diff --git a/.gitignore b/.gitignore
index e5d46f5e2567228ab637f935c9d2dcdc3087f550..6f3a6192a49eb125579c4f47d9ad88445dd6b887 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,9 +1,7 @@
.idea
logs
-tests/basic_nes_tests_alba.py
-tests/test_bash_nord3v2-alba.cmd
notebooks/.ipynb_checkpoints
.ipynb_checkpoints
nes/__pycache__
nes/nc_projections/__pycache__
-jupyter_notebooks
\ No newline at end of file
+*.pyc
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 20473a13240b11f756bcdcd2e9c36341dc0c05ae..172fe41d268a2124cbbab9003441fc11d80f33a9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,12 +1,26 @@
# NES CHANGELOG
-### 1.0.1
-* Release date: 2023/01/18
+### 1.1.0
+* Release date: 2023/03/01
* Changes and new features:
- * Horizontal Interpolation: Lat-Lon to cartesian method improved (aligned with PROVIDENTIA)
- * NetCDF metadata: level.positive attribute used properly.
- * Vertical Interpolation: will switch from 'up' to 'down' if vertical direction is reversed in that process.
-
+ * Improve Lat-Lon to Cartesian coordinates method (used in Providentia).
+ * Function to_shapefile() to create shapefiles from a NES object without losing data from the original grid and being able to select the time and level.
+ * Function from_shapefile() to create a new grid with data from a shapefile after doing a spatial join.
+ * Function create_shapefile() can now be used in parallel.
+ * Function calculate_grid_area() to calculate the area of each cell in a grid.
+ * Function calculate_geometry_area() to calculate the area of each cell given a set of geometries.
+ * Function get_spatial_bounds_mesh_format() to get the lon-lat boundaries in a mesh format (used in pcolormesh).
+ * Bugs fixing:
+ * Correct the dimensions of the resulting points datasets from any interpolation.
+ * Amend the interpolation method to take into account the cases in which the distance among points equals zero.
+ * Correct the way we retrieve the level positive value.
+ * Correct how to calculate the spatial bounds of LCC and Mercator grids: the dimensions were flipped.
+ * Correct how to calculate the spatial bounds for all grids: use read axis limits instead of write axis limits.
+ * Calculate centroids from coordinates in the creation of shapefiles, instead of using the geopandas function 'centroid', that raises a warning for possible errors.
+ * Enable selection of variables on the creation of shapefiles.
+ * Correct read and write parallel limits.
+ * Correct data type in the parallelization of points datasets.
+ * Correct error that appear when trying to select coordinates and write the file.
### 1.0.0
* Release date: 2022/11/24
diff --git a/nes/__init__.py b/nes/__init__.py
index 13543cff291da0fa8fbcad12a0f3bec500aa8a01..625d722e7839209b9eaeede4a00b786605269ff5 100644
--- a/nes/__init__.py
+++ b/nes/__init__.py
@@ -4,3 +4,4 @@ __version__ = "1.0.1"
from .load_nes import open_netcdf, concatenate_netcdfs
from .create_nes import create_nes, from_shapefile
from .nc_projections import *
+from .methods.cell_measures import calculate_geometry_area
\ No newline at end of file
diff --git a/nes/create_nes.py b/nes/create_nes.py
index a4e484f88f66ea2a309afd58413ebb62f79d07ca..33f0bf63eb76714623e9b906770014355a96ea7d 100644
--- a/nes/create_nes.py
+++ b/nes/create_nes.py
@@ -61,6 +61,8 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala
required_vars = ['lat', 'lon']
elif projection == 'regular':
required_vars = ['lat_orig', 'lon_orig', 'inc_lat', 'inc_lon', 'n_lat', 'n_lon']
+ elif projection == 'global':
+ required_vars = ['inc_lat', 'inc_lon']
elif projection == 'rotated':
required_vars = ['centre_lat', 'centre_lon', 'west_boundary', 'south_boundary', 'inc_rlat', 'inc_rlon']
elif projection == 'lcc':
@@ -76,6 +78,12 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala
msg += 'For a {} projection, it is necessary to define {}'.format(projection, required_vars)
raise ValueError(msg)
+ for var in kwargs_list:
+ if var not in required_vars:
+ msg = 'Variable {0} has been defined. '.format(var)
+ msg += 'For a {} projection, you can only define {}'.format(projection, required_vars)
+ raise ValueError(msg)
+
if projection is None:
if parallel_method == 'Y':
warnings.warn("Parallel method cannot be 'Y' to create points NES. Setting it to 'X'")
@@ -86,7 +94,7 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala
avoid_first_hours=avoid_first_hours, avoid_last_hours=avoid_last_hours,
first_level=first_level, last_level=last_level, balanced=balanced,
create_nes=True, strlen=strlen, times=times, **kwargs)
- elif projection == 'regular':
+ elif projection in ['regular', 'global']:
nessy = LatLonNes(comm=comm, dataset=None, xarray=False, info=info, parallel_method=parallel_method,
avoid_first_hours=avoid_first_hours, avoid_last_hours=avoid_last_hours,
first_level=first_level, last_level=last_level, balanced=balanced,
@@ -112,7 +120,7 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala
return nessy
-def from_shapefile(path, method=None, **kwargs):
+def from_shapefile(path, method=None, parallel_method='Y', **kwargs):
"""
Create NES from shapefile data.
@@ -125,23 +133,25 @@ def from_shapefile(path, method=None, **kwargs):
----------
path : str
Path to shapefile.
- kwargs :
- Projection and projection dependent parameters to create it from scratch.
-
method : str
Overlay method. Accepted values: ['nearest', 'intersection', None].
+ parallel_method : str
+ Indicates the parallelization method that you want. Default: 'Y'.
+ accepted values: ['X', 'Y', 'T'].
+ kwargs :
+ Projection and projection dependent parameters to create it from scratch.
"""
# Create NES
- nessy = create_nes(comm=None, info=False, **kwargs)
+ nessy = create_nes(comm=None, info=False, parallel_method=parallel_method, **kwargs)
# Create shapefile for grid
nessy.create_shapefile()
# Read shapefile
- mask = gpd.read_file(path)
+ shapefile = gpd.read_file(path)
# Make spatial join
- nessy.spatial_join(mask, method=method)
+ nessy.spatial_join(shapefile, method=method)
return nessy
diff --git a/nes/load_nes.py b/nes/load_nes.py
index a6e2f86954d450549024823923e3f79bfa15cb44..5bfd333fc4661ec1f9feffe82dfe4d726051b13e 100644
--- a/nes/load_nes.py
+++ b/nes/load_nes.py
@@ -122,6 +122,8 @@ def __is_rotated(dataset):
if 'rotated_pole' in dataset.variables.keys():
return True
+ elif ('rlat' in dataset.dimensions) and ('rlon' in dataset.dimensions):
+ return True
else:
return False
diff --git a/nes/methods/cell_measures.py b/nes/methods/cell_measures.py
new file mode 100644
index 0000000000000000000000000000000000000000..d93000ecfa0e68815209862317311e462fb90efe
--- /dev/null
+++ b/nes/methods/cell_measures.py
@@ -0,0 +1,265 @@
+#!/usr/bin/env python
+
+import numpy as np
+from copy import deepcopy
+
+
+def calculate_grid_area(self):
+ """
+ Get coordinate bounds and call function to calculate the area of each cell of a grid.
+
+ Parameters
+ ----------
+ self : nes.Nes
+ Source projection Nes Object.
+ """
+
+ # Create bounds if they do not exist
+ if self._lat_bnds is None or self._lon_bnds is None:
+ self.create_spatial_bounds()
+
+ # Get spatial number of vertices
+ spatial_nv = self.lat_bnds['data'].shape[-1]
+
+ # Reshape bounds
+ if spatial_nv == 2:
+
+ aux_shape = (self.lat_bnds['data'].shape[0], self.lon_bnds['data'].shape[0], 4)
+ lon_bnds_aux = np.empty(aux_shape)
+ lon_bnds_aux[:, :, 0] = self.lon_bnds['data'][np.newaxis, :, 0]
+ lon_bnds_aux[:, :, 1] = self.lon_bnds['data'][np.newaxis, :, 1]
+ lon_bnds_aux[:, :, 2] = self.lon_bnds['data'][np.newaxis, :, 1]
+ lon_bnds_aux[:, :, 3] = self.lon_bnds['data'][np.newaxis, :, 0]
+
+ lon_bnds = lon_bnds_aux
+ del lon_bnds_aux
+
+ lat_bnds_aux = np.empty(aux_shape)
+ lat_bnds_aux[:, :, 0] = self.lat_bnds['data'][:, np.newaxis, 0]
+ lat_bnds_aux[:, :, 1] = self.lat_bnds['data'][:, np.newaxis, 0]
+ lat_bnds_aux[:, :, 2] = self.lat_bnds['data'][:, np.newaxis, 1]
+ lat_bnds_aux[:, :, 3] = self.lat_bnds['data'][:, np.newaxis, 1]
+
+ lat_bnds = lat_bnds_aux
+ del lat_bnds_aux
+
+ else:
+ lon_bnds = self.lon_bnds['data']
+ lat_bnds = self.lat_bnds['data']
+
+ # Reshape bounds and assign as grid corner coordinates
+ grid_corner_lon = deepcopy(lon_bnds).reshape(lon_bnds.shape[0]*lon_bnds.shape[1],
+ lon_bnds.shape[2])
+ grid_corner_lat = deepcopy(lat_bnds).reshape(lat_bnds.shape[0]*lat_bnds.shape[1],
+ lat_bnds.shape[2])
+
+ # Calculate cell areas
+ grid_area = calculate_cell_area(grid_corner_lon, grid_corner_lat,
+ earth_radius_minor_axis=self.earth_radius[0],
+ earth_radius_major_axis=self.earth_radius[1])
+
+ return grid_area
+
+
+def calculate_geometry_area(geometry_list, earth_radius_minor_axis=6356752.3142,
+ earth_radius_major_axis=6378137.0):
+ """
+ Get coordinate bounds and call function to calculate the area of each cell of a set of geometries.
+
+ Parameters
+ ----------
+ geometry_list : list
+ List with polygon geometries.
+ earth_radius_minor_axis : float
+ Radius of the minor axis of the Earth.
+ earth_radius_major_axis : float
+ Radius of the major axis of the Earth.
+ """
+
+ geometry_area = np.empty(shape=(len(geometry_list,)))
+
+ for geom_ind in range(0, len(geometry_list)):
+
+ # Calculate the area of each geometry in multipolygon and collection objects
+ if geometry_list[geom_ind].type in ['MultiPolygon', 'GeometryCollection']:
+ multi_geom_area = 0
+ for multi_geom_ind in range(0, len(geometry_list[geom_ind].geoms)):
+ if geometry_list[geom_ind].geoms[multi_geom_ind].type == 'Point':
+ continue
+ geometry_corner_lon, geometry_corner_lat = geometry_list[geom_ind].geoms[multi_geom_ind].exterior.coords.xy
+ geometry_corner_lon = np.array(geometry_corner_lon)
+ geometry_corner_lat = np.array(geometry_corner_lat)
+ geom_area = mod_huiliers_area(geometry_corner_lon, geometry_corner_lat)
+ multi_geom_area += geom_area
+ geometry_area[geom_ind] = multi_geom_area * earth_radius_minor_axis * earth_radius_major_axis
+
+ # Calculate the area of each geometry
+ else:
+ geometry_corner_lon, geometry_corner_lat = geometry_list[geom_ind].exterior.coords.xy
+ geometry_corner_lon = np.array(geometry_corner_lon)
+ geometry_corner_lat = np.array(geometry_corner_lat)
+ geom_area = mod_huiliers_area(geometry_corner_lon, geometry_corner_lat)
+ geometry_area[geom_ind] = geom_area * earth_radius_minor_axis * earth_radius_major_axis
+
+ return geometry_area
+
+
+def calculate_cell_area(grid_corner_lon, grid_corner_lat,
+ earth_radius_minor_axis=6356752.3142,
+ earth_radius_major_axis=6378137.0):
+ """
+ Calculate the area of each cell of a grid.
+
+ Parameters
+ ----------
+ grid_corner_lon : np.array
+ Array with longitude bounds of grid.
+ grid_corner_lat : np.array
+ Array with longitude bounds of grid.
+ earth_radius_minor_axis : float
+ Radius of the minor axis of the Earth.
+ earth_radius_major_axis : float
+ Radius of the major axis of the Earth.
+ """
+
+ # Calculate area for each grid cell
+ n_cells = grid_corner_lon.shape[0]
+ area = np.empty(shape=(n_cells,))
+ for i in range(0, n_cells):
+ area[i] = mod_huiliers_area(grid_corner_lon[i], grid_corner_lat[i])
+
+ return area*earth_radius_minor_axis*earth_radius_major_axis
+
+
+def mod_huiliers_area(cell_corner_lon, cell_corner_lat):
+ """
+ Calculate the area of each cell according to Huilier's theorem.
+ Reference: CDO (https://earth.bsc.es/gitlab/ces/cdo/)
+
+ Parameters
+ ----------
+ cell_corner_lon : np.array
+ Longitude boundaries of each cell
+ cell_corner_lat : np.array
+ Latitude boundaries of each cell
+ """
+
+ sum = 0
+
+ # Get points 0 (bottom left) and 1 (bottom right) in Earth coordinates
+ point_0 = lon_lat_to_cartesian(cell_corner_lon[0], cell_corner_lat[0], earth_radius_major_axis=1)
+ point_1 = lon_lat_to_cartesian(cell_corner_lon[1], cell_corner_lat[1], earth_radius_major_axis=1)
+ point_0, point_1 = point_0[0], point_1[0]
+
+ # Get number of vertices
+ if cell_corner_lat[0] == cell_corner_lat[-1]:
+ spatial_nv = len(cell_corner_lon) - 1
+ else:
+ spatial_nv = len(cell_corner_lon)
+
+ for i in range(2, spatial_nv):
+
+ # Get point 2 (top right) in Earth coordinates
+ point_2 = lon_lat_to_cartesian(cell_corner_lon[i], cell_corner_lat[i], earth_radius_major_axis=1)
+ point_2 = point_2[0]
+
+ # Calculate area of triangle between points 0, 1 and 2
+ sum += tri_area(point_0, point_1, point_2)
+
+ # Copy to calculate area of next triangle
+ if i == (spatial_nv - 1):
+ point_1 = deepcopy(point_2)
+
+ return sum
+
+
+def tri_area(point_0, point_1, point_2):
+ """
+ Calculate area between three points that form a triangle.
+ Reference: CDO (https://earth.bsc.es/gitlab/ces/cdo/)
+
+ Parameters
+ ----------
+ point_0 : np.array
+ Position of first point in cartesian coordinates.
+ point_1 : np.array
+ Position of second point in cartesian coordinates.
+ point_2 : np.array
+ Position of third point in cartesian coordinates.
+ """
+
+ # Get length of side a (between point 0 and 1)
+ tmp_vec = cross_product(point_0, point_1)
+ sina = norm(tmp_vec)
+ a = np.arcsin(sina)
+
+ # Get length of side b (between point 0 and 2)
+ tmp_vec = cross_product(point_0, point_2)
+ sinb = norm(tmp_vec)
+ b = np.arcsin(sinb)
+
+ # Get length of side c (between point 1 and 2)
+ tmp_vec = cross_product(point_2, point_1)
+ sinc = norm(tmp_vec)
+ c = np.arcsin(sinc)
+
+ # Calculate area
+ s = 0.5*(a+b+c)
+ t = np.tan(s*0.5) * np.tan((s - a)*0.5) * np.tan((s - b)*0.5) * np.tan((s - c)*0.5)
+ area = np.fabs(4.0 * np.arctan(np.sqrt(np.fabs(t))))
+
+ return area
+
+
+def cross_product(a, b):
+ """
+ Calculate cross product between two points.
+
+ Parameters
+ ----------
+ a : np.array
+ Position of point a in cartesian coordinates.
+ b : np.array
+ Position of point b in cartesian coordinates.
+ """
+
+ return [a[1]*b[2] - a[2]*b[1],
+ a[2]*b[0] - a[0]*b[2],
+ a[0]*b[1] - a[1]*b[0]]
+
+
+def norm(cp):
+ """
+ Normalize the result of the cross product operation.
+
+ Parameters
+ ----------
+ cp : np.array
+ Cross product between two points.
+ """
+
+ return np.sqrt(cp[0]*cp[0] + cp[1]*cp[1] + cp[2]*cp[2])
+
+
+def lon_lat_to_cartesian(lon, lat, earth_radius_major_axis=6378137.0):
+ """
+ Calculate lon, lat coordinates of a point on a sphere.
+
+ Parameters
+ ----------
+ lon : np.array
+ Longitude values.
+ lat : np.array
+ Latitude values.
+ earth_radius_major_axis : float
+ Radius of the major axis of the Earth.
+ """
+
+ lon_r = np.radians(lon)
+ lat_r = np.radians(lat)
+
+ x = earth_radius_major_axis * np.cos(lat_r) * np.cos(lon_r)
+ y = earth_radius_major_axis * np.cos(lat_r) * np.sin(lon_r)
+ z = earth_radius_major_axis * np.sin(lat_r)
+
+ return np.column_stack([x, y, z])
diff --git a/nes/methods/horizontal_interpolation.py b/nes/methods/horizontal_interpolation.py
index b5d5d185b5402e270c2db2658983361b910defcb..5fc29d87aa68f277eb18744349feada904e72291 100644
--- a/nes/methods/horizontal_interpolation.py
+++ b/nes/methods/horizontal_interpolation.py
@@ -3,6 +3,7 @@
import sys
import warnings
import numpy as np
+import pandas as pd
import os
import nes
from mpi4py import MPI
@@ -10,11 +11,16 @@ from scipy import spatial
from filelock import FileLock
from datetime import datetime
from warnings import warn
+import copy
import pyproj
+# CONSTANTS
+NEAREST_OPTS = ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']
+CONSERVATIVE_OPTS = ['Conservative', 'Area_Conservative', 'cons', 'conservative', 'area']
+
def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='NearestNeighbour', n_neighbours=4,
- info=False, to_providentia=False):
+ info=False, to_providentia=False, only_create_wm=False, wm=None):
"""
Horizontal methods from one grid to another one.
@@ -27,23 +33,44 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares
weight_matrix_path : str, None
Path to the weight matrix to read/create.
kind : str
- Kind of horizontal methods. Accepted values: ['NearestNeighbour'].
+ Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative'].
n_neighbours : int
Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4.
info : bool
Indicates if you want to print extra info during the methods process.
to_providentia : bool
Indicates if we want the interpolated grid in Providentia format.
+ only_create_wm : bool
+ Indicates if you want to only create the Weight Matrix.
+ wm : Nes
+ Weight matrix Nes File
"""
-
+ if info and self.master:
+ print("Creating Weight Matrix")
# Obtain weight matrix
if self.parallel_method == 'T':
- weights, idx = get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours)
+ weights, idx = get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours,
+ only_create_wm, wm)
elif self.parallel_method in ['Y', 'X']:
- weights, idx = get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours)
+ weights, idx = get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours,
+ only_create_wm, wm)
else:
raise NotImplemented("Parallel method {0} is not implemented yet for horizontal interpolations. Use 'T'".format(
self.parallel_method))
+ if info and self.master:
+ print("Weight Matrix done!")
+ if only_create_wm:
+ # weights for only_create is the WM NES object
+ return weights
+
+ # idx[idx < 0] = np.nan
+ idx = np.ma.masked_array(idx, mask=idx == -999)
+ # idx = np.array(idx, dtype=float)
+ # idx[idx < 0] = np.nan
+ # weights[weights < 0] = np.nan
+ weights = np.ma.masked_array(weights, mask=weights == -999)
+ # weights = np.array(weights, dtype=float)
+ # weights[weights < 0] = np.nan
# Copy NES
final_dst = dst_grid.copy()
@@ -62,6 +89,8 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares
final_dst.hours_start = self.hours_start
final_dst.hours_end = self.hours_end
+ if info and self.master:
+ print("Applying weights")
# Apply weights
for var_name, var_info in self.variables.items():
if info and self.master:
@@ -82,8 +111,8 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares
for time in range(dst_shape[0]):
for lev in range(dst_shape[1]):
src_aux = get_src_data(self.comm, var_info['data'][time, lev], idx, self.parallel_method)
- # src_aux = np.take(src_data[time, lev], idx)
final_dst.variables[var_name]['data'][time, lev] = np.sum(weights * src_aux, axis=1)
+
if isinstance(dst_grid, nes.PointsNes):
# Removing level axis
if src_shape[1] != 1:
@@ -95,6 +124,9 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares
final_dst.global_attrs = self.global_attrs
+ if info and self.master:
+ print("Formatting")
+
if to_providentia:
# self = experiment to interpolate (regular, rotated, etc.)
# final_dst = interpolated experiment (points)
@@ -155,13 +187,14 @@ def get_src_data(comm, var_data, idx, parallel_method):
var_data = comm.bcast(var_data)
- var_data = np.take(var_data, idx)
+ var_data = np.pad(var_data, [1, 1], 'constant', constant_values=np.nan).take(idx + 1, mode='clip')
+ #var_data = np.take(var_data, idx)
return var_data
# noinspection DuplicatedCode
-def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours):
+def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm):
"""
To obtain the weights and source data index through the T axis.
@@ -174,51 +207,75 @@ def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbour
weight_matrix_path : str, None
Path to the weight matrix to read/create.
kind : str
- Kind of horizontal methods. Accepted values: ['NearestNeighbour'].
+ Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative'].
n_neighbours : int
Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4.
+ only_create : bool
+ Indicates if you want to only create the Weight Matrix.
+ wm : Nes
+ Weight matrix Nes File
Returns
-------
tuple
Weights and source data index.
"""
+ if wm is not None:
+ weight_matrix = wm
- if weight_matrix_path is not None:
- with FileLock(weight_matrix_path.replace('.nc', '.lock')):
- if self.master:
- if os.path.isfile(weight_matrix_path):
+ elif weight_matrix_path is not None:
+ with FileLock(weight_matrix_path + "{0:03d}.lock".format(self.rank)):
+ if os.path.isfile(weight_matrix_path):
+ if self.master:
weight_matrix = read_weight_matrix(weight_matrix_path, comm=MPI.COMM_SELF)
- if len(weight_matrix.lev['data']) != n_neighbours:
- warn("The selected weight matrix does not have the same number of nearest neighbours." +
- "Re-calculating again but not saving it.")
- if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']:
- weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
- else:
- raise NotImplementedError(kind)
else:
- if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']:
- weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
+ weight_matrix = True
+ if kind in NEAREST_OPTS:
+ if self.master:
+ if len(weight_matrix.lev['data']) != n_neighbours:
+ warn("The selected weight matrix does not have the same number of nearest neighbours." +
+ "Re-calculating again but not saving it.")
+ weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
else:
- raise NotImplementedError(kind)
- if weight_matrix_path is not None:
- weight_matrix.to_netcdf(weight_matrix_path)
+ weight_matrix = True
+
else:
- weight_matrix = None
+ if self.master:
+ if kind in NEAREST_OPTS:
+ weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours,
+ wm_path=weight_matrix_path)
+ elif kind in CONSERVATIVE_OPTS:
+ weight_matrix = create_area_conservative_weight_matrix(self, dst_grid,
+ wm_path=weight_matrix_path)
+ else:
+ raise NotImplementedError(kind)
+ else:
+ weight_matrix = True
+
+ if os.path.exists(weight_matrix_path + "{0:03d}.lock".format(self.rank)):
+ os.remove(weight_matrix_path + "{0:03d}.lock".format(self.rank))
else:
- if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']:
- if self.master:
+ if self.master:
+ if kind in NEAREST_OPTS:
weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
+ elif kind in CONSERVATIVE_OPTS:
+ weight_matrix = create_area_conservative_weight_matrix(self, dst_grid)
else:
- weight_matrix = None
+ raise NotImplementedError(kind)
else:
- raise NotImplementedError(kind)
+ weight_matrix = True
+
+ if only_create:
+ return weight_matrix, None
- # Normalize to 1
if self.master:
- weights = np.array(np.array(weight_matrix.variables['inverse_dists']['data'], dtype=np.float64) /
- np.array(weight_matrix.variables['inverse_dists']['data'], dtype=np.float64).sum(axis=1),
- dtype=np.float64)
+ if kind in NEAREST_OPTS:
+ # Normalize to 1
+ weights = np.array(np.array(weight_matrix.variables['weight']['data'], dtype=np.float64) /
+ np.array(weight_matrix.variables['weight']['data'], dtype=np.float64).sum(axis=1),
+ dtype=np.float64)
+ else:
+ weights = np.array(weight_matrix.variables['weight']['data'], dtype=np.float64)
idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=int)
else:
weights = None
@@ -231,7 +288,7 @@ def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbour
# noinspection DuplicatedCode
-def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours):
+def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm):
"""
To obtain the weights and source data index through the X or Y axis.
@@ -244,63 +301,83 @@ def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbou
weight_matrix_path : str, None
Path to the weight matrix to read/create.
kind : str
- Kind of horizontal methods. Accepted values: ['NearestNeighbour']
+ Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative'].
n_neighbours : int
Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4.
+ only_create : bool
+ Indicates if you want to only create the Weight Matrix.
+ wm : Nes
+ Weight matrix Nes File
Returns
-------
tuple
Weights and source data index.
"""
-
if isinstance(dst_grid, nes.PointsNes) and weight_matrix_path is not None:
if self.master:
warn("To point weight matrix cannot be saved.")
weight_matrix_path = None
- if weight_matrix_path is not None:
- with FileLock(weight_matrix_path.replace('.nc', '.lock')):
- if self.master:
- if os.path.isfile(weight_matrix_path):
+ if wm is not None:
+ weight_matrix = wm
+
+ elif weight_matrix_path is not None:
+ with FileLock(weight_matrix_path + "{0:03d}.lock".format(self.rank)):
+ if os.path.isfile(weight_matrix_path):
+ if self.master:
weight_matrix = read_weight_matrix(weight_matrix_path, comm=MPI.COMM_SELF)
+ else:
+ weight_matrix = True
+ if kind in NEAREST_OPTS:
if len(weight_matrix.lev['data']) != n_neighbours:
warn("The selected weight matrix does not have the same number of nearest neighbours." +
"Re-calculating again but not saving it.")
- if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']:
+ if self.master:
weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
else:
- raise NotImplementedError(kind)
- else:
- if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']:
- weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
- else:
- raise NotImplementedError(kind)
- if weight_matrix_path is not None:
- weight_matrix.to_netcdf(weight_matrix_path)
+ weight_matrix = True
else:
- weight_matrix = None
+ if kind in NEAREST_OPTS:
+ if self.master:
+ weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours,
+ wm_path=weight_matrix_path)
+ else:
+ weight_matrix = True
+ elif kind in CONSERVATIVE_OPTS:
+ weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, wm_path=weight_matrix_path)
+ else:
+ raise NotImplementedError(kind)
+
+ if os.path.exists(weight_matrix_path + "{0:03d}.lock".format(self.rank)):
+ os.remove(weight_matrix_path + "{0:03d}.lock".format(self.rank))
else:
- if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']:
- if self.master:
- weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
- else:
- weight_matrix = None
+ if kind in NEAREST_OPTS:
+ weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
+ elif kind in CONSERVATIVE_OPTS:
+ weight_matrix = create_area_conservative_weight_matrix(self, dst_grid)
else:
raise NotImplementedError(kind)
+ if only_create:
+ return weight_matrix, None
+
# Normalize to 1
if self.master:
- weights = np.array(np.array(weight_matrix.variables['inverse_dists']['data'], dtype=np.float64) /
- np.array(weight_matrix.variables['inverse_dists']['data'], dtype=np.float64).sum(axis=1),
- dtype=np.float64)
- idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=int)
+ if kind in NEAREST_OPTS:
+ weights = np.array(np.array(weight_matrix.variables['weight']['data'], dtype=np.float64) /
+ np.array(weight_matrix.variables['weight']['data'], dtype=np.float64).sum(axis=1),
+ dtype=np.float64)
+ else:
+ weights = np.array(weight_matrix.variables['weight']['data'], dtype=np.float64)
+ idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=np.int64)
else:
weights = None
idx = None
weights = self.comm.bcast(weights, root=0)
idx = self.comm.bcast(idx, root=0)
+
# if isinstance(dst_grid, nes.PointsNes):
# print("weights 1 ->", weights.shape)
# print("idx 1 ->", idx.shape)
@@ -335,14 +412,17 @@ def read_weight_matrix(weight_matrix_path, comm=None, parallel_method='T'):
nes.Nes
Weight matrix.
"""
-
weight_matrix = nes.open_netcdf(path=weight_matrix_path, comm=comm, parallel_method=parallel_method, balanced=True)
weight_matrix.load()
+ # In previous versions of NES weight was called inverse_dists
+ if 'inverse_dists' in weight_matrix.variables.keys():
+ weight_matrix.variables['weight'] = weight_matrix.variables['inverse_dists']
+
return weight_matrix
-def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, info=False):
+def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, wm_path=None, info=False):
"""
To create the weight matrix with the nearest neighbours method.
@@ -412,6 +492,8 @@ def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, info=False):
weight_matrix = dst_grid.copy()
weight_matrix.time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)]
weight_matrix._time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)]
+ weight_matrix._time_bnds = None
+ weight_matrix.time_bnds = None
weight_matrix.last_level = None
weight_matrix.first_level = 0
weight_matrix.hours_start = 0
@@ -423,12 +505,165 @@ def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, info=False):
inverse_dists = np.reciprocal(dists)
inverse_dists_transf = inverse_dists.T.reshape((1, n_neighbours, dst_lon.shape[0], dst_lon.shape[1]))
- weight_matrix.variables['inverse_dists'] = {'data': inverse_dists_transf, 'units': 'm'}
+ weight_matrix.variables['weight'] = {'data': inverse_dists_transf, 'units': 'm'}
idx_transf = idx.T.reshape((1, n_neighbours, dst_lon.shape[0], dst_lon.shape[1]))
weight_matrix.variables['idx'] = {'data': idx_transf, 'units': ''}
weight_matrix.lev = {'data': np.arange(inverse_dists_transf.shape[1]), 'units': ''}
weight_matrix._lev = {'data': np.arange(inverse_dists_transf.shape[1]), 'units': ''}
+ if wm_path is not None:
+ weight_matrix.to_netcdf(wm_path)
+
+ return weight_matrix
+
+
+def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, info=False):
+ """
+ To create the weight matrix with the area conservative method.
+
+ Parameters
+ ----------
+ self : nes.Nes
+ Source projection Nes Object.
+ dst_nes : nes.Nes
+ Final projection Nes object.
+
+ info: bool
+ Indicates if you want to print extra info during the methods process.
+
+ Returns
+ -------
+ nes.Nes
+ Weight matrix.
+ """
+ if info and self.master:
+ print("\tCreating area conservative Weight Matrix")
+ sys.stdout.flush()
+ # Get a portion of the destiny grid
+ if dst_nes.shapefile is None:
+ dst_nes.create_shapefile()
+ dst_grid = copy.deepcopy(dst_nes.shapefile)
+
+ # Get the complete source grid
+ if self.shapefile is None:
+ self.create_shapefile()
+ src_grid = copy.deepcopy(self.shapefile)
+
+ if self.parallel_method == 'T':
+ # All process has the same shapefile
+ pass
+ else:
+ src_grid = self.comm.gather(src_grid, root=0)
+ if self.master:
+ src_grid = pd.concat(src_grid)
+ src_grid = self.comm.bcast(src_grid)
+
+ my_crs = pyproj.CRS.from_proj4("+proj=latlon")
+ # Normalizing projections
+ dst_grid.to_crs(crs=my_crs, inplace=True)
+ dst_grid['FID_dst'] = dst_grid.index
+ dst_grid = dst_grid.reset_index()
+
+ # src_grid.to_crs(crs=pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84').crs, inplace=True)
+ src_grid.to_crs(crs=my_crs, inplace=True)
+ src_grid['FID_src'] = src_grid.index
+
+ src_grid = src_grid.reset_index()
+
+ if info and self.master:
+ print("\t\tGrids created and ready to interpolate")
+ sys.stdout.flush()
+
+ # Get intersected areas
+ inp, res = dst_grid.sindex.query_bulk(src_grid.geometry, predicate='intersects')
+
+ # Calculate intersected areas and fractions
+ intersection = pd.DataFrame(columns=["FID_src", "FID_dst"])
+ intersection['INP'] = np.array(inp, dtype=np.uint32)
+ intersection['RES'] = np.array(res, dtype=np.uint32)
+ intersection['FID_src'] = np.array(src_grid.loc[inp, 'FID_src'], dtype=np.uint32)
+ intersection['FID_dst'] = np.array(dst_grid.loc[res, 'FID_dst'], dtype=np.uint32)
+
+ intersection['geometry_src'] = src_grid.loc[inp, 'geometry'].values
+ intersection['geometry_dst'] = dst_grid.loc[res, 'geometry'].values
+
+ if True:
+ # No Warnings Zone
+ warnings.filterwarnings('ignore')
+ intersection['intersect_area'] = intersection.apply(
+ lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area, axis=1)
+ intersection.drop(intersection[intersection['intersect_area'] <= 0].index, inplace=True)
+
+ intersection["src_area"] = src_grid.loc[intersection['FID_src'], 'geometry'].area.values
+
+ warnings.filterwarnings('default')
+
+ intersection['weight'] = intersection['intersect_area'] / intersection["src_area"]
+
+ # Format & Clean
+ intersection.drop(columns=["geometry_src", "geometry_dst", "src_area", "intersect_area"], inplace=True)
+
+ if info and self.master:
+ print("\t\tWeights calculated. Formatting weight matrix.")
+ sys.stdout.flush()
+
+ # Initialising weight matrix
+ if self.parallel_method != 'T':
+ intersection = self.comm.gather(intersection, root=0)
+ if self.master:
+ if self.parallel_method != 'T':
+ intersection = pd.concat(intersection)
+ intersection = intersection.set_index(['FID_dst', intersection.groupby('FID_dst').cumcount()]).rename_axis(
+ ('FID', 'level')).sort_index()
+ intersection.rename(columns={"FID_src": "idx"}, inplace=True)
+ weight_matrix = dst_nes.copy()
+ weight_matrix.time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)]
+ weight_matrix._time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)]
+ weight_matrix._time_bnds = None
+ weight_matrix.time_bnds = None
+ weight_matrix.last_level = None
+ weight_matrix.first_level = 0
+ weight_matrix.hours_start = 0
+ weight_matrix.hours_end = 0
+
+ weight_matrix.set_communicator(MPI.COMM_SELF)
+ weight_matrix.set_levels({'data': np.arange(intersection.index.get_level_values('level').max() + 1),
+ 'dimensions': ('lev',),
+ 'units': '',
+ 'positive': 'up'})
+
+ # Creating Weight matrix empty variables
+ if len(weight_matrix._lat['data'].shape) == 1:
+ shape = (1, len(weight_matrix.lev['data']),
+ weight_matrix._lat['data'].shape[0], weight_matrix._lon['data'].shape[0],)
+ shape_flat = (1, len(weight_matrix.lev['data']),
+ weight_matrix._lat['data'].shape[0] * weight_matrix._lon['data'].shape[0],)
+ else:
+ shape = (1, len(weight_matrix.lev['data']),
+ weight_matrix._lat['data'].shape[0], weight_matrix._lat['data'].shape[1],)
+ shape_flat = (1, len(weight_matrix.lev['data']),
+ weight_matrix._lat['data'].shape[0] * weight_matrix._lat['data'].shape[1],)
+
+ weight_matrix.variables['weight'] = {'data': np.empty(shape_flat), 'units': '-'}
+ weight_matrix.variables['weight']['data'][:] = -999
+ weight_matrix.variables['idx'] = {'data': np.empty(shape_flat), 'units': '-'}
+ weight_matrix.variables['idx']['data'][:] = -999
+
+ # Filling Weight matrix variables
+ for aux_lev in weight_matrix.lev['data']:
+ aux_data = intersection.xs(level='level', key=aux_lev)
+ weight_matrix.variables['weight']['data'][0, aux_lev, aux_data.index] = aux_data.loc[:, 'weight'].values
+ weight_matrix.variables['idx']['data'][0, aux_lev, aux_data.index] = aux_data.loc[:, 'idx'].values
+ # Re-shaping
+ weight_matrix.variables['weight']['data'] = weight_matrix.variables['weight']['data'].reshape(shape)
+ weight_matrix.variables['idx']['data'] = weight_matrix.variables['idx']['data'].reshape(shape)
+ if wm_path is not None:
+ if info and self.master:
+ print("\t\tWeight matrix saved at {0}".format(wm_path))
+ sys.stdout.flush()
+ weight_matrix.to_netcdf(wm_path)
+ else:
+ weight_matrix = True
return weight_matrix
diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py
index 9cb632810e93f26f24c9e9575eeb6517e564e8b6..fcb4397c662602ced009ed4ba95d1d24d23c083b 100644
--- a/nes/nc_projections/default_nes.py
+++ b/nes/nc_projections/default_nes.py
@@ -13,14 +13,17 @@ from mpi4py import MPI
from cfunits import Units
from numpy.ma.core import MaskError
import geopandas as gpd
-from shapely.geometry import Polygon
+from shapely.geometry import Polygon, Point
from copy import deepcopy, copy
import datetime
+import pyproj
from ..methods import vertical_interpolation
from ..methods import horizontal_interpolation
+from ..methods import cell_measures
from ..nes_formats import to_netcdf_cams_ra
from ..nc_projections import *
+
class Nes(object):
"""
@@ -155,6 +158,9 @@ class Nes(object):
# Define parallel method
self.parallel_method = parallel_method
+ # Get minor and major axes of Earth
+ self.earth_radius = self.get_earth_radius('WGS84')
+
# NetCDF object
if create_nes:
@@ -182,6 +188,9 @@ class Nes(object):
self.lev = deepcopy(self._lev)
self.lat_bnds, self.lon_bnds = self._lat_bnds, self._lon_bnds
+ # Cell measures screening
+ self.cell_measures = self.__get_cell_measures(create_nes)
+
# Set NetCDF attributes
self.global_attrs = self.__get_global_attributes(create_nes)
@@ -213,6 +222,9 @@ class Nes(object):
self._lon = self._get_coordinate_dimension(['lon', 'longitude'])
self._lat_bnds, self._lon_bnds = self.__get_coordinates_bnds()
+ # Complete cell measures
+ self._cell_measures = self.__get_cell_measures()
+
# Set axis limits for parallel reading
self.read_axis_limits = self.get_read_axis_limits()
@@ -225,6 +237,9 @@ class Nes(object):
self.lat_bnds = self._get_coordinate_values(self._lat_bnds, 'Y', bounds=True)
self.lon_bnds = self._get_coordinate_values(self._lon_bnds, 'X', bounds=True)
+ # Cell measures screening
+ self.cell_measures = self._get_cell_measures_values(self._cell_measures)
+
# Set axis limits for parallel writing
self.write_axis_limits = self.get_write_axis_limits()
@@ -306,6 +321,11 @@ class Nes(object):
del self.lat_bnds
del self._lon_bnds
del self.lon_bnds
+ del self.shapefile
+ for cell_measure in self.cell_measures.keys():
+ if self.cell_measures[cell_measure]['data'] is not None:
+ del self.cell_measures[cell_measure]['data']
+ del self.cell_measures
except AttributeError:
pass
@@ -343,7 +363,7 @@ class Nes(object):
return None
- def copy(self, copy_vars=False, copy_comm=False):
+ def copy(self, copy_vars=False):
"""
Copy the Nes object.
The copy will avoid to copy the communicator, dataset and variables by default.
@@ -352,8 +372,6 @@ class Nes(object):
----------
copy_vars: bool
Indicates if you want to copy the variables (in lazy mode).
- copy_comm: bool
- Indicates if you want to copy the communicator.
Returns
-------
@@ -362,16 +380,9 @@ class Nes(object):
"""
nessy = deepcopy(self)
-
- if copy_comm:
- nessy.comm = self.comm
-
+ nessy.netcdf = None
if copy_vars:
- if not hasattr(nessy, 'netcdf'):
- nessy.netcdf = None
- nessy.variables = self._get_lazy_variables()
- else:
- nessy.variables = nessy._get_lazy_variables()
+ nessy.variables = nessy._get_lazy_variables()
else:
nessy.variables = {}
@@ -430,6 +441,7 @@ class Nes(object):
self.read_axis_limits = self.get_read_axis_limits()
self.write_axis_limits = self.get_write_axis_limits()
+
return None
def set_levels(self, levels):
@@ -490,8 +502,7 @@ class Nes(object):
inc : float
Increment between centre values.
spatial_nv : int
- Non mandatory parameter that informs the number of vertices that must have the
- boundaries. Default: 2.
+ Non mandatory parameter that informs the number of vertices that the boundaries must have. Default: 2.
inverse : bool
For some grid latitudes.
@@ -530,19 +541,67 @@ class Nes(object):
inc_lat = np.abs(np.mean(np.diff(self._lat['data'])))
lat_bnds = self.create_single_spatial_bounds(self._lat['data'], inc_lat, spatial_nv=2)
- self._lat_bnds = deepcopy(lat_bnds)
- self.lat_bnds = lat_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'],
- :]
-
+ self._lat_bnds = {}
+ self._lat_bnds['data'] = deepcopy(lat_bnds)
+ self.lat_bnds = {}
+ self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ :]
+
inc_lon = np.abs(np.mean(np.diff(self._lon['data'])))
lon_bnds = self.create_single_spatial_bounds(self._lon['data'], inc_lon, spatial_nv=2)
- self._lon_bnds = deepcopy(lon_bnds)
- self.lon_bnds = lon_bnds[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'],
- :]
+ self._lon_bnds = {}
+ self._lon_bnds['data'] = deepcopy(lon_bnds)
+ self.lon_bnds = {}
+ self.lon_bnds['data'] = lon_bnds[self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
+ :]
return None
+ def get_spatial_bounds_mesh_format(self):
+ """
+ Get the spatial bounds in the pcolormesh format:
+
+ see: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pcolormesh.html
+
+ Returns
+ -------
+ lon_bnds_mesh : numpy.array
+ Longitude boundaries in the mesh format
+ lat_bnds_mesh : numpy.array
+ Latitude boundaries in the mesh format
+ """
+ if self.size > 1:
+ raise RuntimeError("NES.get_spatial_bounds_mesh_format() function only works in serial mode.")
+ if self.lat_bnds is None:
+ self.create_spatial_bounds()
+
+ if self.lat_bnds['data'].shape[-1] == 2:
+ # get the lat_b and lon_b first rows
+ lat_b_0 = np.append(self.lat_bnds['data'][:, 0], self.lat_bnds['data'][-1, -1])
+ lon_b_0 = np.append(self.lon_bnds['data'][:, 0], self.lon_bnds['data'][-1, -1])
+ # expand lat_band lon_b in 2D
+ lat_bnds_mesh = np.tile(lat_b_0, (len(self.lon['data']) + 1, 1)).transpose()
+ lon_bnds_mesh = np.tile(lon_b_0, (len(self.lat['data']) + 1, 1))
+
+ elif self.lat_bnds['data'].shape[-1] == 4:
+ # Irregular quadrilateral polygon cell definition
+ lat_bnds_mesh = np.empty((self.lat['data'].shape[0] + 1, self.lat['data'].shape[1] + 1))
+ lat_bnds_mesh[:-1, :-1] = self.lat_bnds['data'][:, :, 0]
+ lat_bnds_mesh[:-1, 1:] = self.lat_bnds['data'][:, :, 1]
+ lat_bnds_mesh[1:, 1:] = self.lat_bnds['data'][:, :, 2]
+ lat_bnds_mesh[1:, :-1] = self.lat_bnds['data'][:, :, 3]
+
+ lon_bnds_mesh = np.empty((self.lat['data'].shape[0] + 1, self.lat['data'].shape[1] + 1))
+ lon_bnds_mesh[:-1, :-1] = self.lon_bnds['data'][:, :, 0]
+ lon_bnds_mesh[:-1, 1:] = self.lon_bnds['data'][:, :, 1]
+ lon_bnds_mesh[1:, 1:] = self.lon_bnds['data'][:, :, 2]
+ lon_bnds_mesh[1:, :-1] = self.lon_bnds['data'][:, :, 3]
+ else:
+ raise RuntimeError("Invalid number of vertices: {0}".format(self.lat_bnds['data'].shape[-1] ))
+
+ return lon_bnds_mesh, lat_bnds_mesh
+
def free_vars(self, var_list):
"""
Erase the selected variables from the variables information.
@@ -705,17 +764,18 @@ class Nes(object):
self.lat_bnds = self._get_coordinate_values(self._lat_bnds, 'Y', bounds=True)
self.lon_bnds = self._get_coordinate_values(self._lon_bnds, 'X', bounds=True)
+ self.filter_coordinates_selection()
+
# Removing complete coordinates
self.write_axis_limits = self.get_write_axis_limits()
- self.filter_coordinates_selection()
-
return None
def filter_coordinates_selection(self):
idx = self.get_idx_intervals()
self._time = self._time[idx['idx_t_min']:idx['idx_t_max']]
+ self._lev['data'] = self._lev['data'][idx['idx_z_min']:idx['idx_z_max']]
if len(self._lat['data'].shape) == 1:
# Regular projection
@@ -736,11 +796,22 @@ class Nes(object):
if self._lon_bnds is not None:
self._lon_bnds = self._lon_bnds[idx['idx_y_min']:idx['idx_y_max'], idx['idx_x_min']:idx['idx_x_max'], :]
+ self.hours_start = 0
+ self.hours_end = 0
+ self.last_level = None
+ self.first_level = None
+ self.lat_min = None
+ self.lat_max = None
+ self.lon_max = None
+ self.lon_min = None
+
return None
def get_idx_intervals(self):
idx = {'idx_t_min': self.get_time_id(self.hours_start, first=True),
- 'idx_t_max': self.get_time_id(self.hours_end, first=False)}
+ 'idx_t_max': self.get_time_id(self.hours_end, first=False),
+ 'idx_z_min': self.first_level,
+ 'idx_z_max': self.last_level}
# Axis Y
if self.lat_min is None:
@@ -748,7 +819,7 @@ class Nes(object):
else:
idx['idx_y_min'] = self.get_coordinate_id(self._lat['data'], self.lat_min, axis=0)
if self.lat_max is None:
- idx['idx_y_max'] = self._lat['data'].shape[0] + 1
+ idx['idx_y_max'] = self._lat['data'].shape[0]
else:
idx['idx_y_max'] = self.get_coordinate_id(self._lat['data'], self.lat_max, axis=0) + 1
@@ -768,7 +839,7 @@ class Nes(object):
axis = 1
idx['idx_x_min'] = self.get_coordinate_id(self._lon['data'], self.lon_min, axis=axis)
if self.lon_max is None:
- idx['idx_x_max'] = self._lon['data'].shape[-1] + 1
+ idx['idx_x_max'] = self._lon['data'].shape[-1]
else:
if len(self._lon['data'].shape) == 1:
axis = 0
@@ -978,7 +1049,6 @@ class Nes(object):
Dictionary with the 4D limits of the rank data to read.
t_min, t_max, z_min, z_max, y_min, y_max, x_min and x_max.
"""
-
if self.balanced:
return self.get_read_axis_limits_balanced()
else:
@@ -1001,7 +1071,6 @@ class Nes(object):
't_min': None, 't_max': None}
idx = self.get_idx_intervals()
-
if self.parallel_method == 'Y':
y_len = idx['idx_y_max'] - idx['idx_y_min']
if y_len < self.size:
@@ -1009,7 +1078,7 @@ class Nes(object):
self.size, y_len))
axis_limits['y_min'] = ((y_len // self.size) * self.rank) + idx['idx_y_min']
if self.rank + 1 < self.size:
- axis_limits['y_max'] = ((y_len // self.size) * (self.rank + 1)) + idx['idx_y_max']
+ axis_limits['y_max'] = ((y_len // self.size) * (self.rank + 1)) + idx['idx_y_min']
else:
axis_limits['y_max'] = idx['idx_y_max']
@@ -1027,7 +1096,7 @@ class Nes(object):
self.size, x_len))
axis_limits['x_min'] = ((x_len // self.size) * self.rank) + idx['idx_x_min']
if self.rank + 1 < self.size:
- axis_limits['x_max'] = ((x_len // self.size) * (self.rank + 1)) + idx['idx_x_max']
+ axis_limits['x_max'] = ((x_len // self.size) * (self.rank + 1)) + idx['idx_x_min']
else:
axis_limits['x_max'] = idx['idx_x_max']
@@ -1039,13 +1108,13 @@ class Nes(object):
axis_limits['t_max'] = idx['idx_t_max']
elif self.parallel_method == 'T':
- t_len = idx['idx_t_min'] - idx['idx_t_max']
+ t_len = idx['idx_t_max'] - idx['idx_t_min']
if t_len < self.size:
raise IndexError('More processors (size={0}) selected than T elements (size={1})'.format(
self.size, t_len))
axis_limits['t_min'] = ((t_len // self.size) * self.rank) + idx['idx_t_min']
if self.rank + 1 < self.size:
- axis_limits['t_max'] = ((t_len // self.size) * (self.rank + 1)) + idx['idx_t_max']
+ axis_limits['t_max'] = ((t_len // self.size) * (self.rank + 1)) + idx['idx_t_min']
# Non parallel filters
axis_limits['y_min'] = idx['idx_y_min']
@@ -1091,6 +1160,7 @@ class Nes(object):
min_axis = 'y_min'
max_axis = 'y_max'
to_add = idx['idx_y_min']
+
elif self.parallel_method == 'X':
len_to_split = idx['idx_x_max'] - idx['idx_x_min']
if len_to_split < self.size:
@@ -1282,10 +1352,9 @@ class Nes(object):
Close the NetCDF with netcdf4-python.
"""
- if hasattr(self, 'netcdf'):
- if self.netcdf is not None:
- self.netcdf.close()
- self.netcdf = None
+ if (hasattr(self, 'netcdf')) and (self.netcdf is not None):
+ self.netcdf.close()
+ self.netcdf = None
return None
@@ -1345,7 +1414,7 @@ class Nes(object):
CF compliant time.
"""
- units = time.units
+ units = self.__parse_time_unit(time.units)
if not hasattr(time, 'calendar'):
calendar = 'standard'
else:
@@ -1355,7 +1424,12 @@ class Nes(object):
units = 'days since ' + time.units.split('since')[1].lstrip()
time = self.__get_dates_from_months(time, units, calendar)
- return time, units, calendar
+ time_data = time[:]
+
+ if len(time_data) == 1 and np.isnan(time_data[0]):
+ time_data[0] = 0
+
+ return time_data, units, calendar
@staticmethod
def __parse_time_unit(t_units):
@@ -1394,8 +1468,9 @@ class Nes(object):
else:
if self.master:
nc_var = self.netcdf.variables['time']
- nc_var, units, calendar = self.__parse_time(nc_var)
- time = num2date(nc_var[:], self.__parse_time_unit(units), calendar=calendar)
+ time_data, units, calendar = self.__parse_time(nc_var)
+
+ time = num2date(time_data, units, calendar=calendar)
time = [aux.replace(second=0, microsecond=0) for aux in time]
else:
time = None
@@ -1465,11 +1540,13 @@ class Nes(object):
if self.master:
if not create_nes:
if 'lat_bnds' in self.netcdf.variables.keys():
- lat_bnds = self.netcdf.variables['lat_bnds'][:]
+ lat_bnds = {}
+ lat_bnds['data'] = self.netcdf.variables['lat_bnds'][:]
else:
lat_bnds = None
if 'lon_bnds' in self.netcdf.variables.keys():
- lon_bnds = self.netcdf.variables['lon_bnds'][:]
+ lon_bnds = {}
+ lon_bnds['data'] = self.netcdf.variables['lon_bnds'][:]
else:
lon_bnds = None
else:
@@ -1485,6 +1562,33 @@ class Nes(object):
return lat_bnds, lon_bnds
+ def __get_cell_measures(self, create_nes=False):
+ """
+ Get the NetCDF cell measures values.
+
+ Parameters
+ ----------
+ create_nes : bool
+ Indicates if you want to create the object from scratch (True) or through an existing file.
+
+ Returns
+ -------
+ cell_measures : dict
+ Dictionary of cell measures of the NetCDF data.
+ """
+
+ cell_measures = {}
+ if self.master:
+ if not create_nes:
+ if 'cell_area' in self.netcdf.variables.keys():
+ cell_measures['cell_area'] = {}
+ cell_measures['cell_area']['data'] = self.netcdf.variables['cell_area'][:]
+ cell_measures = self.comm.bcast(cell_measures, root=0)
+
+ self.free_vars(['cell_area'])
+
+ return cell_measures
+
def _get_coordinate_dimension(self, possible_names):
"""
Read the coordinate dimension data.
@@ -1557,7 +1661,8 @@ class Nes(object):
if coordinate_len == 1:
values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max']]
elif coordinate_len == 2:
- values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
else:
raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format(
dim=values['data'].shape))
@@ -1565,7 +1670,8 @@ class Nes(object):
if coordinate_len == 1:
values['data'] = values['data'][self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
elif coordinate_len == 2:
- values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
else:
raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format(
dim=values['data'].shape))
@@ -1578,6 +1684,44 @@ class Nes(object):
return values
+ def _get_cell_measures_values(self, cell_measures_info):
+ """
+ Get the cell measures data of the current portion.
+
+ Parameters
+ ----------
+ cell_measures_info : dict, list
+ Dictionary with the 'data' key with the cell measures variable values. and the attributes as other keys.
+
+ Returns
+ -------
+ values : dict
+ Dictionary with the portion of data corresponding to the rank.
+ """
+
+ if cell_measures_info is None:
+ return None
+
+ cell_measures_values = {}
+
+ for cell_measures_var in cell_measures_info.keys():
+
+ values = deepcopy(cell_measures_info[cell_measures_var])
+ coordinate_len = len(values['data'].shape)
+
+ if coordinate_len == 1:
+ values['data'] = values['data'][self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ elif coordinate_len == 2:
+ values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ else:
+ raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format(
+ dim=values['data'].shape))
+
+ cell_measures_values[cell_measures_var] = values
+
+ return cell_measures_values
+
def _get_lazy_variables(self):
"""
Get all the variables information.
@@ -1599,33 +1743,26 @@ class Nes(object):
else:
if self.master:
variables = {}
- if self.netcdf is not None:
- # Initialise data
- for var_name, var_info in self.netcdf.variables.items():
- variables[var_name] = {}
- variables[var_name]['data'] = None
- variables[var_name]['dimensions'] = var_info.dimensions
-
- # Avoid some attributes
- for attrname in var_info.ncattrs():
- if attrname not in ['missing_value', '_FillValue']:
- value = getattr(var_info, attrname)
- if value in ['unitless', '-']:
- value = ''
- variables[var_name][attrname] = value
- else:
- # Unload loaded data (used in to_shapefile with data from interpolated or loaded datasets)
- for var_name, var_info in self.variables.items():
- variables[var_name] = {}
- variables[var_name]['data'] = None
- variables[var_name]['dimensions'] = var_info['dimensions']
+ # Initialise data
+ for var_name, var_info in self.netcdf.variables.items():
+ variables[var_name] = {}
+ variables[var_name]['data'] = None
+ variables[var_name]['dimensions'] = var_info.dimensions
+
+ # Avoid some attributes
+ for attrname in var_info.ncattrs():
+ if attrname not in ['missing_value', '_FillValue']:
+ value = getattr(var_info, attrname)
+ if value in ['unitless', '-']:
+ value = ''
+ variables[var_name][attrname] = value
else:
variables = None
variables = self.comm.bcast(variables, root=0)
return variables
- def _read_variable(self, var_name, nc_var=None, var_dims=None):
+ def _read_variable(self, var_name):
"""
Read the corresponding variable data according to the current rank.
@@ -1640,9 +1777,8 @@ class Nes(object):
Portion of the variable data corresponding to the rank.
"""
- if nc_var is None and var_dims is None:
- nc_var = self.netcdf.variables[var_name]
- var_dims = nc_var.dimensions
+ nc_var = self.netcdf.variables[var_name]
+ var_dims = nc_var.dimensions
# Read data in 4 dimensions
if len(var_dims) < 2:
@@ -1672,7 +1808,7 @@ class Nes(object):
var_name))
# Missing to nan
try:
- data[data.mask == True] = np.nan
+ data[data.shapefile == True] = np.nan
except (AttributeError, MaskError, ValueError):
pass
@@ -1686,12 +1822,12 @@ class Nes(object):
Parameters
----------
- var_list : list, str
+ var_list : list, str, None
List (or single string) of the variables to be loaded.
"""
if (self.__ini_path is None) and (self.dataset is None) and (self.netcdf is None):
- raise RuntimeError('Only data from existent files can be loaded.')
+ raise RuntimeError('Only data from existing files can be loaded.')
if self.netcdf is None:
self.__open_dataset()
@@ -1927,10 +2063,6 @@ class Nes(object):
if self._time_bnds is not None:
netcdf.createDimension('time_nv', 2)
- # Create spatial_nv (number of vertices) dimension
- if (self._lat_bnds is not None) and (self._lon_bnds is not None):
- netcdf.createDimension('spatial_nv', 2)
-
# Create lev, lon and lat dimensions
netcdf.createDimension('lev', len(self.lev['data']))
netcdf.createDimension('lon', len(self._lon['data']))
@@ -1950,8 +2082,7 @@ class Nes(object):
# TIMES
time_var = netcdf.createVariable('time', np.float64, ('time',), zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
- time_var.units = 'hours since {0}'.format(
- self._time[self.get_time_id(self.hours_start, first=True)].strftime('%Y-%m-%d %H:%M:%S'))
+ time_var.units = 'hours since {0}'.format( self._time[0].strftime('%Y-%m-%d %H:%M:%S'))
time_var.standard_name = 'time'
time_var.calendar = 'standard'
time_var.long_name = 'time'
@@ -1959,9 +2090,7 @@ class Nes(object):
time_var.bounds = 'time_bnds'
if self.size > 1:
time_var.set_collective(True)
- time_var[:] = date2num(self._time[self.get_time_id(self.hours_start, first=True):
- self.get_time_id(self.hours_end, first=False)],
- time_var.units, time_var.calendar)
+ time_var[:] = date2num(self._time[:], time_var.units, time_var.calendar)
# TIME BOUNDS
if self._time_bnds is not None:
@@ -2002,7 +2131,7 @@ class Nes(object):
zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
if self.size > 1:
lat_bnds_var.set_collective(True)
- lat_bnds_var[:] = self._lat_bnds[:]
+ lat_bnds_var[:] = self._lat_bnds['data']
# LONGITUDES
lon = netcdf.createVariable('lon', np.float64, self._lon_dim, zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
@@ -2022,10 +2151,29 @@ class Nes(object):
zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
if self.size > 1:
lon_bnds_var.set_collective(True)
- lon_bnds_var[:] = self._lon_bnds[:]
+ lon_bnds_var[:] = self._lon_bnds['data']
return None
+ def _create_cell_measures(self, netcdf):
+
+ # CELL AREA
+ if 'cell_area' in self.cell_measures.keys():
+ cell_area = netcdf.createVariable('cell_area', np.float64, self._var_dim,
+ zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
+ if self.size > 1:
+ cell_area.set_collective(True)
+ cell_area[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] = self.cell_measures[
+ 'cell_area']['data']
+
+ cell_area.long_name = 'area of grid cell'
+ cell_area.standard_name = 'cell_area'
+ cell_area.units = 'm2'
+
+ for var_name in self.variables.keys():
+ self.variables[var_name]['cell_measures'] = 'area: cell_area'
+
def _create_variables(self, netcdf, chunking=False):
"""
Create the netCDF file variables.
@@ -2041,7 +2189,8 @@ class Nes(object):
for i, (var_name, var_dict) in enumerate(self.variables.items()):
if var_dict['data'] is not None:
if self.info:
- print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(self.rank, var_name, i + 1, len(self.variables)))
+ print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(
+ self.rank, var_name, i + 1, len(self.variables)))
try:
if not chunking:
var = netcdf.createVariable(var_name, var_dict['data'].dtype, ('time', 'lev',) + self._var_dim,
@@ -2055,7 +2204,8 @@ class Nes(object):
chunk_size = None
chunk_size = self.comm.bcast(chunk_size, root=0)
var = netcdf.createVariable(var_name, var_dict['data'].dtype, ('time', 'lev',) + self._var_dim,
- zlib=self.zip_lvl > 0, complevel=self.zip_lvl, chunksizes=chunk_size)
+ zlib=self.zip_lvl > 0, complevel=self.zip_lvl,
+ chunksizes=chunk_size)
if self.info:
print("Rank {0:03d}: Var {1} created ({2}/{3})".format(
self.rank, var_name, i + 1, len(self.variables)))
@@ -2179,6 +2329,11 @@ class Nes(object):
if self.info:
print("Rank {0:03d}: Dimensions done".format(self.rank))
+ # Create cell measures
+ self._create_cell_measures(netcdf)
+ if self.info:
+ print("Rank {0:03d}: Cell measures done".format(self.rank))
+
# Create variables
self._create_variables(netcdf, chunking=chunking)
@@ -2380,27 +2535,32 @@ class Nes(object):
def create_shapefile(self):
"""
Create spatial geodataframe (shapefile).
+
+ Returns
+ -------
+ shapefile : GeoPandasDataFrame
+ Shapefile dataframe.
"""
if self._lat_bnds is None or self._lon_bnds is None:
self.create_spatial_bounds()
# Reshape arrays to create geometry
- aux_shape = (self.lat_bnds.shape[0], self.lon_bnds.shape[0], 4)
+ aux_shape = (self.lat_bnds['data'].shape[0], self.lon_bnds['data'].shape[0], 4)
lon_bnds_aux = np.empty(aux_shape)
- lon_bnds_aux[:, :, 0] = self.lon_bnds[np.newaxis, :, 0]
- lon_bnds_aux[:, :, 1] = self.lon_bnds[np.newaxis, :, 1]
- lon_bnds_aux[:, :, 2] = self.lon_bnds[np.newaxis, :, 1]
- lon_bnds_aux[:, :, 3] = self.lon_bnds[np.newaxis, :, 0]
+ lon_bnds_aux[:, :, 0] = self.lon_bnds['data'][np.newaxis, :, 0]
+ lon_bnds_aux[:, :, 1] = self.lon_bnds['data'][np.newaxis, :, 1]
+ lon_bnds_aux[:, :, 2] = self.lon_bnds['data'][np.newaxis, :, 1]
+ lon_bnds_aux[:, :, 3] = self.lon_bnds['data'][np.newaxis, :, 0]
lon_bnds = lon_bnds_aux
del lon_bnds_aux
lat_bnds_aux = np.empty(aux_shape)
- lat_bnds_aux[:, :, 0] = self.lat_bnds[:, np.newaxis, 0]
- lat_bnds_aux[:, :, 1] = self.lat_bnds[:, np.newaxis, 0]
- lat_bnds_aux[:, :, 2] = self.lat_bnds[:, np.newaxis, 1]
- lat_bnds_aux[:, :, 3] = self.lat_bnds[:, np.newaxis, 1]
+ lat_bnds_aux[:, :, 0] = self.lat_bnds['data'][:, np.newaxis, 0]
+ lat_bnds_aux[:, :, 1] = self.lat_bnds['data'][:, np.newaxis, 0]
+ lat_bnds_aux[:, :, 2] = self.lat_bnds['data'][:, np.newaxis, 1]
+ lat_bnds_aux[:, :, 3] = self.lat_bnds['data'][:, np.newaxis, 1]
lat_bnds = lat_bnds_aux
del lat_bnds_aux
@@ -2456,10 +2616,9 @@ class Nes(object):
"""
Create shapefile from NES data.
- 1. Check 2D data is selected.
- 2. Create grid shapefile.
- 3. Add variables to shapefile (as independent function).
- 4. Write shapefile.
+ 1. Create grid shapefile.
+ 2. Add variables to shapefile (as independent function).
+ 3. Write shapefile.
Parameters
----------
@@ -2469,193 +2628,225 @@ class Nes(object):
Time stamp to select.
lev : int
Vertical level to select.
- var_list : list, str
+ var_list : list, str, None
List (or single string) of the variables to be loaded and saved in the shapefile.
"""
-
- # Make a copy, so we do not lose data of original dataset
- aux_nessy = self.copy(copy_vars=True, copy_comm=True)
-
+
+ # If list is not defined, get all variables
if var_list is None:
- var_list = deepcopy(aux_nessy.variables).keys()
+ var_list = list(self.variables.keys())
else:
- # Transform to list if there is only one element
if isinstance(var_list, str):
var_list = [var_list]
- # Check if variable exists
- for var_name in var_list:
- if var_name not in aux_nessy.variables.keys():
- raise ValueError('{} does not exist in dataset.'.format(var_name))
- # Skip variables (if desired)
- aux_nessy.keep_vars(var_list)
-
- # Check if time and level have to be selected
- sel_time, sel_lev = False, False
- data_list = deepcopy(aux_nessy.variables)
- for var_name in data_list.keys():
- dim = data_list[var_name]['dimensions']
- if 'time' in dim:
- sel_time = True
- elif 'lev' in dim:
- sel_lev = True
- break
+
+ # Add warning for unloaded variables
+ unloaded_vars = []
+ for var_name in var_list:
+ if self.variables[var_name]['data'] is None:
+ unloaded_vars.append(var_name)
+ if len(unloaded_vars) > 0:
+ raise ValueError('The variables {0} need to be loaded/created before using to_shapefile.'.format(
+ unloaded_vars))
# Select first vertical level (if needed)
- if sel_lev:
- if lev is None:
- msg = 'No vertical level has been specified. The first one will be selected.'
- warnings.warn(msg)
- aux_nessy.sel(lev_min=0, lev_max=0)
- else:
- if lev not in aux_nessy.lev['data']:
- raise ValueError('Level {} is not available. Choose from {}'.format(lev, aux_nessy.lev['data']))
- aux_nessy.sel(lev_min=lev, lev_max=lev)
+ if lev is None:
+ msg = 'No vertical level has been specified. The first one will be selected.'
+ warnings.warn(msg)
+ idx_lev = 0
+ else:
+ if lev not in self.lev['data']:
+ raise ValueError('Level {} is not available. Choose from {}'.format(lev, self.lev['data']))
+ idx_lev = lev
# Select first time (if needed)
- if sel_time:
- if time is None:
- msg = 'No time has been specified. The first one will be selected.'
- warnings.warn(msg)
- aux_nessy.sel(time_min=aux_nessy.time[0], time_max=aux_nessy.time[0])
- else:
- if time not in aux_nessy.time:
- raise ValueError('Time {} is not available. Choose from {}'.format(time, aux_nessy.time))
- aux_nessy.sel(time_min=time, time_max=time)
+ if time is None:
+ msg = 'No time has been specified. The first one will be selected.'
+ warnings.warn(msg)
+ idx_time = 0
+ else:
+ if time not in self.time:
+ raise ValueError('Time {} is not available. Choose from {}'.format(time, self.time))
+ idx_time = self.time.index(time)
# Create shapefile
- aux_nessy.create_shapefile()
+ self.create_shapefile()
# Load variables from original file and get data for selected time / level
- self.add_variables_to_shapefile(aux_nessy, var_list)
-
- # Add data to shapefile
- self.shapefile = aux_nessy.shapefile
+ self.add_variables_to_shapefile(var_list, idx_lev, idx_time)
# Write shapefile
- aux_nessy.write_shapefile(path)
+ self.write_shapefile(path)
return None
- def add_variables_to_shapefile(self, aux_nessy, var_list):
+ def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0):
"""
Add variables data to shapefile.
- var_list : list, str
+ var_list : list
List (or single string) of the variables to be loaded and saved in the shapefile.
+ idx_lev : int
+ Index of vertical level for which the data will be saved in the shapefile.
+ idx_time : int
+ Index of time for which the data will be saved in the shapefile.
"""
- if var_list:
-
- # Load data
- # We cannot load it directly with the function load() because aux_nessy.netcdf is None
- for i, var_name in enumerate(var_list):
- if self.info:
- print("Rank {0:03d}: Loading {1} var ({2}/{3})".format(self.rank, var_name, i + 1,
- len(var_list)))
-
- # Add data
- if aux_nessy.variables[var_name]['data'] is None:
-
- # Get data from variables in original file
- nc_var = self.variables[var_name]['data']
- var_dims = self.variables[var_name]['dimensions']
-
- # Do not add data if there is no data loaded in original
- if self.variables[var_name]['data'] is None:
- msg = 'Data for {} was not loaded, it will not be saved in the shapefile. '.format(var_name)
- msg += 'Load the data using load() if you want to include it.'
- print(msg)
- return
-
- # Add data
- aux_nessy.variables[var_name]['data'] = aux_nessy._read_variable(var_name, nc_var, var_dims)
-
- else:
- if self.master:
- print("Data for {0} was previously loaded. Skipping variable.".format(var_name))
-
- if self.info:
- print("Rank {0:03d}: Loaded {1} var ({2})".format(self.rank, var_name,
- aux_nessy.variables[var_name]['data'].shape))
-
- # Add data
- for var_name in var_list:
- aux_nessy.shapefile[var_name] = aux_nessy.variables[var_name]['data'][:].ravel()
+ for var_name in var_list:
+ self.shapefile[var_name] = self.variables[var_name]['data'][idx_time, idx_lev, :].ravel()
return None
- def spatial_join(self, mask, method=None):
+ def spatial_join(self, ext_shp, method=None, var_list=None, info=False):
"""
Compute overlay intersection of two GeoPandasDataFrames.
Parameters
----------
- mask : GeoPandasDataFrame
- File from where the data will be obtained on the intersection.
+ ext_shp : GeoPandasDataFrame, str
+ File or path from where the data will be obtained on the intersection.
method : str
- Overlay method. Accepted values: ['nearest', 'intersection', None].
+ Overlay method. Accepted values: ['nearest', 'intersection', 'centroid'].
+ var_list : list, None
+ Variables that will be included in the resulting shapefile.
"""
+ if isinstance(ext_shp, str):
+ ext_shp = gpd.read_file(ext_shp)
- # Nearest centroids to the mask polygons
- if method == 'nearest':
-
- # Make copy
- shapefile_aux = deepcopy(self.shapefile)
+ # Create shapefile if it does not exist
+ if self.shapefile is None:
+ msg = 'Shapefile does not exist. It will be created now.'
+ warnings.warn(msg)
+ grid_shp = self.create_shapefile()
+ else:
+ grid_shp = self.shapefile
+ grid_shp = copy(grid_shp)
+
+ # Get variables of interest from shapefile
+ if var_list is not None:
+ if isinstance(var_list, str):
+ var_list = [var_list]
+ ext_shp = ext_shp.loc[:, var_list + ['geometry']]
+ else:
+ var_list = ext_shp.columns.to_list()
+ var_list.remove('geometry')
- # Get centroids of shapefile to mask
- shapefile_aux.geometry = shapefile_aux.centroid
+ ext_shp = ext_shp.to_crs(grid_shp.crs)
- # Calculate spatial joint by distance
- shapefile_aux = gpd.sjoin_nearest(shapefile_aux, mask.to_crs(shapefile_aux.crs), distance_col='distance')
+ # Nearest centroids to the shapefile polygons
+ if method == 'nearest':
+ if self.master and info:
+ print("Nearest spatial joint")
+ # Get centroids
+ centroids_gdf = self.get_centroids_from_coordinates()
+
+ # From geodetic coordinates (e.g. 4326) to meters (e.g. 4328) to use sjoin_nearest
+ # TODO: Check if the projection 4328 does not distort the coordinates too much
+ # https://gis.stackexchange.com/questions/372564/userwarning-when-trying-to-get-centroid-from-a-polygon-geopandas
+ ext_shp = ext_shp.to_crs('EPSG:4328')
+ centroids_gdf = centroids_gdf.to_crs('EPSG:4328')
+ # Calculate spatial joint by distance
+ aux_grid = gpd.sjoin_nearest(centroids_gdf, ext_shp, distance_col='distance')
+
# Get data from closest shapes to centroids
- del shapefile_aux['geometry'], shapefile_aux['index_right']
- self.shapefile.loc[shapefile_aux.index, shapefile_aux.columns] = shapefile_aux
+ del aux_grid['geometry'], aux_grid['index_right']
+ self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid
- # Intersect the areas of the mask polygons, outside of the mask there will be NaN
+ # Intersect the areas of the shapefile polygons, outside the shapefile there will be NaN
elif method == 'intersection':
-
+ if self.master and info:
+ print("Intersection spatial joint")
+ grid_shp['FID_grid'] = grid_shp.index
+ grid_shp = grid_shp.reset_index()
# Get intersected areas
- inp, res = mask.sindex.query_bulk(self.shapefile.geometry, predicate='intersects')
- print('Rank {0:03d}: {1} intersected areas found'.format(self.rank, len(inp)))
-
+ # inp, res = shapefile.sindex.query_bulk(self.shapefile.geometry, predicate='intersects')
+ inp, res = grid_shp.sindex.query_bulk(ext_shp.geometry, predicate='intersects')
+
+ if self.master and info:
+ print('Rank {0:03d}: {1} intersected areas found'.format(self.rank, len(inp)))
+
# Calculate intersected areas and fractions
- intersection = pd.concat([self.shapefile.geometry[inp].reset_index(), mask.geometry[res].reset_index()],
- axis=1, ignore_index=True)
- intersection.columns = (list(self.shapefile.geometry[inp].reset_index().columns) +
- list(mask.geometry[res].reset_index().rename(columns={'geometry': 'geometry_mask',
- 'index': 'index_mask'}).columns))
- intersection['area'] = intersection.apply(lambda x: x['geometry'].intersection(x['geometry_mask']).buffer(0).area,
- axis=1)
- intersection['fraction'] = intersection.apply(lambda x: x['area'] / x['geometry'].area, axis=1)
-
- # Choose biggest area from intersected areas with multiple options
- intersection.sort_values('fraction', ascending=False, inplace=True)
+ intersection = pd.DataFrame()
+ intersection['FID'] = np.array(grid_shp.loc[res, 'FID_grid'], dtype=np.uint32)
+ intersection['ext_shp_id'] = np.array(inp, dtype=np.uint32)
+
+ # intersection['geometry_ext'] = ext_shp.loc[inp, 'geometry'].values
+ # intersection['geometry_grid'] = grid_shp.loc[res, 'geometry'].values
+ if True:
+ # No Warnings Zone
+ counts = intersection['FID'].value_counts()
+ warnings.filterwarnings('ignore')
+
+ intersection.loc[:, 'weight'] = 1.
+ for i, row in intersection.iterrows():
+ if i % 1000 == 0 and self.master and info:
+ print('\tRank {0:03d}: {1:.3f} %'.format(self.rank, i*100 / len(intersection)))
+ # Filter to do not calculate percentages over 100% grid cells spatial joint
+ if counts[row['FID']] > 1:
+ intersection.loc[i, 'weight'] = grid_shp.loc[res[i], 'geometry'].intersection(
+ ext_shp.loc[inp[i], 'geometry']).area / grid_shp.loc[res[i], 'geometry'].area
+ # intersection['intersect_area'] = intersection.apply(
+ # lambda x: x['geometry_grid'].intersection(x['geometry_ext']).area, axis=1)
+ intersection.drop(intersection[intersection['weight'] <= 0].index, inplace=True)
+
+ warnings.filterwarnings('default')
+
+ # Choose the biggest area from intersected areas with multiple options
+ intersection.sort_values('weight', ascending=False, inplace=True)
intersection = intersection.drop_duplicates(subset='FID', keep="first")
intersection = intersection.sort_values('FID').set_index('FID')
- # Get data from mask
- del mask['geometry']
- self.shapefile.loc[intersection.index, mask.columns] = np.array(mask.loc[intersection.index_mask, :])
-
- # Centroids that fall on the mask polygons, outside of the mask there will be NaN
- elif method is None:
+ for var_name in var_list:
+ self.shapefile.loc[intersection.index, var_name] = np.array(
+ ext_shp.loc[intersection['ext_shp_id'], var_name])
- # Make copy
- shapefile_aux = deepcopy(self.shapefile)
+ # Centroids that fall on the shapefile polygons, outside the shapefile there will be NaN
+ elif method == 'centroid':
- # Get centroids of shapefile to mask
- shapefile_aux.geometry = shapefile_aux.centroid
+ # Get centroids
+ centroids_gdf = self.get_centroids_from_coordinates()
# Calculate spatial joint
- shapefile_aux = gpd.sjoin(shapefile_aux, mask.to_crs(shapefile_aux.crs))
+ aux_grid = gpd.sjoin(centroids_gdf, ext_shp, predicate='within')
# Get data from shapes where there are centroids, rest will be NaN
- del shapefile_aux['geometry'], shapefile_aux['index_right']
- self.shapefile.loc[shapefile_aux.index, shapefile_aux.columns] = shapefile_aux
-
+ del aux_grid['geometry'], aux_grid['index_right']
+ self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid
+
+ else:
+ accepted_values = ['nearest', 'intersection', 'centroid']
+ raise NotImplementedError('{0} is not implemented. Choose from: {1}'.format(method, accepted_values))
+
return None
+ def get_centroids_from_coordinates(self):
+ """
+ Get centroids from geographical coordinates.
+
+ Returns
+ -------
+ centroids_gdf: GeoPandasDataFrame
+ Centroids dataframe.
+ """
+
+ # Get centroids from coordinates
+ centroids = []
+ for lat_ind in range(0, len(self.lat['data'])):
+ for lon_ind in range(0, len(self.lon['data'])):
+ centroids.append(Point(self.lon['data'][lon_ind],
+ self.lat['data'][lat_ind]))
+
+ # Create dataframe cointaining all points
+ fids = np.arange(len(self._lat['data']) * len(self._lon['data']))
+ fids = fids.reshape((len(self._lat['data']), len(self._lon['data'])))
+ fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
+ geometry=centroids,
+ crs="EPSG:4326")
+
+ return centroids_gdf
+
def __gather_data_py_object(self):
"""
Gather all the variable data into the MPI rank 0 to perform a serial write.
@@ -2715,7 +2906,8 @@ class Nes(object):
data_list[var_name]['data'] = np.concatenate(data_aux, axis=axis)
except Exception as e:
print("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name))
- sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name))
+ sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(
+ var_name))
print(e)
sys.stderr.write(str(e))
# print(e, file=sys.stderr)
@@ -2800,7 +2992,8 @@ class Nes(object):
data_list[var_name]['data'] = np.concatenate(recvbuf, axis=axis)
except Exception as e:
print("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name))
- sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name))
+ sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(
+ var_name))
print(e)
sys.stderr.write(str(e))
# print(e, file=sys.stderr)
@@ -2813,7 +3006,33 @@ class Nes(object):
# ==================================================================================================================
# Extra Methods
# ==================================================================================================================
-
+ @staticmethod
+ def lon_lat_to_cartesian_ecef(lon, lat):
+ """
+ # Convert observational/model geographic longitude/latitude coordinates to cartesian ECEF (Earth Centred,
+ # Earth Fixed) coordinates, assuming WGS84 datum and ellipsoid, and that all heights = 0.
+ # ECEF coordiantes represent positions (in meters) as X, Y, Z coordinates, approximating the earth surface
+ # as an ellipsoid of revolution.
+ # This conversion is for the subsequent calculation of euclidean distances of the model gridcell centres
+ # from each observational station.
+ # Defining the distance between two points on the earth's surface as simply the euclidean distance
+ # between the two lat/lon pairs could lead to inaccurate results depending on the distance
+ # between two points (i.e. 1 deg. of longitude varies with latitude).
+
+ Parameters
+ ----------
+ lon : np.array
+ Longitude values.
+ lat : np.array
+ Latitude values.
+ """
+
+ lla = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84')
+ ecef = pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84')
+ x, y, z = pyproj.transform(lla, ecef, lon, lat, np.zeros(lon.shape), radians=False)
+
+ return np.column_stack([x, y, z])
+
def add_4d_vertical_info(self, info_to_add):
"""
To add the vertical information from other source.
@@ -2829,7 +3048,7 @@ class Nes(object):
def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear', extrapolate=None, info=None):
"""
- Vertical methods method.
+ Vertical interpolation function.
Parameters
----------
@@ -2850,7 +3069,7 @@ class Nes(object):
self, new_levels, new_src_vertical=new_src_vertical, kind=kind, extrapolate=extrapolate, info=info)
def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='NearestNeighbour', n_neighbours=4,
- info=False, to_providentia=False):
+ info=False, to_providentia=False, only_create_wm=False, wm=None):
"""
Horizontal methods from the current grid to another one.
@@ -2861,15 +3080,68 @@ class Nes(object):
weight_matrix_path : str, None
Path to the weight matrix to read/create.
kind : str
- Kind of horizontal methods. choices = ['NearestNeighbour'].
+ Kind of horizontal methods. choices = ['NearestNeighbour', 'Conservative'].
n_neighbours: int
Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4.
info: bool
Indicates if you want to print extra info during the methods process.
to_providentia : bool
Indicates if we want the interpolated grid in Providentia format.
+ only_create_wm : bool
+ Indicates if you want to only create the Weight Matrix.
+ wm : Nes
+ Weight matrix Nes File
"""
return horizontal_interpolation.interpolate_horizontal(
self, dst_grid, weight_matrix_path=weight_matrix_path, kind=kind, n_neighbours=n_neighbours, info=info,
- to_providentia=to_providentia)
+ to_providentia=to_providentia, only_create_wm=only_create_wm, wm=wm)
+
+ def calculate_grid_area(self):
+ """
+ Get coordinate bounds and call function to calculate the area of each cell of a grid.
+
+ Parameters
+ ----------
+ self : nes.Nes
+ Source projection Nes Object.
+ """
+
+ grid_area = cell_measures.calculate_grid_area(self)
+
+ return grid_area
+
+ @staticmethod
+ def calculate_geometry_area(geometry_list, earth_radius_minor_axis=6356752.3142,
+ earth_radius_major_axis=6378137.0):
+ """
+ Get coordinate bounds and call function to calculate the area of each cell of a set of geometries.
+
+ Parameters
+ ----------
+ geometry_list : list
+ List with polygon geometries.
+ earth_radius_minor_axis : float
+ Radius of the minor axis of the Earth.
+ earth_radius_major_axis : float
+ Radius of the major axis of the Earth.
+ """
+
+ return cell_measures.calculate_geometry_area(geometry_list, earth_radius_minor_axis=earth_radius_minor_axis,
+ earth_radius_major_axis=earth_radius_major_axis)
+
+ @staticmethod
+ def get_earth_radius(ellps):
+ """
+ Get minor and major axis of Earth.
+
+ Parameters
+ ----------
+ ellps : str
+ Spatial reference system.
+ """
+
+ # WGS84 with radius defined in Cartopy source code
+ earth_radius_dict = {'WGS84': [6356752.3142, 6378137.0]}
+
+ return earth_radius_dict[ellps]
diff --git a/nes/nc_projections/latlon_nes.py b/nes/nc_projections/latlon_nes.py
index b1e996f4780f4a4a31d3c4b3c8174d016675b71e..2dfd51310f4bd56d7f14d9d12f46c2130e15aced 100644
--- a/nes/nc_projections/latlon_nes.py
+++ b/nes/nc_projections/latlon_nes.py
@@ -99,6 +99,24 @@ class LatLonNes(Nes):
return new
+ def _create_dimensions(self, netcdf):
+ """
+ Create 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat'.
+
+ Parameters
+ ----------
+ netcdf : Dataset
+ NetCDF object.
+ """
+
+ super(LatLonNes, self)._create_dimensions(netcdf)
+
+ # Create spatial_nv (number of vertices) dimension
+ if (self._lat_bnds is not None) and (self._lon_bnds is not None):
+ netcdf.createDimension('spatial_nv', 2)
+
+ return None
+
def _create_centre_coordinates(self, **kwargs):
"""
Calculate centre latitudes and longitudes from grid details.
@@ -111,19 +129,32 @@ class LatLonNes(Nes):
Dictionary with data of centre longitudes in 1D
"""
+ inc_lat = kwargs['inc_lat']
+ inc_lon = kwargs['inc_lon']
+
+ # Global domain
+ if len(kwargs) == 2:
+ lat_orig = -90
+ lon_orig = -180
+ n_lat = int(180 // inc_lat)
+ n_lon = int(360 // inc_lon)
+
+ # Other domains
+ else:
+ lat_orig = kwargs['lat_orig']
+ lon_orig = kwargs['lon_orig']
+ n_lat = kwargs['n_lat']
+ n_lon = kwargs['n_lon']
+
# Calculate centre latitudes
- lat_c_orig = kwargs['lat_orig'] + (kwargs['inc_lat'] / 2)
- centre_lat_data = np.linspace(
- lat_c_orig, lat_c_orig + (kwargs['inc_lat'] * (kwargs['n_lat'] - 1)), kwargs['n_lat'])
- centre_lat = {'data': centre_lat_data}
+ lat_c_orig = lat_orig + (inc_lat / 2)
+ centre_lat = np.linspace(lat_c_orig, lat_c_orig + (inc_lat * (n_lat - 1)), n_lat)
# Calculate centre longitudes
- lon_c_orig = kwargs['lon_orig'] + (kwargs['inc_lon'] / 2)
- centre_lon_data = np.linspace(
- lon_c_orig, lon_c_orig + (kwargs['inc_lon'] * (kwargs['n_lon'] - 1)), kwargs['n_lon'])
- centre_lon = {'data': centre_lon_data}
+ lon_c_orig = lon_orig + (inc_lon / 2)
+ centre_lon = np.linspace(lon_c_orig, lon_c_orig + (inc_lon * (n_lon - 1)), n_lon)
- return centre_lat, centre_lon
+ return {'data': centre_lat}, {'data': centre_lon}
def create_providentia_exp_centre_coordinates(self):
"""
@@ -239,3 +270,19 @@ class LatLonNes(Nes):
"""
return super(LatLonNes, self).to_grib2(path, grib_keys, grib_template_path, lat_flip=lat_flip, info=info)
+
+ def calculate_grid_area(self):
+ """
+ Get coordinate bounds and call function to calculate the area of each cell of a grid.
+
+ Parameters
+ ----------
+ self : nes.Nes
+ Source projection Nes Object.
+ """
+
+ grid_area = super(LatLonNes, self).calculate_grid_area()
+ self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0],
+ self.lon_bnds['data'].shape[0]])}
+
+ return None
diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py
index 610397631bf6a1692ffbca3bb6368aa88012f9c7..ae471d8bb2cdf192ea92ab696e31e549b4041a8c 100644
--- a/nes/nc_projections/lcc_nes.py
+++ b/nes/nc_projections/lcc_nes.py
@@ -1,12 +1,13 @@
#!/usr/bin/env python
+import warnings
import numpy as np
import pandas as pd
from cfunits import Units
from pyproj import Proj
from copy import deepcopy
import geopandas as gpd
-from shapely.geometry import Polygon
+from shapely.geometry import Polygon, Point
from .default_nes import Nes
@@ -152,23 +153,28 @@ class LCCNes(Nes):
projection_data = {'data': None,
'dimensions': (),
'grid_mapping_name': 'lambert_conformal_conic',
- 'standard_parallel': [kwargs['lat_1'], kwargs['lat_2']],
- 'longitude_of_central_meridian': kwargs['lon_0'],
- 'latitude_of_projection_origin': kwargs['lat_0'],
+ 'standard_parallel': [str(kwargs['lat_1']), str(kwargs['lat_2'])],
+ 'longitude_of_central_meridian': str(kwargs['lon_0']),
+ 'latitude_of_projection_origin': str(kwargs['lat_0']),
}
else:
- projection_data = self.variables['Lambert_conformal']
- if not isinstance(projection_data['standard_parallel'], list):
- projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0],
- projection_data['standard_parallel'].split(', ')[1]]
- self.free_vars('Lambert_conformal')
+ if 'Lambert_conformal' in self.variables.keys():
+ projection_data = self.variables['Lambert_conformal']
+ if not isinstance(projection_data['standard_parallel'], list):
+ projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0],
+ projection_data['standard_parallel'].split(', ')[1]]
+ self.free_vars('Lambert_conformal')
+ else:
+ msg = 'There is no variable called Lambert_conformal, projection has not been defined.'
+ warnings.warn(msg)
+ projection_data = None
return projection_data
def _create_dimensions(self, netcdf):
"""
- Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions ('lev', 'time').
+ Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat'
Parameters
----------
@@ -240,11 +246,11 @@ class LCCNes(Nes):
self._x = {'data': np.linspace(kwargs['x_0'] + (kwargs['inc_x'] / 2),
kwargs['x_0'] + (kwargs['inc_x'] / 2) +
(kwargs['inc_x'] * (kwargs['nx'] - 1)), kwargs['nx'],
- dtype=np.float)}
+ dtype=np.float64)}
self._y = {'data': np.linspace(kwargs['y_0'] + (kwargs['inc_y'] / 2),
kwargs['y_0'] + (kwargs['inc_y'] / 2) +
(kwargs['inc_y'] * (kwargs['ny'] - 1)), kwargs['ny'],
- dtype=np.float)}
+ dtype=np.float64)}
# Create a regular grid in metres (1D to 2D)
x = np.array([self._x['data']] * len(self._y['data']))
@@ -253,7 +259,7 @@ class LCCNes(Nes):
self.projection = Proj(
proj='lcc',
ellps='WGS84',
- R=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code)
+ R=self.earth_radius[0],
lat_1=kwargs['lat_1'],
lat_2=kwargs['lat_2'],
lon_0=kwargs['lon_0'],
@@ -261,16 +267,14 @@ class LCCNes(Nes):
to_meter=1,
x_0=0,
y_0=0,
- a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code)
+ a=self.earth_radius[1],
k_0=1.0
)
# Calculate centre latitudes and longitudes (UTM to LCC)
- centre_lon_data, centre_lat_data = self.projection(x, y, inverse=True)
- centre_lat = {'data': centre_lat_data}
- centre_lon = {'data': centre_lon_data}
+ centre_lon, centre_lat = self.projection(x, y, inverse=True)
- return centre_lat, centre_lon
+ return {'data': centre_lat}, {'data': centre_lon}
def create_providentia_exp_centre_coordinates(self):
"""
@@ -330,7 +334,7 @@ class LCCNes(Nes):
self.projection = Proj(
proj='lcc',
ellps='WGS84',
- R=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code)
+ R=self.earth_radius[0],
lat_1=float(self.projection_data['standard_parallel'][0]),
lat_2=float(self.projection_data['standard_parallel'][1]),
lon_0=float(self.projection_data['longitude_of_central_meridian']),
@@ -338,7 +342,7 @@ class LCCNes(Nes):
to_meter=1,
x_0=0,
y_0=0,
- a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code)
+ a=self.earth_radius[1],
k_0=1.0
)
grid_edge_lon_data, grid_edge_lat_data = self.projection(x_grid_edge, y_grid_edge, inverse=True)
@@ -357,18 +361,18 @@ class LCCNes(Nes):
# Calculate LCC coordinates bounds
inc_x = np.abs(np.mean(np.diff(self._x['data'])))
- x_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T,
- inc_x, spatial_nv=4, inverse=True)
+ x_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])),
+ inc_x, spatial_nv=4)
inc_y = np.abs(np.mean(np.diff(self._y['data'])))
- y_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])),
- inc_y, spatial_nv=4)
+ y_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T,
+ inc_y, spatial_nv=4, inverse=True)
# Transform LCC bounds to regular bounds
self.projection = Proj(
proj='lcc',
ellps='WGS84',
- R=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code)
+ R=self.earth_radius[0],
lat_1=float(self.projection_data['standard_parallel'][0]),
lat_2=float(self.projection_data['standard_parallel'][1]),
lon_0=float(self.projection_data['longitude_of_central_meridian']),
@@ -376,21 +380,25 @@ class LCCNes(Nes):
to_meter=1,
x_0=0,
y_0=0,
- a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code)
+ a=self.earth_radius[1],
k_0=1.0
)
lon_bnds, lat_bnds = self.projection(x_bnds, y_bnds, inverse=True)
# Obtain regular coordinates bounds
- self._lat_bnds = deepcopy(lat_bnds)
- self.lat_bnds = lat_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'],
- self.write_axis_limits['x_min']:self.write_axis_limits['x_max'],
- :]
-
- self._lon_bnds = deepcopy(lon_bnds)
- self.lon_bnds = lon_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'],
- self.write_axis_limits['x_min']:self.write_axis_limits['x_max'],
- :]
+ self._lat_bnds = {}
+ self._lat_bnds['data'] = deepcopy(lat_bnds)
+ self.lat_bnds = {}
+ self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
+ :]
+
+ self._lon_bnds = {}
+ self._lon_bnds['data'] = deepcopy(lon_bnds)
+ self.lon_bnds = {}
+ self.lon_bnds['data'] = lon_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
+ :]
return None
@@ -420,11 +428,12 @@ class LCCNes(Nes):
netcdf4-python Dataset
"""
- mapping = netcdf.createVariable('Lambert_conformal', 'c')
- mapping.grid_mapping_name = self.projection_data['grid_mapping_name']
- mapping.standard_parallel = self.projection_data['standard_parallel']
- mapping.longitude_of_central_meridian = self.projection_data['longitude_of_central_meridian']
- mapping.latitude_of_projection_origin = self.projection_data['latitude_of_projection_origin']
+ if self.projection_data is not None:
+ mapping = netcdf.createVariable('Lambert_conformal', 'c')
+ mapping.grid_mapping_name = self.projection_data['grid_mapping_name']
+ mapping.standard_parallel = self.projection_data['standard_parallel']
+ mapping.longitude_of_central_meridian = self.projection_data['longitude_of_central_meridian']
+ mapping.latitude_of_projection_origin = self.projection_data['latitude_of_projection_origin']
return None
@@ -449,6 +458,11 @@ class LCCNes(Nes):
def create_shapefile(self):
"""
Create spatial geodataframe (shapefile).
+
+ Returns
+ -------
+ shapefile : GeoPandasDataFrame
+ Shapefile dataframe.
"""
# Get latitude and longitude cell boundaries
@@ -456,10 +470,12 @@ class LCCNes(Nes):
self.create_spatial_bounds()
# Reshape arrays to create geometry
- aux_b_lats = self.lat_bnds.reshape((self.lat_bnds.shape[0] * self.lat_bnds.shape[1], self.lat_bnds.shape[2]))
- aux_b_lons = self.lon_bnds.reshape((self.lon_bnds.shape[0] * self.lon_bnds.shape[1], self.lon_bnds.shape[2]))
+ aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1],
+ self.lat_bnds['data'].shape[2]))
+ aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1],
+ self.lon_bnds['data'].shape[2]))
- # Create dataframe cointaining all polygons
+ # Get polygons from bounds
geometry = []
for i in range(aux_b_lons.shape[0]):
geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]),
@@ -467,8 +483,10 @@ class LCCNes(Nes):
(aux_b_lons[i, 2], aux_b_lats[i, 2]),
(aux_b_lons[i, 3], aux_b_lats[i, 3]),
(aux_b_lons[i, 0], aux_b_lats[i, 0])]))
+
+ # Create dataframe cointaining all polygons
fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1])
- fids = fids.reshape((self._lat['data'].shape[0],self._lat['data'].shape[1]))
+ fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1]))
fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
@@ -477,3 +495,47 @@ class LCCNes(Nes):
self.shapefile = gdf
return gdf
+
+ def get_centroids_from_coordinates(self):
+ """
+ Get centroids from geographical coordinates.
+
+ Returns
+ -------
+ centroids_gdf: GeoPandasDataFrame
+ Centroids dataframe.
+ """
+
+ # Get centroids from coordinates
+ centroids = []
+ for lat_ind in range(0, self.lon['data'].shape[0]):
+ for lon_ind in range(0, self.lon['data'].shape[1]):
+ centroids.append(Point(self.lon['data'][lat_ind, lon_ind],
+ self.lat['data'][lat_ind, lon_ind]))
+
+ # Create dataframe cointaining all points
+ fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1])
+ fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1]))
+ fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
+ geometry=centroids,
+ crs="EPSG:4326")
+
+ return centroids_gdf
+
+ def calculate_grid_area(self):
+ """
+ Get coordinate bounds and call function to calculate the area of each cell of a grid.
+
+ Parameters
+ ----------
+ self : nes.Nes
+ Source projection Nes Object.
+ """
+
+ grid_area = super(LCCNes, self).calculate_grid_area()
+ self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0],
+ self.lon_bnds['data'].shape[1]])}
+
+ return None
diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py
index 8b79b9e6aa2c5dc7e8605ba45ac74635db31563f..eec3fb0af780c3b7cc6883d68742ae11c1010f80 100644
--- a/nes/nc_projections/mercator_nes.py
+++ b/nes/nc_projections/mercator_nes.py
@@ -1,12 +1,13 @@
#!/usr/bin/env python
+import warnings
import numpy as np
import pandas as pd
from cfunits import Units
from pyproj import Proj
from copy import deepcopy
import geopandas as gpd
-from shapely.geometry import Polygon
+from shapely.geometry import Polygon, Point
from nes.nc_projections.default_nes import Nes
@@ -157,14 +158,19 @@ class MercatorNes(Nes):
}
else:
- projection_data = self.variables['mercator']
- self.free_vars('mercator')
+ if 'mercator' in self.variables.keys():
+ projection_data = self.variables['mercator']
+ self.free_vars('mercator')
+ else:
+ msg = 'There is no variable called mercator, projection has not been defined.'
+ warnings.warn(msg)
+ projection_data = None
return projection_data
def _create_dimensions(self, netcdf):
"""
- Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions ('lev', 'time').
+ Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat'
Parameters
----------
@@ -236,13 +242,13 @@ class MercatorNes(Nes):
self._x = {'data': np.linspace(kwargs['x_0'] + (kwargs['inc_x'] / 2),
kwargs['x_0'] + (kwargs['inc_x'] / 2) +
(kwargs['inc_x'] * (kwargs['nx'] - 1)), kwargs['nx'],
- dtype=np.float)}
+ dtype=np.float64)}
self._y = {'data': np.linspace(kwargs['y_0'] + (kwargs['inc_y'] / 2),
kwargs['y_0'] + (kwargs['inc_y'] / 2) +
(kwargs['inc_y'] * (kwargs['ny'] - 1)), kwargs['ny'],
- dtype=np.float)}
+ dtype=np.float64)}
# Create a regular grid in metres (1D to 2D)
x = np.array([self._x['data']] * len(self._y['data']))
@@ -250,18 +256,16 @@ class MercatorNes(Nes):
self.projection = Proj(
proj='merc',
- a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code)
- b=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code)
+ a=self.earth_radius[1],
+ b=self.earth_radius[0],
lat_ts=kwargs['lat_ts'],
lon_0=kwargs['lon_0'],
)
# Calculate centre latitudes and longitudes (UTM to Mercator)
- centre_lon_data, centre_lat_data = self.projection(x, y, inverse=True)
- centre_lat = {'data': centre_lat_data}
- centre_lon = {'data': centre_lon_data}
+ centre_lon, centre_lat = self.projection(x, y, inverse=True)
- return centre_lat, centre_lon
+ return {'data': centre_lat}, {'data': centre_lon}
def create_providentia_exp_centre_coordinates(self):
"""
@@ -320,8 +324,8 @@ class MercatorNes(Nes):
# Get edges for regular coordinates
self.projection = Proj(
proj='merc',
- a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code)
- b=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code)
+ a=self.earth_radius[1],
+ b=self.earth_radius[0],
lat_ts=float(self.projection_data['standard_parallel'][0]),
lon_0=float(self.projection_data['longitude_of_projection_origin']),
)
@@ -341,33 +345,37 @@ class MercatorNes(Nes):
# Calculate Mercator coordinates bounds
inc_x = np.abs(np.mean(np.diff(self._x['data'])))
- x_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T,
- inc_x, spatial_nv=4, inverse=True)
+ x_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])),
+ inc_x, spatial_nv=4)
inc_y = np.abs(np.mean(np.diff(self._y['data'])))
- y_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])),
- inc_y, spatial_nv=4)
+ y_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T,
+ inc_y, spatial_nv=4, inverse=True)
# Transform Mercator bounds to regular bounds
self.projection = Proj(
proj='merc',
- a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code)
- b=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code)
+ a=self.earth_radius[1],
+ b=self.earth_radius[0],
lat_ts=float(self.projection_data['standard_parallel'][0]),
lon_0=float(self.projection_data['longitude_of_projection_origin']),
)
lon_bnds, lat_bnds = self.projection(x_bnds, y_bnds, inverse=True)
# Obtain regular coordinates bounds
- self._lat_bnds = deepcopy(lat_bnds)
- self.lat_bnds = lat_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'],
- self.write_axis_limits['x_min']:self.write_axis_limits['x_max'],
- :]
-
- self._lon_bnds = deepcopy(lon_bnds)
- self.lon_bnds = lon_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'],
- self.write_axis_limits['x_min']:self.write_axis_limits['x_max'],
- :]
+ self._lat_bnds = {}
+ self._lat_bnds['data'] = deepcopy(lat_bnds)
+ self.lat_bnds = {}
+ self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
+ :]
+
+ self._lon_bnds = {}
+ self._lon_bnds['data'] = deepcopy(lon_bnds)
+ self.lon_bnds = {}
+ self.lon_bnds['data'] = lon_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
+ :]
return None
@@ -397,10 +405,11 @@ class MercatorNes(Nes):
netcdf4-python Dataset.
"""
- mapping = netcdf.createVariable('mercator', 'c')
- mapping.grid_mapping_name = self.projection_data['grid_mapping_name']
- mapping.standard_parallel = self.projection_data['standard_parallel']
- mapping.longitude_of_projection_origin = self.projection_data['longitude_of_projection_origin']
+ if self.projection_data is not None:
+ mapping = netcdf.createVariable('mercator', 'c')
+ mapping.grid_mapping_name = self.projection_data['grid_mapping_name']
+ mapping.standard_parallel = self.projection_data['standard_parallel']
+ mapping.longitude_of_projection_origin = self.projection_data['longitude_of_projection_origin']
return None
@@ -425,6 +434,11 @@ class MercatorNes(Nes):
def create_shapefile(self):
"""
Create spatial geodataframe (shapefile).
+
+ Returns
+ -------
+ shapefile : GeoPandasDataFrame
+ Shapefile dataframe.
"""
# Get latitude and longitude cell boundaries
@@ -432,10 +446,12 @@ class MercatorNes(Nes):
self.create_spatial_bounds()
# Reshape arrays to create geometry
- aux_b_lats = self.lat_bnds.reshape((self.lat_bnds.shape[0] * self.lat_bnds.shape[1], self.lat_bnds.shape[2]))
- aux_b_lons = self.lon_bnds.reshape((self.lon_bnds.shape[0] * self.lon_bnds.shape[1], self.lon_bnds.shape[2]))
+ aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1],
+ self.lat_bnds['data'].shape[2]))
+ aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1],
+ self.lon_bnds['data'].shape[2]))
- # Create dataframe cointaining all polygons
+ # Get polygons from bounds
geometry = []
for i in range(aux_b_lons.shape[0]):
geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]),
@@ -443,8 +459,10 @@ class MercatorNes(Nes):
(aux_b_lons[i, 2], aux_b_lats[i, 2]),
(aux_b_lons[i, 3], aux_b_lats[i, 3]),
(aux_b_lons[i, 0], aux_b_lats[i, 0])]))
+
+ # Create dataframe cointaining all polygons
fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1])
- fids = fids.reshape((self._lat['data'].shape[0],self._lat['data'].shape[1]))
+ fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1]))
fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
@@ -453,3 +471,47 @@ class MercatorNes(Nes):
self.shapefile = gdf
return gdf
+
+ def get_centroids_from_coordinates(self):
+ """
+ Get centroids from geographical coordinates.
+
+ Returns
+ -------
+ centroids_gdf: GeoPandasDataFrame
+ Centroids dataframe.
+ """
+
+ # Get centroids from coordinates
+ centroids = []
+ for lat_ind in range(0, self.lon['data'].shape[0]):
+ for lon_ind in range(0, self.lon['data'].shape[1]):
+ centroids.append(Point(self.lon['data'][lat_ind, lon_ind],
+ self.lat['data'][lat_ind, lon_ind]))
+
+ # Create dataframe cointaining all points
+ fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1])
+ fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1]))
+ fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
+ geometry=centroids,
+ crs="EPSG:4326")
+
+ return centroids_gdf
+
+ def calculate_grid_area(self):
+ """
+ Get coordinate bounds and call function to calculate the area of each cell of a grid.
+
+ Parameters
+ ----------
+ self : nes.Nes
+ Source projection Nes Object.
+ """
+
+ grid_area = super(MercatorNes, self).calculate_grid_area()
+ self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0],
+ self.lon_bnds['data'].shape[1]])}
+
+ return None
diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py
index 744ff206ff3c6ba1b229d378b5aa82d926f9cabd..9c3d859b48ad1c157095d42b570e9977ddd1f0e0 100644
--- a/nes/nc_projections/points_nes.py
+++ b/nes/nc_projections/points_nes.py
@@ -121,7 +121,7 @@ class PointsNes(Nes):
def _create_dimensions(self, netcdf):
"""
- Create 'lev', 'time_nv', 'station', 'spatial_nv' and 'strlen' dimensions.
+ Create 'time', 'time_nv', 'station' and 'strlen' dimensions.
Parameters
----------
@@ -278,7 +278,7 @@ class PointsNes(Nes):
return strlen
- def _read_variable(self, var_name, nc_var=None, var_dims=None):
+ def _read_variable(self, var_name):
"""
Read the corresponding variable data according to the current rank.
@@ -293,9 +293,8 @@ class PointsNes(Nes):
Portion of the variable data corresponding to the rank.
"""
- if nc_var is None and var_dims is None:
- nc_var = self.netcdf.variables[var_name]
- var_dims = nc_var.dimensions
+ nc_var = self.netcdf.variables[var_name]
+ var_dims = nc_var.dimensions
# Read data in 1 or 2 dimensions
if len(var_dims) < 2:
@@ -378,8 +377,9 @@ class PointsNes(Nes):
if 'strlen' not in var_dims:
var_dims += ('strlen',)
var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore')
- for v in var_dict['data']]).astype('S' + str(self.strlen)))
- var_dtype = 'S' + str(self.strlen)
+ for v in var_dict['data']]).astype('S' +
+ str(self.strlen)))
+ var_dtype = 'S1'
except:
pass
@@ -553,14 +553,12 @@ class PointsNes(Nes):
"""
# Calculate centre latitudes
- centre_lat_data = kwargs['lat']
- centre_lat = {'data': centre_lat_data}
+ centre_lat = kwargs['lat']
# Calculate centre longitudes
- centre_lon_data = kwargs['lon']
- centre_lon = {'data': centre_lon_data}
+ centre_lon = kwargs['lon']
- return centre_lat, centre_lon
+ return {'data': centre_lat}, {'data': centre_lon}
def _create_metadata(self, netcdf):
"""
@@ -650,19 +648,62 @@ class PointsNes(Nes):
def create_shapefile(self):
"""
Create spatial geodataframe (shapefile).
+
+ Returns
+ -------
+ shapefile : GeoPandasDataFrame
+ Shapefile dataframe.
"""
# Create dataframe cointaining all points
- geometry = gpd.points_from_xy(self.lon['data'], self.lat['data'])
- fids = np.arange(len(self._lon['data']))
- fids = fids[self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
- gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids),
- geometry=geometry,
- crs="EPSG:4326")
+ gdf = self.get_centroids_from_coordinates()
self.shapefile = gdf
return gdf
+ def get_centroids_from_coordinates(self):
+ """
+ Get centroids from geographical coordinates.
+
+ Returns
+ -------
+ centroids_gdf: GeoPandasDataFrame
+ Centroids dataframe.
+ """
+
+ # Get centroids from coordinates
+ centroids = gpd.points_from_xy(self.lon['data'], self.lat['data'])
+
+ # Create dataframe cointaining all points
+ fids = np.arange(len(self._lon['data']))
+ fids = fids[self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids),
+ geometry=centroids,
+ crs="EPSG:4326")
+
+ return centroids_gdf
+
+ def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0):
+ """
+ Add variables data to shapefile.
+
+ var_list : list, str
+ List (or single string) of the variables to be loaded and saved in the shapefile.
+ idx_lev : int
+ Index of vertical level for which the data will be saved in the shapefile.
+ idx_time : int
+ Index of time for which the data will be saved in the shapefile.
+ """
+
+ if idx_lev != 0:
+ msg = 'Error: Points dataset has no level (Level: {0}).'.format(idx_lev)
+ raise ValueError(msg)
+
+ for var_name in var_list:
+ self.shapefile[var_name] = self.variables[var_name]['data'][idx_time, :].ravel()
+
+ return None
+
@staticmethod
def _get_axis_index_(axis):
if axis == 'T':
diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py
index 53a0ee286a5d5652b9296e14e82785ddce343f52..1d95fa18baa925d5b930d411cea97d1737e7c5dd 100644
--- a/nes/nc_projections/points_nes_ghost.py
+++ b/nes/nc_projections/points_nes_ghost.py
@@ -130,7 +130,7 @@ class PointsNesGHOST(PointsNes):
def _create_dimensions(self, netcdf):
"""
Create 'N_flag_codes' and 'N_qa_codes' dimensions and the super dimensions
- ('lev', 'time_nv', 'station', 'spatial_nv' and 'strlen').
+ 'time', 'time_nv', 'station', and 'strlen'.
Parameters
----------
@@ -272,7 +272,7 @@ class PointsNesGHOST(PointsNes):
return values
- def _read_variable(self, var_name, nc_var=None, var_dims=None):
+ def _read_variable(self, var_name):
"""
Read the corresponding variable data according to the current rank.
@@ -287,9 +287,8 @@ class PointsNesGHOST(PointsNes):
Portion of the variable data corresponding to the rank.
"""
- if nc_var is None and var_dims is None:
- nc_var = self.netcdf.variables[var_name]
- var_dims = nc_var.dimensions
+ nc_var = self.netcdf.variables[var_name]
+ var_dims = nc_var.dimensions
# Read data in 1 or 2 dimensions
if len(var_dims) < 2:
@@ -362,6 +361,7 @@ class PointsNesGHOST(PointsNes):
var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore')
for v in var_dict['data']]).astype('S' +
str(self.strlen)))
+ var_dtype = 'S1'
except:
pass
@@ -586,11 +586,12 @@ class PointsNesGHOST(PointsNes):
Indicates if you want a chunked netCDF output. Only available with non serial writes. Default: False.
"""
- if not serial:
+ if (not serial) and (self.size > 1):
msg = 'WARNING!!! '
msg += 'GHOST datasets cannot be written in parallel yet. '
msg += 'Changing to serial mode.'
warnings.warn(msg)
+
super(PointsNesGHOST, self).to_netcdf(path, compression_level=compression_level,
serial=True, info=info, chunking=chunking)
@@ -766,6 +767,27 @@ class PointsNesGHOST(PointsNes):
return metadata_variables[GHOST_version]
+ def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0):
+ """
+ Add variables data to shapefile.
+
+ var_list : list, str
+ List (or single string) of the variables to be loaded and saved in the shapefile.
+ idx_lev : int
+ Index of vertical level for which the data will be saved in the shapefile.
+ idx_time : int
+ Index of time for which the data will be saved in the shapefile.
+ """
+
+ if idx_lev != 0:
+ msg = 'Error: Points dataset has no level (Level: {0}).'.format(idx_lev)
+ raise ValueError(msg)
+
+ for var_name in var_list:
+ self.shapefile[var_name] = self.variables[var_name]['data'][:, idx_time].ravel()
+
+ return None
+
@staticmethod
def _get_axis_index_(axis):
if axis == 'T':
diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py
index df515c17bade9436b2043f4745bd5f73a0eee4de..737392f31da97abc6ca00aa3075127dc36305b51 100644
--- a/nes/nc_projections/points_nes_providentia.py
+++ b/nes/nc_projections/points_nes_providentia.py
@@ -172,7 +172,7 @@ class PointsNesProvidentia(PointsNes):
def _create_dimensions(self, netcdf):
"""
Create 'grid_edge', 'model_latitude' and 'model_longitude' dimensions and the super dimensions
- ('lev', 'time_nv', 'station', 'spatial_nv', 'strlen').
+ 'time', 'time_nv', 'station', and 'strlen'.
Parameters
----------
@@ -308,7 +308,7 @@ class PointsNesProvidentia(PointsNes):
return values
- def _read_variable(self, var_name, nc_var=None, var_dims=None):
+ def _read_variable(self, var_name):
"""
Read the corresponding variable data according to the current rank.
@@ -323,9 +323,8 @@ class PointsNesProvidentia(PointsNes):
Portion of the variable data corresponding to the rank.
"""
- if nc_var is None and var_dims is None:
- nc_var = self.netcdf.variables[var_name]
- var_dims = nc_var.dimensions
+ nc_var = self.netcdf.variables[var_name]
+ var_dims = nc_var.dimensions
# Read data in 1, 2 or 3 dimensions
if len(var_dims) < 2:
@@ -398,6 +397,7 @@ class PointsNesProvidentia(PointsNes):
var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore')
for v in var_dict['data']]).astype('S' +
str(self.strlen)))
+ var_dtype = 'S1'
except:
pass
@@ -588,7 +588,7 @@ class PointsNesProvidentia(PointsNes):
Indicates if you want a chunked netCDF output. Only available with non serial writes. Default: False.
"""
- if not serial:
+ if (not serial) and (self.size > 1):
msg = 'WARNING!!! '
msg += 'Providentia datasets cannot be written in parallel yet. '
msg += 'Changing to serial mode.'
@@ -599,6 +599,27 @@ class PointsNesProvidentia(PointsNes):
return None
+ def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0):
+ """
+ Add variables data to shapefile.
+
+ var_list : list, str
+ List (or single string) of the variables to be loaded and saved in the shapefile.
+ idx_lev : int
+ Index of vertical level for which the data will be saved in the shapefile.
+ idx_time : int
+ Index of time for which the data will be saved in the shapefile.
+ """
+
+ if idx_lev != 0:
+ msg = 'Error: Points dataset has no level (Level: {0}).'.format(idx_lev)
+ raise ValueError(msg)
+
+ for var_name in var_list:
+ self.shapefile[var_name] = self.variables[var_name]['data'][:, idx_time].ravel()
+
+ return None
+
@staticmethod
def _get_axis_index_(axis):
if axis == 'T':
diff --git a/nes/nc_projections/rotated_nes.py b/nes/nc_projections/rotated_nes.py
index b968dd724f49a5577ca5654a84a1ae1a87c596ba..5e7cd32aa5c7976bd26577a4d8895590226aeb74 100644
--- a/nes/nc_projections/rotated_nes.py
+++ b/nes/nc_projections/rotated_nes.py
@@ -1,12 +1,13 @@
#!/usr/bin/env python
+import warnings
import numpy as np
import pandas as pd
import math
from cfunits import Units
from copy import deepcopy
import geopandas as gpd
-from shapely.geometry import Polygon
+from shapely.geometry import Polygon, Point
from .default_nes import Nes
@@ -156,16 +157,20 @@ class RotatedNes(Nes):
'grid_north_pole_latitude': 90 - kwargs['centre_lat'],
'grid_north_pole_longitude': -180 + kwargs['centre_lon'],
}
-
else:
- projection_data = self.variables['rotated_pole']
- self.free_vars('rotated_pole')
-
+ if 'rotated_pole' in self.variables.keys():
+ projection_data = self.variables['rotated_pole']
+ self.free_vars('rotated_pole')
+ else:
+ msg = 'There is no variable called rotated_pole, projection has not been defined.'
+ warnings.warn(msg)
+ projection_data = None
+
return projection_data
def _create_dimensions(self, netcdf):
"""
- Create 'rlat', 'rlon' and 'spatial_nv' dimensions and the super dimensions ('lev', 'time').
+ Create 'rlat', 'rlon' and 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat'.
Parameters
----------
@@ -182,6 +187,7 @@ class RotatedNes(Nes):
# Create spatial_nv (number of vertices) dimension
if (self._lat_bnds is not None) and (self._lon_bnds is not None):
netcdf.createDimension('spatial_nv', 4)
+ pass
return None
@@ -234,15 +240,16 @@ class RotatedNes(Nes):
"""
# Calculate rotated latitudes
- self.n_lat = int((abs(kwargs['south_boundary']) / kwargs['inc_rlat']) * 2 + 1)
- self.rotated_lat = np.linspace(kwargs['south_boundary'], kwargs['south_boundary'] +
- (kwargs['inc_rlat'] * (self.n_lat - 1)), self.n_lat)
+ n_lat = int((abs(kwargs['south_boundary']) / kwargs['inc_rlat']) * 2 + 1)
+ rlat = np.linspace(kwargs['south_boundary'], kwargs['south_boundary'] +
+ (kwargs['inc_rlat'] * (n_lat - 1)), n_lat)
+
# Calculate rotated longitudes
- self.n_lon = int((abs(kwargs['west_boundary']) / kwargs['inc_rlon']) * 2 + 1)
- self.rotated_lon = np.linspace(kwargs['west_boundary'], kwargs['west_boundary'] +
- (kwargs['inc_rlon'] * (self.n_lon - 1)), self.n_lon)
+ n_lon = int((abs(kwargs['west_boundary']) / kwargs['inc_rlon']) * 2 + 1)
+ rlon = np.linspace(kwargs['west_boundary'], kwargs['west_boundary'] +
+ (kwargs['inc_rlon'] * (n_lon - 1)), n_lon)
- return {'data': self.rotated_lat}, {'data': self.rotated_lon}
+ return {'data': rlat}, {'data': rlon}
def rotated2latlon(self, lon_deg, lat_deg, lon_min=-180, **kwargs):
"""
@@ -318,13 +325,11 @@ class RotatedNes(Nes):
self._rlat, self._rlon = self._create_rotated_coordinates(**kwargs)
# Calculate centre latitudes and longitudes (1D to 2D)
- centre_lon_data, centre_lat_data = self.rotated2latlon(np.array([self.rotated_lon] * len(self.rotated_lat)),
- np.array([self.rotated_lat] * len(self.rotated_lon)).T,
- **kwargs)
- centre_lon = {'data': centre_lon_data}
- centre_lat = {'data': centre_lat_data}
+ centre_lon, centre_lat = self.rotated2latlon(np.array([self._rlon['data']] * len(self._rlat['data'])),
+ np.array([self._rlat['data']] * len(self._rlon['data'])).T,
+ **kwargs)
- return centre_lat, centre_lon
+ return {'data': centre_lat}, {'data': centre_lon}
def create_providentia_exp_centre_coordinates(self):
"""
@@ -409,15 +414,19 @@ class RotatedNes(Nes):
lon_bnds, lat_bnds = self.rotated2latlon(rlon_bnds, rlat_bnds)
# Obtain regular coordinates bounds
- self._lat_bnds = deepcopy(lat_bnds)
- self.lat_bnds = lat_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'],
- self.write_axis_limits['x_min']:self.write_axis_limits['x_max'],
- :]
-
- self._lon_bnds = deepcopy(lon_bnds)
- self.lon_bnds = lon_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'],
- self.write_axis_limits['x_min']:self.write_axis_limits['x_max'],
- :]
+ self._lat_bnds = {}
+ self._lat_bnds['data'] = deepcopy(lat_bnds)
+ self.lat_bnds = {}
+ self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
+ :]
+
+ self._lon_bnds = {}
+ self._lon_bnds['data'] = deepcopy(lon_bnds)
+ self.lon_bnds = {}
+ self.lon_bnds['data']= lon_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
+ :]
return None
@@ -447,10 +456,11 @@ class RotatedNes(Nes):
netcdf4-python Dataset.
"""
- mapping = netcdf.createVariable('rotated_pole', 'c')
- mapping.grid_mapping_name = self.projection_data['grid_mapping_name']
- mapping.grid_north_pole_latitude = self.projection_data['grid_north_pole_latitude']
- mapping.grid_north_pole_longitude = self.projection_data['grid_north_pole_longitude']
+ if self.projection_data is not None:
+ mapping = netcdf.createVariable('rotated_pole', 'c')
+ mapping.grid_mapping_name = self.projection_data['grid_mapping_name']
+ mapping.grid_north_pole_latitude = self.projection_data['grid_north_pole_latitude']
+ mapping.grid_north_pole_longitude = self.projection_data['grid_north_pole_longitude']
return None
@@ -475,16 +485,23 @@ class RotatedNes(Nes):
def create_shapefile(self):
"""
Create spatial geodataframe (shapefile).
+
+ Returns
+ -------
+ shapefile : GeoPandasDataFrame
+ Shapefile dataframe.
"""
if self._lat_bnds is None or self._lon_bnds is None:
self.create_spatial_bounds()
# Reshape arrays to create geometry
- aux_b_lats = self.lat_bnds.reshape((self.lat_bnds.shape[0] * self.lat_bnds.shape[1], self.lat_bnds.shape[2]))
- aux_b_lons = self.lon_bnds.reshape((self.lon_bnds.shape[0] * self.lon_bnds.shape[1], self.lon_bnds.shape[2]))
-
- # Create dataframe cointaining all polygons
+ aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1],
+ self.lat_bnds['data'].shape[2]))
+ aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1],
+ self.lon_bnds['data'].shape[2]))
+
+ # Get polygons from bounds
geometry = []
for i in range(aux_b_lons.shape[0]):
geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]),
@@ -492,8 +509,10 @@ class RotatedNes(Nes):
(aux_b_lons[i, 2], aux_b_lats[i, 2]),
(aux_b_lons[i, 3], aux_b_lats[i, 3]),
(aux_b_lons[i, 0], aux_b_lats[i, 0])]))
+
+ # Create dataframe cointaining all polygons
fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1])
- fids = fids.reshape((self._lat['data'].shape[0],self._lat['data'].shape[1]))
+ fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1]))
fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
@@ -502,3 +521,47 @@ class RotatedNes(Nes):
self.shapefile = gdf
return gdf
+
+ def get_centroids_from_coordinates(self):
+ """
+ Get centroids from geographical coordinates.
+
+ Returns
+ -------
+ centroids_gdf: GeoPandasDataFrame
+ Centroids dataframe.
+ """
+
+ # Get centroids from coordinates
+ centroids = []
+ for lat_ind in range(0, self.lon['data'].shape[0]):
+ for lon_ind in range(0, self.lon['data'].shape[1]):
+ centroids.append(Point(self.lon['data'][lat_ind, lon_ind],
+ self.lat['data'][lat_ind, lon_ind]))
+
+ # Create dataframe cointaining all points
+ fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1])
+ fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1]))
+ fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
+ self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]
+ centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
+ geometry=centroids,
+ crs="EPSG:4326")
+
+ return centroids_gdf
+
+ def calculate_grid_area(self):
+ """
+ Get coordinate bounds and call function to calculate the area of each cell of a grid.
+
+ Parameters
+ ----------
+ self : nes.Nes
+ Source projection Nes Object.
+ """
+
+ grid_area = super(RotatedNes, self).calculate_grid_area()
+ self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0],
+ self.lon_bnds['data'].shape[1]])}
+
+ return None
diff --git a/requirements.txt b/requirements.txt
index 74a35d2b0177f006ea26eeb08b997d3cc0c4bc1f..a40856e17a26c8879f41f335d629cc759a16e843 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,14 +1,16 @@
-pycodestyle~=2.8.0
-geopandas~=0.10.2
-pandas~=1.2.2
-netcdf4
-numpy~=1.21.5
-timezonefinder~=5.2.0
+pycodestyle>=2.10.0
+geopandas>=0.10.2
+rtree>=0.9.0
+pandas>=1.3.5
+netcdf4>=1.6.2
+numpy>=1.20.0
pyproj~=3.2.1
-setuptools~=47.1.0
-pytest~=6.2.5
-Shapely~=1.8.0
-scipy
-filelock
-pyproj~=3.2.1
-eccodes-python~=0.9.5
\ No newline at end of file
+setuptools>=66.1.1
+pytest>=7.2.1
+Shapely>=2.0.0
+scipy>=1.7.3
+filelock>=3.9.0
+eccodes-python~=0.9.5
+cfunits>=3.3.5
+xarray>=0.20.2
+mpi4py>=3.1.4
\ No newline at end of file
diff --git a/tests/1-test_read_write_size.py b/tests/1-test_read_write_size.py
deleted file mode 100644
index e545fadf190480843312c1797f9d0e10a1752d2e..0000000000000000000000000000000000000000
--- a/tests/1-test_read_write_size.py
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/usr/bin/env python
-import timeit
-import sys
-from nes import *
-import pandas as pd
-# from mpi4py import MPI
-
-# test_list = [{'in_file': "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc",
-# 'prefix': 'small'},
-# {'in_file': "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2021080212.nc",
-# 'prefix': 'medium'},
-# {'in_file': "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2017123000.nc",
-# 'prefix': 'large'},
-# ]
-test_list = [{'in_file': "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2017123000.nc",
- 'prefix': 'large'},
- ]
-# parallel_method = 'X'
-# parallel_method = 'Y'
-parallel_method = 'T'
-
-
-times = pd.DataFrame(index=[0])
-# size = MPI.COMM_WORLD.Get_size()
-
-for test in test_list:
- # ======================================================================================================================
-
- st_time = timeit.default_timer()
- # ===== Reading
- nessy = open_netcdf(path=test['in_file'], parallel_method=parallel_method)
- size = nessy.size
- if nessy.master:
- print("\n=====", test['prefix'], "=====")
-
- nessy.keep_vars(['O3', 'T', 'U', 'V', 'layer_thickness'])
- # nessy.keep_vars(['O3'])
- # ===== END Reading
- spent_time = timeit.default_timer() - st_time
- if nessy.master:
- print("Init time {0:02d}:{1:02.3f} (tot: {2:.3f} s)".format(
- int(spent_time // 60), spent_time - (int(spent_time // 60) * 60), spent_time))
- sys.stdout.flush()
- times["{0}_Init".format(test['prefix'])] = spent_time
-
- # ======================================================================================================================
-
- st_time = timeit.default_timer()
- # ===== Loading
- nessy.load()
- # ===== END Loading
- spent_time = timeit.default_timer() - st_time
- if nessy.master:
- print("Loading time {min:02d}:{sec:02.3f} (tot: {tot:.3f} s ; var: {var:.3f}s/var)".format(
- min=int(spent_time // 60), sec=spent_time - (int(spent_time // 60) * 60),
- var=spent_time / len(nessy.variables.keys()), tot=spent_time))
- sys.stdout.flush()
- times["{0}_Load".format(test['prefix'])] = spent_time
-
- # ======================================================================================================================
-
- if test['prefix'] not in ['large']:
- st_time = timeit.default_timer()
- # ===== Serial Write
- nessy.to_netcdf('nc_test_{0}_{1}_serial.nc'.format(test['prefix'], nessy.size), serial=True)
- # ===== END Serial Write
- spent_time = timeit.default_timer() - st_time
- if nessy.master:
- print("Writing in serial time {min:02d}:{sec:02.3f} (tot: {tot:.3f} s ; var: {var:.3f}s/var)".format(
- min=int(spent_time // 60), sec=spent_time - (int(spent_time // 60) * 60),
- var=spent_time / len(nessy.variables.keys()), tot=spent_time))
- sys.stdout.flush()
- times["{0}_Serial".format(test['prefix'])] = spent_time
-
- # ======================================================================================================================
-
- st_time = timeit.default_timer()
- # ===== Parallel Chunk Write
- nessy.to_netcdf('nc_test_{0}_{1}_parallel_chunked.nc'.format(test['prefix'], nessy.size), chunking=True)
- # ===== END Parallel Chunk Write
- spent_time = timeit.default_timer() - st_time
- if nessy.master:
- print("Writing in chunked parallel time {min:02d}:{sec:02.3f} (tot: {tot:.3f} s ; var: {var:.3f}s/var)".format(
- min=int(spent_time // 60), sec=spent_time - (int(spent_time // 60) * 60),
- var=spent_time / len(nessy.variables.keys()), tot=spent_time))
- sys.stdout.flush()
- times["{0}_Chunk".format(test['prefix'])] = spent_time
-
- # ======================================================================================================================
-
- st_time = timeit.default_timer()
- # ===== Parallel Write
- nessy.to_netcdf('nc_test_{0}_{1}_parallel.nc'.format(test['prefix'], nessy.size))
- # ===== END Parallel Write
- spent_time = timeit.default_timer() - st_time
- if nessy.master:
- print("Writing in parallel time {min:02d}:{sec:02.3f} (tot: {tot:.3f} s ; var: {var:.3f}s/var)".format(
- min=int(spent_time // 60), sec=spent_time - (int(spent_time // 60) * 60),
- var=spent_time / len(nessy.variables.keys()), tot=spent_time))
- sys.stdout.flush()
- times["{0}_Parallel".format(test['prefix'])] = spent_time
-
- # ======================================================================================================================
-
-
-times.to_csv("times_{0}.csv".format(str(size).zfill(3)))
diff --git a/tests/1.1-test_read_write_projection.py b/tests/1.1-test_read_write_projection.py
new file mode 100644
index 0000000000000000000000000000000000000000..37fd9cd6d57cc6ebea829a624f809ee2e3fcac26
--- /dev/null
+++ b/tests/1.1-test_read_write_projection.py
@@ -0,0 +1,216 @@
+#!/usr/bin/env python
+
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+
+result_path = "Times_test_1.1_read_write_projection_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'write'],
+ columns=['1.1.1.Regular', '1.1.2.Rotated', '1.1.3.Points', '1.1.4.Points_GHOST',
+ '1.1.5.LCC', '1.1.6.Mercator'])
+
+# ======================================================================================================================
+# ============================================= REGULAR ========================================================
+# ======================================================================================================================
+
+test_name = '1.1.1.Regular'
+if rank == 0:
+ print(test_name)
+
+# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc
+# Regular lat-lon grid from MONARCH
+path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# LOAD VARIABLES
+variables = ['O3']
+nessy.keep_vars(variables)
+nessy.load()
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ============================================= ROTATED ========================================================
+# ======================================================================================================================
+
+test_name = '1.1.2.Rotated'
+if rank == 0:
+ print(test_name)
+
+# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc
+# Rotated grid from MONARCH
+path = '/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# LOAD VARIABLES
+variables = ['O3_all']
+nessy.keep_vars(variables)
+nessy.load()
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+
+# ======================================================================================================================
+# ============================================= LCC ============================================================
+# ======================================================================================================================
+
+test_name = '1.1.5.LCC'
+if rank == 0:
+ print(test_name)
+
+# Original path: /esarchive/exp/snes/a5g1/ip/daily_max/sconco3/sconco3_2022111500.nc
+# LCC grid with a coverage over the Iberian Peninsula (4x4km)
+path = '/gpfs/projects/bsc32/models/NES_tutorial_data/sconco3_2022111500.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# LOAD VARIABLES
+nessy.load()
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ============================================= MERCATOR ========================================================
+# ======================================================================================================================
+
+test_name = '1.1.6.Mercator'
+if rank == 0:
+ print(test_name)
+
+# Original path: None (generated with NES)
+# Mercator grid
+path = '/gpfs/projects/bsc32/models/NES_tutorial_data/mercator_grid.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# LOAD VARIABLES
+nessy.load()
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
+
+# ======================================================================================================================
+# ============================================= POINTS =========================================================
+# ======================================================================================================================
+
+test_name = '1.1.3.Points'
+if rank == 0:
+ print(test_name)
+
+# Original path: /esarchive/obs/nilu/ebas/daily/pm10/pm10_201507.nc
+# Points grid from EBAS network
+path = '/gpfs/projects/bsc32/models/NES_tutorial_data/pm10_201507.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# LOAD VARIABLES
+nessy.load()
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ============================================= POINTS GHOST ===================================================
+# ======================================================================================================================
+
+test_name = '1.1.4.Points_GHOST'
+if rank == 0:
+ print(test_name)
+
+path = '/gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.4/hourly/sconco3/sconco3_201906.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# LOAD VARIABLES
+nessy.load()
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
diff --git a/tests/1.2-test_create_projection.py b/tests/1.2-test_create_projection.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6e41254bcd23035df7ea9333687f311e569f142
--- /dev/null
+++ b/tests/1.2-test_create_projection.py
@@ -0,0 +1,189 @@
+#!/usr/bin/env python
+
+import sys
+from mpi4py import MPI
+import pandas as pd
+import timeit
+from nes import *
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+
+result_path = "Times_test_1.2_create_projection_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['create', 'write'],
+ columns=['1.2.1.Regular', '1.2.2.Rotated', '1.2.3.LCC', '1.2.4.Mercator', '1.2.5.Global'])
+
+# ======================================================================================================================
+# ============================================= REGULAR ========================================================
+# ======================================================================================================================
+
+test_name = '1.2.1.Regular'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_orig = 41.1
+lon_orig = 1.8
+inc_lat = 0.01
+inc_lon = 0.01
+n_lat = 100
+n_lon = 100
+nessy = create_nes(projection='regular', parallel_method=parallel_method,
+ lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, n_lat=n_lat, n_lon=n_lon)
+
+comm.Barrier()
+result.loc['create', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ============================================= ROTATED ========================================================
+# ======================================================================================================================
+
+test_name = '1.2.2.Rotated'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+centre_lat = 51
+centre_lon = 10
+west_boundary = -35
+south_boundary = -27
+inc_rlat = 0.2
+inc_rlon = 0.2
+nessy = create_nes(projection='rotated', parallel_method=parallel_method,
+ centre_lat=centre_lat, centre_lon=centre_lon,
+ west_boundary=west_boundary, south_boundary=south_boundary,
+ inc_rlat=inc_rlat, inc_rlon=inc_rlon)
+
+comm.Barrier()
+result.loc['create', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ============================================= LCC ========================================================
+# ======================================================================================================================
+
+test_name = '1.2.3.LCC'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+nessy = create_nes(projection='lcc', parallel_method=parallel_method,
+ lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)
+
+comm.Barrier()
+result.loc['create', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ============================================= MERCATOR ========================================================
+# ======================================================================================================================
+
+test_name = '1.2.4.Mercator'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_ts = -1.5
+lon_0 = -18.0
+nx = 210
+ny = 236
+inc_x = 50000
+inc_y = 50000
+x_0 = -126017.5
+y_0 = -5407460.0
+nessy = create_nes(projection='mercator', parallel_method=parallel_method,
+ lat_ts=lat_ts, lon_0=lon_0, nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)
+
+comm.Barrier()
+result.loc['create', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ============================================== GLOBAL ========================================================
+# ======================================================================================================================
+
+test_name = '1.2.5.Global'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+inc_lat = 0.1
+inc_lon = 0.1
+nessy = create_nes(projection='global', parallel_method=parallel_method, inc_lat=inc_lat, inc_lon=inc_lon)
+
+comm.Barrier()
+result.loc['create', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/1.3-test_selecting.py b/tests/1.3-test_selecting.py
new file mode 100644
index 0000000000000000000000000000000000000000..72ab997edfd221cc944309136e194ba57e04a40f
--- /dev/null
+++ b/tests/1.3-test_selecting.py
@@ -0,0 +1,184 @@
+#!/usr/bin/env python
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+import os, sys
+from datetime import datetime
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+serial_write = True
+
+result_path = "Times_test_1.3.Selecting_{0}_{1:03d}.csv".format(parallel_method, size)
+
+result = pd.DataFrame(index=['read', 'calcul', 'write'],
+ columns=['1.3.1.LatLon', '1.3.2.Level', '1.3.3.Time', '1.3.4.Time_min', '1.3.5.Time_max'])
+
+# NAMEE
+src_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc"
+var_list = ['O3']
+
+# ======================================================================================================================
+# ====================================== '1.3.1.LatLon' =====================================================
+# ======================================================================================================================
+test_name = '1.3.1.LatLon'
+
+if rank == 0:
+ print(test_name)
+
+st_time = timeit.default_timer()
+
+# Source data
+nessy = open_netcdf(src_path, parallel_method=parallel_method, balanced=True)
+nessy.keep_vars(var_list)
+nessy.sel(lat_min=35, lat_max=45, lon_min=-9, lon_max=5)
+
+nessy.load()
+
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write)
+
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== 1.3.2.Level =====================================================
+# ======================================================================================================================
+test_name = '1.3.2.Level'
+
+if rank == 0:
+ print(test_name)
+
+st_time = timeit.default_timer()
+
+# Source data
+nessy = open_netcdf(src_path, parallel_method=parallel_method)
+nessy.keep_vars(var_list)
+nessy.sel(lev_min=3, lev_max=5)
+
+nessy.load()
+
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write)
+
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== 1.3.3.Time =====================================================
+# ======================================================================================================================
+test_name = '1.3.3.Time'
+
+if rank == 0:
+ print(test_name)
+
+st_time = timeit.default_timer()
+
+# Source data
+nessy = open_netcdf(src_path, parallel_method=parallel_method)
+nessy.keep_vars(var_list)
+nessy.sel(time_min=datetime(year=2022, month=11, day=16, hour=0),
+ time_max=datetime(year=2022, month=11, day=16, hour=0))
+
+nessy.load()
+
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write)
+
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== '1.3.4.Time_min' =====================================================
+# ======================================================================================================================
+test_name = '1.3.4.Time_min'
+
+if rank == 0:
+ print(test_name)
+
+st_time = timeit.default_timer()
+
+# Source data
+nessy = open_netcdf(src_path, parallel_method=parallel_method)
+nessy.keep_vars(var_list)
+nessy.sel(time_min=datetime(year=2022, month=11, day=16, hour=0))
+
+nessy.load()
+
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write)
+
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== '1.3.5.Time_max' =====================================================
+# ======================================================================================================================
+test_name = '1.3.5.Time_max'
+
+if rank == 0:
+ print(test_name)
+
+st_time = timeit.default_timer()
+
+# Source data
+nessy = open_netcdf(src_path, parallel_method=parallel_method)
+nessy.keep_vars(var_list)
+nessy.sel(time_max=datetime(year=2022, month=11, day=16, hour=0))
+
+nessy.load()
+
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write)
+
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
+ print("TEST PASSED SUCCESSFULLY")
diff --git a/tests/2-test_read_write_projection.py b/tests/2-test_read_write_projection.py
deleted file mode 100644
index 5f4590c1482f5e2690643caeaaf765ab9db2cbb2..0000000000000000000000000000000000000000
--- a/tests/2-test_read_write_projection.py
+++ /dev/null
@@ -1,135 +0,0 @@
-#!/usr/bin/env python
-import sys
-import timeit
-import pandas as pd
-from mpi4py import MPI
-from nes import *
-
-paths = {'regular_file': {'path': '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc',
- 'projection': 'regular',
- 'variables': ['O3'],
- 'parallel_methods': ['X', 'Y', 'T']},
- 'rotated_file': {'path': '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc',
- 'projection': 'rotated',
- 'variables': ['O3_all'],
- 'parallel_methods': ['X', 'Y', 'T']},
- 'points_file': {'path': '/esarchive/obs/eea/eionet/hourly/pm10/pm10_202107.nc',
- 'projection': 'points',
- 'variables': [], # all
- 'parallel_methods': ['X', 'T']},
- 'points_ghost_file': {'path': '/gpfs/projects/bsc32/AC_cache/obs/ghost/EANET/1.4/daily/sconcso4/sconcso4_201911.nc',
- 'projection': 'points_ghost',
- 'variables': [], # all
- 'parallel_methods': ['X', 'T']},
- 'lcc_file': {'path': '/esarchive/exp/wrf-hermes-cmaq/b075/eu/hourly/pm10/pm10_2022062600.nc',
- 'projection': 'lcc',
- 'variables': [], # all
- 'parallel_methods': ['X', 'Y', 'T']},
- 'mercator_file': {'path': '/esarchive/scratch/avilanova/software/NES/tutorials/data/mercator_grid_example.nc',
- 'projection': 'mercator',
- 'variables': [], # all
- 'parallel_methods': ['X', 'Y', 'T']}
- }
-
-results = []
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-size = comm.Get_size()
-
-for name, dict in paths.items():
-
- path = dict['path']
- projection = dict['projection']
- variables = dict['variables']
- parallel_methods = dict['parallel_methods']
-
- for parallel_method in parallel_methods:
-
- if rank == 0:
- print('TEST TO USE {0} GRID IN {1} FOR {2} USING {3} NODES'.format(
- projection.upper(), parallel_method, path, size))
- sys.stdout.flush()
-
- try:
-
- # Read
- start_time = timeit.default_timer()
- nessy_1 = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method)
- open_time = timeit.default_timer() - start_time
-
- # Select variables and load
- start_time = timeit.default_timer()
- if len(variables) > 0:
- nessy_1.keep_vars(variables)
- nessy_1.load()
- load_time = timeit.default_timer() - start_time
- comm.Barrier()
-
- # Write in serial
- if rank == 0:
- print('WRITE IN SERIAL')
- sys.stdout.flush()
- start_time = timeit.default_timer()
- nessy_1.to_netcdf('{0}_{1}_file_{2}_serial.nc'.format(size, projection, parallel_method),
- info=True, serial=True)
- serial_time = timeit.default_timer() - start_time
- comm.Barrier()
-
- # Write in parallel
- if rank == 0:
- print('WRITE IN PARALLEL')
- sys.stdout.flush()
- start_time = timeit.default_timer()
- nessy_1.to_netcdf('{0}_{1}_file_{2}_parallel.nc'.format(size, projection, parallel_method),
- info=True)
- parallel_time = timeit.default_timer() - start_time
- comm.Barrier()
-
- # Write in chunks
- if rank == 0:
- print('WRITE IN CHUNKS')
- sys.stdout.flush()
- start_time = timeit.default_timer()
- nessy_1.to_netcdf('{0}_{1}_file_{2}_chunking.nc'.format(size, projection, parallel_method),
- info=True, chunking=True)
- chunking_time = timeit.default_timer() - start_time
- comm.Barrier()
-
- # Close everything
- del nessy_1
-
- if rank == 0:
- print('Test was successful for {0} projection in {1}'.format(projection, parallel_method))
- sys.stdout.flush()
-
- # End timer and save results
- results.append({'Projection': projection,
- 'Method': parallel_method,
- 'Open': '{min:02d}:{sec:02.3f}'.format(
- min=int(open_time // 60), sec=open_time - (int(open_time // 60) * 60)),
- 'Load': '{min:02d}:{sec:02.3f}'.format(
- min=int(load_time // 60), sec=load_time - (int(load_time // 60) * 60)),
- 'Serial': '{min:02d}:{sec:02.3f}'.format(
- min=int(serial_time // 60), sec=serial_time - (int(serial_time // 60) * 60)),
- 'Chunking': '{min:02d}:{sec:02.3f}'.format(
- min=int(chunking_time // 60), sec=chunking_time - (int(chunking_time // 60) * 60)),
- 'Parallel': '{min:02d}:{sec:02.3f}'.format(
- min=int(parallel_time // 60), sec=parallel_time - (int(parallel_time // 60) * 60))
- })
-
- comm.Barrier()
-
- except Exception as e:
- print(e)
-
- sys.stdout.flush()
-
-comm.Barrier()
-
-if rank == 0:
- table = pd.DataFrame(results)
- print('RESULTS TABLE')
- print(table)
- table.to_csv('{0}_results.csv'.format(size))
- sys.stdout.flush()
diff --git a/tests/2-test_read_write_projection_nord3v2.bash b/tests/2-test_read_write_projection_nord3v2.bash
deleted file mode 100644
index 4f7eac151bfa80b6a16328289db257bb4d9952d4..0000000000000000000000000000000000000000
--- a/tests/2-test_read_write_projection_nord3v2.bash
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/bin/bash
-
-#EXPORTPATH="/esarchive/scratch/avilanova/software/NES"
-EXPORTPATH="/gpfs/projects/bsc32/models/NES"
-SRCPATH="/gpfs/projects/bsc32/models/NES/tests"
-EXE="2-test_read_write_projection.py"
-
-module purge
-module load Python/3.7.4-GCCcore-8.3.0
-module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4
-module load cfunits/1.8-foss-2019b-Python-3.7.4
-module load xarray/0.17.0-foss-2019b-Python-3.7.4
-module load pandas/1.2.4-foss-2019b-Python-3.7.4
-module load mpi4py/3.0.3-foss-2019b-Python-3.7.4
-module load filelock/3.7.1-foss-2019b-Python-3.7.4
-module load pyproj/2.5.0-foss-2019b-Python-3.7.4
-module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4
-module load geopandas/0.8.1-foss-2019b-Python-3.7.4
-module load Shapely/1.7.1-foss-2019b-Python-3.7.4
-
-for nprocs in 1 2 4 8 16 32
-do
- JOB_ID=`sbatch --ntasks=${nprocs} --exclusive --job-name=nes_${nprocs} --output=./log_nord3v2_NES_${nprocs}_%J.out --error=./log_nord3v2_NES_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"`
-done
\ No newline at end of file
diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py
new file mode 100644
index 0000000000000000000000000000000000000000..8aedaba3c58dda1b7daa9da3de868f0f8621ad59
--- /dev/null
+++ b/tests/2.1-test_spatial_join.py
@@ -0,0 +1,214 @@
+#!/usr/bin/env python
+
+import sys
+from mpi4py import MPI
+import pandas as pd
+import timeit
+from nes import *
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+
+result_path = "Times_test_2.1_spatial_join_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'calculate', 'write'],
+ columns=['2.1.1.Existing_file_centroid', '2.1.2.New_file_centroid',
+ '2.1.3.Existing_file_nearest', '2.1.4.New_file_nearest',
+ '2.1.5.Existing_file_intersection', '2.1.6.New_file_intersection'])
+
+# ===== PATH TO MASK ===== #
+shapefile_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp'
+
+# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc
+# Regular lat-lon grid from MONARCH
+original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc'
+
+# ======================================================================================================================
+# =================================== CENTROID EXISTING FILE ===================================================
+# ======================================================================================================================
+
+test_name = '2.1.1.Existing_file_centroid'
+if rank == 0:
+ print(test_name)
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(original_path, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# SPATIAL JOIN
+# Method can be centroid, nearest and intersection
+st_time = timeit.default_timer()
+nessy.spatial_join(shapefile_path, method='centroid')
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# =================================== CENTROID FROM NEW FILE ===================================================
+# ======================================================================================================================
+
+test_name = '2.1.2.New_file_centroid'
+if rank == 0:
+ print(test_name)
+
+# DEFINE PROJECTION
+st_time = timeit.default_timer()
+projection = 'regular'
+lat_orig = 41.1
+lon_orig = 1.8
+inc_lat = 0.2
+inc_lon = 0.2
+n_lat = 100
+n_lon = 100
+
+# SPATIAL JOIN
+# Method can be centroid, nearest and intersection
+nessy = from_shapefile(shapefile_path, method='centroid', projection=projection,
+ lat_orig=lat_orig, lon_orig=lon_orig,
+ inc_lat=inc_lat, inc_lon=inc_lon,
+ n_lat=n_lat, n_lon=n_lon)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# =================================== NEAREST EXISTING FILE ===================================================
+# ======================================================================================================================
+
+test_name = '2.1.3.Existing_file_nearest'
+if rank == 0:
+ print(test_name)
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(original_path, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# SPATIAL JOIN
+# Method can be centroid, nearest and intersection
+st_time = timeit.default_timer()
+nessy.spatial_join(shapefile_path, method='nearest')
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# =================================== NEAREST FROM NEW FILE ===================================================
+# ======================================================================================================================
+
+test_name = '2.1.4.New_file_nearest'
+if rank == 0:
+ print(test_name)
+
+# DEFINE PROJECTION
+st_time = timeit.default_timer()
+projection = 'regular'
+lat_orig = 41.1
+lon_orig = 1.8
+inc_lat = 0.2
+inc_lon = 0.2
+n_lat = 100
+n_lon = 100
+
+# SPATIAL JOIN
+# Method can be centroid, nearest and intersection
+nessy = from_shapefile(shapefile_path, method='nearest', projection=projection,
+ lat_orig=lat_orig, lon_orig=lon_orig,
+ inc_lat=inc_lat, inc_lon=inc_lon,
+ n_lat=n_lat, n_lon=n_lon)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+
+# ======================================================================================================================
+# =================================== INTERSECTION EXISTING FILE ===================================================
+# ======================================================================================================================
+
+test_name = '2.1.5.Existing_file_intersection'
+if rank == 0:
+ print(test_name)
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(original_path, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# SPATIAL JOIN
+# Method can be centroid, nearest and intersection
+st_time = timeit.default_timer()
+nessy.spatial_join(shapefile_path, method='intersection')
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+
+# ======================================================================================================================
+# =================================== INTERSECTION FROM NEW FILE ===================================================
+# ======================================================================================================================
+
+test_name = '2.1.6.New_file_intersection'
+if rank == 0:
+ print(test_name)
+
+# DEFINE PROJECTION
+st_time = timeit.default_timer()
+projection = 'regular'
+lat_orig = 41.1
+lon_orig = 1.8
+inc_lat = 0.2
+inc_lon = 0.2
+n_lat = 100
+n_lon = 100
+
+# SPATIAL JOIN
+# Method can be centroid, nearest and intersection
+nessy = from_shapefile(shapefile_path, method='intersection', projection=projection,
+ lat_orig=lat_orig, lon_orig=lon_orig,
+ inc_lat=inc_lat, inc_lon=inc_lon,
+ n_lat=n_lat, n_lon=n_lon)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/2.2-test_create_shapefile.py b/tests/2.2-test_create_shapefile.py
new file mode 100644
index 0000000000000000000000000000000000000000..d41b59304d5158ffb1f9d53f4f45615e57a41574
--- /dev/null
+++ b/tests/2.2-test_create_shapefile.py
@@ -0,0 +1,200 @@
+#!/usr/bin/env python
+
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+import datetime
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+
+result_path = "Times_test_2.2_create_shapefile_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'calculate'],
+ columns=['2.2.1.Existing', '2.2.2.New_Regular',
+ '2.2.3.New_Rotated', '2.2.4.New_LCC', '2.2.5.New_Mercator'])
+
+# ======================================================================================================================
+# ===================================== CREATE SHAPEFILE FROM EXISTING GRID ==========================================
+# ======================================================================================================================
+
+test_name = '2.2.1.Existing'
+if rank == 0:
+ print(test_name)
+
+# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc
+# Regular lat-lon grid from MONARCH
+path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(path=path, info=True, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# LOAD VARIABLES
+nessy.load()
+
+# CREATE SHAPEFILE
+st_time = timeit.default_timer()
+nessy.to_shapefile(path='regular_shp',
+ time=datetime.datetime(2019, 1, 1, 10, 0),
+ lev=0, var_list=['sconcno2'])
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+print('FROM EXISTING GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ===================================== CREATE SHAPEFILE FROM NEW REGULAR GRID =======================================
+# ======================================================================================================================
+
+test_name = '2.2.2.New_Regular'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_orig = 41.1
+lon_orig = 1.8
+inc_lat = 0.1
+inc_lon = 0.1
+n_lat = 50
+n_lon = 100
+nessy = create_nes(comm=None, info=False, projection='regular',
+ lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon,
+ n_lat=n_lat, n_lon=n_lon)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CREATE SHAPEFILE
+st_time = timeit.default_timer()
+nessy.create_shapefile()
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+print('FROM NEW REGULAR GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ===================================== CREATE SHAPEFILE FROM NEW ROTATED GRID =======================================
+# ======================================================================================================================
+
+test_name = '2.2.3.New_Rotated'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+centre_lat = 51
+centre_lon = 10
+west_boundary = -35
+south_boundary = -27
+inc_rlat = 0.2
+inc_rlon = 0.2
+nessy = create_nes(comm=None, info=False, projection='rotated',
+ centre_lat=centre_lat, centre_lon=centre_lon,
+ west_boundary=west_boundary, south_boundary=south_boundary,
+ inc_rlat=inc_rlat, inc_rlon=inc_rlon)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CREATE SHAPEFILE
+st_time = timeit.default_timer()
+nessy.create_shapefile()
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+print('FROM NEW ROTATED GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ===================================== CREATE SHAPEFILE FROM NEW LCC GRID ===========================================
+# ======================================================================================================================
+
+test_name = '2.2.4.New_LCC'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 100
+ny = 200
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+nessy = create_nes(comm=None, info=False, projection='lcc',
+ lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CREATE SHAPEFILE
+st_time = timeit.default_timer()
+nessy.create_shapefile()
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+print('FROM NEW LCC GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ===================================== CREATE SHAPEFILE FROM NEW MERCATOR GRID ======================================
+# ======================================================================================================================
+
+test_name = '2-2.5.New_Mercator'
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_ts = -1.5
+lon_0 = -18.0
+nx = 100
+ny = 50
+inc_x = 50000
+inc_y = 50000
+x_0 = -126017.5
+y_0 = -5407460.0
+nessy = create_nes(comm=None, info=False, projection='mercator',
+ lat_ts=lat_ts, lon_0=lon_0, nx=nx, ny=ny,
+ inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CREATE SHAPEFILE
+st_time = timeit.default_timer()
+nessy.create_shapefile()
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+print('FROM NEW MERCATOR GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/2.3-test_bounds.py b/tests/2.3-test_bounds.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bee2ede76cee4e96dd3e9031ce4209505e292a7
--- /dev/null
+++ b/tests/2.3-test_bounds.py
@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+
+result_path = "Times_test_2.3_bounds_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'calculate', 'write'],
+ columns=['2.3.1.With_bounds', '2.3.2.Without_bounds', "2.3.3.Create_new"])
+
+# ======================================================================================================================
+# ===================================== FILE WITH EXISTING BOUNDS ====================================================
+# ======================================================================================================================
+
+test_name = "2.3.1.With_bounds"
+if rank == 0:
+ print(test_name)
+
+# READ
+st_time = timeit.default_timer()
+# Original path: /gpfs/scratch/bsc32/bsc32538/HERMESv3/OUT_Complete_single/GFAS_p13h/HERMESv3_GR_GFAS_d01_2022050100.nc
+# Rotated grid from HERMES
+path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/HERMESv3_GR_GFAS_d01_2022050100.nc'
+nessy_1 = open_netcdf(path=path_1, parallel_method=parallel_method, info=True)
+
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# EXPLORE BOUNDS
+st_time = timeit.default_timer()
+print('FILE WITH EXISTING BOUNDS - Rank', rank, '-', 'Lat bounds', nessy_1.lat_bnds)
+print('FILE WITH EXISTING BOUNDS - Rank', rank, '-', 'Lon bounds', nessy_1.lon_bnds)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+nessy_1.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+# REOPEN
+nessy_2 = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+
+# LOAD DATA AND EXPLORE BOUNDS
+print('FILE WITH EXISTING BOUNDS AFTER WRITE - Rank', rank, '-', 'Lat bounds', nessy_2.lat_bnds)
+print('FILE WITH EXISTING BOUNDS AFTER WRITE - Rank', rank, '-', 'Lon bounds', nessy_2.lon_bnds)
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# =================================== FILE WITHOUT EXISTING BOUNDS ===================================================
+# ======================================================================================================================
+
+test_name = '2.3.2.Without_bounds'
+if rank == 0:
+ print(test_name)
+
+# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc
+# Rotated grid from MONARCH
+st_time = timeit.default_timer()
+path_3 = "/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc"
+nessy_3 = open_netcdf(path=path_3, parallel_method=parallel_method, info=True)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CREATE BOUNDS
+st_time = timeit.default_timer()
+nessy_3.create_spatial_bounds()
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# EXPLORE BOUNDS
+print('FILE WITHOUT EXISTING BOUNDS - Rank', rank, '-', 'Lat bounds', nessy_3.lat_bnds)
+print('FILE WITHOUT EXISTING BOUNDS - Rank', rank, '-', 'Lon bounds', nessy_3.lon_bnds)
+
+# WRITE
+st_time = timeit.default_timer()
+nessy_3.to_netcdf('/tmp/bounds_file_2.nc', info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+# REOPEN
+nessy_4 = open_netcdf('/tmp/bounds_file_2.nc', info=True)
+
+# LOAD DATA AND EXPLORE BOUNDS
+print('FILE WITH EXISTING BOUNDS AFTER WRITE - Rank', rank, '-', 'Lat bounds', nessy_4.lat_bnds)
+print('FILE WITH EXISTING BOUNDS AFTER WRITE - Rank', rank, '-', 'Lon bounds', nessy_4.lon_bnds)
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ==================================== CREATE NES REGULAR LAT-LON ====================================================
+# ======================================================================================================================
+
+test_name = "2.3.3.Create_new"
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_orig = 41.1
+lon_orig = 1.8
+inc_lat = 0.2
+inc_lon = 0.2
+n_lat = 100
+n_lon = 100
+nessy_5 = create_nes(comm=None, parallel_method=parallel_method, info=True, projection='regular',
+ lat_orig=lat_orig, lon_orig=lon_orig,
+ inc_lat=inc_lat, inc_lon=inc_lon,
+ n_lat=n_lat, n_lon=n_lon)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CREATE BOUNDS
+st_time = timeit.default_timer()
+nessy_5.create_spatial_bounds()
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# EXPLORE BOUNDS
+print('FROM NEW GRID - Rank', rank, '-', 'Lat bounds', nessy_5.lat_bnds)
+print('FROM NEW GRID - Rank', rank, '-', 'Lon bounds', nessy_5.lon_bnds)
+
+# WRITE
+st_time = timeit.default_timer()
+nessy_5.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+# REOPENcomm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+nessy_6 = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True)
+
+# LOAD DATA AND EXPLORE BOUNDS
+print('FROM NEW GRID AFTER WRITE - Rank', rank, '-', 'Lat bounds', nessy_6.lat_bnds)
+print('FROM NEW GRID AFTER WRITE - Rank', rank, '-', 'Lon bounds', nessy_6.lon_bnds)
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/2.4-test_cell_area.py b/tests/2.4-test_cell_area.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c9b152c2c1b7e7dc9c0d25b06eaa10a8b79a4cb
--- /dev/null
+++ b/tests/2.4-test_cell_area.py
@@ -0,0 +1,194 @@
+#!/usr/bin/env python
+
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+
+result_path = "Times_test_2.4_cell_area_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'calculate', 'write'],
+ columns=['2.4.1.New_file_grid_area', '2.4.2.New_file_geometry_area',
+ '2.4.3.Existing_file_grid_area', '2.4.4.Existing_file_geometry_area'])
+
+# ======================================================================================================================
+# ===================================== CALCULATE CELLS AREA FROM NEW GRID ===========================================
+# ======================================================================================================================
+
+test_name = "2.4.1.New_file_grid_area"
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 20
+ny = 40
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+nessy = create_nes(comm=None, info=False, projection='lcc',
+ lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CALCULATE AREA OF EACH CELL IN GRID
+st_time = timeit.default_timer()
+nessy.calculate_grid_area()
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# EXPLORE GRID AREA
+print('Rank {0:03d}: Calculate grid cell area: {1}'.format(rank, nessy.cell_measures['cell_area']))
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+# REOPEN
+# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+
+# EXPLORE GRID AREA
+print('Rank {0:03d}: Write grid cell area: {1}'.format(rank, nessy.cell_measures['cell_area']))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+del nessy
+
+# ======================================================================================================================
+# ===================================== CALCULATE CELLS AREA FROM GEOMETRIES =========================================
+# ======================================================================================================================
+
+test_name = "2.4.2.New_file_geometry_area"
+if rank == 0:
+ print(test_name)
+
+# CREATE GRID
+st_time = timeit.default_timer()
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 20
+ny = 40
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+nessy = create_nes(comm=None, info=False, projection='lcc',
+ lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CALCULATE AREA OF EACH CELL POLYGON
+st_time = timeit.default_timer()
+nessy.create_shapefile()
+geometry_list = nessy.shapefile['geometry'].values
+geometry_area = calculate_geometry_area(geometry_list)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# EXPLORE GEOMETRIES AREA
+print('Rank {0:03d}: Calculate geometry cell area: {1}'.format(rank, geometry_area))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ===================================== CALCULATE CELLS AREA FROM EXISTING GRID ======================================
+# ======================================================================================================================
+
+test_name = '2.4.3.Existing_file_grid_area'
+if rank == 0:
+ print(test_name)
+
+# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc
+# Rotated grid from MONARCH
+original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(original_path, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CALCULATE AREA OF EACH CELL IN GRID
+st_time = timeit.default_timer()
+nessy.calculate_grid_area()
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# EXPLORE GRID AREA
+print('Rank {0:03d}: Calculate grid cell area: {1}'.format(rank, nessy.cell_measures['cell_area']))
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+# REOPEN
+# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+del nessy
+
+# ======================================================================================================================
+# ===================================== CALCULATE CELLS AREA FROM GEOMETRIES FROM EXISTING GRID ======================
+# ======================================================================================================================
+
+test_name = '2.4.4.Existing_file_geometry_area'
+if rank == 0:
+ print(test_name)
+
+# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc
+# Rotated grid from MONARCH
+original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(original_path, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# CALCULATE AREA OF EACH CELL POLYGON
+st_time = timeit.default_timer()
+nessy.create_shapefile()
+geometry_list = nessy.shapefile['geometry'].values
+geometry_area = calculate_geometry_area(geometry_list)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# EXPLORE GEOMETRIES AREA
+print('Rank {0:03d}: Calculate geometry cell area: {1}'.format(rank, geometry_area))
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+del nessy
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/3-test_spatial_join.py b/tests/3-test_spatial_join.py
deleted file mode 100644
index a438eefd5c0974125cf169f49b99545544e29439..0000000000000000000000000000000000000000
--- a/tests/3-test_spatial_join.py
+++ /dev/null
@@ -1,124 +0,0 @@
-#!/usr/bin/env python
-import geopandas as gpd
-import pandas as pd
-import timeit
-import sys
-from mpi4py import MPI
-from nes import *
-
-# Hide warning
-pd.options.mode.chained_assignment = None
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-size = comm.Get_size()
-
-results = []
-for method in ['spatial_overlay', 'spatial_join']:
- for projection in ['regular', 'rotated']:
- for projection_type in ['created', 'read']:
-
- # Regular projection
- if projection == 'regular':
- # Create dataset and get shapefile
- if projection_type == 'created':
- lat_orig = 41.1
- lon_orig = 1.8
- inc_lat = 0.2
- inc_lon = 0.2
- n_lat = 100
- n_lon = 100
- coordinates = create_nes(comm=None, info=True,
- projection='regular',
- lat_orig=lat_orig,
- lon_orig=lon_orig,
- inc_lat=inc_lat,
- inc_lon=inc_lon,
- n_lat=n_lat,
- n_lon=n_lon)
- coordinates.create_shapefile()
-
- # Open dataset and get shapefile
- elif projection_type == 'read':
- coordinates_path = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc'
- coordinates = open_netcdf(path=coordinates_path, info=True)
- coordinates.create_shapefile()
- coordinates.keep_vars(['O3'])
- coordinates.load()
- coordinates.shapefile['O3'] = coordinates.variables['O3']['data'][-1, -1, :].ravel()
-
- # Rotated projection
- elif projection == 'rotated':
- # Create dataset and get shapefile
- if projection_type == 'created':
- centre_lat = 51
- centre_lon = 10
- west_boundary = -35
- south_boundary = -27
- inc_rlat = 0.2
- inc_rlon = 0.2
- coordinates = create_nes(comm=None, info=True,
- projection='rotated',
- centre_lat=centre_lat,
- centre_lon=centre_lon,
- west_boundary=west_boundary,
- south_boundary=south_boundary,
- inc_rlat=inc_rlat,
- inc_rlon=inc_rlon)
- coordinates.create_shapefile()
-
- # Open dataset and get shapefile
- elif projection_type == 'read':
- coordinates_path = '/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc'
- coordinates = open_netcdf(path=coordinates_path, info=True)
- coordinates.create_shapefile()
- coordinates.keep_vars(['O3'])
- coordinates.load()
- coordinates.shapefile['O3'] = coordinates.variables['O3']['data'][-1, -1, :].ravel()
-
- coordinates.write_shapefile('coordinates_{0}_{1}'.format(projection,
- projection_type))
-
- mask_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp'
- mask = gpd.read_file(mask_path)
-
- # Spatial overlay (old method)
- if method == 'spatial_overlay':
- start_time = timeit.default_timer()
- intersection = coordinates.shapefile.copy()
- #intersection['area'] = intersection.geometry.area
- intersection = coordinates.spatial_overlays(intersection, mask)
- #intersection.rename(columns={'idx1': 'FID', 'idx2': 'shp_id'}, inplace=True)
- #intersection['fraction'] = intersection.geometry.area / intersection['area']
- #intersection.sort_values('fraction', ascending=False, inplace=True)
- #intersection = intersection.drop_duplicates(subset='FID', keep="first")
- #intersection.set_index('FID', inplace=True)
- #coordinates.loc[intersection.index, coordinates.shp_colname] = intersection[coordinates.shp_colname]
- time = timeit.default_timer() - start_time
-
- # Spatial join (new method)
- elif method == 'spatial_join':
- start_time = timeit.default_timer()
- coordinates.spatial_join(mask, method='intersection')
- time = timeit.default_timer() - start_time
-
- coordinates.write_shapefile('masked_coordinates_{0}_{1}_{2}'.format(projection,
- projection_type,
- method))
-
- results = []
- results.append({'Projection': projection,
- 'Projection type': projection_type,
- 'Method': '{min:02d}:{sec:02.3f}'.format(
- min=int(time // 60), sec=time - (int(time // 60) * 60))
- })
-
- comm.Barrier()
-
-comm.Barrier()
-
-if rank == 0:
- table = pd.DataFrame(results)
- print('RESULTS TABLE')
- print(table)
- sys.stdout.flush()
diff --git a/tests/3-test_spatial_join_nord3v2.bash b/tests/3-test_spatial_join_nord3v2.bash
deleted file mode 100644
index 079aa7928696599a280f2c555704dbd543b65385..0000000000000000000000000000000000000000
--- a/tests/3-test_spatial_join_nord3v2.bash
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/bin/bash
-
-EXPORTPATH="/esarchive/scratch/avilanova/software/NES"
-SRCPATH="/esarchive/scratch/avilanova/software/NES/tests"
-EXE="3-test_spatial_join.py"
-
-module purge
-module load Python/3.7.4-GCCcore-8.3.0
-module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4
-module load cfunits/1.8-foss-2019b-Python-3.7.4
-module load xarray/0.17.0-foss-2019b-Python-3.7.4
-module load pandas/1.2.4-foss-2019b-Python-3.7.4
-module load mpi4py/3.0.3-foss-2019b-Python-3.7.4
-module load filelock/3.7.1-foss-2019b-Python-3.7.4
-module load pyproj/2.5.0-foss-2019b-Python-3.7.4
-module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4
-module load geopandas/0.8.1-foss-2019b-Python-3.7.4
-module load Shapely/1.7.1-foss-2019b-Python-3.7.4
-
-for nprocs in 1
-do
- JOB_ID=`sbatch --ntasks=${nprocs} --exclusive --job-name=nes_${nprocs} --output=./log_nord3v2_NES_${nprocs}_%J.out --error=./log_nord3v2_NES_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"`
-done
\ No newline at end of file
diff --git a/tests/3.1-test_vertical_interp.py b/tests/3.1-test_vertical_interp.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7bcfed201572bdbe10fb50c1c1314f8367398f8
--- /dev/null
+++ b/tests/3.1-test_vertical_interp.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+import os, sys
+from datetime import datetime
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'T'
+
+result_path = "Times_test_3.1_vertical_interp_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'calculate'],
+ columns=['3.1.1.Interp'])
+
+# ======================================================================================================================
+# =============================================== VERTICAL INTERPOLATION =============================================
+# ======================================================================================================================
+
+test_name = '3.1.1.Interp'
+if rank == 0:
+ print(test_name)
+
+# READ
+st_time = timeit.default_timer()
+
+# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc
+# Rotated grid from MONARCH
+source_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'
+
+# Read source data
+source_data = open_netcdf(path=source_path, info=True)
+
+# Select time and load variables
+source_data.keep_vars(['O3', 'mid_layer_height_agl'])
+source_data.load()
+
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# INTERPOLATE
+st_time = timeit.default_timer()
+source_data.vertical_var_name = 'mid_layer_height_agl'
+level_list = [0., 50., 100., 250., 500., 750., 1000., 2000., 3000., 5000.]
+interp_nes = source_data.interpolate_vertical(level_list, info=True, kind='linear', extrapolate=None)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+interp_nes.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/3.2-test_horiz_interp_bilinear.py b/tests/3.2-test_horiz_interp_bilinear.py
new file mode 100644
index 0000000000000000000000000000000000000000..097085a7b85704be793ec60831419c652453dd56
--- /dev/null
+++ b/tests/3.2-test_horiz_interp_bilinear.py
@@ -0,0 +1,220 @@
+#!/usr/bin/env python
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+import os, sys
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'T'
+
+result_path = "Times_test_3.2_horiz_interp_bilinear_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'calculate', 'write'],
+ columns=['3.2.1.Only interp', '3.2.2.Create_WM', "3.2.3.Use_WM", "3.2.4.Read_WM"])
+
+# NAMEE
+src_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc"
+var_list = ['O3']
+
+# ======================================================================================================================
+# ====================================== Only interp =====================================================
+# ======================================================================================================================
+test_name = '3.2.1.NN_Only interp'
+if rank == 0:
+ print(test_name)
+
+# READING
+st_time = timeit.default_timer()
+
+# Source data
+src_nes = open_netcdf(src_path, parallel_method=parallel_method)
+src_nes.keep_vars(var_list)
+src_nes.load()
+
+# Destination Grid
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method,
+ times=src_nes.get_full_times())
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+
+interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='NN')
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+interp_nes.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== Create_WM =====================================================
+# ======================================================================================================================
+test_name = '3.2.2.NN_Create_WM'
+if rank == 0:
+ print(test_name)
+
+# READ
+st_time = timeit.default_timer()
+
+# Read source data
+src_nes = open_netcdf(src_path, parallel_method=parallel_method)
+src_nes.keep_vars(var_list)
+src_nes.load()
+
+# Destination Grid
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method,
+ times=src_nes.get_full_times())
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# Cleaning WM
+if os.path.exists("NN_WM_NAMEE_to_IP.nc") and rank == 0:
+ os.remove("NN_WM_NAMEE_to_IP.nc")
+comm.Barrier()
+
+st_time = timeit.default_timer()
+
+wm_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='NN', info=True,
+ weight_matrix_path="NN_WM_NAMEE_to_IP.nc", only_create_wm=True)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== Use_WM =====================================================
+# ======================================================================================================================
+test_name = "3.2.3.NN_Use_WM"
+if rank == 0:
+ print(test_name)
+
+# READING
+st_time = timeit.default_timer()
+
+# Source data
+src_nes = open_netcdf(src_path, parallel_method=parallel_method)
+src_nes.keep_vars(var_list)
+src_nes.load()
+
+# Destination Grid
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+
+dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method,
+ times=src_nes.get_full_times())
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+
+interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='NN', wm=wm_nes)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+interp_nes.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== Read_WM =====================================================
+# ======================================================================================================================
+test_name = "3.2.4.NN_Read_WM"
+if rank == 0:
+ print(test_name)
+
+# READING
+st_time = timeit.default_timer()
+
+# Source data
+src_nes = open_netcdf(src_path, parallel_method=parallel_method)
+src_nes.keep_vars(var_list)
+src_nes.load()
+
+# Destination Grid
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+
+dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method,
+ times=src_nes.get_full_times())
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+
+interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='NN',
+ weight_matrix_path="NN_WM_NAMEE_to_IP.nc")
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+interp_nes.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/3.3-test_horiz_interp_conservative.py b/tests/3.3-test_horiz_interp_conservative.py
new file mode 100644
index 0000000000000000000000000000000000000000..51981244a7e9a54a3cbc38defe32f4fe9f000f44
--- /dev/null
+++ b/tests/3.3-test_horiz_interp_conservative.py
@@ -0,0 +1,234 @@
+#!/usr/bin/env python
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+import os, sys
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+
+result_path = "Times_test_3.3_horiz_interp_conservative.py_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'calculate', 'write'],
+ columns=['3.3.1.Only interp', '3.3.2.Create_WM', "3.3.3.Use_WM", "3.3.4.Read_WM"])
+
+# NAMEE
+src_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc"
+var_list = ['O3']
+
+# ======================================================================================================================
+# ====================================== Only interp =====================================================
+# ======================================================================================================================
+
+test_name = '3.3.1.Only interp'
+if rank == 0:
+ print(test_name)
+
+# READ
+# final_dst.variables[var_name]['data'][time, lev] = np.sum(weights * src_aux, axis=1)
+
+st_time = timeit.default_timer()
+
+# Read source data
+src_nes = open_netcdf(src_path, parallel_method=parallel_method)
+src_nes.keep_vars(var_list)
+src_nes.load()
+
+# Create destination grid
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method,
+ times=src_nes.get_full_times())
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+st_time = timeit.default_timer()
+
+# INTERPOLATE
+interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative')
+# interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', weight_matrix_path='T_WM.nc')
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+interp_nes.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=True)
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== Create_WM =====================================================
+# ======================================================================================================================
+
+test_name = '3.3.2.Create_WM'
+if rank == 0:
+ print(test_name)
+
+# READING
+st_time = timeit.default_timer()
+
+# Read source data
+src_nes = open_netcdf(src_path, parallel_method=parallel_method)
+src_nes.keep_vars(var_list)
+src_nes.load()
+
+# Create destination grid
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method,
+ times=src_nes.get_full_times())
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# Cleaning WM
+if os.path.exists("CONS_WM_NAMEE_to_IP.nc") and rank == 0:
+ os.remove("CONS_WM_NAMEE_to_IP.nc")
+comm.Barrier()
+
+# INTERPOLATE
+st_time = timeit.default_timer()
+wm_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True,
+ weight_matrix_path="CONS_WM_NAMEE_to_IP.nc", only_create_wm=True)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+# st_time = timeit.default_timer()
+# interp_nes.to_netcdf(test_name.replace(' ', '_') + ".nc")
+# comm.Barrier()
+# result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== Use_WM =====================================================
+# ======================================================================================================================
+
+test_name = "3.3.3.Use_WM"
+if rank == 0:
+ print(test_name)
+
+# READ
+st_time = timeit.default_timer()
+
+# Read source data
+src_nes = open_netcdf(src_path, parallel_method=parallel_method)
+src_nes.keep_vars(var_list)
+src_nes.load()
+
+# Create destination grid
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method,
+ times=src_nes.get_full_times())
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# INTERPOLATE
+st_time = timeit.default_timer()
+interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', wm=wm_nes)
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+interp_nes.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+# ======================================================================================================================
+# ====================================== Read_WM =====================================================
+# ======================================================================================================================
+
+test_name = "3.3.4.Read_WM"
+if rank == 0:
+ print(test_name)
+
+# READ
+st_time = timeit.default_timer()
+
+# Read source data
+src_nes = open_netcdf(src_path, parallel_method=parallel_method)
+src_nes.keep_vars(var_list)
+src_nes.load()
+
+# Create destination grid
+lat_1 = 37
+lat_2 = 43
+lon_0 = -3
+lat_0 = 40
+nx = 397
+ny = 397
+inc_x = 4000
+inc_y = 4000
+x_0 = -807847.688
+y_0 = -797137.125
+dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,
+ nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method,
+ times=src_nes.get_full_times())
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# INTERPOLATE
+st_time = timeit.default_timer()
+interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', weight_matrix_path="CONS_WM_NAMEE_to_IP.nc")
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+interp_nes.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/4-test_bounds.py b/tests/4-test_bounds.py
deleted file mode 100644
index e3d3063fddd238f0dc601a6a1c2837e1bcd98e94..0000000000000000000000000000000000000000
--- a/tests/4-test_bounds.py
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/usr/bin/env python
-import sys
-import timeit
-import pandas as pd
-from mpi4py import MPI
-from nes import *
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-
-for projection_type in ['read', 'created']:
-
- # Open dataset
- if projection_type == 'read':
- test_path = "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc"
- nessy = open_netcdf(path=test_path, info=True)
-
- # Create dataset
- elif projection_type == 'created':
- lat_orig = 41.1
- lon_orig = 1.8
- inc_lat = 0.2
- inc_lon = 0.2
- n_lat = 100
- n_lon = 100
- nessy = create_nes(comm=None, info=True, projection='regular',
- lat_orig=lat_orig, lon_orig=lon_orig,
- inc_lat=inc_lat, inc_lon=inc_lon,
- n_lat=n_lat, n_lon=n_lon)
-
- # Add bounds
- nessy.create_spatial_bounds()
-
- print('NES', projection_type, '-', 'Rank', rank, '-', nessy)
- print('NES', projection_type, '-', 'Rank', rank, '-', 'Lat bounds',
- nessy.lat_bnds)
- print('NES', projection_type, '-', 'Rank', rank, '-', 'Lon bounds',
- nessy.lon_bnds)
-
- comm.Barrier()
- sys.stdout.flush()
\ No newline at end of file
diff --git a/tests/4-test_bounds_nord3v2.bash b/tests/4-test_bounds_nord3v2.bash
deleted file mode 100644
index fc6e06b0824d8682c80372867da0d429df1a80b6..0000000000000000000000000000000000000000
--- a/tests/4-test_bounds_nord3v2.bash
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/bin/bash
-
-EXPORTPATH="/esarchive/scratch/avilanova/software/NES"
-SRCPATH="/esarchive/scratch/avilanova/software/NES/tests"
-EXE="4-test_bounds.py"
-
-module purge
-module load Python/3.7.4-GCCcore-8.3.0
-module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4
-module load cfunits/1.8-foss-2019b-Python-3.7.4
-module load xarray/0.17.0-foss-2019b-Python-3.7.4
-module load pandas/1.2.4-foss-2019b-Python-3.7.4
-module load mpi4py/3.0.3-foss-2019b-Python-3.7.4
-module load filelock/3.7.1-foss-2019b-Python-3.7.4
-module load pyproj/2.5.0-foss-2019b-Python-3.7.4
-module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4
-module load geopandas/0.8.1-foss-2019b-Python-3.7.4
-module load Shapely/1.7.1-foss-2019b-Python-3.7.4
-
-for nprocs in 1 2
-do
- JOB_ID=`sbatch --ntasks=${nprocs} --exclusive --job-name=nes_${nprocs} --output=./log_nord3v2_NES_${nprocs}_%J.out --error=./log_nord3v2_NES_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"`
-done
\ No newline at end of file
diff --git a/tests/4.1-test_daily_stats.py b/tests/4.1-test_daily_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd00407b3206e3927de1bc3eb0dd5755b7c47ea7
--- /dev/null
+++ b/tests/4.1-test_daily_stats.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+
+import sys
+import timeit
+import pandas as pd
+from mpi4py import MPI
+from nes import *
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+parallel_method = 'Y'
+
+result_path = "Times_test_4.1_daily_stats_{0}_{1:03d}.csv".format(parallel_method, size)
+result = pd.DataFrame(index=['read', 'calculate', 'write'],
+ columns=['4.1.1.Mean'])
+
+# ======================================================================================================================
+# ================================================== CALCULATE MEAN ==================================================
+# ======================================================================================================================
+
+test_name = '4.1.1.Mean'
+if rank == 0:
+ print(test_name)
+
+# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc
+# Rotated grid from MONARCH
+cams_file = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'
+
+# READ
+st_time = timeit.default_timer()
+nessy = open_netcdf(path=cams_file, info=True, parallel_method=parallel_method)
+comm.Barrier()
+result.loc['read', test_name] = timeit.default_timer() - st_time
+
+# LOAD VARIABLES
+nessy.keep_vars('O3')
+nessy.load()
+
+# CALCULATE MEAN
+st_time = timeit.default_timer()
+nessy.daily_statistic(op="mean")
+print(nessy.variables['O3']['cell_methods'])
+comm.Barrier()
+result.loc['calculate', test_name] = timeit.default_timer() - st_time
+
+# WRITE
+st_time = timeit.default_timer()
+nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size))
+comm.Barrier()
+result.loc['write', test_name] = timeit.default_timer() - st_time
+
+comm.Barrier()
+if rank == 0:
+ print(result.loc[:, test_name])
+sys.stdout.flush()
+
+if rank == 0:
+ result.to_csv(result_path)
diff --git a/tests/run_scalability_tests_nord3v2.sh b/tests/run_scalability_tests_nord3v2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..bece03097051b1c2993a2865da42b8a8db36f411
--- /dev/null
+++ b/tests/run_scalability_tests_nord3v2.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+
+EXPORTPATH="/gpfs/projects/bsc32/models/NES_master"
+SRCPATH="/gpfs/projects/bsc32/models/NES_master/tests"
+
+module purge
+module load Python/3.7.4-GCCcore-8.3.0
+module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4
+module load cfunits/1.8-foss-2019b-Python-3.7.4
+module load xarray/0.17.0-foss-2019b-Python-3.7.4
+module load pandas/1.2.4-foss-2019b-Python-3.7.4
+module load mpi4py/3.0.3-foss-2019b-Python-3.7.4
+module load filelock/3.7.1-foss-2019b-Python-3.7.4
+module load pyproj/2.5.0-foss-2019b-Python-3.7.4
+module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4
+module load geopandas/0.10.2-foss-2019b-Python-3.7.4
+module load Shapely/1.7.1-foss-2019b-Python-3.7.4
+
+#EXE="1.1-test_read_write_projection.py"
+#EXE="1.2-test_create_projection.py"
+#EXE="2.1-test_spatial_join.py"
+#EXE="2.2-test_create_shapefile.py"
+#EXE="2.3-test_bounds.py"
+#EXE="2.4-test_cell_area.py"
+#EXE="3.1-test_vertical_interp.py"
+#EXE="3.2-test_horiz_interp_bilinear.py"
+#EXE="3.3-test_horiz_interp_conservative.py"
+#EXE="4.1-test_daily_stats.py"
+
+# for nprocs in 1 2 4 8 16
+# do
+# JOB_ID=`sbatch --ntasks=${nprocs} --qos=debug --exclusive --job-name=NES_${EXE}_${nprocs} --output=./log_NES-${EXE}_nord3v2_${nprocs}_%J.out --error=./log_NES-${EXE}_nord3v2_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"`
+# done
+
+for EXE in "1.1-test_read_write_projection.py" "1.2-test_create_projection.py" "2.1-test_spatial_join.py" "2.2-test_create_shapefile.py" "2.3-test_bounds.py" "2.4-test_cell_area.py" "3.1-test_vertical_interp.py" "3.2-test_horiz_interp_bilinear.py" "3.3-test_horiz_interp_conservative.py" "4.1-test_daily_stats.py"
+ do
+ for nprocs in 1 2 4 8 16
+ do
+ JOB_ID=`sbatch --ntasks=${nprocs} --qos=debug --exclusive --job-name=NES_${EXE}_${nprocs} --output=./log_NES-${EXE}_nord3v2_${nprocs}_%J.out --error=./log_NES-${EXE}_nord3v2_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"`
+ done
+ done
diff --git a/tests/sbatch_test_MN4.cmd b/tests/sbatch_test_MN4.cmd
new file mode 100644
index 0000000000000000000000000000000000000000..09011158b106e8716aab17388ac4c3e5a26c1569
--- /dev/null
+++ b/tests/sbatch_test_MN4.cmd
@@ -0,0 +1,35 @@
+#!/bin/bash
+
+#SBATCH --qos=debug
+#SBATCH -A bsc32
+#SBATCH --cpus-per-task=1
+#SBATCH -n 1
+#SBATCH -t 00:30:00
+#SBATCH -J NES_mn4_tests
+#SBATCH --output=log_mn4_NES_%j.out
+#SBATCH --error=log_mn4_NES_%j.err
+#SBATCH --exclusive
+
+### ulimit -s 128000
+
+module purge
+module use /gpfs/projects/bsc32/software/suselinux/11/modules/all
+
+module load Python/3.7.4-GCCcore-8.3.0
+module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4
+module load cfunits/1.8-foss-2019b-Python-3.7.4
+module load xarray/0.17.0-foss-2019b-Python-3.7.4
+module load OpenMPI/4.0.5-GCC-8.3.0-mn4
+module load filelock/3.7.1-foss-2019b-Python-3.7.4
+module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4
+module load pyproj/2.5.0-foss-2019b-Python-3.7.4
+module load geopandas/0.8.1-foss-2019b-Python-3.7.4
+module load Shapely/1.7.1-foss-2019b-Python-3.7.4
+
+# export PYTHONPATH=/gpfs/projects/bsc32/models/NES_master:${PYTHONPATH}
+# cd /gpfs/projects/bsc32/models/NES_master/tests
+
+export PYTHONPATH=/gpfs/scratch/bsc32/bsc32538/NES_tests/NES:${PYTHONPATH}
+cd /gpfs/scratch/bsc32/bsc32538/NES_tests/NES/tests
+
+mpirun --mca mpi_warn_on_fork 0 -np 1 python test_interp_conservative.py
diff --git a/tests/test_bash_mn4.cmd b/tests/test_bash_mn4.cmd
index db2758fcffd54ce03e81b5a2b4e4d8af072ea07e..9dfd27dbb3826faa0805dc9d1a552a5fcf81424f 100644
--- a/tests/test_bash_mn4.cmd
+++ b/tests/test_bash_mn4.cmd
@@ -4,10 +4,10 @@
#SBATCH -A bsc32
#SBATCH --cpus-per-task=1
#SBATCH -n 4
-#SBATCH -t 00:30:00
-#SBATCH -J test_nes
-#SBATCH --output=log_mn4_NES_%j.out
-#SBATCH --error=log_mn4_NES_%j.err
+#SBATCH -t 02:00:00
+#SBATCH -J NES-test
+#SBATCH --output=log_NES-tests_mn4_%j.out
+#SBATCH --error=log_NES-tests_mn4_%j.err
#SBATCH --exclusive
### ulimit -s 128000
@@ -15,18 +15,13 @@
module purge
module use /gpfs/projects/bsc32/software/suselinux/11/modules/all
-module load Python/3.7.4-GCCcore-8.3.0
-module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4
-module load cfunits/1.8-foss-2019b-Python-3.7.4
-module load xarray/0.17.0-foss-2019b-Python-3.7.4
-module load OpenMPI/4.0.5-GCC-8.3.0-mn4
-module load filelock/3.7.1-foss-2019b-Python-3.7.4
-module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4
-module load pyproj/2.5.0-foss-2019b-Python-3.7.4
-module load geopandas/0.8.1-foss-2019b-Python-3.7.4
-module load Shapely/1.7.1-foss-2019b-Python-3.7.4
+# TODO change module when installed
+module load NES/1.0.0-mn4-foss-2019b-Python-3.7.4
-export PYTHONPATH=/gpfs/projects/bsc32/models/NES:${PYTHONPATH}
-cd /gpfs/projects/bsc32/models/NES/tests
+cd /gpfs/projects/bsc32/models/NES_master/tests
-mpirun --mca mpi_warn_on_fork 0 -np 4 python basic_nes_tests.py
+#mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.1-test_read_write_projection
+#mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.1-test_spatial_join.py
+#mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.3-test_bounds.py
+mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.4-test_cell_area.py
+mpirun --mca mpi_warn_on_fork 0 -np 4 python 5.3-test_horiz_interp_conservative.py
diff --git a/tests/test_bash_nord3v2.cmd b/tests/test_bash_nord3v2.cmd
index da0f4d7bf1829f732d82e21237872333048060c8..e585eab22d231734eae8dc1545e973ab200c8ae0 100644
--- a/tests/test_bash_nord3v2.cmd
+++ b/tests/test_bash_nord3v2.cmd
@@ -1,32 +1,26 @@
#!/bin/bash
-####SBATCH --qos=debug
+#SBATCH --qos=debug
#SBATCH -A bsc32
#SBATCH --cpus-per-task=1
#SBATCH -n 4
-#SBATCH -t 00:10:00
-#SBATCH -J test_nes
-#SBATCH --output=log_nord3v2_NES_%j.out
-#SBATCH --error=log_nord3v2_NES_%j.err
+#SBATCH -t 02:00:00
+#SBATCH -J NES-test
+#SBATCH --output=log_NES-tests_nord3v2_%j.out
+#SBATCH --error=log_NES-tests_nord3v2_%j.err
#SBATCH --exclusive
### ulimit -s 128000
module purge
-module load Python/3.7.4-GCCcore-8.3.0
-module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4
-module load cfunits/1.8-foss-2019b-Python-3.7.4
-module load xarray/0.17.0-foss-2019b-Python-3.7.4
-module load pandas/1.2.4-foss-2019b-Python-3.7.4
-module load mpi4py/3.0.3-foss-2019b-Python-3.7.4
-module load filelock/3.7.1-foss-2019b-Python-3.7.4
-module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4
-module load pyproj/2.5.0-foss-2019b-Python-3.7.4
-module load geopandas/0.8.1-foss-2019b-Python-3.7.4
-module load Shapely/1.7.1-foss-2019b-Python-3.7.4
+# TODO change module when installed
+module load NES/1.0.0-nord3-v2-foss-2019b-Python-3.7.4
-export PYTHONPATH=/gpfs/projects/bsc32/models/NES:${PYTHONPATH}
-cd /gpfs/projects/bsc32/models/NES/tests
+cd /gpfs/projects/bsc32/models/NES_master/tests
-mpirun --mca mpi_warn_on_fork 0 -np 4 python 2-nes_tests_by_projection.py
+#mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.1-test_read_write_projection
+#mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.1-test_spatial_join.py
+#mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.3-test_bounds.py
+mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.4-test_cell_area.py
+mpirun --mca mpi_warn_on_fork 0 -np 4 python 5.3-test_horiz_interp_conservative.py
diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb
index d726d988b91d86014d429d6d04067f0f5bc78e02..06542ff4bd7c67656035b23c128e50d3a662da03 100644
--- a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb
+++ b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# How to read and write regular grids"
+ "# How to read and write regular lat-lon grids"
]
},
{
@@ -13,8 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
- "from netCDF4 import Dataset\n",
"from nes import *"
]
},
@@ -24,7 +22,9 @@
"metadata": {},
"outputs": [],
"source": [
- "nc_path_1 = '/gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc'"
+ "# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc\n",
+ "# Regular lat-lon grid from MONARCH\n",
+ "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc'"
]
},
{
@@ -34,484 +34,6 @@
"## 1. Read dataset"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 25, lev: 1, lat: 115, lon: 165)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-01-01 ... 2019-01-02\n",
- " * lev (lev) float64 0.0\n",
- " * lat (lat) float64 33.9 34.0 34.1 34.2 34.3 ... 45.0 45.1 45.2 45.3\n",
- " * lon (lon) float64 -11.8 -11.7 -11.6 -11.5 -11.4 ... 4.3 4.4 4.5 4.6\n",
- "Data variables:\n",
- " sconcno2 (time, lev, lat, lon) float32 ...\n",
- " crs |S1 b''\n",
- "Attributes:\n",
- " Domain: Regional\n",
- " Conventions: CF-1.7\n",
- " history: MONARCHv1.0 netcdf file.\n",
- " comment: Generated on marenostrum4 Dimensions: time : 25lev : 1lat : 115lon : 165
Coordinates: (4)
time
(time)
datetime64[ns]
2019-01-01 ... 2019-01-02
standard_name : time long_name : time array(['2019-01-01T00:00:00.000000000', '2019-01-01T01:00:00.000000000',\n",
- " '2019-01-01T02:00:00.000000000', '2019-01-01T03:00:00.000000000',\n",
- " '2019-01-01T04:00:00.000000000', '2019-01-01T05:00:00.000000000',\n",
- " '2019-01-01T06:00:00.000000000', '2019-01-01T07:00:00.000000000',\n",
- " '2019-01-01T08:00:00.000000000', '2019-01-01T09:00:00.000000000',\n",
- " '2019-01-01T10:00:00.000000000', '2019-01-01T11:00:00.000000000',\n",
- " '2019-01-01T12:00:00.000000000', '2019-01-01T13:00:00.000000000',\n",
- " '2019-01-01T14:00:00.000000000', '2019-01-01T15:00:00.000000000',\n",
- " '2019-01-01T16:00:00.000000000', '2019-01-01T17:00:00.000000000',\n",
- " '2019-01-01T18:00:00.000000000', '2019-01-01T19:00:00.000000000',\n",
- " '2019-01-01T20:00:00.000000000', '2019-01-01T21:00:00.000000000',\n",
- " '2019-01-01T22:00:00.000000000', '2019-01-01T23:00:00.000000000',\n",
- " '2019-01-02T00:00:00.000000000'], dtype='datetime64[ns]') lev
(lev)
float64
0.0
lat
(lat)
float64
33.9 34.0 34.1 ... 45.1 45.2 45.3
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([33.9, 34. , 34.1, 34.2, 34.3, 34.4, 34.5, 34.6, 34.7, 34.8, 34.9, 35. ,\n",
- " 35.1, 35.2, 35.3, 35.4, 35.5, 35.6, 35.7, 35.8, 35.9, 36. , 36.1, 36.2,\n",
- " 36.3, 36.4, 36.5, 36.6, 36.7, 36.8, 36.9, 37. , 37.1, 37.2, 37.3, 37.4,\n",
- " 37.5, 37.6, 37.7, 37.8, 37.9, 38. , 38.1, 38.2, 38.3, 38.4, 38.5, 38.6,\n",
- " 38.7, 38.8, 38.9, 39. , 39.1, 39.2, 39.3, 39.4, 39.5, 39.6, 39.7, 39.8,\n",
- " 39.9, 40. , 40.1, 40.2, 40.3, 40.4, 40.5, 40.6, 40.7, 40.8, 40.9, 41. ,\n",
- " 41.1, 41.2, 41.3, 41.4, 41.5, 41.6, 41.7, 41.8, 41.9, 42. , 42.1, 42.2,\n",
- " 42.3, 42.4, 42.5, 42.6, 42.7, 42.8, 42.9, 43. , 43.1, 43.2, 43.3, 43.4,\n",
- " 43.5, 43.6, 43.7, 43.8, 43.9, 44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6,\n",
- " 44.7, 44.8, 44.9, 45. , 45.1, 45.2, 45.3]) lon
(lon)
float64
-11.8 -11.7 -11.6 ... 4.4 4.5 4.6
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([-1.180000e+01, -1.170000e+01, -1.160000e+01, -1.150000e+01,\n",
- " -1.140000e+01, -1.130000e+01, -1.120000e+01, -1.110000e+01,\n",
- " -1.100000e+01, -1.090000e+01, -1.080000e+01, -1.070000e+01,\n",
- " -1.060000e+01, -1.050000e+01, -1.040000e+01, -1.030000e+01,\n",
- " -1.020000e+01, -1.010000e+01, -1.000000e+01, -9.900000e+00,\n",
- " -9.800000e+00, -9.700000e+00, -9.600000e+00, -9.500000e+00,\n",
- " -9.400000e+00, -9.300000e+00, -9.200000e+00, -9.100000e+00,\n",
- " -9.000000e+00, -8.900000e+00, -8.800000e+00, -8.700000e+00,\n",
- " -8.600000e+00, -8.500000e+00, -8.400000e+00, -8.300000e+00,\n",
- " -8.200000e+00, -8.100000e+00, -8.000000e+00, -7.900000e+00,\n",
- " -7.800000e+00, -7.700000e+00, -7.600000e+00, -7.500000e+00,\n",
- " -7.400000e+00, -7.300000e+00, -7.200000e+00, -7.100000e+00,\n",
- " -7.000000e+00, -6.900000e+00, -6.800000e+00, -6.700000e+00,\n",
- " -6.600000e+00, -6.500000e+00, -6.400000e+00, -6.300000e+00,\n",
- " -6.200000e+00, -6.100000e+00, -6.000000e+00, -5.900000e+00,\n",
- " -5.800000e+00, -5.700000e+00, -5.600000e+00, -5.500000e+00,\n",
- " -5.400000e+00, -5.300000e+00, -5.200000e+00, -5.100000e+00,\n",
- " -5.000000e+00, -4.900000e+00, -4.800000e+00, -4.700000e+00,\n",
- " -4.600000e+00, -4.500000e+00, -4.400000e+00, -4.300000e+00,\n",
- " -4.200000e+00, -4.100000e+00, -4.000000e+00, -3.900000e+00,\n",
- " -3.800000e+00, -3.700000e+00, -3.600000e+00, -3.500000e+00,\n",
- " -3.400000e+00, -3.300000e+00, -3.200000e+00, -3.100000e+00,\n",
- " -3.000000e+00, -2.900000e+00, -2.800000e+00, -2.700000e+00,\n",
- " -2.600000e+00, -2.500000e+00, -2.400000e+00, -2.300000e+00,\n",
- " -2.200000e+00, -2.100000e+00, -2.000000e+00, -1.900000e+00,\n",
- " -1.800000e+00, -1.700000e+00, -1.600000e+00, -1.500000e+00,\n",
- " -1.400000e+00, -1.300000e+00, -1.200000e+00, -1.100000e+00,\n",
- " -1.000000e+00, -9.000000e-01, -8.000000e-01, -7.000000e-01,\n",
- " -6.000000e-01, -5.000000e-01, -4.000000e-01, -3.000000e-01,\n",
- " -2.000000e-01, -1.000000e-01, 3.552714e-15, 1.000000e-01,\n",
- " 2.000000e-01, 3.000000e-01, 4.000000e-01, 5.000000e-01,\n",
- " 6.000000e-01, 7.000000e-01, 8.000000e-01, 9.000000e-01,\n",
- " 1.000000e+00, 1.100000e+00, 1.200000e+00, 1.300000e+00,\n",
- " 1.400000e+00, 1.500000e+00, 1.600000e+00, 1.700000e+00,\n",
- " 1.800000e+00, 1.900000e+00, 2.000000e+00, 2.100000e+00,\n",
- " 2.200000e+00, 2.300000e+00, 2.400000e+00, 2.500000e+00,\n",
- " 2.600000e+00, 2.700000e+00, 2.800000e+00, 2.900000e+00,\n",
- " 3.000000e+00, 3.100000e+00, 3.200000e+00, 3.300000e+00,\n",
- " 3.400000e+00, 3.500000e+00, 3.600000e+00, 3.700000e+00,\n",
- " 3.800000e+00, 3.900000e+00, 4.000000e+00, 4.100000e+00,\n",
- " 4.200000e+00, 4.300000e+00, 4.400000e+00, 4.500000e+00,\n",
- " 4.600000e+00]) Data variables: (2)
Attributes: (4)
Domain : Regional Conventions : CF-1.7 history : MONARCHv1.0 netcdf file. comment : Generated on marenostrum4 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 25, lev: 1, lat: 115, lon: 165)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-01-01 ... 2019-01-02\n",
- " * lev (lev) float64 0.0\n",
- " * lat (lat) float64 33.9 34.0 34.1 34.2 34.3 ... 45.0 45.1 45.2 45.3\n",
- " * lon (lon) float64 -11.8 -11.7 -11.6 -11.5 -11.4 ... 4.3 4.4 4.5 4.6\n",
- "Data variables:\n",
- " sconcno2 (time, lev, lat, lon) float32 ...\n",
- " crs |S1 ...\n",
- "Attributes:\n",
- " Domain: Regional\n",
- " Conventions: CF-1.7\n",
- " history: MONARCHv1.0 netcdf file.\n",
- " comment: Generated on marenostrum4"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset(nc_path_1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with NES"
- ]
- },
{
"cell_type": "code",
"execution_count": 4,
@@ -520,7 +42,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 4,
@@ -874,13 +396,6 @@
"## 2. Write dataset"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Write with NES"
- ]
- },
{
"cell_type": "code",
"execution_count": 12,
@@ -911,7 +426,7 @@
"toc-hr-collapsed": true
},
"source": [
- "### Reopen with NES"
+ "## 3. Reopen dataset"
]
},
{
@@ -922,7 +437,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 13,
@@ -934,477 +449,6 @@
"nessy_2 = open_netcdf('regular_file_1.nc', info=True)\n",
"nessy_2"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Reopen with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 25, lev: 1, lat: 115, lon: 165)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-01-01 ... 2019-01-02\n",
- " * lev (lev) float64 0.0\n",
- " * lat (lat) float64 33.9 34.0 34.1 34.2 34.3 ... 45.0 45.1 45.2 45.3\n",
- " * lon (lon) float64 -11.8 -11.7 -11.6 -11.5 -11.4 ... 4.3 4.4 4.5 4.6\n",
- "Data variables:\n",
- " sconcno2 (time, lev, lat, lon) float32 ...\n",
- " crs |S1 b''\n",
- "Attributes:\n",
- " Domain: Regional\n",
- " Conventions: CF-1.7\n",
- " history: MONARCHv1.0 netcdf file.\n",
- " comment: Generated on marenostrum4 Dimensions: time : 25lev : 1lat : 115lon : 165
Coordinates: (4)
time
(time)
datetime64[ns]
2019-01-01 ... 2019-01-02
standard_name : time long_name : time array(['2019-01-01T00:00:00.000000000', '2019-01-01T01:00:00.000000000',\n",
- " '2019-01-01T02:00:00.000000000', '2019-01-01T03:00:00.000000000',\n",
- " '2019-01-01T04:00:00.000000000', '2019-01-01T05:00:00.000000000',\n",
- " '2019-01-01T06:00:00.000000000', '2019-01-01T07:00:00.000000000',\n",
- " '2019-01-01T08:00:00.000000000', '2019-01-01T09:00:00.000000000',\n",
- " '2019-01-01T10:00:00.000000000', '2019-01-01T11:00:00.000000000',\n",
- " '2019-01-01T12:00:00.000000000', '2019-01-01T13:00:00.000000000',\n",
- " '2019-01-01T14:00:00.000000000', '2019-01-01T15:00:00.000000000',\n",
- " '2019-01-01T16:00:00.000000000', '2019-01-01T17:00:00.000000000',\n",
- " '2019-01-01T18:00:00.000000000', '2019-01-01T19:00:00.000000000',\n",
- " '2019-01-01T20:00:00.000000000', '2019-01-01T21:00:00.000000000',\n",
- " '2019-01-01T22:00:00.000000000', '2019-01-01T23:00:00.000000000',\n",
- " '2019-01-02T00:00:00.000000000'], dtype='datetime64[ns]') lev
(lev)
float64
0.0
lat
(lat)
float64
33.9 34.0 34.1 ... 45.1 45.2 45.3
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([33.9, 34. , 34.1, 34.2, 34.3, 34.4, 34.5, 34.6, 34.7, 34.8, 34.9, 35. ,\n",
- " 35.1, 35.2, 35.3, 35.4, 35.5, 35.6, 35.7, 35.8, 35.9, 36. , 36.1, 36.2,\n",
- " 36.3, 36.4, 36.5, 36.6, 36.7, 36.8, 36.9, 37. , 37.1, 37.2, 37.3, 37.4,\n",
- " 37.5, 37.6, 37.7, 37.8, 37.9, 38. , 38.1, 38.2, 38.3, 38.4, 38.5, 38.6,\n",
- " 38.7, 38.8, 38.9, 39. , 39.1, 39.2, 39.3, 39.4, 39.5, 39.6, 39.7, 39.8,\n",
- " 39.9, 40. , 40.1, 40.2, 40.3, 40.4, 40.5, 40.6, 40.7, 40.8, 40.9, 41. ,\n",
- " 41.1, 41.2, 41.3, 41.4, 41.5, 41.6, 41.7, 41.8, 41.9, 42. , 42.1, 42.2,\n",
- " 42.3, 42.4, 42.5, 42.6, 42.7, 42.8, 42.9, 43. , 43.1, 43.2, 43.3, 43.4,\n",
- " 43.5, 43.6, 43.7, 43.8, 43.9, 44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6,\n",
- " 44.7, 44.8, 44.9, 45. , 45.1, 45.2, 45.3]) lon
(lon)
float64
-11.8 -11.7 -11.6 ... 4.4 4.5 4.6
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([-1.180000e+01, -1.170000e+01, -1.160000e+01, -1.150000e+01,\n",
- " -1.140000e+01, -1.130000e+01, -1.120000e+01, -1.110000e+01,\n",
- " -1.100000e+01, -1.090000e+01, -1.080000e+01, -1.070000e+01,\n",
- " -1.060000e+01, -1.050000e+01, -1.040000e+01, -1.030000e+01,\n",
- " -1.020000e+01, -1.010000e+01, -1.000000e+01, -9.900000e+00,\n",
- " -9.800000e+00, -9.700000e+00, -9.600000e+00, -9.500000e+00,\n",
- " -9.400000e+00, -9.300000e+00, -9.200000e+00, -9.100000e+00,\n",
- " -9.000000e+00, -8.900000e+00, -8.800000e+00, -8.700000e+00,\n",
- " -8.600000e+00, -8.500000e+00, -8.400000e+00, -8.300000e+00,\n",
- " -8.200000e+00, -8.100000e+00, -8.000000e+00, -7.900000e+00,\n",
- " -7.800000e+00, -7.700000e+00, -7.600000e+00, -7.500000e+00,\n",
- " -7.400000e+00, -7.300000e+00, -7.200000e+00, -7.100000e+00,\n",
- " -7.000000e+00, -6.900000e+00, -6.800000e+00, -6.700000e+00,\n",
- " -6.600000e+00, -6.500000e+00, -6.400000e+00, -6.300000e+00,\n",
- " -6.200000e+00, -6.100000e+00, -6.000000e+00, -5.900000e+00,\n",
- " -5.800000e+00, -5.700000e+00, -5.600000e+00, -5.500000e+00,\n",
- " -5.400000e+00, -5.300000e+00, -5.200000e+00, -5.100000e+00,\n",
- " -5.000000e+00, -4.900000e+00, -4.800000e+00, -4.700000e+00,\n",
- " -4.600000e+00, -4.500000e+00, -4.400000e+00, -4.300000e+00,\n",
- " -4.200000e+00, -4.100000e+00, -4.000000e+00, -3.900000e+00,\n",
- " -3.800000e+00, -3.700000e+00, -3.600000e+00, -3.500000e+00,\n",
- " -3.400000e+00, -3.300000e+00, -3.200000e+00, -3.100000e+00,\n",
- " -3.000000e+00, -2.900000e+00, -2.800000e+00, -2.700000e+00,\n",
- " -2.600000e+00, -2.500000e+00, -2.400000e+00, -2.300000e+00,\n",
- " -2.200000e+00, -2.100000e+00, -2.000000e+00, -1.900000e+00,\n",
- " -1.800000e+00, -1.700000e+00, -1.600000e+00, -1.500000e+00,\n",
- " -1.400000e+00, -1.300000e+00, -1.200000e+00, -1.100000e+00,\n",
- " -1.000000e+00, -9.000000e-01, -8.000000e-01, -7.000000e-01,\n",
- " -6.000000e-01, -5.000000e-01, -4.000000e-01, -3.000000e-01,\n",
- " -2.000000e-01, -1.000000e-01, 3.552714e-15, 1.000000e-01,\n",
- " 2.000000e-01, 3.000000e-01, 4.000000e-01, 5.000000e-01,\n",
- " 6.000000e-01, 7.000000e-01, 8.000000e-01, 9.000000e-01,\n",
- " 1.000000e+00, 1.100000e+00, 1.200000e+00, 1.300000e+00,\n",
- " 1.400000e+00, 1.500000e+00, 1.600000e+00, 1.700000e+00,\n",
- " 1.800000e+00, 1.900000e+00, 2.000000e+00, 2.100000e+00,\n",
- " 2.200000e+00, 2.300000e+00, 2.400000e+00, 2.500000e+00,\n",
- " 2.600000e+00, 2.700000e+00, 2.800000e+00, 2.900000e+00,\n",
- " 3.000000e+00, 3.100000e+00, 3.200000e+00, 3.300000e+00,\n",
- " 3.400000e+00, 3.500000e+00, 3.600000e+00, 3.700000e+00,\n",
- " 3.800000e+00, 3.900000e+00, 4.000000e+00, 4.100000e+00,\n",
- " 4.200000e+00, 4.300000e+00, 4.400000e+00, 4.500000e+00,\n",
- " 4.600000e+00]) Data variables: (2)
Attributes: (4)
Domain : Regional Conventions : CF-1.7 history : MONARCHv1.0 netcdf file. comment : Generated on marenostrum4 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 25, lev: 1, lat: 115, lon: 165)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-01-01 ... 2019-01-02\n",
- " * lev (lev) float64 0.0\n",
- " * lat (lat) float64 33.9 34.0 34.1 34.2 34.3 ... 45.0 45.1 45.2 45.3\n",
- " * lon (lon) float64 -11.8 -11.7 -11.6 -11.5 -11.4 ... 4.3 4.4 4.5 4.6\n",
- "Data variables:\n",
- " sconcno2 (time, lev, lat, lon) float32 ...\n",
- " crs |S1 ...\n",
- "Attributes:\n",
- " Domain: Regional\n",
- " Conventions: CF-1.7\n",
- " history: MONARCHv1.0 netcdf file.\n",
- " comment: Generated on marenostrum4"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('regular_file_1.nc')"
- ]
}
],
"metadata": {
diff --git a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb
index 290316f7a6a338186c97481c07a6630c16620997..ccb839d0f18cf53a49b23fddf7ccbaa0167d3e59 100644
--- a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb
+++ b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"from nes import *"
]
},
@@ -23,7 +22,9 @@
"metadata": {},
"outputs": [],
"source": [
- "nc_path_1 = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc'"
+ "# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\n",
+ "# Rotated grid from MONARCH\n",
+ "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc'"
]
},
{
@@ -33,13 +34,6 @@
"## 1. Read dataset"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with xarray"
- ]
- },
{
"cell_type": "code",
"execution_count": 3,
@@ -47,416 +41,8 @@
"outputs": [
{
"data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, nv: 2, lev: 24, rlat: 271, rlon: 351)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-08-03\n",
- " * lev (lev) float64 0.0 1.0 2.0 3.0 4.0 ... 19.0 20.0 21.0 22.0 23.0\n",
- " lat (rlat, rlon) float64 16.35 16.43 16.52 ... 58.83 58.68 58.53\n",
- " lon (rlat, rlon) float64 -22.18 -22.02 -21.85 ... 88.05 88.23\n",
- " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
- " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
- "Dimensions without coordinates: nv\n",
- "Data variables:\n",
- " time_bnds (time, nv) datetime64[ns] 2021-08-03 2021-08-07\n",
- " O3_all (time, lev, rlat, rlon) float32 ...\n",
- " rotated_pole |S1 b''\n",
- "Attributes:\n",
- " Conventions: CF-1.7\n",
- " comment: Generated on marenostrum4 Dimensions: time : 1nv : 2lev : 24rlat : 271rlon : 351
Coordinates: (6)
time
(time)
datetime64[ns]
2021-08-03
standard_name : time long_name : time bounds : time_bnds array(['2021-08-03T00:00:00.000000000'], dtype='datetime64[ns]') lev
(lev)
float64
0.0 1.0 2.0 3.0 ... 21.0 22.0 23.0
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,\n",
- " 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.]) lat
(rlat, rlon)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([[16.350338, 16.43293 , 16.515146, ..., 16.515146, 16.43293 , 16.350338],\n",
- " [16.527426, 16.610239, 16.692677, ..., 16.692677, 16.610243, 16.527426],\n",
- " [16.704472, 16.787508, 16.870167, ..., 16.870167, 16.78751 , 16.704472],\n",
- " ...,\n",
- " [58.32095 , 58.472683, 58.62431 , ..., 58.62431 , 58.472683, 58.32095 ],\n",
- " [58.426285, 58.578201, 58.730026, ..., 58.730026, 58.578201, 58.426285],\n",
- " [58.530792, 58.682899, 58.834919, ..., 58.834919, 58.682903, 58.530792]]) lon
(rlat, rlon)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([[-22.181265, -22.016672, -21.851799, ..., 41.851795, 42.016666,\n",
- " 42.181259],\n",
- " [-22.278179, -22.113186, -21.947905, ..., 41.947899, 42.113174,\n",
- " 42.278172],\n",
- " [-22.375267, -22.209873, -22.044189, ..., 42.044186, 42.209873,\n",
- " 42.375263],\n",
- " ...,\n",
- " [-67.577667, -67.397064, -67.215347, ..., 87.21534 , 87.397057,\n",
- " 87.57766 ],\n",
- " [-67.901878, -67.722473, -67.541939, ..., 87.541939, 87.722458,\n",
- " 87.901871],\n",
- " [-68.228035, -68.04982 , -67.870514, ..., 87.870506, 88.04982 ,\n",
- " 88.228035]]) rlat
(rlat)
float64
-27.0 -26.8 -26.6 ... 26.8 27.0
long_name : latitude in rotated pole grid units : 0.0174532925199433 rad standard_name : grid_latitude array([-27. , -26.799999, -26.6 , ..., 26.6 , 26.800001,\n",
- " 27. ]) rlon
(rlon)
float64
-35.0 -34.8 -34.6 ... 34.8 35.0
long_name : longitude in rotated pole grid units : 0.0174532925199433 rad standard_name : grid_longitude array([-35. , -34.799999, -34.599998, ..., 34.600002, 34.799999,\n",
- " 35. ]) Data variables: (3)
Attributes: (2)
Conventions : CF-1.7 comment : Generated on marenostrum4 "
- ],
"text/plain": [
- "\n",
- "Dimensions: (time: 1, nv: 2, lev: 24, rlat: 271, rlon: 351)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-08-03\n",
- " * lev (lev) float64 0.0 1.0 2.0 3.0 4.0 ... 19.0 20.0 21.0 22.0 23.0\n",
- " lat (rlat, rlon) float64 ...\n",
- " lon (rlat, rlon) float64 ...\n",
- " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
- " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
- "Dimensions without coordinates: nv\n",
- "Data variables:\n",
- " time_bnds (time, nv) datetime64[ns] ...\n",
- " O3_all (time, lev, rlat, rlon) float32 ...\n",
- " rotated_pole |S1 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7\n",
- " comment: Generated on marenostrum4"
+ ""
]
},
"execution_count": 3,
@@ -464,33 +50,6 @@
"output_type": "execute_result"
}
],
- "source": [
- "xr.open_dataset(nc_path_1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with NES"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
"nessy_1 = open_netcdf(path=nc_path_1, info=True)\n",
"nessy_1"
@@ -498,7 +57,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -507,7 +66,7 @@
"[datetime.datetime(2021, 8, 3, 0, 0)]"
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -518,7 +77,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -534,7 +93,7 @@
" 'positive': 'up'}"
]
},
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -545,7 +104,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -574,7 +133,7 @@
" 'standard_name': 'latitude'}"
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -585,7 +144,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -614,7 +173,7 @@
" 'standard_name': 'longitude'}"
]
},
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -625,7 +184,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -643,7 +202,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -746,7 +305,7 @@
" 'grid_mapping': 'rotated_pole'}}"
]
},
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -762,16 +321,9 @@
"## 2. Write dataset"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Write with NES"
- ]
- },
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -781,6 +333,7 @@
"Rank 000: Creating rotated_file_1.nc\n",
"Rank 000: NetCDF ready to write\n",
"Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n",
"Rank 000: Writing O3_all var (1/1)\n",
"Rank 000: Var O3_all created (1/1)\n",
"Rank 000: Filling O3_all)\n",
@@ -799,21 +352,21 @@
"toc-hr-collapsed": true
},
"source": [
- "### Reopen with NES"
+ "## 3. Reopen dataset"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -822,441 +375,6 @@
"nessy_2 = open_netcdf('rotated_file_1.nc', info=True)\n",
"nessy_2"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Reopen with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, time_nv: 2, lev: 24, rlat: 271, rlon: 351)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-08-03\n",
- " * lev (lev) float64 0.0 1.0 2.0 3.0 4.0 ... 19.0 20.0 21.0 22.0 23.0\n",
- " lat (rlat, rlon) float64 16.35 16.43 16.52 ... 58.83 58.68 58.53\n",
- " lon (rlat, rlon) float64 -22.18 -22.02 -21.85 ... 88.05 88.23\n",
- " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
- " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
- "Dimensions without coordinates: time_nv\n",
- "Data variables:\n",
- " time_bnds (time, time_nv) datetime64[ns] 2021-08-03 2021-08-07\n",
- " O3_all (time, lev, rlat, rlon) float32 ...\n",
- " rotated_pole |S1 b''\n",
- "Attributes:\n",
- " Conventions: CF-1.7\n",
- " comment: Generated on marenostrum4 Dimensions: time : 1time_nv : 2lev : 24rlat : 271rlon : 351
Coordinates: (6)
time
(time)
datetime64[ns]
2021-08-03
standard_name : time long_name : time bounds : time_bnds array(['2021-08-03T00:00:00.000000000'], dtype='datetime64[ns]') lev
(lev)
float64
0.0 1.0 2.0 3.0 ... 21.0 22.0 23.0
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,\n",
- " 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.]) lat
(rlat, rlon)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([[16.350338, 16.43293 , 16.515146, ..., 16.515146, 16.43293 , 16.350338],\n",
- " [16.527426, 16.610239, 16.692677, ..., 16.692677, 16.610243, 16.527426],\n",
- " [16.704472, 16.787508, 16.870167, ..., 16.870167, 16.78751 , 16.704472],\n",
- " ...,\n",
- " [58.32095 , 58.472683, 58.62431 , ..., 58.62431 , 58.472683, 58.32095 ],\n",
- " [58.426285, 58.578201, 58.730026, ..., 58.730026, 58.578201, 58.426285],\n",
- " [58.530792, 58.682899, 58.834919, ..., 58.834919, 58.682903, 58.530792]]) lon
(rlat, rlon)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([[-22.181265, -22.016672, -21.851799, ..., 41.851795, 42.016666,\n",
- " 42.181259],\n",
- " [-22.278179, -22.113186, -21.947905, ..., 41.947899, 42.113174,\n",
- " 42.278172],\n",
- " [-22.375267, -22.209873, -22.044189, ..., 42.044186, 42.209873,\n",
- " 42.375263],\n",
- " ...,\n",
- " [-67.577667, -67.397064, -67.215347, ..., 87.21534 , 87.397057,\n",
- " 87.57766 ],\n",
- " [-67.901878, -67.722473, -67.541939, ..., 87.541939, 87.722458,\n",
- " 87.901871],\n",
- " [-68.228035, -68.04982 , -67.870514, ..., 87.870506, 88.04982 ,\n",
- " 88.228035]]) rlat
(rlat)
float64
-27.0 -26.8 -26.6 ... 26.8 27.0
long_name : latitude in rotated pole grid units : 0.0174532925199433 rad standard_name : grid_latitude array([-27. , -26.799999, -26.6 , ..., 26.6 , 26.800001,\n",
- " 27. ]) rlon
(rlon)
float64
-35.0 -34.8 -34.6 ... 34.8 35.0
long_name : longitude in rotated pole grid units : 0.0174532925199433 rad standard_name : grid_longitude array([-35. , -34.799999, -34.599998, ..., 34.600002, 34.799999,\n",
- " 35. ]) Data variables: (3)
Attributes: (2)
Conventions : CF-1.7 comment : Generated on marenostrum4 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 1, time_nv: 2, lev: 24, rlat: 271, rlon: 351)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-08-03\n",
- " * lev (lev) float64 0.0 1.0 2.0 3.0 4.0 ... 19.0 20.0 21.0 22.0 23.0\n",
- " lat (rlat, rlon) float64 ...\n",
- " lon (rlat, rlon) float64 ...\n",
- " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
- " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
- "Dimensions without coordinates: time_nv\n",
- "Data variables:\n",
- " time_bnds (time, time_nv) datetime64[ns] ...\n",
- " O3_all (time, lev, rlat, rlon) float32 ...\n",
- " rotated_pole |S1 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7\n",
- " comment: Generated on marenostrum4"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('rotated_file_1.nc')"
- ]
}
],
"metadata": {
diff --git a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb
index 4f791b2054555ff216031bad6bf549c9fff1db8b..80dcc1d691fd4c8179fd4e0542f4ef085737425c 100644
--- a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb
+++ b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb
@@ -13,8 +13,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from nes import *\n",
- "import xarray as xr"
+ "from nes import *"
]
},
{
@@ -23,22 +22,23 @@
"metadata": {},
"outputs": [],
"source": [
- "# nc_path_1 = '/esarchive/obs/eea/eionet/hourly/pm10/pm10_202107.nc' # EIONET\n",
- "nc_path_1 = '/esarchive/obs/nilu/ebas/daily/pm10/pm10_201507.nc' # EBAS"
+ "# Original path: /esarchive/obs/nilu/ebas/daily/pm10/pm10_201507.nc\n",
+ "# Points grid from EBAS network\n",
+ "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/pm10_201507.nc'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 1. Read and write - Non-GHOST type"
+ "## 1. Non-GHOST"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Open with xarray"
+ "### 1.1. Read dataset"
]
},
{
@@ -48,642 +48,8 @@
"outputs": [
{
"data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (station: 84, time: 31)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2015-07-01 ... 2015-0...\n",
- "Dimensions without coordinates: station\n",
- "Data variables: (12/19)\n",
- " station_start_date (station) |S75 b'1980-01-01' ... b'nan'\n",
- " station_zone (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
- " street_type (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
- " country_code (station) |S75 b'CH' b'CH' ... b'NL' b'IT'\n",
- " ccaa (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
- " station_name (station) |S75 b'payerne' ... b'lamezia terme'\n",
- " ... ...\n",
- " station_code (station) |S75 b'CH0002R' ... b'IT0016R'\n",
- " longitude (station) float32 6.944 8.905 ... 6.277 16.23\n",
- " station_end_date (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
- " station_rural_back (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
- " latitude (station) float32 46.81 47.48 ... 53.33 38.88\n",
- " station_ozone_classification (station) |S75 b'rural' b'rural' ... b'nan' "
- ],
"text/plain": [
- "\n",
- "Dimensions: (station: 84, time: 31)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2015-07-01 ... 2015-0...\n",
- "Dimensions without coordinates: station\n",
- "Data variables: (12/19)\n",
- " station_start_date (station) |S75 ...\n",
- " station_zone (station) |S75 ...\n",
- " street_type (station) |S75 ...\n",
- " country_code (station) |S75 ...\n",
- " ccaa (station) |S75 ...\n",
- " station_name (station) |S75 ...\n",
- " ... ...\n",
- " station_code (station) |S75 ...\n",
- " longitude (station) float32 ...\n",
- " station_end_date (station) |S75 ...\n",
- " station_rural_back (station) |S75 ...\n",
- " latitude (station) float32 ...\n",
- " station_ozone_classification (station) |S75 ..."
+ ""
]
},
"execution_count": 3,
@@ -691,33 +57,6 @@
"output_type": "execute_result"
}
],
- "source": [
- "xr.open_dataset(nc_path_1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with NES"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
"nessy_1 = open_netcdf(path=nc_path_1, info=True, parallel_method='X')\n",
"nessy_1"
@@ -725,7 +64,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -764,7 +103,7 @@
" datetime.datetime(2015, 7, 2, 6, 0)]"
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -775,7 +114,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -784,7 +123,7 @@
"{'data': array([0]), 'units': ''}"
]
},
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -795,7 +134,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -808,7 +147,7 @@
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83])}"
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -819,7 +158,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -852,7 +191,7 @@
" 'axis': 'Y'}"
]
},
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -863,7 +202,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -900,7 +239,7 @@
" 'axis': 'X'}"
]
},
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -911,7 +250,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -961,7 +300,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -1243,7 +582,7 @@
" 'standard_name': ''}}"
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -1254,7 +593,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1288,7 +627,7 @@
" 'long_name': 'pm10_mass pm10'}"
]
},
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -1301,12 +640,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Write with NES"
+ "### 1.2. Write dataset"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -1316,6 +655,7 @@
"Rank 000: Creating points_file_1.nc\n",
"Rank 000: NetCDF ready to write\n",
"Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n",
"Rank 000: Writing station_start_date var (1/17)\n",
"Rank 000: Var station_start_date created (1/17)\n",
"Rank 000: Var station_start_date data (1/17)\n",
@@ -1397,21 +737,21 @@
"toc-hr-collapsed": true
},
"source": [
- "### Reopen with NES"
+ "### 1.3. Reopen dataset"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -1425,595 +765,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Reopen with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 31, station: 84, strlen: 75)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2015-07-01 ... 2015-0...\n",
- " * station (station) float64 0.0 1.0 2.0 ... 82.0 83.0\n",
- " lat (station) float64 46.81 47.48 ... 53.33 38.88\n",
- " lon (station) float64 6.944 8.905 ... 6.277 16.23\n",
- "Dimensions without coordinates: strlen\n",
- "Data variables: (12/17)\n",
- " station_start_date (station, strlen) object '1' '9' '8' ... '' ''\n",
- " station_zone (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
- " street_type (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
- " country_code (station, strlen) object 'C' 'H' '' ... '' ''\n",
- " ccaa (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
- " station_name (station, strlen) object 'p' 'a' 'y' ... '' ''\n",
- " ... ...\n",
- " country (station, strlen) object 's' 'w' 'i' ... '' ''\n",
- " altitude (station) float32 489.0 538.0 ... 1.0 6.0\n",
- " station_code (station, strlen) object 'C' 'H' '0' ... '' ''\n",
- " station_end_date (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
- " station_rural_back (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
- " station_ozone_classification (station, strlen) object 'r' 'u' 'r' ... '' ''\n",
- "Attributes:\n",
- " Conventions: CF-1.7 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 31, station: 84, strlen: 75)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2015-07-01 ... 2015-0...\n",
- " * station (station) float64 0.0 1.0 2.0 ... 82.0 83.0\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- "Dimensions without coordinates: strlen\n",
- "Data variables: (12/17)\n",
- " station_start_date (station, strlen) object ...\n",
- " station_zone (station, strlen) object ...\n",
- " street_type (station, strlen) object ...\n",
- " country_code (station, strlen) object ...\n",
- " ccaa (station, strlen) object ...\n",
- " station_name (station, strlen) object ...\n",
- " ... ...\n",
- " country (station, strlen) object ...\n",
- " altitude (station) float32 ...\n",
- " station_code (station, strlen) object ...\n",
- " station_end_date (station, strlen) object ...\n",
- " station_rural_back (station, strlen) object ...\n",
- " station_ozone_classification (station, strlen) object ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('points_file_1.nc')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Read and write - GHOST type"
+ "## 2. GHOST"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -2028,623 +785,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Open with xarray"
+ "### 2.1. Read dataset"
]
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (station: 3, time: 30, N_flag_codes: 190, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
- "Data variables: (12/177)\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NMVOC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NOx_emissions (station) float32 ...\n",
- " ... ...\n",
- " street_type (station) object ...\n",
- " street_width (station) float32 ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- " sconcso4_prefiltered_defaultqa (station, time) float32 ...\n",
- "Attributes:\n",
- " title: Surface sulphate data in the EANET network in 2019-11.\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " version: 1.4 Dimensions: station : 3time : 30N_flag_codes : 190N_qa_codes : 77
Coordinates: (1)
Data variables: (177)
ASTER_v3_altitude
(station)
float32
...
standard_name : ASTER v3 altitude long_name : ASTER v3 altitude, relative to EGM96 geoid datum units : m description : Altitude from ASTER v3 digital elevation model, relative to EGM96 geoid vertical datum, in metres. The dataset was generated using 1,880,306 Level-1A scenes (taken from the NASA TERRA spacecraft) acquired between March 1, 2000 and November 30, 2013. The ASTER GDEM was created by stacking all individual cloud-masked scene DEMs and non-cloud-masked scene DEMs, then applying various algorithms to remove abnormal data. A statistical approach is not always effective for anomaly removal in areas with a limited number of images. Several existing reference DEMs were used to replace residual anomalies caused by the insufficient number of stacked scenes. In addition to ASTER GDEM, the ASTER Global Water Body Database (ASTWBD) was generated as a by-product to correct elevation values of water body surfaces like sea, rivers, and lakes. The ASTWBD was applied to GDEM to provide proper elevation values for water body surfaces. The sea and lake have a flattened elevation value. The river has a stepped-down elevation value from the upper stream to the lower stream. Native resolution of 1 arc second ~= 30m at the equator. array([ 60., 52., 200.], dtype=float32) EDGAR_v4.3.2_annual_average_BC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average BC emissions long_name : EDGAR v4.3.2 annual average black carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average BC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([1.235562e-15, 5.161284e-16, 1.677018e-13], dtype=float32) EDGAR_v4.3.2_annual_average_CO_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average CO emissions long_name : EDGAR v4.3.2 annual average carbon monoxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average CO emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([3.399153e-14, 1.419923e-14, 4.695353e-13], dtype=float32) EDGAR_v4.3.2_annual_average_NH3_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NH3 emissions long_name : EDGAR v4.3.2 annual average ammonia emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NH3 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 7.552762e-16], dtype=float32) EDGAR_v4.3.2_annual_average_NMVOC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NMVOC emissions long_name : EDGAR v4.3.2 annual average non-methane volatile organic compound emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NMVOC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([1.545073e-14, 6.454220e-15, 2.849663e-12], dtype=float32) EDGAR_v4.3.2_annual_average_NOx_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NOx emissions long_name : EDGAR v4.3.2 annual average emissions of nitrogen oxides units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NOx emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([3.955383e-13, 1.652272e-13, 8.565082e-12], dtype=float32) EDGAR_v4.3.2_annual_average_OC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average OC emissions long_name : EDGAR v4.3.2 annual average organic carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average OC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([6.080775e-16, 2.540110e-16, 8.383988e-14], dtype=float32) EDGAR_v4.3.2_annual_average_PM10_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average PM10 emissions long_name : EDGAR v4.3.2 annual average PM10 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average PM10 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([6.180300e-15, 2.581682e-15, 8.388205e-13], dtype=float32) EDGAR_v4.3.2_annual_average_SO2_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average SO2 emissions long_name : EDGAR v4.3.2 annual average sulphur dioxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average SO2 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([3.090143e-14, 1.290841e-14, 5.084733e-12], dtype=float32) EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions long_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average biogenic PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 2.124066e-16], dtype=float32) EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions long_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([6.180300e-15, 2.581682e-15, 8.383800e-13], dtype=float32) ESDAC_Iwahashi_landform_classification
(station)
object
...
standard_name : ESDAC Iwahashi landform classification long_name : ESDAC Iwahashi landform classification units : unitless description : European Soil Data Centre (ESDAC) Iwahashi landform classification. The classification presents relief classes which are classified using an unsupervised nested-means algorithms and a three part geometric signature. Slope gradient, surface texture and local convexity are calculated based on the SRTM30 digital elevation model, within a given window size and classified according to the inherent data set properties. This is a dynamic landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity'], dtype=object) ESDAC_Meybeck_landform_classification
(station)
object
...
standard_name : ESDAC Meybeck landform classification long_name : ESDAC Meybeck landform classification units : unitless description : European Soil Data Centre (ESDAC) Meybeck landform classification. The classification presents relief classes which are calculated based on the relief roughness. Roughness and elevation are classified based on a digital elevation model according to static thresholds, with a given window size. This is a static landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water', 'water', 'very low plateaus'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 25km long_name : ESDAC modal Iwahashi landform classification in 25km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 25km around station location. array(['water', 'water', 'steep - fine texture - high convexity'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 5km long_name : ESDAC modal Iwahashi landform classification in 5km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 5km around station location. array(['water', 'water', 'steep - fine texture - low convexity'], dtype=object) ESDAC_modal_Meybeck_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 25km long_name : ESDAC modal Meybeck landform classification in 25km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 25km around station location. array(['water', 'water', 'hills'], dtype=object) ESDAC_modal_Meybeck_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 5km long_name : ESDAC modal Meybeck landform classification in 5km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 5km around station location. array(['water', 'water', 'hills'], dtype=object) ETOPO1_altitude
(station)
float32
...
standard_name : ETOPO1 altitude long_name : ETOPO1 altitude, relative to sea level datum units : m description : Altitude from ETOPO1 digital elevation model, relative to sea level vertical datum, in metres. Over Antarctica and Greenland the elevation given is on top of the ice sheets. Native resolution of 1 arc minute. A correction for coastal sites is made: if the derived altitude is <= -5 m, the maximum altitude of the neighbouring grid boxes will be used instead. If all neighbouring grid boxes have altitudes <= -5 m, the original value will be retained. array([ 4., -1., 280.], dtype=float32) ETOPO1_max_altitude_difference_5km
(station)
float32
...
standard_name : ETOPO1 max altitude difference 5km long_name : ETOPO1 maximum altitude difference between the ETOPO1_altitude and all ETOPO1 altitudes in 5km radius units : m description : Altitude difference between the ETOPO1_altitude, and the minimum ETOP1 altitude in a radius of 5 km around the station location, in metres. array([ 10., 66., 109.], dtype=float32) GHOST_version
(station)
object
...
standard_name : GHOST version long_name : Globally Harmonised Observational Surface Treatment (GHOST) version units : unitless description : Version of the Globally Harmonised Observational Surface Treatment (GHOST). array(['1.4', '1.4', '1.4'], dtype=object) GHSL_average_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL average built up area density 25km long_name : GHSL average built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 25km around the station location. array([1.171513, 1.151988, 1.243206], dtype=float32) GHSL_average_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL average built up area density 5km long_name : GHSL average built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 5km around the station location. array([0.987336, 2.102892, 0.154906], dtype=float32) GHSL_average_population_density_25km
(station)
float32
...
standard_name : GHSL average population density 25km long_name : GHSL average population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 25km around the station location. array([ 74.10099, 81.21062, 174.84006], dtype=float32) GHSL_average_population_density_5km
(station)
float32
...
standard_name : GHSL average population density 5km long_name : GHSL average population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 5km around the station location. array([ 28.34486 , 138.51115 , 10.174279], dtype=float32) GHSL_built_up_area_density
(station)
float32
...
standard_name : GHSL built up area density long_name : GHSL built up area density units : % description : Global Human Settlement Layer (GHSL) built up area density (technical label: GHS_BUILT_LDSMT_GLOBE_R2018A), in units of built-up area percent per gridcell (0-100). The product is a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). Native resolution of 0.25 x 0.25 kilometres. array([5.9664, 0. , 0. ], dtype=float32) GHSL_max_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL max built up area density 25km long_name : GHSL max built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 25km around the station location. array([100., 100., 100.], dtype=float32) GHSL_max_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL max built up area density 5km long_name : GHSL max built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 5km around the station location. array([59., 80., 29.], dtype=float32) GHSL_max_population_density_25km
(station)
float32
...
standard_name : GHSL max population density 25km long_name : GHSL max population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 25km around the station location. array([34752., 9012., 19701.], dtype=float32) GHSL_max_population_density_5km
(station)
float32
...
standard_name : GHSL max population density 5km long_name : GHSL max population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 5km around the station location. array([1658., 4708., 1593.], dtype=float32) GHSL_modal_settlement_model_classification_25km
(station)
object
...
standard_name : GHSL modal settlement model classification 25km long_name : GHSL modal settlement model classification in 25km radius units : unitless description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 25km around station location. array(['water', 'water', 'very low density rural'], dtype=object) GHSL_modal_settlement_model_classification_5km
(station)
object
...
standard_name : GHSL modal settlement model classification 5km long_name : GHSL modal settlement model classification in 5km radius units : unitless description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 5km around station location. array(['water', 'water', 'very low density rural'], dtype=object) GHSL_population_density
(station)
float32
...
standard_name : GHSL population density long_name : GHSL population density units : xx km–2 description : Global Human Settlement Layer (GHSL) population density (technical label: GHS_POP_MT_GLOBE_R2019A), in populus per squared kilometre. It depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the GHSL global layer per corresponding epoch. Native resolution of 0.25 x 0.25 kilometres. array([166.15518, 0. , 0. ], dtype=float32) GHSL_settlement_model_classification
(station)
object
...
standard_name : GHSL settlement model classification long_name : GHSL settlement model classification units : unitless description : Global Human Settlement Layer (GHSL) settlement model classification (technical label: GHS_SMOD_POPMT_GLOBE_R2019A). The classification delineates and classify settlement typologies via a logic of population size, population and built-up area densities as a refinement of the ‘degree of urbanization’ method described by EUROSTAT. The classification is derived by using the GHS_POP_MT_GLOBE_R2019A and GHS_BUILT_LDSMT_GLOBE_R2018A products. The GHS Settlement Model grid is an improvement of the GHS Settlement Grid (R2016A) introducing a more detailed classification of settlements in two levels, also called ‘refined degree of urbanization’. The Settlement Model is provided at detailed level (Second Level - L2). The First Level, as a porting of the Degree of Urbanization adopted by EUROSTAT can be obtained aggregating L2. Native resolution of 1.0 x 1.0 kilometres. array(['low density rural', 'low density rural', 'very low density rural'],\n",
- " dtype=object) GPW_average_population_density_25km
(station)
float32
...
standard_name : GPW average population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 25 km around the station location. array([125.20069, 79.76857, 251.70555], dtype=float32) GPW_average_population_density_5km
(station)
float32
...
standard_name : GPW average population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 5 km around the station location. array([ 76.15041, 151.78712, 35.03873], dtype=float32) GPW_max_population_density_25km
(station)
float32
...
standard_name : GPW max population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 25 km around the station location. array([1020.8104 , 295.55426, 3971.275 ], dtype=float32) GPW_max_population_density_5km
(station)
float32
...
standard_name : GPW max population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 5 km around the station location. array([130.01776 , 295.55423 , 35.038742], dtype=float32) GPW_population_density
(station)
float32
...
standard_name : GPW population density long_name : GPW population density units : xx km–2 description : Gridded Population of the World (GPW), population density, in populus per squared kilometre, from either version 3 and 4 of the provided gridded datasets, dependent on the data year: v3 (1990-2000), v4 (2000-2015). Native resolution of 0.04166 x 0.04166 for v3 data; native resolution of 0.0083 x 0.0083 degrees for v4 data. array([130.01775, 295.5542 , 35.03873], dtype=float32) GSFC_coastline_proximity
(station)
float32
...
standard_name : GSFC coastline proximity long_name : GSFC proximity to the coastline units : km description : Proximity to the coastline provided by the NASA Goddard Space Flight Center (GSFC) Ocean Color Group, in kilometres, produced using the Generic Mapping Tools package. Native resolution of 0.01 x 0.01 degrees. Negative distances represent locations over land (including land-locked bodies of water), while positive distances represent locations over the ocean. There is an uncertainty of up to 1 km in the computed distance at any given point. array([ 0., -2., -40.], dtype=float32) Joly-Peuch_classification_code
(station)
float32
...
standard_name : Joly-Peuch classification code long_name : Joly-Peuch classification code units : unitless description : Joly-Peuch European classification code (range of 1-10) designed to objectively stratify stations between those diplaying rural and urban signatures (most rural == 1, most urban == 10). This classification is objectively made per species. The species that this is done for are: O3, NO2, SO2, CO, PM10, PM2.5. See reference here: https://www.sciencedirect.com/science/article/abs/pii/S1352231011012088 array([nan, nan, nan], dtype=float32) Koppen-Geiger_classification
(station)
object
...
standard_name : Koppen-Geiger classification long_name : Koppen-Geiger classification units : unitless description : Koppen-Geiger classification, classifying the global climates into 5 main groups (30 total groups with subcategories). Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". See citation: Beck, H.E., N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Nature Scientific Data, 2018. array(['water', 'water', 'cold - no dry season - hot summer'], dtype=object) Koppen-Geiger_modal_classification_25km
(station)
object
...
standard_name : Koppen-Geiger modal classification 25km long_name : Koppen-Geiger classification units : unitless description : Modal Koppen-Geiger classification in radius of 25km around station location. array(['water', 'water', 'cold - no dry season - hot summer'], dtype=object) Koppen-Geiger_modal_classification_5km
(station)
object
...
standard_name : Koppen-Geiger modal classification 5km long_name : Koppen-Geiger classification units : unitless description : Modal Koppen-Geiger classification in radius of 5km around station location. array(['water', 'water', 'cold - no dry season - hot summer'], dtype=object) MODIS_MCD12C1_v6_IGBP_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP land use long_name : MODIS MCD12C1 v6 IGBP land use units : unitless description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water bodies', 'croplands', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_LAI
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI long_name : MODIS MCD12C1 v6 Leaf Area Index units : unitless description : Majority Leaf Area Index class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water bodies', 'water bodies', 'savannas'], dtype=object) MODIS_MCD12C1_v6_UMD_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD land use long_name : MODIS MCD12C1 v6 UMD land use units : unitless description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water bodies', 'water bodies', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 25km long_name : MODIS MCD12C1 v6 IGBP modal land use in 25km radius units : unitless description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['water bodies', 'water bodies', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 5km long_name : MODIS MCD12C1 v6 IGBP modal land use in 5km radius units : unitless description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['water bodies', 'croplands', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_modal_LAI_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 25km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 25km radius units : unitless description : Modal Leaf Area Index in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['water bodies', 'water bodies', 'deciduous broadleaf forests'],\n",
- " dtype=object) MODIS_MCD12C1_v6_modal_LAI_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 5km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 5km radius units : unitless description : Modal Leaf Area Index in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['water bodies', 'water bodies', 'savannas'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 25km long_name : MODIS MCD12C1 v6 UMD modal land use in 25km radius units : unitless description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['water bodies', 'water bodies', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 5km long_name : MODIS MCD12C1 v6 UMD modal land use in 5km radius units : unitless description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['water bodies', 'water bodies', 'woody savannas'], dtype=object) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 25km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([12., 8., 17.], dtype=float32) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 5km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 9., 12., 10.], dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 25km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([62., 58., 63.], dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 5km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([20., 22., 23.], dtype=float32) NOAA-DMSP-OLS_v4_nighttime_stable_lights
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 nighttime stable lights long_name : NOAA DMSP-OLS version 4 nighttime stable lights units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 nighttime stable lights. Native resolution of 0.0083 x 0.0083 degrees. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([11., 14., 8.], dtype=float32) OMI_level3_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column annual average NO2 long_name : OMI level3 column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([8.610014e+15, 4.759712e+15, 6.198418e+15], dtype=float32) OMI_level3_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column cloud screened annual average NO2 long_name : OMI level3 column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([8.410300e+15, 4.600654e+15, 6.476759e+15], dtype=float32) OMI_level3_tropospheric_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column annual average NO2 long_name : OMI level3 tropospheric column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([5.951614e+15, 2.243110e+15, 3.619630e+15], dtype=float32) OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column cloud screened annual average NO2 long_name : OMI level3 tropospheric column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([5.723139e+15, 2.023781e+15, 3.770765e+15], dtype=float32) UMBC_anthrome_classification
(station)
object
...
standard_name : UMBC anthrome classification long_name : UMBC anthrome classification units : unitless description : University of Maryland Baltimore County (UMBC) anthrome classification, describing the anthropogenic land use (for the year 2000). There are 20 distinct classifications. Native resolution of 0.0833 x 0.0833 degrees. A correction for costal sites is made: if the native anthrome class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['residential woodlands', 'populated woodlands', 'residential woodlands'],\n",
- " dtype=object) UMBC_modal_anthrome_classification_25km
(station)
object
...
standard_name : UMBC modal anthrome classification 25km long_name : UMBC modal anthrome classification in 25km radius units : unitless description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 25km around station location. array(['water', 'water', 'residential woodlands'], dtype=object) UMBC_modal_anthrome_classification_5km
(station)
object
...
standard_name : UMBC modal anthrome classification 5km long_name : UMBC modal anthrome classification in 5km radius units : unitless description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 5km around station location. array(['residential woodlands', 'populated woodlands', 'residential woodlands'],\n",
- " dtype=object) WMO_region
(station)
object
...
standard_name : WMO region long_name : WMO region code units : unitless description : World Meteorological Organization (WMO) region of station. The available regions are: Africa, Asia, South America, "Northern America, Central America and the Caribbean", South-West Pacific, Europe and Antarctica. array(['Asia', 'Asia', 'Asia'], dtype=object) WWF_TEOW_biogeographical_realm
(station)
object
...
standard_name : WWF TEOW biogeographical realm long_name : WWF TEOW biogeographical realm units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 8 biogeographical realms. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['water', 'palearctic', 'palearctic'], dtype=object) WWF_TEOW_biome
(station)
object
...
standard_name : WWF TEOW biome long_name : WWF TEOW biome units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 14 biomes. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['water', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests'], dtype=object) WWF_TEOW_terrestrial_ecoregion
(station)
object
...
standard_name : WWF TEOW terrestrial ecoregion long_name : WWF TEOW terrestrial ecoregion units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 825 terrestrial ecoregions. Ecoregions are relatively large units of land containing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['water', 'southern korea evergreen forests',\n",
- " 'central korean deciduous forests'], dtype=object) administrative_country_division_1
(station)
object
...
standard_name : administrative country division 1 long_name : administrative country division 1 units : unitless description : Name of the first (i.e. largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['Incheon', 'Jeju-Do', 'Jeollabuk-Do'], dtype=object) administrative_country_division_2
(station)
object
...
standard_name : administrative country division 2 long_name : administrative country division 2 units : unitless description : Name of the second (i.e. second largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['nan', 'nan', 'nan'], dtype=object) altitude
(station)
float32
...
standard_name : altitude long_name : altitude relative to mean sea level units : m description : Altitude of the ground level at the station, relative to the stated vertical datum, in metres. array([ 60., 37., 217.], dtype=float32) annual_native_max_gap_percent
(station, time)
uint8
...
standard_name : annual native max gap percent long_name : annual native max gap percent units : % description : Percentage of the maximum data gap in the annual averaged measurement UTC window filled by native resolution data, relative to the total window length. array([[5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
- " 5, 5, 5, 5, 5, 5, 5],\n",
- " [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
- " 8, 8, 8, 8, 8, 8, 8],\n",
- " [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
- " 6, 6, 6, 6, 6, 6, 6]], dtype=uint8) annual_native_representativity_percent
(station, time)
uint8
...
standard_name : annual native representativity percent long_name : annual native representativity percent units : % description : Percentage of the annual averaged measurement UTC window represented by native resolution data. array([[16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,\n",
- " 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16],\n",
- " [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
- " 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12],\n",
- " [13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
- " 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13]], dtype=uint8) area_classification
(station)
object
...
standard_name : area classification long_name : standardised network provided area classification units : unitless description : Standardised network provided classification, describing type of area a measurement station is situated in. array(['rural-regional', 'rural-remote', 'rural-regional'], dtype=object) associated_networks
(station)
object
...
standard_name : other associated networks long_name : other associated networks units : unitless description : String pair of associated network name and station reference. Format: network1:station_reference1;network2:station_reference2 array(['nan', 'nan', 'nan'], dtype=object) city
(station)
object
...
standard_name : city long_name : city units : unitless description : Name of the city the station is located in. array(['Seogeom-Ri', 'Gaigeturi', 'Imsil'], dtype=object) climatology
(station)
object
...
standard_name : climatology long_name : climatology units : unitless description : Name of the climatology of which the observations pertain to. array(['nan', 'nan', 'nan'], dtype=object) contact_email_address
(station)
object
...
standard_name : contact email address long_name : contact email address units : unitless description : Email address of the principal data contact for the specific reported data. array(['eanetdata@acap.asia', 'eanetdata@acap.asia', 'eanetdata@acap.asia'],\n",
- " dtype=object) contact_institution
(station)
object
...
standard_name : contact institution long_name : contact institution units : unitless description : Institution of the principal data contact for the specific reported data. array(['EANET', 'EANET', 'EANET'], dtype=object) contact_name
(station)
object
...
standard_name : contact name long_name : contact name units : unitless description : Full name of the principal data contact for the specific reported data. array(['nan', 'nan', 'nan'], dtype=object) country
(station)
object
...
standard_name : country long_name : country units : unitless description : Name of the country the station is located in. array(['South Korea', 'South Korea', 'South Korea'], dtype=object) daily_native_max_gap_percent
(station, time)
uint8
...
standard_name : daily native max gap percent long_name : daily native max gap percent units : % description : Percentage of the maximum data gap in the daily averaged measurement UTC window filled by native resolution data, relative to the total window length. array([[100, 100, 100, 100, 100, 100, 100, 4, 96, 4, 96, 100, 100, 100,\n",
- " 100, 4, 96, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 4,\n",
- " 0, 96],\n",
- " [100, 100, 100, 4, 96, 100, 100, 100, 100, 4, 96, 100, 100, 100,\n",
- " 100, 4, 96, 100, 100, 100, 100, 4, 96, 100, 100, 100, 100, 4,\n",
- " 96, 100],\n",
- " [100, 100, 100, 4, 96, 100, 100, 100, 100, 4, 96, 100, 100, 100,\n",
- " 100, 4, 96, 100, 100, 100, 100, 4, 96, 100, 100, 100, 100, 100,\n",
- " 100, 100]], dtype=uint8) daily_native_representativity_percent
(station, time)
uint8
...
standard_name : daily native representativity percent long_name : daily native representativity percent units : % description : Percentage of the daily averaged measurement UTC window represented by native resolution data. array([[ 0, 0, 0, 0, 0, 0, 0, 96, 4, 96, 4, 0, 0, 0,\n",
- " 0, 96, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 96,\n",
- " 100, 4],\n",
- " [ 0, 0, 0, 96, 4, 0, 0, 0, 0, 96, 4, 0, 0, 0,\n",
- " 0, 96, 4, 0, 0, 0, 0, 96, 4, 0, 0, 0, 0, 96,\n",
- " 4, 0],\n",
- " [ 0, 0, 0, 96, 4, 0, 0, 0, 0, 96, 4, 0, 0, 0,\n",
- " 0, 96, 4, 0, 0, 0, 0, 96, 4, 0, 0, 0, 0, 0,\n",
- " 0, 0]], dtype=uint8) daily_passing_vehicles
(station)
float32
...
standard_name : daily passing vehicles long_name : average daily number of passing vehicles units : unitless description : Average number of vehicles passing daily. array([nan, nan, nan], dtype=float32) data_level
(station)
object
...
standard_name : data level long_name : data level units : unitless description : Data level of data reported. This varies per network. If data level is variable per measurement, and not static per reported file, then this is set as "variable". If there is no reported data level this is set as "none" array(['none', 'none', 'none'], dtype=object) data_licence
(station)
object
...
standard_name : data licence long_name : data licence units : unitless description : Information pertaining to the data licence governing the redistribution/publication of the ingested network data. array(['Rights reserved to the Network Center for the EANET: https://monitoring.eanet.asia/document/public/index',\n",
- " 'Rights reserved to the Network Center for the EANET: https://monitoring.eanet.asia/document/public/index',\n",
- " 'Rights reserved to the Network Center for the EANET: https://monitoring.eanet.asia/document/public/index'],\n",
- " dtype=object) day_night_code
(station, time)
uint8
...
standard_name : day/night code long_name : day/night code per measurement units : unitless description : Binary indication if measurement was made during the day or night. Day=0, Night=1. The classification is made by calculating the solar elevation angle for a latitude/longitude/measurement height at a mid-measurement window timestamp. If the solar elevation angle is > 0, it is classed as daytime, otherwise it is nightime. Classification is 255 if cannot be made. array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255],\n",
- " [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255],\n",
- " [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255]], dtype=uint8) daytime_traffic_speed
(station)
float32
...
standard_name : daytime traffic speed long_name : average daytime speed of passing traffic units : km hr-1 description : Average daytime speed of the passing traffic where measurements are being made (if applicable), in kilometres per hour. array([nan, nan, nan], dtype=float32) derived_uncertainty_per_measurement
(station, time)
float32
...
standard_name : derived measurement uncertainty per measurement long_name : derived measurement uncertainty per measurement units : ug m-3 description : Derived measurement uncertainty (±) of methodology, for a specific measurement. This is calculated through the quadratic addition of reported (or if not available, documented) accuracy and precision metrics. This is given in absolute terms in ug m-3. array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan],\n",
- " [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan],\n",
- " [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan]], dtype=float32) distance_to_building
(station)
float32
...
standard_name : distance to building long_name : distance to the nearest building units : m description : Distance to the nearest building of the inlet/instrument/sampler, in metres. array([nan, nan, nan], dtype=float32) distance_to_junction
(station)
float32
...
standard_name : distance to junction long_name : distance to the nearest road junction units : m description : Distance to the nearest road junction of the inlet/instrument/sampler, in metres. array([nan, nan, nan], dtype=float32) distance_to_kerb
(station)
float32
...
standard_name : distance to kerb long_name : distance to the street kerb units : m description : Distance to the street kerb of the inlet/instrument/sampler, in metres. array([nan, nan, nan], dtype=float32) distance_to_source
(station)
float32
...
standard_name : distance to source long_name : distance to the main emission source units : km description : Distance to the main emission source (see variable: "main_emission_source") of the inlet/instrument/sampler, in kilometres. array([nan, nan, nan], dtype=float32) ellipsoid
(station)
object
...
standard_name : ellipsoid long_name : ellipsoid units : unitless description : The ellipsoidal model of the earth used as a basis for 2D and 3D geographic coordinate systems. array(['WGS 84', 'WGS 84', 'WGS 84'], dtype=object) flag
(station, time, N_flag_codes)
float32
...
standard_name : flags long_name : data reporter provided standardised flags units : unitless description : List of associated data flag codes per measurement, indicating the data quality of a specific measurement, provided by the data reporter. Fill value code of 255. array([[[ 4., nan, ..., nan, nan],\n",
- " [ 4., nan, ..., nan, nan],\n",
- " ...,\n",
- " [ 0., nan, ..., nan, nan],\n",
- " [ 4., nan, ..., nan, nan]],\n",
- "\n",
- " [[ 4., nan, ..., nan, nan],\n",
- " [ 4., nan, ..., nan, nan],\n",
- " ...,\n",
- " [ 4., nan, ..., nan, nan],\n",
- " [ 4., nan, ..., nan, nan]],\n",
- "\n",
- " [[ 4., nan, ..., nan, nan],\n",
- " [ 4., nan, ..., nan, nan],\n",
- " ...,\n",
- " [ 4., nan, ..., nan, nan],\n",
- " [ 4., nan, ..., nan, nan]]], dtype=float32) horizontal_datum
(station)
object
...
standard_name : horizontal datum long_name : horizontal datum units : unitless description : Name of the horizontal datum used in defining geodetic latitudes and longitudes on the Earth's surface. The datum is set when positioning an ellipsoid model of the Earth to an anchor point. If not explicitely stated then this is assumed to be 'World Geodetic System 1984'. array(['WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984'], dtype=object) land_use
(station)
object
...
standard_name : land use long_name : standardised network provided land use type units : unitless description : Standardised network provided classification, describing the dominant land use in the area of the reporting station. array(['nan', 'nan', 'nan'], dtype=object) latitude
(station)
float64
...
standard_name : latitude long_name : latitude units : decimal degrees North description : Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum. axis : Y array([37.708889, 33.292222, 35.6025 ]) longitude
(station)
float64
...
standard_name : longitude long_name : longitude units : decimal degrees East description : Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum. axis : X array([126.273889, 126.161944, 127.181389]) main_emission_source
(station)
object
...
standard_name : main emission source long_name : standardised network provided main emission source units : unitless description : Standardised network provided classification, describing the main emission source influencing air measured at a station. array(['nan', 'nan', 'nan'], dtype=object) measurement_altitude
(station)
float32
...
standard_name : measurement altitude long_name : measurement altitude relative to mean sea level units : m description : Altitude of the inlet/instrument/sampler, relative to the stated vertical datum, in metres. array([ 63., 40., 220.], dtype=float32) measurement_methodology
(station)
object
...
standard_name : measurement methodology long_name : standardised measurement methodology units : unitless description : Standardised name of the measurement methodology. array(['ion chromatography', 'ion chromatography', 'ion chromatography'],\n",
- " dtype=object) measurement_scale
(station)
object
...
standard_name : measurement scale long_name : standardised network provided measurement scale units : unitless description : Standardised network provided classification, a denotation of the geographic scope of the air quality measurements made. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_calibration_scale
(station)
object
...
standard_name : measuring instrument calibration scale long_name : measuring instrument calibration scale name units : unitless description : Name of calibration scale used for the calibration of the measuring instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_absorption_cross_section
(station)
object
...
standard_name : measuring instrument documented absorption cross section long_name : measuring instrument documented absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in instrumental manual/documentation. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_accuracy
(station)
object
...
standard_name : measuring instrument documented measurement accuracy long_name : measuring instrument documented measurement accuracy units : ug m-3 description : Measurement accuracy (±), as given in the instrumental manual/documentation. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_flow_rate
(station)
object
...
standard_name : measuring instrument documented flow rate long_name : measuring instrument documented flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented lower limit of detection long_name : measuring instrument documented lower limit of detection units : ug m-3 description : Lower limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([nan, nan, nan], dtype=float32) measuring_instrument_documented_measurement_resolution
(station)
float32
...
standard_name : measuring instrument documented measurement resolution long_name : measuring instrument documented measurement resolution units : ug m-3 description : Measurement resolution, as given in instrumental manual/documentation. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([nan, nan, nan], dtype=float32) measuring_instrument_documented_precision
(station)
object
...
standard_name : measuring instrument documented measurement precision long_name : measuring instrument documented measurement precision units : ug m-3 description : Measurement precision (±), as given in instrumental manual/documentation. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_span_drift
(station)
object
...
standard_name : measuring instrument documented span drift long_name : measuring instrument documented span drift units : ug m-3 description : Span drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_uncertainty
(station)
object
...
standard_name : measuring instrument documented measurement uncertainty long_name : measuring instrument documented measurement uncertainty units : ug m-3 description : Measurement uncertainty (±), as given in the instrumental manual/documentation. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented upper limit of detection long_name : measuring instrument documented upper limit of detection units : ug m-3 description : Upper limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([nan, nan, nan], dtype=float32) measuring_instrument_documented_zero_drift
(station)
object
...
standard_name : measuring instrument documented zero drift long_name : measuring instrument documented zero drift units : ug m-3 description : Zero drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_zonal_drift
(station)
object
...
standard_name : measuring instrument documented zonal drift long_name : measuring instrument documented zonal drift units : ug m-3 description : Zonal drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_further_details
(station)
object
...
standard_name : measuring instrument further details long_name : measuring instrument further details units : unitless description : Further associated details regarding the specifics of the measurement methodology/instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_inlet_information
(station)
object
...
standard_name : measuring instrument inlet information long_name : measuring instrument measurement inlet information units : unitless description : Description of sampling inlet of the measuring instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_manual_name
(station)
object
...
standard_name : measuring instrument manual name long_name : measuring instrument manual name units : unitless description : Path to the location in the esarchive of the manual for the specific measuring instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_name
(station)
object
...
standard_name : measuring instrument name long_name : standardised measuring instrument name units : unitless description : Standardised name of the measuring instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_process_details
(station)
object
...
standard_name : measuring instrument process details long_name : measuring instrument process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the measurement methodology/instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_absorption_cross_section
(station)
object
...
standard_name : measuring instrument reported absorption cross section long_name : measuring instrument reported absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in metadata. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_accuracy
(station)
object
...
standard_name : measuring instrument reported measurement accuracy long_name : measuring instrument reported measurement accuracy units : ug m-3 description : Measurement accuracy (±), as given in metadata. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_flow_rate
(station)
object
...
standard_name : measuring instrument reported flow rate long_name : measuring instrument reported flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported lower limit of detection long_name : measuring instrument reported lower limit of detection units : ug m-3 description : Lower limit of detection of measurement methodology, as given in metadata. array([nan, nan, nan], dtype=float32) measuring_instrument_reported_measurement_resolution
(station)
float32
...
standard_name : measuring instrument reported measurement resolution long_name : measuring instrument reported measurement resolution units : ug m-3 description : Measurement resolution, as given in metadata. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([nan, nan, nan], dtype=float32) measuring_instrument_reported_precision
(station)
object
...
standard_name : measuring instrument reported measurement precision long_name : measuring instrument reported measurement precision units : ug m-3 description : Measurement precision (±), as given in metadata. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_span_drift
(station)
object
...
standard_name : measuring instrument reported span drift long_name : measuring instrument reported span drift units : ug m-3 description : Span drift of measuring instrument per unit of time, as given in metadata. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_uncertainty
(station)
object
...
standard_name : measuring instrument reported measurement uncertainty long_name : measuring instrument reported measurement uncertainty units : ug m-3 description : Measurement uncertainty (±), as given in metadata. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). It can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_units
(station)
object
...
standard_name : measuring instrument reported units long_name : measuring instrument reported measurement units units : unitless description : Units that the measured parameter are natively reported in. array(['ug m-3', 'ug m-3', 'ug m-3'], dtype=object) measuring_instrument_reported_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported upper limit of detection long_name : measuring instrument reported upper limit of detection units : ug m-3 description : Upper limit of detection of measurement methodology, as given in metadata. array([nan, nan, nan], dtype=float32) measuring_instrument_reported_zero_drift
(station)
object
...
standard_name : measuring instrument reported zero drift long_name : measuring instrument reported zero drift units : ug m-3 description : Zero drift of measuring instrument per unit of time, as given in metadata. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_zonal_drift
(station)
object
...
standard_name : measuring instrument reported zonal drift long_name : measuring instrument reported zonal drift units : ug m-3 description : Zonal drift of measuring instrument per unit of time, as given in metadata. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_sampling_type
(station)
object
...
standard_name : measuring instrument sampling type long_name : standardised sampling type of the measuring instrument units : unitless description : Standardised name of the measuring instrument sampling type. array(['injection', 'injection', 'injection'], dtype=object) monthly_native_max_gap_percent
(station, time)
uint8
...
standard_name : monthly native max gap percent long_name : monthly native max gap percent units : % description : Percentage of the maximum data gap in the monthly averaged measurement UTC window filled by native resolution data, relative to the total window length. array([[37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37,\n",
- " 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37],\n",
- " [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
- " 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],\n",
- " [27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,\n",
- " 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27]], dtype=uint8) monthly_native_representativity_percent
(station, time)
uint8
...
standard_name : monthly native representativity percent long_name : monthly native representativity percent units : % description : Percentage of the monthly averaged measurement UTC window represented by native resolution data. array([[17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
- " 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],\n",
- " [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
- " 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],\n",
- " [13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
- " 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13]], dtype=uint8) network
(station)
object
...
standard_name : network long_name : data network units : unitless description : The name of the network which reports data for the specific station in question. array(['EANET', 'EANET', 'EANET'], dtype=object) network_maintenance_details
(station)
object
...
standard_name : network maintenance details long_name : network maintenance details units : unitless description : Extra details provided by the reporting network about the operational maintenance done at the station. array(['nan', 'nan', 'nan'], dtype=object) network_miscellaneous_details
(station)
object
...
standard_name : network miscellaneous details long_name : network miscellaneous details units : unitless description : Extra miscellanous details provided by the reporting network. array(['nan', 'nan', 'nan'], dtype=object) network_provided_volume_standard_pressure
(station)
float64
...
standard_name : network provided volume standard pressure long_name : network provided volume standard pressure units : hPa description : The pressure (in hPa) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typically normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported pressure should be reported as the internal pressure of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas pressure is assumed to be the known network standard pressure for the measured component. array([1013.25, 1013.25, 1013.25]) network_provided_volume_standard_temperature
(station)
float64
...
standard_name : network provided volume standard temperature long_name : network provided volume standard temperature units : K description : The temperature (in Kelvin) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typically normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported temperature should be reported as the internal temperature of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas temperature is assumed to be the known network standard temperature for the measured component. array([293.15, 293.15, 293.15]) network_qa_details
(station)
object
...
standard_name : network qa details long_name : network qa details units : unitless description : Extra details provided by the reporting network about the in-network quality assurance of measurements. array(['nan', 'nan', 'nan'], dtype=object) network_sampling_details
(station)
object
...
standard_name : network sampling details long_name : network sampling details units : unitless description : Extra details provided by the reporting network about the sampling methods employed. array(['nan', 'nan', 'nan'], dtype=object) network_uncertainty_details
(station)
object
...
standard_name : network uncertainty details long_name : network uncertainty details units : unitless description : Extra details provided by the reporting network about the uncertainties involved with the measurement methods employed. array(['nan', 'nan', 'nan'], dtype=object) population
(station)
float32
...
standard_name : population long_name : population units : unitless description : Population size of the nearest urban settlement. array([nan, nan, nan], dtype=float32) primary_sampling_further_details
(station)
object
...
standard_name : primary sampling further details long_name : primary sampling further details units : unitless description : Further associated details regarding the specifics of the primary sampling instrument/type. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_documented_flow_rate
(station)
object
...
standard_name : primary sampling instrument documented flow rate long_name : primary sampling instrument documented sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_manual_name
(station)
object
...
standard_name : primary sampling instrument manual name long_name : primary sampling instrument manual name units : unitless description : Path to the location in the esarchive of the manual for the specific primary sampling instrument. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_name
(station)
object
...
standard_name : primary sampling instrument name long_name : standardised primary sampling instrument name units : unitless description : Standardised name of the primary sampling instrument (if no specific instrument is used, or known, this is the standardised primary sampling type). array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_reported_flow_rate
(station)
object
...
standard_name : primary sampling instrument reported flow rate long_name : primary sampling instrument reported sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_process_details
(station)
object
...
standard_name : primary sampling process details long_name : primary sampling process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the primary sampling type/instrument. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_type
(station)
object
...
standard_name : primary sampling type long_name : standardised primary sampling type units : unitless description : Standardised primary sampling type. array(['low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous'], dtype=object) principal_investigator_email_address
(station)
object
...
standard_name : principal investigator email address long_name : principal investigator email address units : unitless description : Email address of the principal scientific investigator for the specific reported data. array(['nan', 'nan', 'nan'], dtype=object) principal_investigator_institution
(station)
object
...
standard_name : principal investigator institution long_name : principal investigator institution units : unitless description : Institution of the principal scientific investigator for the specific reported data. array(['nan', 'nan', 'nan'], dtype=object) principal_investigator_name
(station)
object
...
standard_name : principal investigator name long_name : principal investigator name units : unitless description : Full name of the principal scientific investigator for the specific reported data. array(['nan', 'nan', 'nan'], dtype=object) process_warnings
(station)
object
...
standard_name : process warnings long_name : process warnings units : unitless description : Warnings accumulated through GHOST processing regarding the data that should be considered. array(['Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. '],\n",
- " dtype=object) projection
(station)
object
...
standard_name : projection long_name : projection units : unitless description : Name of the projected coordinate system of the original provided station position x, y coordinates. If the original coordinates are not projected, then this is set as 'geographic'. array(['GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC'], dtype=object) qa
(station, time, N_qa_codes)
float32
...
standard_name : qa long_name : qa standardised data flags units : unitless description : List of derived quality assurance flag codes per measurement, informing on the quality associated with each observation, determined by multiple scientific quality control/assurance checks. Fill value code of 255. array([[[ 0., 15., ..., nan, nan],\n",
- " [ 0., 15., ..., nan, nan],\n",
- " ...,\n",
- " [15., 19., ..., nan, nan],\n",
- " [15., 19., ..., nan, nan]],\n",
- "\n",
- " [[ 0., 15., ..., nan, nan],\n",
- " [ 0., 15., ..., nan, nan],\n",
- " ...,\n",
- " [15., 19., ..., nan, nan],\n",
- " [ 0., 15., ..., nan, nan]],\n",
- "\n",
- " [[ 0., 15., ..., nan, nan],\n",
- " [ 0., 15., ..., nan, nan],\n",
- " ...,\n",
- " [ 0., 15., ..., nan, nan],\n",
- " [ 0., 15., ..., nan, nan]]], dtype=float32) reported_uncertainty_per_measurement
(station, time)
float32
...
standard_name : reported measurement uncertainty per measurement long_name : reported measurement uncertainty per measurement units : ug m-3 description : Reported measurement uncertainty (±) of methodology, for a specific measurement. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This is given in absolute terms in ug m-3. array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan],\n",
- " [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan],\n",
- " [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan]], dtype=float32) representative_radius
(station)
float32
...
standard_name : representative_radius long_name : representative_radius units : km description : Radius of representativity of the measurements made (i.e. for what distance scale around the sampling point would the measurements be very similar?), given in kilometres. A quantitative version of the "measurement_scale" classification. array([nan, nan, nan], dtype=float32) retrieval_algorithm
(station)
object
...
standard_name : retrieval algorithm long_name : retrieval algorithm units : unitless description : The name of the retrieval algorithm. Remote sensing algorithms are used to retrieve the aerosol optical properties (as aerosol optical depths or single scattering albedo among others) using remote-sensing radiances for multiple wavelengths from ground stations or on satellite platforms. Each algorithm is particularly designed considering the characteristics of the sensor and other ancillary information. array(['nan', 'nan', 'nan'], dtype=object) sample_preparation_further_details
(station)
object
...
standard_name : sample preparation further details long_name : sample preparation further details units : unitless description : Further associated details regarding the specifics of the sample preparation types/instruments. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan'], dtype=object) sample_preparation_process_details
(station)
object
...
standard_name : sample preparation process details long_name : sample preparation process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the sample preparation types/techniques. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan'], dtype=object) sample_preparation_techniques
(station)
object
...
standard_name : sample preparation techniques long_name : standardised specific preparation techniques units : unitless description : Standardised sample preparation techniques utilised in the measurement process. Mutiple names are separated by ";". array(['3-Stage Filter Pack', '3-Stage Filter Pack', '3-Stage Filter Pack'],\n",
- " dtype=object) sample_preparation_types
(station)
object
...
standard_name : sample preparation types long_name : standardised sample preparation types units : unitless description : Standardised sample preparation types utilised in the measurement process. Mutiple types are separated by ";". array(['filter pack', 'filter pack', 'filter pack'], dtype=object) sampling_height
(station)
float32
...
standard_name : sampling height long_name : sampling height relative to ground units : m description : Height above the ground of the inlet/instrument/sampler, in metres. array([3., 3., 3.], dtype=float32) sconcso4
(station, time)
float32
...
standard_name : sulphate long_name : sulphate units : ug m-3 description : Measured value of surface sulphate for the stated temporal resolution. array([[ nan, nan, nan, nan, nan, nan, nan,\n",
- " 2.31 , 2.31 , 1.12 , 1.12 , nan, nan, nan,\n",
- " nan, 1.71 , 1.71 , nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, 1.38 ,\n",
- " 1.284167, 1.28 ],\n",
- " [ nan, nan, nan, 0.74 , 0.74 , nan, nan,\n",
- " nan, nan, 3.41 , 3.41 , nan, nan, nan,\n",
- " nan, 0.74 , 0.74 , nan, nan, nan, nan,\n",
- " 1.2 , 1.2 , nan, nan, nan, nan, 1.76 ,\n",
- " 1.76 , nan],\n",
- " [ nan, nan, nan, 3.05 , 3.05 , nan, nan,\n",
- " nan, nan, 2.44 , 2.44 , nan, nan, nan,\n",
- " nan, 2.24 , 2.24 , nan, nan, nan, nan,\n",
- " 1.37 , 1.37 , nan, nan, nan, nan, nan,\n",
- " nan, nan]], dtype=float32) season_code
(station, time)
uint8
...
standard_name : season code long_name : season code per measurement units : unitless description : Code decreeing if measurement was made during the Spring, Summer, Autumn or Winter Seasons. Spring=0, Summer=1, Autumn=2, Winter=3. The classification is made by evaluating which season the local-time of a mid-measurement window timestamp falls in. array([[255, 255, 255, 255, 255, 255, 255, 2, 2, 2, 2, 255, 255, 255,\n",
- " 255, 2, 2, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 2,\n",
- " 2, 2],\n",
- " [255, 255, 255, 2, 2, 255, 255, 255, 255, 2, 2, 255, 255, 255,\n",
- " 255, 2, 2, 255, 255, 255, 255, 2, 2, 255, 255, 255, 255, 2,\n",
- " 2, 255],\n",
- " [255, 255, 255, 2, 2, 255, 255, 255, 255, 2, 2, 255, 255, 255,\n",
- " 255, 2, 2, 255, 255, 255, 255, 2, 2, 255, 255, 255, 255, 255,\n",
- " 255, 255]], dtype=uint8) station_classification
(station)
object
...
standard_name : station classification long_name : standardised network provided station classification units : unitless description : Standardised network provided classification, categorising the type of air measured by a station. array(['nan', 'nan', 'nan'], dtype=object) station_name
(station)
object
...
standard_name : station name long_name : station name units : unitless description : Name of station where the measurement was conducted. array(['Kanghwa', 'Cheju (Kosan)', 'Imsil'], dtype=object) station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : unitless description : reference ID for station. array(['KRA001_IC', 'KRA002_IC', 'KRA003_IC'], dtype=object) station_timezone
(station)
object
...
standard_name : station timezone long_name : station timezone units : unitless description : Name of the local timezone that the measuring station is located in. This is automatically generated using Timezone Finder Python package (taking longitude and latitude as inputs). array(['Asia/Seoul', 'Asia/Seoul', 'Asia/Seoul'], dtype=object) street_type
(station)
object
...
standard_name : street type long_name : type of street units : unitless description : Type of street where measurements are being made (if applicable). array(['nan', 'nan', 'nan'], dtype=object) street_width
(station)
float32
...
standard_name : street width long_name : width of the street units : m description : Width of the street where measurements are being made (if applicable), in metres. array([nan, nan, nan], dtype=float32) terrain
(station)
object
...
standard_name : terrain long_name : standardised network provided terrain type units : unitless description : Standardised network provided classification, describing the dominant terrain in the area of the reporting station. array(['nan', 'nan', 'nan'], dtype=object) vertical_datum
(station)
object
...
standard_name : vertical datum long_name : vertical datum units : unitless description : Name of the vertical datum used to define vertical elevation on the Earth. The datum is a surface of zero elevation to which other heights can be reference against. If not explicitely stated then this is assumed to be 'tidal - mean sea level'. array(['TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL'], dtype=object) weekday_weekend_code
(station, time)
uint8
...
standard_name : weekday/weekend code long_name : weekday/weekend code per measurement units : unitless description : Binary indication if measurement was made during the weekday or weekend. Weekday=0, Weekend=1. The classification is made by evaluating if the local-time of a mid-measurement window timestamp falls on a weekday or on the weekend. Classification is 255 if cannot be made. array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255],\n",
- " [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255],\n",
- " [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255]], dtype=uint8) sconcso4_prefiltered_defaultqa
(station, time)
float32
...
standard_name : sulphate long_name : sulphate units : ug m-3 description : Measured value of surface sulphate for the stated temporal resolution. Prefiltered by default QA. array([[ nan, nan, nan, nan, nan, nan, nan,\n",
- " 2.31 , 2.31 , 1.12 , 1.12 , nan, nan, nan,\n",
- " nan, 1.71 , 1.71 , nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, 1.38 ,\n",
- " 1.284167, 1.28 ],\n",
- " [ nan, nan, nan, 0.74 , 0.74 , nan, nan,\n",
- " nan, nan, 3.41 , 3.41 , nan, nan, nan,\n",
- " nan, 0.74 , 0.74 , nan, nan, nan, nan,\n",
- " 1.2 , 1.2 , nan, nan, nan, nan, 1.76 ,\n",
- " 1.76 , nan],\n",
- " [ nan, nan, nan, 3.05 , 3.05 , nan, nan,\n",
- " nan, nan, 2.44 , 2.44 , nan, nan, nan,\n",
- " nan, 2.24 , 2.24 , nan, nan, nan, nan,\n",
- " 1.37 , 1.37 , nan, nan, nan, nan, nan,\n",
- " nan, nan]], dtype=float32) Attributes: (6)
title : Surface sulphate data in the EANET network in 2019-11. institution : Barcelona Supercomputing Center source : Surface observations creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es version : 1.4 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (station: 3, time: 30, N_flag_codes: 190, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
- "Data variables: (12/177)\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NMVOC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NOx_emissions (station) float32 ...\n",
- " ... ...\n",
- " street_type (station) object ...\n",
- " street_width (station) float32 ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- " sconcso4_prefiltered_defaultqa (station, time) float32 ...\n",
- "Attributes:\n",
- " title: Surface sulphate data in the EANET network in 2019-11.\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " version: 1.4"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset(nc_path_2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with NES"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 18,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -2656,7 +811,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -2694,7 +849,7 @@
" datetime.datetime(2019, 11, 30, 0, 0)]"
]
},
- "execution_count": 19,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -2705,7 +860,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -2714,7 +869,7 @@
"{'data': array([0]), 'units': ''}"
]
},
- "execution_count": 20,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -2725,7 +880,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -2734,7 +889,7 @@
"{'data': array([0, 1, 2])}"
]
},
- "execution_count": 21,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -2745,7 +900,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -2762,7 +917,7 @@
" 'axis': 'Y'}"
]
},
- "execution_count": 22,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -2773,7 +928,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -2790,7 +945,7 @@
" 'axis': 'X'}"
]
},
- "execution_count": 23,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -2801,7 +956,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -3165,12 +1320,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Write with NES"
+ "### 2.2. Write dataset"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -3178,8 +1333,23 @@
"output_type": "stream",
"text": [
"Rank 000: Creating points_file_2.nc\n",
- "Rank 000: NetCDF ready to write\n",
+ "Rank 000: NetCDF ready to write\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:592: UserWarning: WARNING!!! GHOST datasets cannot be written in parallel yet. Changing to serial mode.\n",
+ " warnings.warn(msg)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n",
"Rank 000: Writing ASTER_v3_altitude var (1/173)\n",
"Rank 000: Var ASTER_v3_altitude created (1/173)\n",
"Rank 000: Var ASTER_v3_altitude data (1/173)\n",
@@ -3341,21 +1511,7 @@
"Rank 000: Var Joly-Peuch_classification_code data (40/173)\n",
"Rank 000: Var Joly-Peuch_classification_code completed (40/173)\n",
"Rank 000: Writing Koppen-Geiger_classification var (41/173)\n",
- "Rank 000: Var Koppen-Geiger_classification created (41/173)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:592: UserWarning: WARNING!!! GHOST datasets cannot be written in parallel yet. Changing to serial mode.\n",
- " warnings.warn(msg)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "Rank 000: Var Koppen-Geiger_classification created (41/173)\n",
"Rank 000: Var Koppen-Geiger_classification data (41/173)\n",
"Rank 000: Var Koppen-Geiger_classification completed (41/173)\n",
"Rank 000: Writing Koppen-Geiger_modal_classification_25km var (42/173)\n",
@@ -3897,12 +2053,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Reopen with NES"
+ "### 2.3. Reopen dataset"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -4262,10 +2418,10 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 26,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -4280,627 +2436,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Reopen with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 30, station: 3, N_flag_codes: 190, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- " * station (station) float64 ...\n",
- "Dimensions without coordinates: N_flag_codes, N_qa_codes\n",
- "Data variables: (12/177)\n",
- " latitude (station) float64 ...\n",
- " longitude (station) float64 ...\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " ... ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- " sconcso4_prefiltered_defaultqa (station, time) float32 ...\n",
- " flag (station, time, N_flag_codes) int64 ...\n",
- " qa (station, time, N_qa_codes) int64 ...\n",
- "Attributes:\n",
- " title: Surface sulphate data in the EANET network in 2019-11.\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " version: 1.4\n",
- " Conventions: CF-1.7 Dimensions: time : 30station : 3N_flag_codes : 190N_qa_codes : 77
Coordinates: (2)
Data variables: (177)
latitude
(station)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([37.708889, 33.292222, 35.6025 ]) longitude
(station)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([126.273889, 126.161944, 127.181389]) ASTER_v3_altitude
(station)
float32
...
standard_name : ASTER v3 altitude long_name : ASTER v3 altitude, relative to EGM96 geoid datum units : m description : Altitude from ASTER v3 digital elevation model, relative to EGM96 geoid vertical datum, in metres. The dataset was generated using 1,880,306 Level-1A scenes (taken from the NASA TERRA spacecraft) acquired between March 1, 2000 and November 30, 2013. The ASTER GDEM was created by stacking all individual cloud-masked scene DEMs and non-cloud-masked scene DEMs, then applying various algorithms to remove abnormal data. A statistical approach is not always effective for anomaly removal in areas with a limited number of images. Several existing reference DEMs were used to replace residual anomalies caused by the insufficient number of stacked scenes. In addition to ASTER GDEM, the ASTER Global Water Body Database (ASTWBD) was generated as a by-product to correct elevation values of water body surfaces like sea, rivers, and lakes. The ASTWBD was applied to GDEM to provide proper elevation values for water body surfaces. The sea and lake have a flattened elevation value. The river has a stepped-down elevation value from the upper stream to the lower stream. Native resolution of 1 arc second ~= 30m at the equator. array([ 60., 52., 200.], dtype=float32) EDGAR_v4.3.2_annual_average_BC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average BC emissions long_name : EDGAR v4.3.2 annual average black carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average BC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([1.235562e-15, 5.161284e-16, 1.677018e-13], dtype=float32) EDGAR_v4.3.2_annual_average_CO_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average CO emissions long_name : EDGAR v4.3.2 annual average carbon monoxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average CO emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([3.399153e-14, 1.419923e-14, 4.695353e-13], dtype=float32) EDGAR_v4.3.2_annual_average_NH3_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NH3 emissions long_name : EDGAR v4.3.2 annual average ammonia emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NH3 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 7.552762e-16], dtype=float32) EDGAR_v4.3.2_annual_average_NMVOC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NMVOC emissions long_name : EDGAR v4.3.2 annual average non-methane volatile organic compound emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NMVOC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([1.545073e-14, 6.454220e-15, 2.849663e-12], dtype=float32) EDGAR_v4.3.2_annual_average_NOx_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NOx emissions long_name : EDGAR v4.3.2 annual average emissions of nitrogen oxides units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NOx emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([3.955383e-13, 1.652272e-13, 8.565082e-12], dtype=float32) EDGAR_v4.3.2_annual_average_OC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average OC emissions long_name : EDGAR v4.3.2 annual average organic carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average OC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([6.080775e-16, 2.540110e-16, 8.383988e-14], dtype=float32) EDGAR_v4.3.2_annual_average_PM10_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average PM10 emissions long_name : EDGAR v4.3.2 annual average PM10 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average PM10 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([6.180300e-15, 2.581682e-15, 8.388205e-13], dtype=float32) EDGAR_v4.3.2_annual_average_SO2_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average SO2 emissions long_name : EDGAR v4.3.2 annual average sulphur dioxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average SO2 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([3.090143e-14, 1.290841e-14, 5.084733e-12], dtype=float32) EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions long_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average biogenic PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 2.124066e-16], dtype=float32) EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions long_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([6.180300e-15, 2.581682e-15, 8.383800e-13], dtype=float32) ESDAC_Iwahashi_landform_classification
(station)
object
...
standard_name : ESDAC Iwahashi landform classification long_name : ESDAC Iwahashi landform classification units : description : European Soil Data Centre (ESDAC) Iwahashi landform classification. The classification presents relief classes which are classified using an unsupervised nested-means algorithms and a three part geometric signature. Slope gradient, surface texture and local convexity are calculated based on the SRTM30 digital elevation model, within a given window size and classified according to the inherent data set properties. This is a dynamic landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity'], dtype=object) ESDAC_Meybeck_landform_classification
(station)
object
...
standard_name : ESDAC Meybeck landform classification long_name : ESDAC Meybeck landform classification units : description : European Soil Data Centre (ESDAC) Meybeck landform classification. The classification presents relief classes which are calculated based on the relief roughness. Roughness and elevation are classified based on a digital elevation model according to static thresholds, with a given window size. This is a static landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water', 'water', 'very low plateaus'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 25km long_name : ESDAC modal Iwahashi landform classification in 25km radius units : description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 25km around station location. array(['water', 'water', 'steep - fine texture - high convexity'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 5km long_name : ESDAC modal Iwahashi landform classification in 5km radius units : description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 5km around station location. array(['water', 'water', 'steep - fine texture - low convexity'], dtype=object) ESDAC_modal_Meybeck_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 25km long_name : ESDAC modal Meybeck landform classification in 25km radius units : description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 25km around station location. array(['water', 'water', 'hills'], dtype=object) ESDAC_modal_Meybeck_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 5km long_name : ESDAC modal Meybeck landform classification in 5km radius units : description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 5km around station location. array(['water', 'water', 'hills'], dtype=object) ETOPO1_altitude
(station)
float32
...
standard_name : ETOPO1 altitude long_name : ETOPO1 altitude, relative to sea level datum units : m description : Altitude from ETOPO1 digital elevation model, relative to sea level vertical datum, in metres. Over Antarctica and Greenland the elevation given is on top of the ice sheets. Native resolution of 1 arc minute. A correction for coastal sites is made: if the derived altitude is <= -5 m, the maximum altitude of the neighbouring grid boxes will be used instead. If all neighbouring grid boxes have altitudes <= -5 m, the original value will be retained. array([ 4., -1., 280.], dtype=float32) ETOPO1_max_altitude_difference_5km
(station)
float32
...
standard_name : ETOPO1 max altitude difference 5km long_name : ETOPO1 maximum altitude difference between the ETOPO1_altitude and all ETOPO1 altitudes in 5km radius units : m description : Altitude difference between the ETOPO1_altitude, and the minimum ETOP1 altitude in a radius of 5 km around the station location, in metres. array([ 10., 66., 109.], dtype=float32) GHOST_version
(station)
object
...
standard_name : GHOST version long_name : Globally Harmonised Observational Surface Treatment (GHOST) version units : description : Version of the Globally Harmonised Observational Surface Treatment (GHOST). array(['1.4', '1.4', '1.4'], dtype=object) GHSL_average_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL average built up area density 25km long_name : GHSL average built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 25km around the station location. array([1.171513, 1.151988, 1.243206], dtype=float32) GHSL_average_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL average built up area density 5km long_name : GHSL average built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 5km around the station location. array([0.987336, 2.102892, 0.154906], dtype=float32) GHSL_average_population_density_25km
(station)
float32
...
standard_name : GHSL average population density 25km long_name : GHSL average population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 25km around the station location. array([ 74.10099, 81.21062, 174.84006], dtype=float32) GHSL_average_population_density_5km
(station)
float32
...
standard_name : GHSL average population density 5km long_name : GHSL average population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 5km around the station location. array([ 28.34486 , 138.51115 , 10.174279], dtype=float32) GHSL_built_up_area_density
(station)
float32
...
standard_name : GHSL built up area density long_name : GHSL built up area density units : % description : Global Human Settlement Layer (GHSL) built up area density (technical label: GHS_BUILT_LDSMT_GLOBE_R2018A), in units of built-up area percent per gridcell (0-100). The product is a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). Native resolution of 0.25 x 0.25 kilometres. array([5.9664, 0. , 0. ], dtype=float32) GHSL_max_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL max built up area density 25km long_name : GHSL max built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 25km around the station location. array([100., 100., 100.], dtype=float32) GHSL_max_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL max built up area density 5km long_name : GHSL max built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 5km around the station location. array([59., 80., 29.], dtype=float32) GHSL_max_population_density_25km
(station)
float32
...
standard_name : GHSL max population density 25km long_name : GHSL max population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 25km around the station location. array([34752., 9012., 19701.], dtype=float32) GHSL_max_population_density_5km
(station)
float32
...
standard_name : GHSL max population density 5km long_name : GHSL max population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 5km around the station location. array([1658., 4708., 1593.], dtype=float32) GHSL_modal_settlement_model_classification_25km
(station)
object
...
standard_name : GHSL modal settlement model classification 25km long_name : GHSL modal settlement model classification in 25km radius units : description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 25km around station location. array(['water', 'water', 'very low density rural'], dtype=object) GHSL_modal_settlement_model_classification_5km
(station)
object
...
standard_name : GHSL modal settlement model classification 5km long_name : GHSL modal settlement model classification in 5km radius units : description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 5km around station location. array(['water', 'water', 'very low density rural'], dtype=object) GHSL_population_density
(station)
float32
...
standard_name : GHSL population density long_name : GHSL population density units : xx km–2 description : Global Human Settlement Layer (GHSL) population density (technical label: GHS_POP_MT_GLOBE_R2019A), in populus per squared kilometre. It depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the GHSL global layer per corresponding epoch. Native resolution of 0.25 x 0.25 kilometres. array([166.15518, 0. , 0. ], dtype=float32) GHSL_settlement_model_classification
(station)
object
...
standard_name : GHSL settlement model classification long_name : GHSL settlement model classification units : description : Global Human Settlement Layer (GHSL) settlement model classification (technical label: GHS_SMOD_POPMT_GLOBE_R2019A). The classification delineates and classify settlement typologies via a logic of population size, population and built-up area densities as a refinement of the ‘degree of urbanization’ method described by EUROSTAT. The classification is derived by using the GHS_POP_MT_GLOBE_R2019A and GHS_BUILT_LDSMT_GLOBE_R2018A products. The GHS Settlement Model grid is an improvement of the GHS Settlement Grid (R2016A) introducing a more detailed classification of settlements in two levels, also called ‘refined degree of urbanization’. The Settlement Model is provided at detailed level (Second Level - L2). The First Level, as a porting of the Degree of Urbanization adopted by EUROSTAT can be obtained aggregating L2. Native resolution of 1.0 x 1.0 kilometres. array(['low density rural', 'low density rural', 'very low density rural'],\n",
- " dtype=object) GPW_average_population_density_25km
(station)
float32
...
standard_name : GPW average population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 25 km around the station location. array([125.20069, 79.76857, 251.70555], dtype=float32) GPW_average_population_density_5km
(station)
float32
...
standard_name : GPW average population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 5 km around the station location. array([ 76.15041, 151.78712, 35.03873], dtype=float32) GPW_max_population_density_25km
(station)
float32
...
standard_name : GPW max population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 25 km around the station location. array([1020.8104 , 295.55426, 3971.275 ], dtype=float32) GPW_max_population_density_5km
(station)
float32
...
standard_name : GPW max population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 5 km around the station location. array([130.01776 , 295.55423 , 35.038742], dtype=float32) GPW_population_density
(station)
float32
...
standard_name : GPW population density long_name : GPW population density units : xx km–2 description : Gridded Population of the World (GPW), population density, in populus per squared kilometre, from either version 3 and 4 of the provided gridded datasets, dependent on the data year: v3 (1990-2000), v4 (2000-2015). Native resolution of 0.04166 x 0.04166 for v3 data; native resolution of 0.0083 x 0.0083 degrees for v4 data. array([130.01775, 295.5542 , 35.03873], dtype=float32) GSFC_coastline_proximity
(station)
float32
...
standard_name : GSFC coastline proximity long_name : GSFC proximity to the coastline units : km description : Proximity to the coastline provided by the NASA Goddard Space Flight Center (GSFC) Ocean Color Group, in kilometres, produced using the Generic Mapping Tools package. Native resolution of 0.01 x 0.01 degrees. Negative distances represent locations over land (including land-locked bodies of water), while positive distances represent locations over the ocean. There is an uncertainty of up to 1 km in the computed distance at any given point. array([ 0., -2., -40.], dtype=float32) Joly-Peuch_classification_code
(station)
float32
...
standard_name : Joly-Peuch classification code long_name : Joly-Peuch classification code units : description : Joly-Peuch European classification code (range of 1-10) designed to objectively stratify stations between those diplaying rural and urban signatures (most rural == 1, most urban == 10). This classification is objectively made per species. The species that this is done for are: O3, NO2, SO2, CO, PM10, PM2.5. See reference here: https://www.sciencedirect.com/science/article/abs/pii/S1352231011012088 array([nan, nan, nan], dtype=float32) Koppen-Geiger_classification
(station)
object
...
standard_name : Koppen-Geiger classification long_name : Koppen-Geiger classification units : description : Koppen-Geiger classification, classifying the global climates into 5 main groups (30 total groups with subcategories). Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". See citation: Beck, H.E., N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Nature Scientific Data, 2018. array(['water', 'water', 'cold - no dry season - hot summer'], dtype=object) Koppen-Geiger_modal_classification_25km
(station)
object
...
standard_name : Koppen-Geiger modal classification 25km long_name : Koppen-Geiger classification units : description : Modal Koppen-Geiger classification in radius of 25km around station location. array(['water', 'water', 'cold - no dry season - hot summer'], dtype=object) Koppen-Geiger_modal_classification_5km
(station)
object
...
standard_name : Koppen-Geiger modal classification 5km long_name : Koppen-Geiger classification units : description : Modal Koppen-Geiger classification in radius of 5km around station location. array(['water', 'water', 'cold - no dry season - hot summer'], dtype=object) MODIS_MCD12C1_v6_IGBP_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP land use long_name : MODIS MCD12C1 v6 IGBP land use units : description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water bodies', 'croplands', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_LAI
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI long_name : MODIS MCD12C1 v6 Leaf Area Index units : description : Majority Leaf Area Index class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water bodies', 'water bodies', 'savannas'], dtype=object) MODIS_MCD12C1_v6_UMD_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD land use long_name : MODIS MCD12C1 v6 UMD land use units : description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water bodies', 'water bodies', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 25km long_name : MODIS MCD12C1 v6 IGBP modal land use in 25km radius units : description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['water bodies', 'water bodies', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 5km long_name : MODIS MCD12C1 v6 IGBP modal land use in 5km radius units : description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['water bodies', 'croplands', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_modal_LAI_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 25km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 25km radius units : description : Modal Leaf Area Index in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['water bodies', 'water bodies', 'deciduous broadleaf forests'],\n",
- " dtype=object) MODIS_MCD12C1_v6_modal_LAI_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 5km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 5km radius units : description : Modal Leaf Area Index in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['water bodies', 'water bodies', 'savannas'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 25km long_name : MODIS MCD12C1 v6 UMD modal land use in 25km radius units : description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['water bodies', 'water bodies', 'woody savannas'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 5km long_name : MODIS MCD12C1 v6 UMD modal land use in 5km radius units : description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['water bodies', 'water bodies', 'woody savannas'], dtype=object) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 25km radius units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([12., 8., 17.], dtype=float32) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 5km radius units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 9., 12., 10.], dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 25km radius units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([62., 58., 63.], dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 5km radius units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([20., 22., 23.], dtype=float32) NOAA-DMSP-OLS_v4_nighttime_stable_lights
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 nighttime stable lights long_name : NOAA DMSP-OLS version 4 nighttime stable lights units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 nighttime stable lights. Native resolution of 0.0083 x 0.0083 degrees. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([11., 14., 8.], dtype=float32) OMI_level3_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column annual average NO2 long_name : OMI level3 column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([8.610014e+15, 4.759712e+15, 6.198418e+15], dtype=float32) OMI_level3_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column cloud screened annual average NO2 long_name : OMI level3 column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([8.410300e+15, 4.600654e+15, 6.476759e+15], dtype=float32) OMI_level3_tropospheric_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column annual average NO2 long_name : OMI level3 tropospheric column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([5.951614e+15, 2.243110e+15, 3.619630e+15], dtype=float32) OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column cloud screened annual average NO2 long_name : OMI level3 tropospheric column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([5.723139e+15, 2.023781e+15, 3.770765e+15], dtype=float32) UMBC_anthrome_classification
(station)
object
...
standard_name : UMBC anthrome classification long_name : UMBC anthrome classification units : description : University of Maryland Baltimore County (UMBC) anthrome classification, describing the anthropogenic land use (for the year 2000). There are 20 distinct classifications. Native resolution of 0.0833 x 0.0833 degrees. A correction for costal sites is made: if the native anthrome class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['residential woodlands', 'populated woodlands', 'residential woodlands'],\n",
- " dtype=object) UMBC_modal_anthrome_classification_25km
(station)
object
...
standard_name : UMBC modal anthrome classification 25km long_name : UMBC modal anthrome classification in 25km radius units : description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 25km around station location. array(['water', 'water', 'residential woodlands'], dtype=object) UMBC_modal_anthrome_classification_5km
(station)
object
...
standard_name : UMBC modal anthrome classification 5km long_name : UMBC modal anthrome classification in 5km radius units : description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 5km around station location. array(['residential woodlands', 'populated woodlands', 'residential woodlands'],\n",
- " dtype=object) WMO_region
(station)
object
...
standard_name : WMO region long_name : WMO region code units : description : World Meteorological Organization (WMO) region of station. The available regions are: Africa, Asia, South America, "Northern America, Central America and the Caribbean", South-West Pacific, Europe and Antarctica. array(['Asia', 'Asia', 'Asia'], dtype=object) WWF_TEOW_biogeographical_realm
(station)
object
...
standard_name : WWF TEOW biogeographical realm long_name : WWF TEOW biogeographical realm units : description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 8 biogeographical realms. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['water', 'palearctic', 'palearctic'], dtype=object) WWF_TEOW_biome
(station)
object
...
standard_name : WWF TEOW biome long_name : WWF TEOW biome units : description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 14 biomes. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['water', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests'], dtype=object) WWF_TEOW_terrestrial_ecoregion
(station)
object
...
standard_name : WWF TEOW terrestrial ecoregion long_name : WWF TEOW terrestrial ecoregion units : description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 825 terrestrial ecoregions. Ecoregions are relatively large units of land containing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['water', 'southern korea evergreen forests',\n",
- " 'central korean deciduous forests'], dtype=object) administrative_country_division_1
(station)
object
...
standard_name : administrative country division 1 long_name : administrative country division 1 units : description : Name of the first (i.e. largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['Incheon', 'Jeju-Do', 'Jeollabuk-Do'], dtype=object) administrative_country_division_2
(station)
object
...
standard_name : administrative country division 2 long_name : administrative country division 2 units : description : Name of the second (i.e. second largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['nan', 'nan', 'nan'], dtype=object) altitude
(station)
float32
...
standard_name : altitude long_name : altitude relative to mean sea level units : m description : Altitude of the ground level at the station, relative to the stated vertical datum, in metres. array([ 60., 37., 217.], dtype=float32) annual_native_max_gap_percent
(station, time)
uint8
...
standard_name : annual native max gap percent long_name : annual native max gap percent units : % description : Percentage of the maximum data gap in the annual averaged measurement UTC window filled by native resolution data, relative to the total window length. array([[5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
- " 5, 5, 5, 5, 5, 5, 5],\n",
- " [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
- " 8, 8, 8, 8, 8, 8, 8],\n",
- " [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
- " 6, 6, 6, 6, 6, 6, 6]], dtype=uint8) annual_native_representativity_percent
(station, time)
uint8
...
standard_name : annual native representativity percent long_name : annual native representativity percent units : % description : Percentage of the annual averaged measurement UTC window represented by native resolution data. array([[16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,\n",
- " 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16],\n",
- " [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
- " 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12],\n",
- " [13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
- " 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13]], dtype=uint8) area_classification
(station)
object
...
standard_name : area classification long_name : standardised network provided area classification units : description : Standardised network provided classification, describing type of area a measurement station is situated in. array(['rural-regional', 'rural-remote', 'rural-regional'], dtype=object) associated_networks
(station)
object
...
standard_name : other associated networks long_name : other associated networks units : description : String pair of associated network name and station reference. Format: network1:station_reference1;network2:station_reference2 array(['nan', 'nan', 'nan'], dtype=object) city
(station)
object
...
standard_name : city long_name : city units : description : Name of the city the station is located in. array(['Seogeom-Ri', 'Gaigeturi', 'Imsil'], dtype=object) climatology
(station)
object
...
standard_name : climatology long_name : climatology units : description : Name of the climatology of which the observations pertain to. array(['nan', 'nan', 'nan'], dtype=object) contact_email_address
(station)
object
...
standard_name : contact email address long_name : contact email address units : description : Email address of the principal data contact for the specific reported data. array(['eanetdata@acap.asia', 'eanetdata@acap.asia', 'eanetdata@acap.asia'],\n",
- " dtype=object) contact_institution
(station)
object
...
standard_name : contact institution long_name : contact institution units : description : Institution of the principal data contact for the specific reported data. array(['EANET', 'EANET', 'EANET'], dtype=object) contact_name
(station)
object
...
standard_name : contact name long_name : contact name units : description : Full name of the principal data contact for the specific reported data. array(['nan', 'nan', 'nan'], dtype=object) country
(station)
object
...
standard_name : country long_name : country units : description : Name of the country the station is located in. array(['South Korea', 'South Korea', 'South Korea'], dtype=object) daily_native_max_gap_percent
(station, time)
uint8
...
standard_name : daily native max gap percent long_name : daily native max gap percent units : % description : Percentage of the maximum data gap in the daily averaged measurement UTC window filled by native resolution data, relative to the total window length. array([[100, 100, 100, 100, 100, 100, 100, 4, 96, 4, 96, 100, 100, 100,\n",
- " 100, 4, 96, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 4,\n",
- " 0, 96],\n",
- " [100, 100, 100, 4, 96, 100, 100, 100, 100, 4, 96, 100, 100, 100,\n",
- " 100, 4, 96, 100, 100, 100, 100, 4, 96, 100, 100, 100, 100, 4,\n",
- " 96, 100],\n",
- " [100, 100, 100, 4, 96, 100, 100, 100, 100, 4, 96, 100, 100, 100,\n",
- " 100, 4, 96, 100, 100, 100, 100, 4, 96, 100, 100, 100, 100, 100,\n",
- " 100, 100]], dtype=uint8) daily_native_representativity_percent
(station, time)
uint8
...
standard_name : daily native representativity percent long_name : daily native representativity percent units : % description : Percentage of the daily averaged measurement UTC window represented by native resolution data. array([[ 0, 0, 0, 0, 0, 0, 0, 96, 4, 96, 4, 0, 0, 0,\n",
- " 0, 96, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 96,\n",
- " 100, 4],\n",
- " [ 0, 0, 0, 96, 4, 0, 0, 0, 0, 96, 4, 0, 0, 0,\n",
- " 0, 96, 4, 0, 0, 0, 0, 96, 4, 0, 0, 0, 0, 96,\n",
- " 4, 0],\n",
- " [ 0, 0, 0, 96, 4, 0, 0, 0, 0, 96, 4, 0, 0, 0,\n",
- " 0, 96, 4, 0, 0, 0, 0, 96, 4, 0, 0, 0, 0, 0,\n",
- " 0, 0]], dtype=uint8) daily_passing_vehicles
(station)
float32
...
standard_name : daily passing vehicles long_name : average daily number of passing vehicles units : description : Average number of vehicles passing daily. array([nan, nan, nan], dtype=float32) data_level
(station)
object
...
standard_name : data level long_name : data level units : description : Data level of data reported. This varies per network. If data level is variable per measurement, and not static per reported file, then this is set as "variable". If there is no reported data level this is set as "none" array(['none', 'none', 'none'], dtype=object) data_licence
(station)
object
...
standard_name : data licence long_name : data licence units : description : Information pertaining to the data licence governing the redistribution/publication of the ingested network data. array(['Rights reserved to the Network Center for the EANET: https://monitoring.eanet.asia/document/public/index',\n",
- " 'Rights reserved to the Network Center for the EANET: https://monitoring.eanet.asia/document/public/index',\n",
- " 'Rights reserved to the Network Center for the EANET: https://monitoring.eanet.asia/document/public/index'],\n",
- " dtype=object) day_night_code
(station, time)
uint8
...
standard_name : day/night code long_name : day/night code per measurement units : description : Binary indication if measurement was made during the day or night. Day=0, Night=1. The classification is made by calculating the solar elevation angle for a latitude/longitude/measurement height at a mid-measurement window timestamp. If the solar elevation angle is > 0, it is classed as daytime, otherwise it is nightime. Classification is 255 if cannot be made. array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255],\n",
- " [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255],\n",
- " [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255]], dtype=uint8) daytime_traffic_speed
(station)
float32
...
standard_name : daytime traffic speed long_name : average daytime speed of passing traffic units : km hr-1 description : Average daytime speed of the passing traffic where measurements are being made (if applicable), in kilometres per hour. array([nan, nan, nan], dtype=float32) derived_uncertainty_per_measurement
(station, time)
float32
...
standard_name : derived measurement uncertainty per measurement long_name : derived measurement uncertainty per measurement units : ug m-3 description : Derived measurement uncertainty (±) of methodology, for a specific measurement. This is calculated through the quadratic addition of reported (or if not available, documented) accuracy and precision metrics. This is given in absolute terms in ug m-3. array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan],\n",
- " [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan],\n",
- " [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan]], dtype=float32) distance_to_building
(station)
float32
...
standard_name : distance to building long_name : distance to the nearest building units : m description : Distance to the nearest building of the inlet/instrument/sampler, in metres. array([nan, nan, nan], dtype=float32) distance_to_junction
(station)
float32
...
standard_name : distance to junction long_name : distance to the nearest road junction units : m description : Distance to the nearest road junction of the inlet/instrument/sampler, in metres. array([nan, nan, nan], dtype=float32) distance_to_kerb
(station)
float32
...
standard_name : distance to kerb long_name : distance to the street kerb units : m description : Distance to the street kerb of the inlet/instrument/sampler, in metres. array([nan, nan, nan], dtype=float32) distance_to_source
(station)
float32
...
standard_name : distance to source long_name : distance to the main emission source units : km description : Distance to the main emission source (see variable: "main_emission_source") of the inlet/instrument/sampler, in kilometres. array([nan, nan, nan], dtype=float32) ellipsoid
(station)
object
...
standard_name : ellipsoid long_name : ellipsoid units : description : The ellipsoidal model of the earth used as a basis for 2D and 3D geographic coordinate systems. array(['WGS 84', 'WGS 84', 'WGS 84'], dtype=object) horizontal_datum
(station)
object
...
standard_name : horizontal datum long_name : horizontal datum units : description : Name of the horizontal datum used in defining geodetic latitudes and longitudes on the Earth's surface. The datum is set when positioning an ellipsoid model of the Earth to an anchor point. If not explicitely stated then this is assumed to be 'World Geodetic System 1984'. array(['WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984'], dtype=object) land_use
(station)
object
...
standard_name : land use long_name : standardised network provided land use type units : description : Standardised network provided classification, describing the dominant land use in the area of the reporting station. array(['nan', 'nan', 'nan'], dtype=object) main_emission_source
(station)
object
...
standard_name : main emission source long_name : standardised network provided main emission source units : description : Standardised network provided classification, describing the main emission source influencing air measured at a station. array(['nan', 'nan', 'nan'], dtype=object) measurement_altitude
(station)
float32
...
standard_name : measurement altitude long_name : measurement altitude relative to mean sea level units : m description : Altitude of the inlet/instrument/sampler, relative to the stated vertical datum, in metres. array([ 63., 40., 220.], dtype=float32) measurement_methodology
(station)
object
...
standard_name : measurement methodology long_name : standardised measurement methodology units : description : Standardised name of the measurement methodology. array(['ion chromatography', 'ion chromatography', 'ion chromatography'],\n",
- " dtype=object) measurement_scale
(station)
object
...
standard_name : measurement scale long_name : standardised network provided measurement scale units : description : Standardised network provided classification, a denotation of the geographic scope of the air quality measurements made. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_calibration_scale
(station)
object
...
standard_name : measuring instrument calibration scale long_name : measuring instrument calibration scale name units : description : Name of calibration scale used for the calibration of the measuring instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_absorption_cross_section
(station)
object
...
standard_name : measuring instrument documented absorption cross section long_name : measuring instrument documented absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in instrumental manual/documentation. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_accuracy
(station)
object
...
standard_name : measuring instrument documented measurement accuracy long_name : measuring instrument documented measurement accuracy units : ug m-3 description : Measurement accuracy (±), as given in the instrumental manual/documentation. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_flow_rate
(station)
object
...
standard_name : measuring instrument documented flow rate long_name : measuring instrument documented flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented lower limit of detection long_name : measuring instrument documented lower limit of detection units : ug m-3 description : Lower limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([nan, nan, nan], dtype=float32) measuring_instrument_documented_measurement_resolution
(station)
float32
...
standard_name : measuring instrument documented measurement resolution long_name : measuring instrument documented measurement resolution units : ug m-3 description : Measurement resolution, as given in instrumental manual/documentation. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([nan, nan, nan], dtype=float32) measuring_instrument_documented_precision
(station)
object
...
standard_name : measuring instrument documented measurement precision long_name : measuring instrument documented measurement precision units : ug m-3 description : Measurement precision (±), as given in instrumental manual/documentation. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_span_drift
(station)
object
...
standard_name : measuring instrument documented span drift long_name : measuring instrument documented span drift units : ug m-3 description : Span drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_uncertainty
(station)
object
...
standard_name : measuring instrument documented measurement uncertainty long_name : measuring instrument documented measurement uncertainty units : ug m-3 description : Measurement uncertainty (±), as given in the instrumental manual/documentation. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented upper limit of detection long_name : measuring instrument documented upper limit of detection units : ug m-3 description : Upper limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([nan, nan, nan], dtype=float32) measuring_instrument_documented_zero_drift
(station)
object
...
standard_name : measuring instrument documented zero drift long_name : measuring instrument documented zero drift units : ug m-3 description : Zero drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_zonal_drift
(station)
object
...
standard_name : measuring instrument documented zonal drift long_name : measuring instrument documented zonal drift units : ug m-3 description : Zonal drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_further_details
(station)
object
...
standard_name : measuring instrument further details long_name : measuring instrument further details units : description : Further associated details regarding the specifics of the measurement methodology/instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_inlet_information
(station)
object
...
standard_name : measuring instrument inlet information long_name : measuring instrument measurement inlet information units : description : Description of sampling inlet of the measuring instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_manual_name
(station)
object
...
standard_name : measuring instrument manual name long_name : measuring instrument manual name units : description : Path to the location in the esarchive of the manual for the specific measuring instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_name
(station)
object
...
standard_name : measuring instrument name long_name : standardised measuring instrument name units : description : Standardised name of the measuring instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_process_details
(station)
object
...
standard_name : measuring instrument process details long_name : measuring instrument process details units : description : Miscellaneous details regarding assumptions made in the standardisation of the measurement methodology/instrument. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_absorption_cross_section
(station)
object
...
standard_name : measuring instrument reported absorption cross section long_name : measuring instrument reported absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in metadata. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_accuracy
(station)
object
...
standard_name : measuring instrument reported measurement accuracy long_name : measuring instrument reported measurement accuracy units : ug m-3 description : Measurement accuracy (±), as given in metadata. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_flow_rate
(station)
object
...
standard_name : measuring instrument reported flow rate long_name : measuring instrument reported flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported lower limit of detection long_name : measuring instrument reported lower limit of detection units : ug m-3 description : Lower limit of detection of measurement methodology, as given in metadata. array([nan, nan, nan], dtype=float32) measuring_instrument_reported_measurement_resolution
(station)
float32
...
standard_name : measuring instrument reported measurement resolution long_name : measuring instrument reported measurement resolution units : ug m-3 description : Measurement resolution, as given in metadata. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([nan, nan, nan], dtype=float32) measuring_instrument_reported_precision
(station)
object
...
standard_name : measuring instrument reported measurement precision long_name : measuring instrument reported measurement precision units : ug m-3 description : Measurement precision (±), as given in metadata. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_span_drift
(station)
object
...
standard_name : measuring instrument reported span drift long_name : measuring instrument reported span drift units : ug m-3 description : Span drift of measuring instrument per unit of time, as given in metadata. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_uncertainty
(station)
object
...
standard_name : measuring instrument reported measurement uncertainty long_name : measuring instrument reported measurement uncertainty units : ug m-3 description : Measurement uncertainty (±), as given in metadata. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). It can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_units
(station)
object
...
standard_name : measuring instrument reported units long_name : measuring instrument reported measurement units units : description : Units that the measured parameter are natively reported in. array(['ug m-3', 'ug m-3', 'ug m-3'], dtype=object) measuring_instrument_reported_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported upper limit of detection long_name : measuring instrument reported upper limit of detection units : ug m-3 description : Upper limit of detection of measurement methodology, as given in metadata. array([nan, nan, nan], dtype=float32) measuring_instrument_reported_zero_drift
(station)
object
...
standard_name : measuring instrument reported zero drift long_name : measuring instrument reported zero drift units : ug m-3 description : Zero drift of measuring instrument per unit of time, as given in metadata. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_zonal_drift
(station)
object
...
standard_name : measuring instrument reported zonal drift long_name : measuring instrument reported zonal drift units : ug m-3 description : Zonal drift of measuring instrument per unit of time, as given in metadata. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan'], dtype=object) measuring_instrument_sampling_type
(station)
object
...
standard_name : measuring instrument sampling type long_name : standardised sampling type of the measuring instrument units : description : Standardised name of the measuring instrument sampling type. array(['injection', 'injection', 'injection'], dtype=object) monthly_native_max_gap_percent
(station, time)
uint8
...
standard_name : monthly native max gap percent long_name : monthly native max gap percent units : % description : Percentage of the maximum data gap in the monthly averaged measurement UTC window filled by native resolution data, relative to the total window length. array([[37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37,\n",
- " 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37],\n",
- " [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
- " 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],\n",
- " [27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,\n",
- " 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27]], dtype=uint8) monthly_native_representativity_percent
(station, time)
uint8
...
standard_name : monthly native representativity percent long_name : monthly native representativity percent units : % description : Percentage of the monthly averaged measurement UTC window represented by native resolution data. array([[17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
- " 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],\n",
- " [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
- " 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17],\n",
- " [13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
- " 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13]], dtype=uint8) network
(station)
object
...
standard_name : network long_name : data network units : description : The name of the network which reports data for the specific station in question. array(['EANET', 'EANET', 'EANET'], dtype=object) network_maintenance_details
(station)
object
...
standard_name : network maintenance details long_name : network maintenance details units : description : Extra details provided by the reporting network about the operational maintenance done at the station. array(['nan', 'nan', 'nan'], dtype=object) network_miscellaneous_details
(station)
object
...
standard_name : network miscellaneous details long_name : network miscellaneous details units : description : Extra miscellanous details provided by the reporting network. array(['nan', 'nan', 'nan'], dtype=object) network_provided_volume_standard_pressure
(station)
float64
...
standard_name : network provided volume standard pressure long_name : network provided volume standard pressure units : hPa description : The pressure (in hPa) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typically normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported pressure should be reported as the internal pressure of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas pressure is assumed to be the known network standard pressure for the measured component. array([1013.25, 1013.25, 1013.25]) network_provided_volume_standard_temperature
(station)
float64
...
standard_name : network provided volume standard temperature long_name : network provided volume standard temperature units : K description : The temperature (in Kelvin) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typically normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported temperature should be reported as the internal temperature of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas temperature is assumed to be the known network standard temperature for the measured component. array([293.15, 293.15, 293.15]) network_qa_details
(station)
object
...
standard_name : network qa details long_name : network qa details units : description : Extra details provided by the reporting network about the in-network quality assurance of measurements. array(['nan', 'nan', 'nan'], dtype=object) network_sampling_details
(station)
object
...
standard_name : network sampling details long_name : network sampling details units : description : Extra details provided by the reporting network about the sampling methods employed. array(['nan', 'nan', 'nan'], dtype=object) network_uncertainty_details
(station)
object
...
standard_name : network uncertainty details long_name : network uncertainty details units : description : Extra details provided by the reporting network about the uncertainties involved with the measurement methods employed. array(['nan', 'nan', 'nan'], dtype=object) population
(station)
float32
...
standard_name : population long_name : population units : description : Population size of the nearest urban settlement. array([nan, nan, nan], dtype=float32) primary_sampling_further_details
(station)
object
...
standard_name : primary sampling further details long_name : primary sampling further details units : description : Further associated details regarding the specifics of the primary sampling instrument/type. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_documented_flow_rate
(station)
object
...
standard_name : primary sampling instrument documented flow rate long_name : primary sampling instrument documented sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_manual_name
(station)
object
...
standard_name : primary sampling instrument manual name long_name : primary sampling instrument manual name units : description : Path to the location in the esarchive of the manual for the specific primary sampling instrument. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_name
(station)
object
...
standard_name : primary sampling instrument name long_name : standardised primary sampling instrument name units : description : Standardised name of the primary sampling instrument (if no specific instrument is used, or known, this is the standardised primary sampling type). array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_reported_flow_rate
(station)
object
...
standard_name : primary sampling instrument reported flow rate long_name : primary sampling instrument reported sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_process_details
(station)
object
...
standard_name : primary sampling process details long_name : primary sampling process details units : description : Miscellaneous details regarding assumptions made in the standardisation of the primary sampling type/instrument. array(['nan', 'nan', 'nan'], dtype=object) primary_sampling_type
(station)
object
...
standard_name : primary sampling type long_name : standardised primary sampling type units : description : Standardised primary sampling type. array(['low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous'], dtype=object) principal_investigator_email_address
(station)
object
...
standard_name : principal investigator email address long_name : principal investigator email address units : description : Email address of the principal scientific investigator for the specific reported data. array(['nan', 'nan', 'nan'], dtype=object) principal_investigator_institution
(station)
object
...
standard_name : principal investigator institution long_name : principal investigator institution units : description : Institution of the principal scientific investigator for the specific reported data. array(['nan', 'nan', 'nan'], dtype=object) principal_investigator_name
(station)
object
...
standard_name : principal investigator name long_name : principal investigator name units : description : Full name of the principal scientific investigator for the specific reported data. array(['nan', 'nan', 'nan'], dtype=object) process_warnings
(station)
object
...
standard_name : process warnings long_name : process warnings units : description : Warnings accumulated through GHOST processing regarding the data that should be considered. array(['Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. '],\n",
- " dtype=object) projection
(station)
object
...
standard_name : projection long_name : projection units : description : Name of the projected coordinate system of the original provided station position x, y coordinates. If the original coordinates are not projected, then this is set as 'geographic'. array(['GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC'], dtype=object) reported_uncertainty_per_measurement
(station, time)
float32
...
standard_name : reported measurement uncertainty per measurement long_name : reported measurement uncertainty per measurement units : ug m-3 description : Reported measurement uncertainty (±) of methodology, for a specific measurement. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This is given in absolute terms in ug m-3. array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan],\n",
- " [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan],\n",
- " [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan]], dtype=float32) representative_radius
(station)
float32
...
standard_name : representative_radius long_name : representative_radius units : km description : Radius of representativity of the measurements made (i.e. for what distance scale around the sampling point would the measurements be very similar?), given in kilometres. A quantitative version of the "measurement_scale" classification. array([nan, nan, nan], dtype=float32) retrieval_algorithm
(station)
object
...
standard_name : retrieval algorithm long_name : retrieval algorithm units : description : The name of the retrieval algorithm. Remote sensing algorithms are used to retrieve the aerosol optical properties (as aerosol optical depths or single scattering albedo among others) using remote-sensing radiances for multiple wavelengths from ground stations or on satellite platforms. Each algorithm is particularly designed considering the characteristics of the sensor and other ancillary information. array(['nan', 'nan', 'nan'], dtype=object) sample_preparation_further_details
(station)
object
...
standard_name : sample preparation further details long_name : sample preparation further details units : description : Further associated details regarding the specifics of the sample preparation types/instruments. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan'], dtype=object) sample_preparation_process_details
(station)
object
...
standard_name : sample preparation process details long_name : sample preparation process details units : description : Miscellaneous details regarding assumptions made in the standardisation of the sample preparation types/techniques. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan'], dtype=object) sample_preparation_techniques
(station)
object
...
standard_name : sample preparation techniques long_name : standardised specific preparation techniques units : description : Standardised sample preparation techniques utilised in the measurement process. Mutiple names are separated by ";". array(['3-Stage Filter Pack', '3-Stage Filter Pack', '3-Stage Filter Pack'],\n",
- " dtype=object) sample_preparation_types
(station)
object
...
standard_name : sample preparation types long_name : standardised sample preparation types units : description : Standardised sample preparation types utilised in the measurement process. Mutiple types are separated by ";". array(['filter pack', 'filter pack', 'filter pack'], dtype=object) sampling_height
(station)
float32
...
standard_name : sampling height long_name : sampling height relative to ground units : m description : Height above the ground of the inlet/instrument/sampler, in metres. array([3., 3., 3.], dtype=float32) sconcso4
(station, time)
float32
...
standard_name : sulphate long_name : sulphate units : ug m-3 description : Measured value of surface sulphate for the stated temporal resolution. array([[ nan, nan, nan, nan, nan, nan, nan,\n",
- " 2.31 , 2.31 , 1.12 , 1.12 , nan, nan, nan,\n",
- " nan, 1.71 , 1.71 , nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, 1.38 ,\n",
- " 1.284167, 1.28 ],\n",
- " [ nan, nan, nan, 0.74 , 0.74 , nan, nan,\n",
- " nan, nan, 3.41 , 3.41 , nan, nan, nan,\n",
- " nan, 0.74 , 0.74 , nan, nan, nan, nan,\n",
- " 1.2 , 1.2 , nan, nan, nan, nan, 1.76 ,\n",
- " 1.76 , nan],\n",
- " [ nan, nan, nan, 3.05 , 3.05 , nan, nan,\n",
- " nan, nan, 2.44 , 2.44 , nan, nan, nan,\n",
- " nan, 2.24 , 2.24 , nan, nan, nan, nan,\n",
- " 1.37 , 1.37 , nan, nan, nan, nan, nan,\n",
- " nan, nan]], dtype=float32) season_code
(station, time)
uint8
...
standard_name : season code long_name : season code per measurement units : description : Code decreeing if measurement was made during the Spring, Summer, Autumn or Winter Seasons. Spring=0, Summer=1, Autumn=2, Winter=3. The classification is made by evaluating which season the local-time of a mid-measurement window timestamp falls in. array([[255, 255, 255, 255, 255, 255, 255, 2, 2, 2, 2, 255, 255, 255,\n",
- " 255, 2, 2, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 2,\n",
- " 2, 2],\n",
- " [255, 255, 255, 2, 2, 255, 255, 255, 255, 2, 2, 255, 255, 255,\n",
- " 255, 2, 2, 255, 255, 255, 255, 2, 2, 255, 255, 255, 255, 2,\n",
- " 2, 255],\n",
- " [255, 255, 255, 2, 2, 255, 255, 255, 255, 2, 2, 255, 255, 255,\n",
- " 255, 2, 2, 255, 255, 255, 255, 2, 2, 255, 255, 255, 255, 255,\n",
- " 255, 255]], dtype=uint8) station_classification
(station)
object
...
standard_name : station classification long_name : standardised network provided station classification units : description : Standardised network provided classification, categorising the type of air measured by a station. array(['nan', 'nan', 'nan'], dtype=object) station_name
(station)
object
...
standard_name : station name long_name : station name units : description : Name of station where the measurement was conducted. array(['Kanghwa', 'Cheju (Kosan)', 'Imsil'], dtype=object) station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : description : reference ID for station. array(['KRA001_IC', 'KRA002_IC', 'KRA003_IC'], dtype=object) station_timezone
(station)
object
...
standard_name : station timezone long_name : station timezone units : description : Name of the local timezone that the measuring station is located in. This is automatically generated using Timezone Finder Python package (taking longitude and latitude as inputs). array(['Asia/Seoul', 'Asia/Seoul', 'Asia/Seoul'], dtype=object) street_type
(station)
object
...
standard_name : street type long_name : type of street units : description : Type of street where measurements are being made (if applicable). array(['nan', 'nan', 'nan'], dtype=object) street_width
(station)
float32
...
standard_name : street width long_name : width of the street units : m description : Width of the street where measurements are being made (if applicable), in metres. array([nan, nan, nan], dtype=float32) terrain
(station)
object
...
standard_name : terrain long_name : standardised network provided terrain type units : description : Standardised network provided classification, describing the dominant terrain in the area of the reporting station. array(['nan', 'nan', 'nan'], dtype=object) vertical_datum
(station)
object
...
standard_name : vertical datum long_name : vertical datum units : description : Name of the vertical datum used to define vertical elevation on the Earth. The datum is a surface of zero elevation to which other heights can be reference against. If not explicitely stated then this is assumed to be 'tidal - mean sea level'. array(['TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL'], dtype=object) weekday_weekend_code
(station, time)
uint8
...
standard_name : weekday/weekend code long_name : weekday/weekend code per measurement units : description : Binary indication if measurement was made during the weekday or weekend. Weekday=0, Weekend=1. The classification is made by evaluating if the local-time of a mid-measurement window timestamp falls on a weekday or on the weekend. Classification is 255 if cannot be made. array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255],\n",
- " [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255],\n",
- " [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,\n",
- " 255, 255]], dtype=uint8) sconcso4_prefiltered_defaultqa
(station, time)
float32
...
standard_name : sulphate long_name : sulphate units : ug m-3 description : Measured value of surface sulphate for the stated temporal resolution. Prefiltered by default QA. array([[ nan, nan, nan, nan, nan, nan, nan,\n",
- " 2.31 , 2.31 , 1.12 , 1.12 , nan, nan, nan,\n",
- " nan, 1.71 , 1.71 , nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, 1.38 ,\n",
- " 1.284167, 1.28 ],\n",
- " [ nan, nan, nan, 0.74 , 0.74 , nan, nan,\n",
- " nan, nan, 3.41 , 3.41 , nan, nan, nan,\n",
- " nan, 0.74 , 0.74 , nan, nan, nan, nan,\n",
- " 1.2 , 1.2 , nan, nan, nan, nan, 1.76 ,\n",
- " 1.76 , nan],\n",
- " [ nan, nan, nan, 3.05 , 3.05 , nan, nan,\n",
- " nan, nan, 2.44 , 2.44 , nan, nan, nan,\n",
- " nan, 2.24 , 2.24 , nan, nan, nan, nan,\n",
- " 1.37 , 1.37 , nan, nan, nan, nan, nan,\n",
- " nan, nan]], dtype=float32) flag
(station, time, N_flag_codes)
int64
...
units : axis : long_name : standard_name : flag array([[[4, 2, ..., 2, 2],\n",
- " [4, 2, ..., 2, 2],\n",
- " ...,\n",
- " [0, 2, ..., 2, 2],\n",
- " [4, 2, ..., 2, 2]],\n",
- "\n",
- " [[4, 2, ..., 2, 2],\n",
- " [4, 2, ..., 2, 2],\n",
- " ...,\n",
- " [4, 2, ..., 2, 2],\n",
- " [4, 2, ..., 2, 2]],\n",
- "\n",
- " [[4, 2, ..., 2, 2],\n",
- " [4, 2, ..., 2, 2],\n",
- " ...,\n",
- " [4, 2, ..., 2, 2],\n",
- " [4, 2, ..., 2, 2]]]) qa
(station, time, N_qa_codes)
int64
...
units : axis : long_name : standard_name : N_qa_codes array([[[ 0, 15, ..., 2, 2],\n",
- " [ 0, 15, ..., 2, 2],\n",
- " ...,\n",
- " [15, 19, ..., 2, 2],\n",
- " [15, 19, ..., 2, 2]],\n",
- "\n",
- " [[ 0, 15, ..., 2, 2],\n",
- " [ 0, 15, ..., 2, 2],\n",
- " ...,\n",
- " [15, 19, ..., 2, 2],\n",
- " [ 0, 15, ..., 2, 2]],\n",
- "\n",
- " [[ 0, 15, ..., 2, 2],\n",
- " [ 0, 15, ..., 2, 2],\n",
- " ...,\n",
- " [ 0, 15, ..., 2, 2],\n",
- " [ 0, 15, ..., 2, 2]]]) Attributes: (7)
title : Surface sulphate data in the EANET network in 2019-11. institution : Barcelona Supercomputing Center source : Surface observations creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es version : 1.4 Conventions : CF-1.7 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 30, station: 3, N_flag_codes: 190, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- " * station (station) float64 ...\n",
- "Dimensions without coordinates: N_flag_codes, N_qa_codes\n",
- "Data variables: (12/177)\n",
- " latitude (station) float64 ...\n",
- " longitude (station) float64 ...\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " ... ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- " sconcso4_prefiltered_defaultqa (station, time) float32 ...\n",
- " flag (station, time, N_flag_codes) int64 ...\n",
- " qa (station, time, N_qa_codes) int64 ...\n",
- "Attributes:\n",
- " title: Surface sulphate data in the EANET network in 2019-11.\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " version: 1.4\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('points_file_2.nc')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Transform to points"
+ "### 2.4. Transform to points"
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 28,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -4912,7 +2462,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -4974,7 +2524,7 @@
" 'description': 'Measured value of surface sulphate for the stated temporal resolution. Prefiltered by default QA.'}}"
]
},
- "execution_count": 29,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -4982,33 +2532,6 @@
"source": [
"nessy_ghost_3.variables"
]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rank 000: Creating points_file_3.nc\n",
- "Rank 000: NetCDF ready to write\n",
- "Rank 000: Dimensions done\n",
- "Rank 000: Writing sconcso4 var (1/2)\n",
- "Rank 000: Var sconcso4 created (1/2)\n",
- "Rank 000: Var sconcso4 data (1/2)\n",
- "Rank 000: Var sconcso4 completed (1/2)\n",
- "Rank 000: Writing sconcso4_prefiltered_defaultqa var (2/2)\n",
- "Rank 000: Var sconcso4_prefiltered_defaultqa created (2/2)\n",
- "Rank 000: Var sconcso4_prefiltered_defaultqa data (2/2)\n",
- "Rank 000: Var sconcso4_prefiltered_defaultqa completed (2/2)\n"
- ]
- }
- ],
- "source": [
- "nessy_ghost_3.to_netcdf('points_file_3.nc', info=True)"
- ]
}
],
"metadata": {
diff --git a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb
index 209188fe43121fa397429127144af28197826e15..8baf84be11b50a545ada9c0726c264e781d5d388 100644
--- a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb
+++ b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb
@@ -13,8 +13,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from nes import *\n",
- "import xarray as xr"
+ "from nes import *"
]
},
{
@@ -23,450 +22,16 @@
"metadata": {},
"outputs": [],
"source": [
- "nc_path_1 = '/esarchive/exp/wrf-hermes-cmaq/b075/eu/hourly/sconco3/sconco3_2021010100.nc'"
+ "# Original path: /esarchive/exp/snes/a5g1/ip/daily_max/sconco3/sconco3_2022111500.nc\n",
+ "# LCC grid with a coverage over the Iberian Peninsula (4x4km)\n",
+ "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/sconco3_2022111500.nc'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 1. Read and write"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 48, y: 398, x: 478, lev: 1)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-01-01 ... 2021-01-02T23:00:00\n",
- " lat (y, x) float32 ...\n",
- " lon (y, x) float32 ...\n",
- " * x (x) float64 -2.126e+06 -2.114e+06 ... 3.586e+06 3.598e+06\n",
- " * y (y) float64 -2.067e+06 -2.055e+06 ... 2.685e+06 2.697e+06\n",
- " * lev (lev) float32 0.0\n",
- "Data variables:\n",
- " sconco3 (time, lev, y, x) float32 ...\n",
- " Lambert_conformal int32 -2147483647 Dimensions: time : 48y : 398x : 478lev : 1
Coordinates: (6)
time
(time)
datetime64[ns]
2021-01-01 ... 2021-01-02T23:00:00
standard_name : time long_name : time array(['2021-01-01T00:00:00.000000000', '2021-01-01T01:00:00.000000000',\n",
- " '2021-01-01T02:00:00.000000000', '2021-01-01T03:00:00.000000000',\n",
- " '2021-01-01T04:00:00.000000000', '2021-01-01T05:00:00.000000000',\n",
- " '2021-01-01T06:00:00.000000000', '2021-01-01T07:00:00.000000000',\n",
- " '2021-01-01T08:00:00.000000000', '2021-01-01T09:00:00.000000000',\n",
- " '2021-01-01T10:00:00.000000000', '2021-01-01T11:00:00.000000000',\n",
- " '2021-01-01T12:00:00.000000000', '2021-01-01T13:00:00.000000000',\n",
- " '2021-01-01T14:00:00.000000000', '2021-01-01T15:00:00.000000000',\n",
- " '2021-01-01T16:00:00.000000000', '2021-01-01T17:00:00.000000000',\n",
- " '2021-01-01T18:00:00.000000000', '2021-01-01T19:00:00.000000000',\n",
- " '2021-01-01T20:00:00.000000000', '2021-01-01T21:00:00.000000000',\n",
- " '2021-01-01T22:00:00.000000000', '2021-01-01T23:00:00.000000000',\n",
- " '2021-01-02T00:00:00.000000000', '2021-01-02T01:00:00.000000000',\n",
- " '2021-01-02T02:00:00.000000000', '2021-01-02T03:00:00.000000000',\n",
- " '2021-01-02T04:00:00.000000000', '2021-01-02T05:00:00.000000000',\n",
- " '2021-01-02T06:00:00.000000000', '2021-01-02T07:00:00.000000000',\n",
- " '2021-01-02T08:00:00.000000000', '2021-01-02T09:00:00.000000000',\n",
- " '2021-01-02T10:00:00.000000000', '2021-01-02T11:00:00.000000000',\n",
- " '2021-01-02T12:00:00.000000000', '2021-01-02T13:00:00.000000000',\n",
- " '2021-01-02T14:00:00.000000000', '2021-01-02T15:00:00.000000000',\n",
- " '2021-01-02T16:00:00.000000000', '2021-01-02T17:00:00.000000000',\n",
- " '2021-01-02T18:00:00.000000000', '2021-01-02T19:00:00.000000000',\n",
- " '2021-01-02T20:00:00.000000000', '2021-01-02T21:00:00.000000000',\n",
- " '2021-01-02T22:00:00.000000000', '2021-01-02T23:00:00.000000000'],\n",
- " dtype='datetime64[ns]') lat
(y, x)
float32
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude [190244 values with dtype=float32] lon
(y, x)
float32
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude [190244 values with dtype=float32] x
(x)
float64
-2.126e+06 -2.114e+06 ... 3.598e+06
units : 1000 m long_name : x coordinate of projection standard_name : projection_x_coordinate array([-2125847.534765, -2113847.610112, -2101847.010684, ..., 3574152.262286,\n",
- " 3586150.20523 , 3598150.638118]) y
(y)
float64
-2.067e+06 -2.055e+06 ... 2.697e+06
units : 1000 m long_name : y coordinate of projection standard_name : projection_y_coordinate array([-2067138.479486, -2055138.337429, -2043137.546656, ..., 2672862.222453,\n",
- " 2684862.70005 , 2696862.03252 ]) lev
(lev)
float32
0.0
array([0.], dtype=float32) Data variables: (2)
Attributes: (0)
"
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 48, y: 398, x: 478, lev: 1)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-01-01 ... 2021-01-02T23:00:00\n",
- " lat (y, x) float32 ...\n",
- " lon (y, x) float32 ...\n",
- " * x (x) float64 -2.126e+06 -2.114e+06 ... 3.586e+06 3.598e+06\n",
- " * y (y) float64 -2.067e+06 -2.055e+06 ... 2.685e+06 2.697e+06\n",
- " * lev (lev) float32 0.0\n",
- "Data variables:\n",
- " sconco3 (time, lev, y, x) float32 ...\n",
- " Lambert_conformal int32 ..."
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset(nc_path_1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with NES"
+ "## 1. Read dataset"
]
},
{
@@ -477,7 +42,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 4,
@@ -498,54 +63,7 @@
{
"data": {
"text/plain": [
- "[datetime.datetime(2021, 1, 1, 0, 0),\n",
- " datetime.datetime(2021, 1, 1, 1, 0),\n",
- " datetime.datetime(2021, 1, 1, 2, 0),\n",
- " datetime.datetime(2021, 1, 1, 3, 0),\n",
- " datetime.datetime(2021, 1, 1, 4, 0),\n",
- " datetime.datetime(2021, 1, 1, 5, 0),\n",
- " datetime.datetime(2021, 1, 1, 6, 0),\n",
- " datetime.datetime(2021, 1, 1, 7, 0),\n",
- " datetime.datetime(2021, 1, 1, 8, 0),\n",
- " datetime.datetime(2021, 1, 1, 9, 0),\n",
- " datetime.datetime(2021, 1, 1, 10, 0),\n",
- " datetime.datetime(2021, 1, 1, 11, 0),\n",
- " datetime.datetime(2021, 1, 1, 12, 0),\n",
- " datetime.datetime(2021, 1, 1, 13, 0),\n",
- " datetime.datetime(2021, 1, 1, 14, 0),\n",
- " datetime.datetime(2021, 1, 1, 15, 0),\n",
- " datetime.datetime(2021, 1, 1, 16, 0),\n",
- " datetime.datetime(2021, 1, 1, 17, 0),\n",
- " datetime.datetime(2021, 1, 1, 18, 0),\n",
- " datetime.datetime(2021, 1, 1, 19, 0),\n",
- " datetime.datetime(2021, 1, 1, 20, 0),\n",
- " datetime.datetime(2021, 1, 1, 21, 0),\n",
- " datetime.datetime(2021, 1, 1, 22, 0),\n",
- " datetime.datetime(2021, 1, 1, 23, 0),\n",
- " datetime.datetime(2021, 1, 2, 0, 0),\n",
- " datetime.datetime(2021, 1, 2, 1, 0),\n",
- " datetime.datetime(2021, 1, 2, 2, 0),\n",
- " datetime.datetime(2021, 1, 2, 3, 0),\n",
- " datetime.datetime(2021, 1, 2, 4, 0),\n",
- " datetime.datetime(2021, 1, 2, 5, 0),\n",
- " datetime.datetime(2021, 1, 2, 6, 0),\n",
- " datetime.datetime(2021, 1, 2, 7, 0),\n",
- " datetime.datetime(2021, 1, 2, 8, 0),\n",
- " datetime.datetime(2021, 1, 2, 9, 0),\n",
- " datetime.datetime(2021, 1, 2, 10, 0),\n",
- " datetime.datetime(2021, 1, 2, 11, 0),\n",
- " datetime.datetime(2021, 1, 2, 12, 0),\n",
- " datetime.datetime(2021, 1, 2, 13, 0),\n",
- " datetime.datetime(2021, 1, 2, 14, 0),\n",
- " datetime.datetime(2021, 1, 2, 15, 0),\n",
- " datetime.datetime(2021, 1, 2, 16, 0),\n",
- " datetime.datetime(2021, 1, 2, 17, 0),\n",
- " datetime.datetime(2021, 1, 2, 18, 0),\n",
- " datetime.datetime(2021, 1, 2, 19, 0),\n",
- " datetime.datetime(2021, 1, 2, 20, 0),\n",
- " datetime.datetime(2021, 1, 2, 21, 0),\n",
- " datetime.datetime(2021, 1, 2, 22, 0),\n",
- " datetime.datetime(2021, 1, 2, 23, 0)]"
+ "[datetime.datetime(2022, 11, 15, 0, 0), datetime.datetime(2022, 11, 16, 0, 0)]"
]
},
"execution_count": 5,
@@ -567,8 +85,10 @@
"text/plain": [
"{'data': masked_array(data=[0.],\n",
" mask=False,\n",
- " fill_value=1e+20,\n",
- " dtype=float32), 'dimensions': ('lev',), 'positive': 'up'}"
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('lev',),\n",
+ " 'units': '',\n",
+ " 'positive': 'up'}"
]
},
"execution_count": 6,
@@ -588,171 +108,144 @@
{
"data": {
"text/plain": [
- "{'data': masked_array(data=[-2.12584753e+06, -2.11384761e+06, -2.10184701e+06,\n",
- " -2.08984901e+06, -2.07784716e+06, -2.06584784e+06,\n",
- " -2.05384791e+06, -2.04185027e+06, -2.02984883e+06,\n",
- " -2.01784924e+06, -2.00584929e+06, -1.99384819e+06,\n",
- " -1.98184942e+06, -1.96984999e+06, -1.95784950e+06,\n",
- " -1.94584776e+06, -1.93384820e+06, -1.92184804e+06,\n",
- " -1.90984965e+06, -1.89784727e+06, -1.88584974e+06,\n",
- " -1.87384798e+06, -1.86184856e+06, -1.84984781e+06,\n",
- " -1.83784882e+06, -1.82584878e+06, -1.81384730e+06,\n",
- " -1.80184766e+06, -1.78985000e+06, -1.77784784e+06,\n",
- " -1.76584709e+06, -1.75384839e+06, -1.74184822e+06,\n",
- " -1.72984970e+06, -1.71784950e+06, -1.70584804e+06,\n",
- " -1.69384799e+06, -1.68184961e+06, -1.66984941e+06,\n",
- " -1.65784782e+06, -1.64584735e+06, -1.63384873e+06,\n",
- " -1.62184786e+06, -1.60984877e+06, -1.59784781e+06,\n",
- " -1.58584811e+06, -1.57384978e+06, -1.56184949e+06,\n",
- " -1.54984706e+06, -1.53784949e+06, -1.52584971e+06,\n",
- " -1.51384784e+06, -1.50185029e+06, -1.48984741e+06,\n",
- " -1.47784881e+06, -1.46584796e+06, -1.45384829e+06,\n",
- " -1.44184935e+06, -1.42984834e+06, -1.41784813e+06,\n",
- " -1.40584895e+06, -1.39384732e+06, -1.38184958e+06,\n",
- " -1.36984970e+06, -1.35784703e+06, -1.34584852e+06,\n",
- " -1.33384739e+06, -1.32185023e+06, -1.30984708e+06,\n",
- " -1.29784782e+06, -1.28584924e+06, -1.27384787e+06,\n",
- " -1.26185017e+06, -1.24984974e+06, -1.23784984e+06,\n",
- " -1.22584679e+06, -1.21384762e+06, -1.20184864e+06,\n",
- " -1.18985017e+06, -1.17784863e+06, -1.16584730e+06,\n",
- " -1.15384933e+06, -1.14184842e+06, -1.12984761e+06,\n",
- " -1.11785016e+06, -1.10584945e+06, -1.09384884e+06,\n",
- " -1.08184809e+06, -1.06984737e+06, -1.05784994e+06,\n",
- " -1.04584897e+06, -1.03384802e+06, -1.02185016e+06,\n",
- " -1.00984874e+06, -9.97847138e+05, -9.85848430e+05,\n",
- " -9.73849460e+05, -9.61850193e+05, -9.49847198e+05,\n",
- " -9.37847136e+05, -9.25849883e+05, -9.13848882e+05,\n",
- " -9.01847317e+05, -8.89848619e+05, -8.77849203e+05,\n",
- " -8.65849037e+05, -8.53848483e+05, -8.41850255e+05,\n",
- " -8.29848103e+05, -8.17848443e+05, -8.05848159e+05,\n",
- " -7.93850146e+05, -7.81847983e+05, -7.69848242e+05,\n",
- " -7.57847649e+05, -7.45849126e+05, -7.33849816e+05,\n",
- " -7.21849273e+05, -7.09847665e+05, -6.97848323e+05,\n",
- " -6.85847775e+05, -6.73849354e+05, -6.61849656e+05,\n",
- " -6.49848767e+05, -6.37849773e+05, -6.25849455e+05,\n",
- " -6.13847832e+05, -6.01848000e+05, -5.89850037e+05,\n",
- " -5.77847297e+05, -5.65849601e+05, -5.53850360e+05,\n",
- " -5.41849485e+05, -5.29850392e+05, -5.17849446e+05,\n",
- " -5.05850300e+05, -4.93849326e+05, -4.81849796e+05,\n",
- " -4.69848463e+05, -4.57848676e+05, -4.45850309e+05,\n",
- " -4.33849861e+05, -4.21847579e+05, -4.09849797e+05,\n",
- " -3.97850061e+05, -3.85848258e+05, -3.73847618e+05,\n",
- " -3.61848107e+05, -3.49849786e+05, -3.37849091e+05,\n",
- " -3.25849544e+05, -3.13847764e+05, -3.01850147e+05,\n",
- " -2.89850223e+05, -2.77847869e+05, -2.65849682e+05,\n",
- " -2.53848995e+05, -2.41849099e+05, -2.29849935e+05,\n",
- " -2.17848225e+05, -2.05850456e+05, -1.93850007e+05,\n",
- " -1.81850198e+05, -1.69847633e+05, -1.57848901e+05,\n",
- " -1.45847355e+05, -1.33849541e+05, -1.21848866e+05,\n",
- " -1.09848557e+05, -9.78485812e+04, -8.58489110e+04,\n",
- " -7.38495157e+04, -6.18503428e+04, -4.98480588e+04,\n",
- " -3.78492294e+04, -2.58472156e+04, -1.38485865e+04,\n",
- " -1.85000093e+03, 1.01518839e+04, 2.21504974e+04,\n",
- " 3.41524846e+04, 4.61512675e+04, 5.81502025e+04,\n",
- " 7.01526052e+04, 8.21519418e+04, 9.41515115e+04,\n",
- " 1.06151401e+05, 1.18151593e+05, 1.30152150e+05,\n",
- " 1.42149821e+05, 1.54151239e+05, 1.66149805e+05,\n",
- " 1.78152206e+05, 1.90151841e+05, 2.02152103e+05,\n",
- " 2.14149676e+05, 2.26151179e+05, 2.38150101e+05,\n",
- " 2.50149789e+05, 2.62150163e+05, 2.74151423e+05,\n",
- " 2.86150208e+05, 2.98149857e+05, 3.10150460e+05,\n",
- " 3.22152060e+05, 3.34151271e+05, 3.46151519e+05,\n",
- " 3.58149605e+05, 3.70152044e+05, 3.82152331e+05,\n",
- " 3.94150502e+05, 4.06149863e+05, 4.18150448e+05,\n",
- " 4.30152410e+05, 4.42152326e+05, 4.54150354e+05,\n",
- " 4.66149752e+05, 4.78150600e+05, 4.90149630e+05,\n",
- " 5.02150092e+05, 5.14152159e+05, 5.26152471e+05,\n",
- " 5.38151163e+05, 5.50151477e+05, 5.62150197e+05,\n",
- " 5.74150715e+05, 5.86149501e+05, 5.98150157e+05,\n",
- " 6.10149476e+05, 6.22150579e+05, 6.34150250e+05,\n",
- " 6.46151947e+05, 6.58152226e+05, 6.70151301e+05,\n",
- " 6.82152394e+05, 6.94152233e+05, 7.06151050e+05,\n",
- " 7.18151992e+05, 7.30151725e+05, 7.42150415e+05,\n",
- " 7.54151335e+05, 7.66151356e+05, 7.78150447e+05,\n",
- " 7.90151805e+05, 8.02152305e+05, 8.14151909e+05,\n",
- " 8.26150803e+05, 8.38152179e+05, 8.50149463e+05,\n",
- " 8.62152600e+05, 8.74151875e+05, 8.86150469e+05,\n",
- " 8.98151720e+05, 9.10152442e+05, 9.22152590e+05,\n",
- " 9.34152281e+05, 9.46151555e+05, 9.58150185e+05,\n",
- " 9.70151947e+05, 9.82150008e+05, 9.94151009e+05,\n",
- " 1.00615168e+06, 1.01815215e+06, 1.03015237e+06,\n",
- " 1.04215255e+06, 1.05415245e+06, 1.06615219e+06,\n",
- " 1.07815192e+06, 1.09015146e+06, 1.10215106e+06,\n",
- " 1.11415084e+06, 1.12615055e+06, 1.13815031e+06,\n",
- " 1.15015027e+06, 1.16215026e+06, 1.17415050e+06,\n",
- " 1.18615117e+06, 1.19815184e+06, 1.21014970e+06,\n",
- " 1.22215106e+06, 1.23414955e+06, 1.24615161e+06,\n",
- " 1.25815089e+06, 1.27015072e+06, 1.28215091e+06,\n",
- " 1.29415184e+06, 1.30614966e+06, 1.31815171e+06,\n",
- " 1.33015072e+06, 1.34215086e+06, 1.35415143e+06,\n",
- " 1.36614929e+06, 1.37815146e+06, 1.39015111e+06,\n",
- " 1.40215157e+06, 1.41414946e+06, 1.42615154e+06,\n",
- " 1.43815149e+06, 1.45015214e+06, 1.46215061e+06,\n",
- " 1.47414997e+06, 1.48615052e+06, 1.49815230e+06,\n",
- " 1.51015149e+06, 1.52215197e+06, 1.53415035e+06,\n",
- " 1.54615023e+06, 1.55815110e+06, 1.57015010e+06,\n",
- " 1.58215014e+06, 1.59415154e+06, 1.60615118e+06,\n",
- " 1.61815225e+06, 1.63015134e+06, 1.64215177e+06,\n",
- " 1.65415073e+06, 1.66615110e+06, 1.67814931e+06,\n",
- " 1.69014976e+06, 1.70215157e+06, 1.71415178e+06,\n",
- " 1.72615026e+06, 1.73815066e+06, 1.75015254e+06,\n",
- " 1.76214949e+06, 1.77415192e+06, 1.78614949e+06,\n",
- " 1.79815196e+06, 1.81015012e+06, 1.82214994e+06,\n",
- " 1.83415163e+06, 1.84615225e+06, 1.85815149e+06,\n",
- " 1.87014939e+06, 1.88214927e+06, 1.89415152e+06,\n",
- " 1.90615253e+06, 1.91815231e+06, 1.93015091e+06,\n",
- " 1.94215219e+06, 1.95415199e+06, 1.96615088e+06,\n",
- " 1.97815184e+06, 1.99015212e+06, 2.00215141e+06,\n",
- " 2.01414974e+06, 2.02615043e+06, 2.03815022e+06,\n",
- " 2.05014932e+06, 2.06215089e+06, 2.07415183e+06,\n",
- " 2.08615200e+06, 2.09815142e+06, 2.11015031e+06,\n",
- " 2.12215125e+06, 2.13415212e+06, 2.14615216e+06,\n",
- " 2.15815181e+06, 2.17015090e+06, 2.18215236e+06,\n",
- " 2.19415062e+06, 2.20615132e+06, 2.21815157e+06,\n",
- " 2.23015162e+06, 2.24215129e+06, 2.25415041e+06,\n",
- " 2.26614942e+06, 2.27815103e+06, 2.29014950e+06,\n",
- " 2.30215084e+06, 2.31415199e+06, 2.32614967e+06,\n",
- " 2.33815052e+06, 2.35015105e+06, 2.36215151e+06,\n",
- " 2.37415171e+06, 2.38615211e+06, 2.39815209e+06,\n",
- " 2.41014925e+06, 2.42214936e+06, 2.43414979e+06,\n",
- " 2.44615014e+06, 2.45815065e+06, 2.47015092e+06,\n",
- " 2.48215186e+06, 2.49414885e+06, 2.50615033e+06,\n",
- " 2.51815099e+06, 2.53014892e+06, 2.54215075e+06,\n",
- " 2.55414946e+06, 2.56615190e+06, 2.57815081e+06,\n",
- " 2.59015090e+06, 2.60215059e+06, 2.61415105e+06,\n",
- " 2.62615163e+06, 2.63814974e+06, 2.65015084e+06,\n",
- " 2.66215003e+06, 2.67414918e+06, 2.68614955e+06,\n",
- " 2.69814946e+06, 2.71015063e+06, 2.72215238e+06,\n",
- " 2.73415141e+06, 2.74615082e+06, 2.75815088e+06,\n",
- " 2.77015212e+06, 2.78215001e+06, 2.79414938e+06,\n",
- " 2.80614927e+06, 2.81814994e+06, 2.83015143e+06,\n",
- " 2.84215046e+06, 2.85415010e+06, 2.86615064e+06,\n",
- " 2.87815237e+06, 2.89015096e+06, 2.90215132e+06,\n",
- " 2.91415163e+06, 2.92615073e+06, 2.93815062e+06,\n",
- " 2.95015108e+06, 2.96214987e+06, 2.97415232e+06,\n",
- " 2.98614959e+06, 2.99815110e+06, 3.01015052e+06,\n",
- " 3.02215092e+06, 3.03414984e+06, 3.04615228e+06,\n",
- " 3.05814999e+06, 3.07015153e+06, 3.08215198e+06,\n",
- " 3.09414943e+06, 3.10615189e+06, 3.11815252e+06,\n",
- " 3.13015077e+06, 3.14215026e+06, 3.15415157e+06,\n",
- " 3.16615058e+06, 3.17815090e+06, 3.19015010e+06,\n",
- " 3.20214982e+06, 3.21415120e+06, 3.22615069e+06,\n",
- " 3.23815162e+06, 3.25015100e+06, 3.26215215e+06,\n",
- " 3.27415093e+06, 3.28615124e+06, 3.29815011e+06,\n",
- " 3.31015056e+06, 3.32214902e+06, 3.33414971e+06,\n",
- " 3.34615147e+06, 3.35815161e+06, 3.37014985e+06,\n",
- " 3.38215073e+06, 3.39415246e+06, 3.40614937e+06,\n",
- " 3.41815139e+06, 3.43015224e+06, 3.44215134e+06,\n",
- " 3.45415201e+06, 3.46615129e+06, 3.47814888e+06,\n",
- " 3.49015204e+06, 3.50214995e+06, 3.51415017e+06,\n",
- " 3.52615242e+06, 3.53814916e+06, 3.55015129e+06,\n",
- " 3.56214954e+06, 3.57415226e+06, 3.58615021e+06,\n",
- " 3.59815064e+06],\n",
+ "{'data': masked_array(data=[-805847.93512686, -801847.95626194, -797848.36116962,\n",
+ " -793846.40078545, -789847.5662888 , -785846.12161903,\n",
+ " -781847.9213502 , -777847.26711017, -773846.90615214,\n",
+ " -769846.99396972, -765847.29384614, -761847.92217455,\n",
+ " -757846.1635423 , -753847.52001723, -749846.52217576,\n",
+ " -745848.52234501, -741847.97471659, -737847.82062838,\n",
+ " -733848.16920624, -729848.64509128, -725846.4925582 ,\n",
+ " -721847.77716311, -717846.4291098 , -713848.18385516,\n",
+ " -709847.59391026, -705847.0556987 , -701847.07129347,\n",
+ " -697847.20935111, -693847.78875693, -689848.70038656,\n",
+ " -685846.96966084, -681848.43600654, -677847.46490525,\n",
+ " -673846.61202228, -669846.11722529, -665848.81181576,\n",
+ " -661846.09003088, -657846.48649904, -653847.1656709 ,\n",
+ " -649848.22514489, -645846.62952115, -641848.3753264 ,\n",
+ " -637847.33246165, -633846.66099372, -629846.16542218,\n",
+ " -625849.06399662, -621846.24075126, -617846.742536 ,\n",
+ " -613847.47778944, -609848.50762818, -605846.96204506,\n",
+ " -601848.48284466, -597847.42518251, -593846.68501473,\n",
+ " -589846.10959814, -585848.71610943, -581848.79564948,\n",
+ " -577846.10972474, -573846.7167133 , -569847.54064889,\n",
+ " -565848.60940359, -561847.05357627, -557848.69254475,\n",
+ " -553847.58996162, -549846.86638894, -545846.29508862,\n",
+ " -541848.88208792, -537848.88888833, -533849.01764933,\n",
+ " -529846.64404749, -525847.31197283, -521848.2063491 ,\n",
+ " -517846.51047709, -513847.87892857, -509846.65286731,\n",
+ " -505848.46233897, -501847.72451902, -497847.15130423,\n",
+ " -493846.81779096, -489846.74677709, -485846.78715095,\n",
+ " -481847.16055064, -477847.71647316, -473848.40576421,\n",
+ " -469846.57609158, -465847.7446169 , -461846.34045261,\n",
+ " -457847.90894114, -453846.85385685, -449848.90821442,\n",
+ " -445848.3789051 , -441847.83741869, -437847.6443723 ,\n",
+ " -433847.59518169, -429847.7551937 , -425848.12227221,\n",
+ " -421848.6728026 , -417846.57992853, -413847.51344757,\n",
+ " -409848.64629979, -405847.08780312, -401848.59265191,\n",
+ " -397847.34191435, -393846.34416041, -389848.4235981 ,\n",
+ " -385847.7238277 , -381847.30809541, -377846.97879215,\n",
+ " -373846.83202364, -369846.84681622, -365847.0775374 ,\n",
+ " -361847.42868547, -357847.99107807, -353848.67124696,\n",
+ " -349846.63664111, -345847.69195027, -341848.86085595,\n",
+ " -337847.36121013, -333848.87439829, -329847.61272787,\n",
+ " -325846.49417387, -321848.45066265, -317847.67770099,\n",
+ " -313847.00966464, -309846.55672976, -305846.15851356,\n",
+ " -301848.80743028, -297848.73401306, -293848.80400067,\n",
+ " -289848.95618854, -285846.42493523, -281846.89895013,\n",
+ " -277847.46574728, -273848.20872659, -269846.17354669,\n",
+ " -265847.10920286, -261848.18691689, -257846.52291925,\n",
+ " -253847.79909431, -249846.38182197, -245847.88954112,\n",
+ " -241846.71062923, -237848.4302232 , -233847.50534012,\n",
+ " -229846.57370064, -225848.6682608 , -221847.96954579,\n",
+ " -217847.40062777, -213846.91455502, -209846.54248561,\n",
+ " -205846.23970871, -201848.92522171, -197848.80949076,\n",
+ " -193848.78972017, -189848.89278964, -185846.18054808,\n",
+ " -181846.43539071, -177846.75949393, -173847.16965507,\n",
+ " -169847.67238598, -165848.22267293, -161848.86205713,\n",
+ " -157846.73599205, -153847.50569577, -149848.37354183,\n",
+ " -145846.43794118, -141847.43312064, -137848.48966761,\n",
+ " -133846.73134824, -129847.93270571, -125846.31490803,\n",
+ " -121847.60658359, -117848.95756046, -113847.49781463,\n",
+ " -109848.96096089, -105847.60370073, -101846.30276213,\n",
+ " -97847.91828188, -93846.68365713, -89848.3819641 ,\n",
+ " -85847.25535105, -81849.02074623, -77847.97046565,\n",
+ " -73846.9571176 , -69848.82507724, -65847.87702698,\n",
+ " -61846.94727318, -57848.91579031, -53848.04741895,\n",
+ " -49847.20474114, -45846.38561176, -41848.44936691,\n",
+ " -37847.66572384, -33846.89869206, -29849.0176602 ,\n",
+ " -25848.28147743, -21847.55537625, -17846.84480235,\n",
+ " -13849.00537462, -9848.30375156, -5847.60778563,\n",
+ " -1846.91443742, 2150.90966469, 6151.60303648,\n",
+ " 10152.29900308, 14153.00065035, 18153.70795947,\n",
+ " 22151.55623066, 26152.28336887, 30153.01595515,\n",
+ " 34150.89912272, 38151.66872149, 42152.45103505,\n",
+ " 46153.25578283, 50151.21272946, 54152.06744801,\n",
+ " 58152.92875182, 62150.96147069, 66151.89584031,\n",
+ " 70152.84528477, 74150.97124616, 78151.98558124,\n",
+ " 82153.05360533, 86151.27690254, 90152.41390478,\n",
+ " 94153.5898915 , 98151.94351874, 102153.2074843 ,\n",
+ " 106151.65297211, 110153.01227233, 114151.55697637,\n",
+ " 118153.01294512, 122151.66401699, 126153.22941959,\n",
+ " 130152.00748048, 134153.6900796 , 138152.56931872,\n",
+ " 142151.52246648, 146153.39036518, 150152.45286934,\n",
+ " 154151.61081517, 158153.70382188, 162152.98939895,\n",
+ " 166152.33634753, 170151.76293096, 174151.28016777,\n",
+ " 178150.88173441, 182153.40052491, 186153.19533963,\n",
+ " 190153.06235028, 194152.96290358, 198152.99644881,\n",
+ " 202153.07486465, 206153.27077183, 210153.56611891,\n",
+ " 214151.10608157, 218151.60694126, 222152.16839161,\n",
+ " 226152.87079379, 230153.64776205, 234151.70331023,\n",
+ " 238152.70436732, 242150.92782145, 246152.13543546,\n",
+ " 250153.52519863, 254152.10493862, 258153.60950245,\n",
+ " 262152.43578044, 266151.38904911, 270153.31180315,\n",
+ " 274152.49713737, 278151.8150729 , 282151.22462874,\n",
+ " 286153.65230465, 290153.33553062, 294153.18867164,\n",
+ " 298153.07839344, 302153.21805122, 306153.42691301,\n",
+ " 310150.93098161, 314151.4847698 , 318152.11031704,\n",
+ " 322152.92157806, 326150.95435571, 330152.04500714,\n",
+ " 334153.31199356, 338151.8040678 , 342153.37867622,\n",
+ " 346152.21587801, 350151.18447167, 354153.22540454,\n",
+ " 358152.55155502, 362151.97699927, 366151.63196773,\n",
+ " 370151.46387702, 374151.4177032 , 378151.57078458,\n",
+ " 382151.88694777, 386152.36772088, 390153.05436712,\n",
+ " 394151.06168363, 398152.12756765, 402153.304367 ,\n",
+ " 406151.8273034 , 410153.37223178, 414152.2043099 ,\n",
+ " 418151.27749738, 422153.46302984, 426152.94171617,\n",
+ " 430152.56045283, 434152.43006433, 438152.46467961,\n",
+ " 442152.64270581, 446153.12344062, 450150.92899765,\n",
+ " 454151.72730472, 458152.83675802, 462151.25289819,\n",
+ " 466152.75985787, 470151.52828603, 474153.43844753,\n",
+ " 478152.70926043, 482152.16165718, 486151.74706637,\n",
+ " 490151.73959925, 494151.84385635, 498152.23639489,\n",
+ " 502152.79278782, 506153.5906173 , 510151.76413657,\n",
+ " 514153.02507329, 518151.66555909, 522153.39691557,\n",
+ " 526152.45813349, 530151.79893534, 534151.44924002,\n",
+ " 538151.24810644, 542151.30556965, 546151.65154799,\n",
+ " 550152.1208407 , 554152.93807898, 558151.18254747,\n",
+ " 562152.47835114, 566151.14763776, 570152.89868486,\n",
+ " 574152.17188498, 578151.66322488, 582151.40322951,\n",
+ " 586151.33379136, 590151.57528313, 594152.13044127,\n",
+ " 598152.9411577 , 602151.20232222, 606152.49829395,\n",
+ " 610151.27918863, 614153.09671507, 618152.43485402,\n",
+ " 622151.97561113, 626151.78297035, 630151.95457719,\n",
+ " 634152.42928344, 638153.11187408, 642151.13477367,\n",
+ " 646152.49685929, 650151.17004599, 654153.02281532,\n",
+ " 658152.38845054, 662152.03718631, 666151.80122378,\n",
+ " 670152.05376699, 674152.62863479, 678153.42454061,\n",
+ " 682151.53923575, 686153.02204595, 690151.89683146,\n",
+ " 694150.997103 , 698153.36844712, 702153.10002477,\n",
+ " 706153.31121357, 710153.6096021 , 714151.59694722,\n",
+ " 718152.57687259, 722151.14234101, 726152.88547008,\n",
+ " 730152.03491755, 734151.53406926, 738151.42254562,\n",
+ " 742151.62766628, 746152.11304004, 750152.8795744 ,\n",
+ " 754151.17522194, 758152.58442569, 762151.68142302,\n",
+ " 766150.98901518, 770153.53123988, 774153.41718595,\n",
+ " 778150.92318215],\n",
" mask=False,\n",
" fill_value=1e+20),\n",
" 'dimensions': ('x',),\n",
- " 'units': '1000 m',\n",
" 'long_name': 'x coordinate of projection',\n",
+ " 'units': '1000 m',\n",
" 'standard_name': 'projection_x_coordinate'}"
]
},
@@ -773,210 +266,144 @@
{
"data": {
"text/plain": [
- "{'data': masked_array(data=[-2067138.47948561, -2055138.33742867,\n",
- " -2043137.54665598, -2031137.11044915,\n",
- " -2019137.8807928 , -2007138.08379361,\n",
- " -1995137.55201761, -1983138.30318791,\n",
- " -1971139.14829968, -1959137.5926525 ,\n",
- " -1947137.10543589, -1935138.53320712,\n",
- " -1923137.4951208 , -1911137.45400782,\n",
- " -1899138.2432851 , -1887137.51394029,\n",
- " -1875138.43810423, -1863136.92956926,\n",
- " -1851137.89552799, -1839137.1121049 ,\n",
- " -1827137.15927377, -1815138.01203912,\n",
- " -1803138.7795864 , -1791137.70775766,\n",
- " -1779137.3704284 , -1767137.74254063,\n",
- " -1755137.93518378, -1743137.06138263,\n",
- " -1731137.6875659 , -1719137.19999768,\n",
- " -1707136.43834552, -1695137.96319842,\n",
- " -1683137.44174755, -1671137.43454641,\n",
- " -1659137.056387 , -1647138.00256504,\n",
- " -1635137.66921584, -1623136.89251186,\n",
- " -1611137.36489549, -1599137.34432241,\n",
- " -1587137.79320469, -1575137.57084194,\n",
- " -1563136.9109904 , -1551137.37235623,\n",
- " -1539137.94554037, -1527137.1502086 ,\n",
- " -1515138.25410466, -1503138.66687657,\n",
- " -1491138.4899473 , -1479136.59453581,\n",
- " -1467137.47233746, -1455137.55905622,\n",
- " -1443137.68244934, -1431137.81703208,\n",
- " -1419138.78837709, -1407138.99088641,\n",
- " -1395137.42673134, -1383137.47212092,\n",
- " -1371137.40019024, -1359138.03433592,\n",
- " -1347136.9224302 , -1335137.31271794,\n",
- " -1323137.4816182 , -1311137.94634304,\n",
- " -1299137.29022577, -1287137.1818736 ,\n",
- " -1275137.59439096, -1263136.92697495,\n",
- " -1251137.57275311, -1239136.12380517,\n",
- " -1227137.6241066 , -1215137.51625937,\n",
- " -1203137.04074528, -1191137.62692217,\n",
- " -1179138.21301879, -1167137.50751754,\n",
- " -1155137.89726681, -1143138.09129864,\n",
- " -1131138.06316848, -1119137.05825655,\n",
- " -1107137.15205236, -1095138.20305536,\n",
- " -1083138.08298794, -1071137.18627622,\n",
- " -1059138.64697368, -1047138.74478058,\n",
- " -1035138.51485085, -1023137.92955771,\n",
- " -1011137.6894263 , -999137.45744737,\n",
- " -987137.51499851, -975137.83442915,\n",
- " -963137.76831308, -951139.05433923,\n",
- " -939137.39017683, -927137.54863286,\n",
- " -915138.66481437, -903137.47725183,\n",
- " -891138.44385041, -879137.99257723,\n",
- " -867138.69715018, -855137.92810361,\n",
- " -843137.53065969, -831137.99786638,\n",
- " -819138.36299108, -807137.87013262,\n",
- " -795137.32354617, -783137.83855245,\n",
- " -771137.41075253, -759138.09010529,\n",
- " -747137.76958547, -735137.98006167,\n",
- " -723137.5495894 , -711137.6947773 ,\n",
- " -699137.86979959, -687138.5622281 ,\n",
- " -675137.66992397, -663138.06610574,\n",
- " -651137.23497902, -639136.80506847,\n",
- " -627138.30290647, -615137.65946231,\n",
- " -603138.15712078, -591136.87075576,\n",
- " -579137.08014637, -567137.82964577,\n",
- " -555137.63384505, -543137.50696179,\n",
- " -531138.0283756 , -519137.73451411,\n",
- " -507137.93240193, -495137.45153707,\n",
- " -483137.30641057, -471137.15203014,\n",
- " -459138.19530608, -447138.75770337,\n",
- " -435136.43449817, -423137.59544964,\n",
- " -411136.54082615, -399138.59271965,\n",
- " -387138.05393281, -375137.17535161,\n",
- " -363137.35013412, -351138.04215929,\n",
- " -339138.30575139, -327138.29921261,\n",
- " -315137.89921544, -303137.58006488,\n",
- " -291137.12571846, -279137.5132003 ,\n",
- " -267137.79855472, -255137.95212261,\n",
- " -243138.35433354, -231138.15527529,\n",
- " -219137.41721347, -207138.06770319,\n",
- " -195138.02753911, -183138.17757006,\n",
- " -171137.98702931, -159138.24510045,\n",
- " -147137.37488813, -135137.71176692,\n",
- " -123137.99774014, -111137.06580801,\n",
- " -99137.65919525, -87138.52051766,\n",
- " -75137.66532798, -63138.24447606,\n",
- " -51137.45613798, -39138.44989177,\n",
- " -27137.19935201, -15137.26193513,\n",
- " -3138.2870129 , 8862.70158754,\n",
- " 20863.19613188, 32862.09109349,\n",
- " 44862.1850711 , 56862.28417564,\n",
- " 68862.09833787, 80862.79348443,\n",
- " 92862.04079618, 104861.82177242,\n",
- " 116861.75894296, 128861.96917478,\n",
- " 140861.98836512, 152861.43960268,\n",
- " 164861.6613091 , 176862.10360022,\n",
- " 188862.1547975 , 200862.57312437,\n",
- " 212861.43960038, 224862.6824761 ,\n",
- " 236862.02704646, 248862.67280663,\n",
- " 260861.80232975, 272862.69968633,\n",
- " 284863.03968892, 296862.27594177,\n",
- " 308863.36959344, 320862.7767121 ,\n",
- " 332862.79683064, 344861.59736628,\n",
- " 356861.88372131, 368861.82305626,\n",
- " 380861.27672775, 392861.25573027,\n",
- " 404862.83978888, 416862.48790187,\n",
- " 428862.26071291, 440862.59403697,\n",
- " 452861.89377259, 464861.81366973,\n",
- " 476862.70563957, 488861.51974816,\n",
- " 500861.77140022, 512861.95044987,\n",
- " 524862.40904252, 536862.85449188,\n",
- " 548862.09945016, 560862.20196287,\n",
- " 572862.70276656, 584862.09217358,\n",
- " 596862.4276693 , 608861.6280585 ,\n",
- " 620861.42797071, 632862.17940113,\n",
- " 644862.3726426 , 656861.95444672,\n",
- " 668862.65863139, 680862.81014377,\n",
- " 692862.52099743, 704861.41511084,\n",
- " 716862.19408409, 728863.02536633,\n",
- " 740861.50621775, 752862.36459892,\n",
- " 764861.74143799, 776862.50273099,\n",
- " 788863.2968091 , 800862.6963584 ,\n",
- " 812861.94605002, 824861.7205462 ,\n",
- " 836861.80803198, 848862.4778583 ,\n",
- " 860861.89719402, 872862.84895905,\n",
- " 884862.52520112, 896861.84719109,\n",
- " 908861.8945184 , 920862.37268268,\n",
- " 932862.58191684, 944862.38597276,\n",
- " 956862.70585726, 968862.75932956,\n",
- " 980862.97978011, 992862.99006537,\n",
- " 1004862.00768959, 1016862.08692122,\n",
- " 1028861.63462963, 1040862.21737941,\n",
- " 1052861.91891392, 1064861.98284895,\n",
- " 1076862.35437825, 1088862.01009992,\n",
- " 1100862.02850585, 1112862.4370279 ,\n",
- " 1124863.3456949 , 1136862.34934454,\n",
- " 1148862.23038066, 1160861.47692509,\n",
- " 1172862.46616204, 1184862.06404513,\n",
- " 1196861.91951627, 1208861.89356904,\n",
- " 1220862.17889049, 1232861.99106498,\n",
- " 1244862.49052547, 1256862.89251711,\n",
- " 1268862.41196453, 1280861.96984232,\n",
- " 1292861.10334917, 1304861.8676576 ,\n",
- " 1316861.85408047, 1328862.71199087,\n",
- " 1340862.92749166, 1352862.44262531,\n",
- " 1364861.60505252, 1376863.04434379,\n",
- " 1388861.42362458, 1400862.3692699 ,\n",
- " 1412861.20141946, 1424861.92291089,\n",
- " 1436862.85005413, 1448862.46695994,\n",
- " 1460862.42366287, 1472861.93234365,\n",
- " 1484862.55875226, 1496862.0583845 ,\n",
- " 1508862.3186023 , 1520862.95731279,\n",
- " 1532862.13580359, 1544862.14789759,\n",
- " 1556862.20427556, 1568862.0074687 ,\n",
- " 1580861.9026419 , 1592861.99921494,\n",
- " 1604861.10001189, 1616862.31297015,\n",
- " 1628862.98422931, 1640862.32262325,\n",
- " 1652862.62179676, 1664862.12766606,\n",
- " 1676862.23304256, 1688862.2324209 ,\n",
- " 1700861.42018878, 1712862.49792768,\n",
- " 1724861.99388639, 1736861.79393994,\n",
- " 1748861.68713378, 1760862.01617442,\n",
- " 1772861.8994825 , 1784862.35010491,\n",
- " 1796861.98991304, 1808861.01601485,\n",
- " 1820862.85914197, 1832862.09010331,\n",
- " 1844861.90765863, 1856861.83581031,\n",
- " 1868862.30366401, 1880861.87554469,\n",
- " 1892861.61954572, 1904861.96465242,\n",
- " 1916862.20298818, 1928862.26487282,\n",
- " 1940862.25984512, 1952861.70845873,\n",
- " 1964862.6760201 , 1976862.40781729,\n",
- " 1988862.0601863 , 2000862.06152662,\n",
- " 2012861.61160637, 2024861.54817743,\n",
- " 2036862.29978874, 2048862.33737603,\n",
- " 2060862.31546611, 2072862.34356724,\n",
- " 2084862.02940992, 2096862.21104696,\n",
- " 2108862.08564468, 2120862.80904264,\n",
- " 2132861.29995525, 2144863.04431031,\n",
- " 2156861.7680832 , 2168862.22975281,\n",
- " 2180862.0745934 , 2192862.45785697,\n",
- " 2204862.98450722, 2216862.11947367,\n",
- " 2228862.25144406, 2240861.84524301,\n",
- " 2252861.64424617, 2264862.07524215,\n",
- " 2276862.64609012, 2288862.83419518,\n",
- " 2300861.95483109, 2312863.00214068,\n",
- " 2324862.2811275 , 2336862.69096942,\n",
- " 2348862.5961938 , 2360862.83510549,\n",
- " 2372862.49844879, 2384862.20620674,\n",
- " 2396862.09153175, 2408862.0470545 ,\n",
- " 2420862.20439962, 2432861.21701588,\n",
- " 2444862.20681598, 2456862.29183621,\n",
- " 2468862.30979652, 2480862.37103315,\n",
- " 2492861.55935617, 2504862.36887632,\n",
- " 2516862.32624744, 2528862.6837446 ,\n",
- " 2540862.62276294, 2552862.56717894,\n",
- " 2564862.52648303, 2576862.09514914,\n",
- " 2588861.69682198, 2600862.48360977,\n",
- " 2612862.07561471, 2624862.03895504,\n",
- " 2636862.06879401, 2648862.48574049,\n",
- " 2660862.15334881, 2672862.22245282,\n",
- " 2684862.70004968, 2696862.03251993],\n",
+ "{'data': masked_array(data=[-795136.61639098, -791136.41059064, -787136.95398677,\n",
+ " -783136.54507254, -779137.45651076, -775138.11398099,\n",
+ " -771137.96423217, -767137.28208224, -763137.76563627,\n",
+ " -759136.86451451, -755137.97579632, -751137.84855236,\n",
+ " -747137.88310773, -743137.52610199, -739137.32826948,\n",
+ " -735138.01003527, -731136.44930963, -727136.46604007,\n",
+ " -723136.9335004 , -719136.70436349, -715136.7747824 ,\n",
+ " -711138.1405258 , -707137.25658184, -703136.24380026,\n",
+ " -699138.07101214, -695136.37209306, -691135.81316616,\n",
+ " -687136.96503205, -683136.56030037, -679137.01503691,\n",
+ " -675136.48305301, -671136.51150421, -667137.11902392,\n",
+ " -663137.43595774, -659137.1850433 , -655136.51298831,\n",
+ " -651137.66627877, -647136.6998782 , -643136.00824864,\n",
+ " -639136.43767724, -635137.1389174 , -631137.26295508,\n",
+ " -627136.53251755, -623137.61709821, -619136.57328355,\n",
+ " -615137.48924267, -611137.25000216, -607137.14453586,\n",
+ " -603137.57580866, -599136.57208816, -595136.39751736,\n",
+ " -591137.60255377, -587136.66913773, -583137.25996956,\n",
+ " -579136.83276977, -575136.65657788, -571137.15334795,\n",
+ " -567137.62219384, -563137.76707809, -559137.88128765,\n",
+ " -555136.96958736, -551137.57028668, -547136.29592197,\n",
+ " -543136.10784507, -539137.42764623, -535137.86189828,\n",
+ " -531136.83909752, -527136.74963176, -523137.03994595,\n",
+ " -519137.13845806, -515136.34476016, -511136.47861553,\n",
+ " -507137.2624934 , -503137.57295892, -499136.83881607,\n",
+ " -495136.75046434, -491138.15216017, -487136.81367617,\n",
+ " -483138.23091907, -479136.20664698, -475137.63385457,\n",
+ " -471136.88538445, -467136.77262358, -463137.29406559,\n",
+ " -459136.33471237, -455137.27480186, -451137.30068009,\n",
+ " -447137.1096951 , -443136.97649607, -439136.77024811,\n",
+ " -435137.61066859, -431137.80600265, -427137.07891212,\n",
+ " -423137.67014482, -419136.91366753, -415136.77409801,\n",
+ " -411138.3708385 , -407137.77083851, -403137.50736794,\n",
+ " -399136.44144162, -395137.82100067, -391136.28350233,\n",
+ " -387137.61075983, -383137.8539102 , -379137.01168091,\n",
+ " -375137.19411908, -371137.97741569, -367138.23958832,\n",
+ " -363136.56643527, -359137.032515 , -355137.39542249,\n",
+ " -351137.50757774, -347137.09156023, -343137.12013648,\n",
+ " -339137.46190547, -335137.4011641 , -331137.22864502,\n",
+ " -327136.79687085, -323137.7925582 , -319137.68202128,\n",
+ " -315136.60992979, -311137.38282789, -307136.89938064,\n",
+ " -303137.98185358, -299136.68531379, -295137.22773734,\n",
+ " -291136.8000591 , -287137.21861107, -283137.50797208,\n",
+ " -279136.25529925, -275136.83400805, -271136.28970518,\n",
+ " -267136.73009672, -263136.46646462, -259136.90851916,\n",
+ " -255137.48720864, -251138.05534607, -247137.63772891,\n",
+ " -243137.07641722, -239137.34369933, -235136.89696686,\n",
+ " -231137.988885 , -227137.52052539, -223137.17719019,\n",
+ " -219137.80068959, -215137.28134621, -211137.30426063,\n",
+ " -207136.74869985, -203137.99719673, -199136.41079586,\n",
+ " -195137.46861417, -191137.79654696, -187137.39319426,\n",
+ " -183137.24571539, -179137.48313279, -175137.55192516,\n",
+ " -171137.72664199, -167137.16284194, -163138.11192593,\n",
+ " -159136.90971936, -155137.63885172, -151137.47821038,\n",
+ " -147136.29579376, -143136.89472954, -139136.89034263,\n",
+ " -135137.67603237, -131137.43406953, -127137.55784189,\n",
+ " -123137.91519273, -119137.09518443, -115138.19122442,\n",
+ " -111137.54029239, -107137.39303131, -103137.47190727,\n",
+ " -99137.63011412, -95137.44485548, -91137.33597317,\n",
+ " -87138.28984298, -83136.64407898, -79136.90066737,\n",
+ " -75136.38512162, -71136.78105858, -67137.24442899,\n",
+ " -63137.35249174, -59137.67024233, -55136.64202745,\n",
+ " -51137.08423766, -47137.1652011 , -43137.7258817 ,\n",
+ " -39137.22510488, -35137.05587385, -31136.66464553,\n",
+ " -27137.58953575, -23137.30185574, -19137.06371283,\n",
+ " -15137.71596995, -11137.29637321, -7137.61901295,\n",
+ " -3137.28799547, 863.00098038, 4863.24941572,\n",
+ " 8862.89252879, 12863.06439672, 16863.05510887,\n",
+ " 20863.4324407 , 24862.09219132, 28862.40499 ,\n",
+ " 32862.26664082, 36862.79694174, 40862.45798722,\n",
+ " 44862.93579718, 48863.38962098, 52862.13649057,\n",
+ " 56862.5468791 , 60861.95048882, 64862.59950388,\n",
+ " 68862.95606085, 72862.58641333, 76862.76955963,\n",
+ " 80863.36192165, 84863.37771449, 88863.80575256,\n",
+ " 92862.96312886, 96863.7990964 , 100862.10412526,\n",
+ " 104862.78790487, 108863.1943838 , 112862.61381734,\n",
+ " 116862.03501245, 120863.14390749, 124863.27031676,\n",
+ " 128863.12702731, 132862.42539513, 136863.41756142,\n",
+ " 140862.73619004, 144862.48834325, 148861.68823736,\n",
+ " 152862.7331382 , 156863.64995215, 160862.47970676,\n",
+ " 164862.59285906, 168862.58249175, 172862.31920476,\n",
+ " 176862.35657583, 180862.42011234, 184862.93247983,\n",
+ " 188862.63181013, 192863.0590615 , 196861.97915599,\n",
+ " 200862.4725277 , 204863.00126842, 208862.3034493 ,\n",
+ " 212862.34123212, 216862.56411947, 220862.26220624,\n",
+ " 224862.42449941, 228863.47373106, 232862.88437566,\n",
+ " 236863.03987784, 240862.96855469, 244863.22399709,\n",
+ " 248862.26811232, 252862.62965461, 256862.48016488,\n",
+ " 260862.9539953 , 264862.91986326, 268862.52449022,\n",
+ " 272862.19064672, 276863.03840288, 280863.25353619,\n",
+ " 284862.1258368 , 288863.59344583, 292862.60271927,\n",
+ " 296863.36792621, 300862.37510111, 304862.86534092,\n",
+ " 308862.86449151, 312862.51935428, 316861.83146148,\n",
+ " 320862.76370655, 324862.51364452, 328862.9134924 ,\n",
+ " 332861.98874568, 336862.69044933, 340862.36130204,\n",
+ " 344861.97624768, 348861.82757176, 352863.73314478,\n",
+ " 356863.20443515, 360862.62599938, 364862.71164663,\n",
+ " 368862.32925202, 372862.46868182, 376862.28859325,\n",
+ " 380862.90951545, 384862.51652108, 388862.50617549,\n",
+ " 392863.02554629, 396863.23318331, 400862.70908473,\n",
+ " 404862.99544012, 408862.55314023, 412862.79397973,\n",
+ " 416863.28290963, 420862.77179626, 424863.07904209,\n",
+ " 428862.38932847, 432862.94278066, 436862.77840975,\n",
+ " 440863.01708396, 444862.68665592, 448863.18411451,\n",
+ " 452862.54821401, 456862.46732199, 460862.52132826,\n",
+ " 464862.98784592, 468863.17067095, 472863.49313601,\n",
+ " 476863.1132311 , 480862.45427433, 484863.05933278,\n",
+ " 488862.5448325 , 492862.59968152, 496862.80364446,\n",
+ " 500862.31452776, 504862.67559245, 508862.34667757,\n",
+ " 512863.01706504, 516862.57864618, 520862.29880874,\n",
+ " 524862.45517937, 528862.77333847, 532862.41082865,\n",
+ " 536863.0573465 , 540862.45822825, 544862.59532854,\n",
+ " 548862.75610294, 552862.24396131, 556863.44712821,\n",
+ " 560862.43806453, 564862.72539344, 568862.34603078,\n",
+ " 572862.990341 , 576862.40266614, 580862.98812753,\n",
+ " 584862.34464282, 588862.30904336, 592862.4606934 ,\n",
+ " 596863.07723751, 600862.34118273, 604862.91789654,\n",
+ " 608862.42111689, 612863.24039535, 616862.29080023,\n",
+ " 620862.66051503, 624862.80782521, 628863.15679884,\n",
+ " 632862.71751253, 636863.05209059, 640862.60149526,\n",
+ " 644861.93631965, 648863.02461258, 652862.78033962,\n",
+ " 656863.02496098, 660863.06140619, 664863.31395051,\n",
+ " 668862.51604284, 672863.05890353, 676862.13162718,\n",
+ " 680862.69517888, 684862.63729034, 688862.65833449,\n",
+ " 692862.63071 , 696862.26231303, 700862.40048983,\n",
+ " 704862.91778939, 708862.67608951, 712863.51578514,\n",
+ " 716862.47750864, 720862.52388042, 724862.66350396,\n",
+ " 728862.34590843, 732862.97097581, 736862.42466877,\n",
+ " 740862.69536738, 744863.49052236, 748861.99636145,\n",
+ " 752862.44667911, 756862.303728 , 760863.53829826,\n",
+ " 764862.06609825, 768862.12193212, 772862.43751676,\n",
+ " 776863.29054439, 780862.86026755, 784863.39408875,\n",
+ " 788863.62322432],\n",
" mask=False,\n",
" fill_value=1e+20),\n",
" 'dimensions': ('y',),\n",
- " 'units': '1000 m',\n",
" 'long_name': 'y coordinate of projection',\n",
+ " 'units': '1000 m',\n",
" 'standard_name': 'projection_y_coordinate'}"
]
},
@@ -998,22 +425,21 @@
"data": {
"text/plain": [
"{'data': masked_array(\n",
- " data=[[19.706436, 19.728317, 19.750084, ..., 16.264694, 16.22998 ,\n",
- " 16.19516 ],\n",
- " [19.805984, 19.827904, 19.849716, ..., 16.358276, 16.323502,\n",
- " 16.288643],\n",
- " [19.905594, 19.927544, 19.949394, ..., 16.45192 , 16.417084,\n",
- " 16.382141],\n",
+ " data=[[32.51078415, 32.51420212, 32.51760101, ..., 32.54065323,\n",
+ " 32.53738403, 32.53408432],\n",
+ " [32.54635239, 32.54976654, 32.55315399, ..., 32.57624817,\n",
+ " 32.57295609, 32.56965637],\n",
+ " [32.58191681, 32.58533096, 32.58874512, ..., 32.61181641,\n",
+ " 32.60853195, 32.60523224],\n",
" ...,\n",
- " [59.66961 , 59.70968 , 59.74953 , ..., 53.457195, 53.39534 ,\n",
- " 53.33333 ],\n",
- " [59.76223 , 59.802353, 59.842262, ..., 53.541912, 53.47999 ,\n",
- " 53.417908],\n",
- " [59.854744, 59.89492 , 59.934883, ..., 53.62653 , 53.56453 ,\n",
- " 53.502373]],\n",
+ " [46.58963013, 46.59383011, 46.59801102, ..., 46.62640381,\n",
+ " 46.62236404, 46.61830521],\n",
+ " [46.62516785, 46.62936783, 46.63354874, ..., 46.66195679,\n",
+ " 46.65791702, 46.65385818],\n",
+ " [46.66069794, 46.66490173, 46.66908264, ..., 46.69750214,\n",
+ " 46.69345856, 46.6893959 ]],\n",
" mask=False,\n",
- " fill_value=1e+20,\n",
- " dtype=float32),\n",
+ " fill_value=1e+20),\n",
" 'dimensions': ('y', 'x'),\n",
" 'units': 'degrees_north',\n",
" 'axis': 'Y',\n",
@@ -1039,22 +465,21 @@
"data": {
"text/plain": [
"{'data': masked_array(\n",
- " data=[[-22.32898 , -22.223236, -22.117432, ..., 28.619202, 28.716614,\n",
- " 28.813965],\n",
- " [-22.352325, -22.24646 , -22.140533, ..., 28.655334, 28.752869,\n",
- " 28.850311],\n",
- " [-22.375702, -22.269714, -22.163696, ..., 28.69159 , 28.789185,\n",
- " 28.886719],\n",
+ " data=[[-11.54882812, -11.50665283, -11.46447754, ..., 5.17233276,\n",
+ " 5.21453857, 5.25671387],\n",
+ " [-11.55288696, -11.51068115, -11.46847534, ..., 5.1762085 ,\n",
+ " 5.21844482, 5.26065063],\n",
+ " [-11.5569458 , -11.51473999, -11.47250366, ..., 5.18008423,\n",
+ " 5.22235107, 5.2645874 ],\n",
" ...,\n",
- " [-39.438995, -39.25531 , -39.07132 , ..., 53.106964, 53.249176,\n",
- " 53.391052],\n",
- " [-39.518707, -39.334717, -39.15039 , ..., 53.210876, 53.35321 ,\n",
- " 53.49518 ],\n",
- " [-39.598724, -39.41443 , -39.229828, ..., 53.315125, 53.45755 ,\n",
- " 53.59964 ]],\n",
+ " [-13.51443481, -13.46273804, -13.41101074, ..., 7.05273438,\n",
+ " 7.10449219, 7.15625 ],\n",
+ " [-13.52056885, -13.46884155, -13.41708374, ..., 7.05859375,\n",
+ " 7.1104126 , 7.16220093],\n",
+ " [-13.5267334 , -13.47494507, -13.42315674, ..., 7.06448364,\n",
+ " 7.11630249, 7.16812134]],\n",
" mask=False,\n",
- " fill_value=1e+20,\n",
- " dtype=float32),\n",
+ " fill_value=1e+20),\n",
" 'dimensions': ('y', 'x'),\n",
" 'units': 'degrees_east',\n",
" 'axis': 'X',\n",
@@ -1081,7 +506,7 @@
"output_type": "stream",
"text": [
"Rank 000: Loading sconco3 var (1/1)\n",
- "Rank 000: Loaded sconco3 var ((48, 1, 398, 478))\n"
+ "Rank 000: Loaded sconco3 var ((2, 1, 397, 397))\n"
]
}
],
@@ -1098,104 +523,42 @@
"data": {
"text/plain": [
"{'sconco3': {'data': masked_array(\n",
- " data=[[[[0.03952214, 0.0395867 , 0.03965145, ..., 0.03198181,\n",
- " 0.03125041, 0.03153064],\n",
- " [0.03945881, 0.03952107, 0.03958255, ..., 0.03184386,\n",
- " 0.03127047, 0.03173521],\n",
- " [0.03940514, 0.03945867, 0.03951972, ..., 0.03147388,\n",
- " 0.031453 , 0.03222592],\n",
+ " data=[[[[0.0415576 , 0.04147514, 0.04152902, ..., 0.03796541,\n",
+ " 0.03792827, 0.0379032 ],\n",
+ " [0.04146153, 0.04136137, 0.0412977 , ..., 0.0382663 ,\n",
+ " 0.03824312, 0.03822216],\n",
+ " [0.04059546, 0.04081807, 0.04091855, ..., 0.03847397,\n",
+ " 0.03840729, 0.0383645 ],\n",
" ...,\n",
- " [0.03757998, 0.03767523, 0.03775784, ..., 0.02404686,\n",
- " 0.02617675, 0.02804444],\n",
- " [0.03747029, 0.03767779, 0.03778836, ..., 0.02497317,\n",
- " 0.02633872, 0.02821054],\n",
- " [0.03744229, 0.03762932, 0.03774261, ..., 0.02464963,\n",
- " 0.02615741, 0.02828379]]],\n",
+ " [0.04466213, 0.04466087, 0.04464992, ..., 0.03963904,\n",
+ " 0.04006213, 0.04025601],\n",
+ " [0.04465724, 0.04464634, 0.04463853, ..., 0.0397002 ,\n",
+ " 0.0398435 , 0.0401062 ],\n",
+ " [0.04465516, 0.04465496, 0.04463719, ..., 0.03971382,\n",
+ " 0.03972259, 0.03986653]]],\n",
" \n",
" \n",
- " [[[0.03965769, 0.03971414, 0.03978673, ..., 0.03103125,\n",
- " 0.03015577, 0.03036901],\n",
- " [0.03960922, 0.03966256, 0.03972849, ..., 0.03101462,\n",
- " 0.03021824, 0.03060081],\n",
- " [0.03957865, 0.03962931, 0.03968912, ..., 0.03126283,\n",
- " 0.03049327, 0.0311233 ],\n",
+ " [[[0.04273837, 0.04270947, 0.04267247, ..., 0.04349075,\n",
+ " 0.04333649, 0.04320888],\n",
+ " [0.04272898, 0.04268417, 0.04263616, ..., 0.04356325,\n",
+ " 0.04341621, 0.04326808],\n",
+ " [0.04264436, 0.04263159, 0.04260145, ..., 0.04372539,\n",
+ " 0.04355535, 0.04337517],\n",
" ...,\n",
- " [0.03786875, 0.03777716, 0.0376072 , ..., 0.02531419,\n",
- " 0.02676462, 0.02868378],\n",
- " [0.03781448, 0.03783737, 0.03778312, ..., 0.02607518,\n",
- " 0.02714914, 0.02904003],\n",
- " [0.03779451, 0.03781895, 0.03778093, ..., 0.02577951,\n",
- " 0.02662436, 0.02910396]]],\n",
- " \n",
- " \n",
- " [[[0.03982776, 0.03989794, 0.04000261, ..., 0.03058425,\n",
- " 0.02924641, 0.02938778],\n",
- " [0.03979794, 0.03985743, 0.03995337, ..., 0.03089899,\n",
- " 0.02948623, 0.02973373],\n",
- " [0.03978007, 0.03982415, 0.03991285, ..., 0.03140561,\n",
- " 0.03000267, 0.0304553 ],\n",
- " ...,\n",
- " [0.03821166, 0.03814133, 0.0379876 , ..., 0.02608518,\n",
- " 0.02720294, 0.02940145],\n",
- " [0.03820223, 0.03822928, 0.03817524, ..., 0.02666686,\n",
- " 0.02796096, 0.02975006],\n",
- " [0.03819539, 0.03822353, 0.03819501, ..., 0.02689083,\n",
- " 0.02732235, 0.02981647]]],\n",
- " \n",
- " \n",
- " ...,\n",
- " \n",
- " \n",
- " [[[0.04243098, 0.04246398, 0.0425217 , ..., 0.03174707,\n",
- " 0.02986301, 0.02911444],\n",
- " [0.04233237, 0.0423856 , 0.0424809 , ..., 0.03209906,\n",
- " 0.03045077, 0.02993134],\n",
- " [0.04244579, 0.04248011, 0.04255648, ..., 0.03262969,\n",
- " 0.03137314, 0.03106195],\n",
- " ...,\n",
- " [0.03979023, 0.03997482, 0.04005315, ..., 0.03456535,\n",
- " 0.03498862, 0.0354754 ],\n",
- " [0.03967334, 0.0398716 , 0.04000713, ..., 0.03471798,\n",
- " 0.03485566, 0.03500457],\n",
- " [0.03964297, 0.03981132, 0.03993002, ..., 0.03497454,\n",
- " 0.03504148, 0.03509094]]],\n",
- " \n",
- " \n",
- " [[[0.04243221, 0.04248913, 0.04257801, ..., 0.03141348,\n",
- " 0.02992571, 0.02943683],\n",
- " [0.04236476, 0.0424497 , 0.04255884, ..., 0.03173555,\n",
- " 0.03054 , 0.03031359],\n",
- " [0.04247259, 0.04253475, 0.0426126 , ..., 0.03197961,\n",
- " 0.03136487, 0.03140653],\n",
- " ...,\n",
- " [0.03982663, 0.04001996, 0.04009991, ..., 0.03445901,\n",
- " 0.0349366 , 0.03539021],\n",
- " [0.03969444, 0.03990471, 0.04005693, ..., 0.03466238,\n",
- " 0.03480245, 0.03491453],\n",
- " [0.0396612 , 0.03983796, 0.03996958, ..., 0.03486277,\n",
- " 0.03491549, 0.03492822]]],\n",
- " \n",
- " \n",
- " [[[0.04249088, 0.04256602, 0.04264675, ..., 0.03092884,\n",
- " 0.02980646, 0.02965403],\n",
- " [0.042451 , 0.04252698, 0.04259988, ..., 0.03106567,\n",
- " 0.03033506, 0.03051317],\n",
- " [0.04252941, 0.04258011, 0.04264118, ..., 0.0312073 ,\n",
- " 0.03103344, 0.03165624],\n",
- " ...,\n",
- " [0.03986304, 0.04006038, 0.04014875, ..., 0.03444035,\n",
- " 0.03486974, 0.03531818],\n",
- " [0.03971234, 0.03994204, 0.04009009, ..., 0.03459954,\n",
- " 0.0347504 , 0.0348453 ],\n",
- " [0.03967606, 0.03986803, 0.03999919, ..., 0.03475965,\n",
- " 0.03481083, 0.03478444]]]],\n",
+ " [0.04538379, 0.04540338, 0.0454199 , ..., 0.0401468 ,\n",
+ " 0.04034726, 0.04047008],\n",
+ " [0.04552482, 0.04551599, 0.04550386, ..., 0.03996951,\n",
+ " 0.04019922, 0.04038962],\n",
+ " [0.04552515, 0.04551959, 0.04550923, ..., 0.03988018,\n",
+ " 0.03998153, 0.04024737]]]],\n",
" mask=False,\n",
" fill_value=1e+20,\n",
" dtype=float32),\n",
" 'dimensions': ('time', 'lev', 'y', 'x'),\n",
" 'units': 'ppm',\n",
- " 'coordinates': 'lat lon',\n",
- " 'grid_mapping': 'Lambert_conformal'}}"
+ " 'cell_methods': 'time: max (interval: 1hr)',\n",
+ " 'grid_mapping': 'Lambert_conformal',\n",
+ " 'coordinates': 'lat lon'}}"
]
},
"execution_count": 12,
@@ -1207,6 +570,13 @@
"nessy_1.variables"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Write dataset"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 13,
@@ -1235,7 +605,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Reopen with NES"
+ "## 3. Reopen dataset"
]
},
{
@@ -1246,7 +616,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 14,
@@ -1258,439 +628,6 @@
"nessy_2 = open_netcdf('lcc_file_1.nc', info=True)\n",
"nessy_2"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Reopen with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 48, lev: 1, y: 398, x: 478)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-01-01 ... 2021-01-02T23:00:00\n",
- " * lev (lev) float64 0.0\n",
- " lat (y, x) float64 ...\n",
- " lon (y, x) float64 ...\n",
- " * y (y) float64 -2.067e+06 -2.055e+06 ... 2.685e+06 2.697e+06\n",
- " * x (x) float64 -2.126e+06 -2.114e+06 ... 3.586e+06 3.598e+06\n",
- "Data variables:\n",
- " sconco3 (time, lev, y, x) float32 ...\n",
- " Lambert_conformal |S1 b''\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions: time : 48lev : 1y : 398x : 478
Coordinates: (6)
time
(time)
datetime64[ns]
2021-01-01 ... 2021-01-02T23:00:00
standard_name : time long_name : time array(['2021-01-01T00:00:00.000000000', '2021-01-01T01:00:00.000000000',\n",
- " '2021-01-01T02:00:00.000000000', '2021-01-01T03:00:00.000000000',\n",
- " '2021-01-01T04:00:00.000000000', '2021-01-01T05:00:00.000000000',\n",
- " '2021-01-01T06:00:00.000000000', '2021-01-01T07:00:00.000000000',\n",
- " '2021-01-01T08:00:00.000000000', '2021-01-01T09:00:00.000000000',\n",
- " '2021-01-01T10:00:00.000000000', '2021-01-01T11:00:00.000000000',\n",
- " '2021-01-01T12:00:00.000000000', '2021-01-01T13:00:00.000000000',\n",
- " '2021-01-01T14:00:00.000000000', '2021-01-01T15:00:00.000000000',\n",
- " '2021-01-01T16:00:00.000000000', '2021-01-01T17:00:00.000000000',\n",
- " '2021-01-01T18:00:00.000000000', '2021-01-01T19:00:00.000000000',\n",
- " '2021-01-01T20:00:00.000000000', '2021-01-01T21:00:00.000000000',\n",
- " '2021-01-01T22:00:00.000000000', '2021-01-01T23:00:00.000000000',\n",
- " '2021-01-02T00:00:00.000000000', '2021-01-02T01:00:00.000000000',\n",
- " '2021-01-02T02:00:00.000000000', '2021-01-02T03:00:00.000000000',\n",
- " '2021-01-02T04:00:00.000000000', '2021-01-02T05:00:00.000000000',\n",
- " '2021-01-02T06:00:00.000000000', '2021-01-02T07:00:00.000000000',\n",
- " '2021-01-02T08:00:00.000000000', '2021-01-02T09:00:00.000000000',\n",
- " '2021-01-02T10:00:00.000000000', '2021-01-02T11:00:00.000000000',\n",
- " '2021-01-02T12:00:00.000000000', '2021-01-02T13:00:00.000000000',\n",
- " '2021-01-02T14:00:00.000000000', '2021-01-02T15:00:00.000000000',\n",
- " '2021-01-02T16:00:00.000000000', '2021-01-02T17:00:00.000000000',\n",
- " '2021-01-02T18:00:00.000000000', '2021-01-02T19:00:00.000000000',\n",
- " '2021-01-02T20:00:00.000000000', '2021-01-02T21:00:00.000000000',\n",
- " '2021-01-02T22:00:00.000000000', '2021-01-02T23:00:00.000000000'],\n",
- " dtype='datetime64[ns]') lev
(lev)
float64
0.0
lat
(y, x)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude [190244 values with dtype=float64] lon
(y, x)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude [190244 values with dtype=float64] y
(y)
float64
-2.067e+06 -2.055e+06 ... 2.697e+06
long_name : y coordinate of projection units : 1000 m standard_name : projection_y_coordinate array([-2067138.479486, -2055138.337429, -2043137.546656, ..., 2672862.222453,\n",
- " 2684862.70005 , 2696862.03252 ]) x
(x)
float64
-2.126e+06 -2.114e+06 ... 3.598e+06
long_name : x coordinate of projection units : 1000 m standard_name : projection_x_coordinate array([-2125847.534765, -2113847.610112, -2101847.010684, ..., 3574152.262286,\n",
- " 3586150.20523 , 3598150.638118]) Data variables: (2)
Attributes: (1)
"
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 48, lev: 1, y: 398, x: 478)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-01-01 ... 2021-01-02T23:00:00\n",
- " * lev (lev) float64 0.0\n",
- " lat (y, x) float64 ...\n",
- " lon (y, x) float64 ...\n",
- " * y (y) float64 -2.067e+06 -2.055e+06 ... 2.685e+06 2.697e+06\n",
- " * x (x) float64 -2.126e+06 -2.114e+06 ... 3.586e+06 3.598e+06\n",
- "Data variables:\n",
- " sconco3 (time, lev, y, x) float32 ...\n",
- " Lambert_conformal |S1 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('lcc_file_1.nc')"
- ]
}
],
"metadata": {
diff --git a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb
index b80ae837cbaab2662e20d369a5db847583879c50..47357c7f42b5970c08a5624b358841c951416f2f 100644
--- a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb
+++ b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb
@@ -13,8 +13,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from nes import *\n",
- "import xarray as xr"
+ "from nes import *"
]
},
{
@@ -23,22 +22,16 @@
"metadata": {},
"outputs": [],
"source": [
- "# TODO: Change path\n",
- "nc_path_1 = '/gpfs/scratch/bsc32/bsc32538/a4mg/HERMESv3/auxiliar_files/d01/temporal_coords.nc'"
+ "# Original path: None (generated with NES)\n",
+ "# Mercator grid\n",
+ "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/mercator_grid.nc'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 1. Read and write"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with xarray"
+ "## 1. Read dataset"
]
},
{
@@ -47,49 +40,16 @@
"metadata": {},
"outputs": [
{
- "ename": "FileNotFoundError",
- "evalue": "[Errno 2] No such file or directory: b'/gpfs/scratch/bsc32/bsc32538/a4mg/HERMESv3/auxiliar_files/d01/temporal_coords.nc'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/file_manager.py\u001b[0m in \u001b[0;36m_acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 200\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/lru_cache.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 54\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmove_to_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mKeyError\u001b[0m: [, ('/gpfs/scratch/bsc32/bsc32538/a4mg/HERMESv3/auxiliar_files/d01/temporal_coords.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False))]",
- "\nDuring handling of the above exception, another exception occurred:\n",
- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnc_path_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, backend_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0mdrop_variables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_variables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 500\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdecoders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 502\u001b[0m )\n\u001b[1;32m 503\u001b[0m ds = _dataset_from_backend_dataset(\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mopen_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m 558\u001b[0m \u001b[0mpersist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpersist\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m \u001b[0mautoclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautoclose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 561\u001b[0m )\n\u001b[1;32m 562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[0mnetCDF4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 379\u001b[0m )\n\u001b[0;32m--> 380\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mautoclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautoclose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_acquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneeds_lock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_group\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 329\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_remote\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_remote_uri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_acquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 391\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_store_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m_acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_acquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneeds_lock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneeds_lock\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 384\u001b[0m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_nc4_require_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_group\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/file_manager.py\u001b[0m in \u001b[0;36macquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0macquire_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneeds_lock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;34m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_acquire_with_cache_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneeds_lock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/backends/file_manager.py\u001b[0m in \u001b[0;36m_acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"mode\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 206\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"w\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[0;31m# ensure file doesn't get overriden when opened again\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
- "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
- "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: b'/gpfs/scratch/bsc32/bsc32538/a4mg/HERMESv3/auxiliar_files/d01/temporal_coords.nc'"
- ]
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "xr.open_dataset(nc_path_1, decode_times=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Open with NES"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
"source": [
"nessy_1 = open_netcdf(nc_path_1, info=True)\n",
"nessy_1"
@@ -97,99 +57,320 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[datetime.datetime(1996, 12, 31, 0, 0)]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy_1.time"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(data=[0.],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('lev',),\n",
+ " 'units': '1',\n",
+ " 'positive': 'up'}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy_1.lev"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(data=[-1.01017500e+05, -5.10175000e+04, -1.01750000e+03,\n",
+ " 4.89825000e+04, 9.89825000e+04, 1.48982500e+05,\n",
+ " 1.98982500e+05, 2.48982500e+05, 2.98982500e+05,\n",
+ " 3.48982500e+05, 3.98982500e+05, 4.48982500e+05,\n",
+ " 4.98982500e+05, 5.48982500e+05, 5.98982500e+05,\n",
+ " 6.48982500e+05, 6.98982500e+05, 7.48982500e+05,\n",
+ " 7.98982500e+05, 8.48982500e+05, 8.98982500e+05,\n",
+ " 9.48982500e+05, 9.98982500e+05, 1.04898250e+06,\n",
+ " 1.09898250e+06, 1.14898250e+06, 1.19898250e+06,\n",
+ " 1.24898250e+06, 1.29898250e+06, 1.34898250e+06,\n",
+ " 1.39898250e+06, 1.44898250e+06, 1.49898250e+06,\n",
+ " 1.54898250e+06, 1.59898250e+06, 1.64898250e+06,\n",
+ " 1.69898250e+06, 1.74898250e+06, 1.79898250e+06,\n",
+ " 1.84898250e+06, 1.89898250e+06, 1.94898250e+06,\n",
+ " 1.99898250e+06, 2.04898250e+06, 2.09898250e+06,\n",
+ " 2.14898250e+06, 2.19898250e+06, 2.24898250e+06,\n",
+ " 2.29898250e+06, 2.34898250e+06, 2.39898250e+06,\n",
+ " 2.44898250e+06, 2.49898250e+06, 2.54898250e+06,\n",
+ " 2.59898250e+06, 2.64898250e+06, 2.69898250e+06,\n",
+ " 2.74898250e+06, 2.79898250e+06, 2.84898250e+06,\n",
+ " 2.89898250e+06, 2.94898250e+06, 2.99898250e+06,\n",
+ " 3.04898250e+06, 3.09898250e+06, 3.14898250e+06,\n",
+ " 3.19898250e+06, 3.24898250e+06, 3.29898250e+06,\n",
+ " 3.34898250e+06, 3.39898250e+06, 3.44898250e+06,\n",
+ " 3.49898250e+06, 3.54898250e+06, 3.59898250e+06,\n",
+ " 3.64898250e+06, 3.69898250e+06, 3.74898250e+06,\n",
+ " 3.79898250e+06, 3.84898250e+06, 3.89898250e+06,\n",
+ " 3.94898250e+06, 3.99898250e+06, 4.04898250e+06,\n",
+ " 4.09898250e+06, 4.14898250e+06, 4.19898250e+06,\n",
+ " 4.24898250e+06, 4.29898250e+06, 4.34898250e+06,\n",
+ " 4.39898250e+06, 4.44898250e+06, 4.49898250e+06,\n",
+ " 4.54898250e+06, 4.59898250e+06, 4.64898250e+06,\n",
+ " 4.69898250e+06, 4.74898250e+06, 4.79898250e+06,\n",
+ " 4.84898250e+06, 4.89898250e+06, 4.94898250e+06,\n",
+ " 4.99898250e+06, 5.04898250e+06, 5.09898250e+06,\n",
+ " 5.14898250e+06, 5.19898250e+06, 5.24898250e+06,\n",
+ " 5.29898250e+06, 5.34898250e+06, 5.39898250e+06,\n",
+ " 5.44898250e+06, 5.49898250e+06, 5.54898250e+06,\n",
+ " 5.59898250e+06, 5.64898250e+06, 5.69898250e+06,\n",
+ " 5.74898250e+06, 5.79898250e+06, 5.84898250e+06,\n",
+ " 5.89898250e+06, 5.94898250e+06, 5.99898250e+06,\n",
+ " 6.04898250e+06, 6.09898250e+06, 6.14898250e+06,\n",
+ " 6.19898250e+06, 6.24898250e+06, 6.29898250e+06,\n",
+ " 6.34898250e+06, 6.39898250e+06, 6.44898250e+06,\n",
+ " 6.49898250e+06, 6.54898250e+06, 6.59898250e+06,\n",
+ " 6.64898250e+06, 6.69898250e+06, 6.74898250e+06,\n",
+ " 6.79898250e+06, 6.84898250e+06, 6.89898250e+06,\n",
+ " 6.94898250e+06, 6.99898250e+06, 7.04898250e+06,\n",
+ " 7.09898250e+06, 7.14898250e+06, 7.19898250e+06,\n",
+ " 7.24898250e+06, 7.29898250e+06, 7.34898250e+06,\n",
+ " 7.39898250e+06, 7.44898250e+06, 7.49898250e+06,\n",
+ " 7.54898250e+06, 7.59898250e+06, 7.64898250e+06,\n",
+ " 7.69898250e+06, 7.74898250e+06, 7.79898250e+06,\n",
+ " 7.84898250e+06, 7.89898250e+06, 7.94898250e+06,\n",
+ " 7.99898250e+06, 8.04898250e+06, 8.09898250e+06,\n",
+ " 8.14898250e+06, 8.19898250e+06, 8.24898250e+06,\n",
+ " 8.29898250e+06, 8.34898250e+06, 8.39898250e+06,\n",
+ " 8.44898250e+06, 8.49898250e+06, 8.54898250e+06,\n",
+ " 8.59898250e+06, 8.64898250e+06, 8.69898250e+06,\n",
+ " 8.74898250e+06, 8.79898250e+06, 8.84898250e+06,\n",
+ " 8.89898250e+06, 8.94898250e+06, 8.99898250e+06,\n",
+ " 9.04898250e+06, 9.09898250e+06, 9.14898250e+06,\n",
+ " 9.19898250e+06, 9.24898250e+06, 9.29898250e+06,\n",
+ " 9.34898250e+06, 9.39898250e+06, 9.44898250e+06,\n",
+ " 9.49898250e+06, 9.54898250e+06, 9.59898250e+06,\n",
+ " 9.64898250e+06, 9.69898250e+06, 9.74898250e+06,\n",
+ " 9.79898250e+06, 9.84898250e+06, 9.89898250e+06,\n",
+ " 9.94898250e+06, 9.99898250e+06, 1.00489825e+07,\n",
+ " 1.00989825e+07, 1.01489825e+07, 1.01989825e+07,\n",
+ " 1.02489825e+07, 1.02989825e+07, 1.03489825e+07],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('x',),\n",
+ " 'long_name': 'x coordinate of projection',\n",
+ " 'units': 'm',\n",
+ " 'standard_name': 'projection_x_coordinate'}"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy_1.x"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(data=[-5382460., -5332460., -5282460., -5232460., -5182460.,\n",
+ " -5132460., -5082460., -5032460., -4982460., -4932460.,\n",
+ " -4882460., -4832460., -4782460., -4732460., -4682460.,\n",
+ " -4632460., -4582460., -4532460., -4482460., -4432460.,\n",
+ " -4382460., -4332460., -4282460., -4232460., -4182460.,\n",
+ " -4132460., -4082460., -4032460., -3982460., -3932460.,\n",
+ " -3882460., -3832460., -3782460., -3732460., -3682460.,\n",
+ " -3632460., -3582460., -3532460., -3482460., -3432460.,\n",
+ " -3382460., -3332460., -3282460., -3232460., -3182460.,\n",
+ " -3132460., -3082460., -3032460., -2982460., -2932460.,\n",
+ " -2882460., -2832460., -2782460., -2732460., -2682460.,\n",
+ " -2632460., -2582460., -2532460., -2482460., -2432460.,\n",
+ " -2382460., -2332460., -2282460., -2232460., -2182460.,\n",
+ " -2132460., -2082460., -2032460., -1982460., -1932460.,\n",
+ " -1882460., -1832460., -1782460., -1732460., -1682460.,\n",
+ " -1632460., -1582460., -1532460., -1482460., -1432460.,\n",
+ " -1382460., -1332460., -1282460., -1232460., -1182460.,\n",
+ " -1132460., -1082460., -1032460., -982460., -932460.,\n",
+ " -882460., -832460., -782460., -732460., -682460.,\n",
+ " -632460., -582460., -532460., -482460., -432460.,\n",
+ " -382460., -332460., -282460., -232460., -182460.,\n",
+ " -132460., -82460., -32460., 17540., 67540.,\n",
+ " 117540., 167540., 217540., 267540., 317540.,\n",
+ " 367540., 417540., 467540., 517540., 567540.,\n",
+ " 617540., 667540., 717540., 767540., 817540.,\n",
+ " 867540., 917540., 967540., 1017540., 1067540.,\n",
+ " 1117540., 1167540., 1217540., 1267540., 1317540.,\n",
+ " 1367540., 1417540., 1467540., 1517540., 1567540.,\n",
+ " 1617540., 1667540., 1717540., 1767540., 1817540.,\n",
+ " 1867540., 1917540., 1967540., 2017540., 2067540.,\n",
+ " 2117540., 2167540., 2217540., 2267540., 2317540.,\n",
+ " 2367540., 2417540., 2467540., 2517540., 2567540.,\n",
+ " 2617540., 2667540., 2717540., 2767540., 2817540.,\n",
+ " 2867540., 2917540., 2967540., 3017540., 3067540.,\n",
+ " 3117540., 3167540., 3217540., 3267540., 3317540.,\n",
+ " 3367540., 3417540., 3467540., 3517540., 3567540.,\n",
+ " 3617540., 3667540., 3717540., 3767540., 3817540.,\n",
+ " 3867540., 3917540., 3967540., 4017540., 4067540.,\n",
+ " 4117540., 4167540., 4217540., 4267540., 4317540.,\n",
+ " 4367540., 4417540., 4467540., 4517540., 4567540.,\n",
+ " 4617540., 4667540., 4717540., 4767540., 4817540.,\n",
+ " 4867540., 4917540., 4967540., 5017540., 5067540.,\n",
+ " 5117540., 5167540., 5217540., 5267540., 5317540.,\n",
+ " 5367540., 5417540., 5467540., 5517540., 5567540.,\n",
+ " 5617540., 5667540., 5717540., 5767540., 5817540.,\n",
+ " 5867540., 5917540., 5967540., 6017540., 6067540.,\n",
+ " 6117540., 6167540., 6217540., 6267540., 6317540.,\n",
+ " 6367540.],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('y',),\n",
+ " 'long_name': 'y coordinate of projection',\n",
+ " 'units': 'm',\n",
+ " 'standard_name': 'projection_y_coordinate'}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy_1.y"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[-43.6653826 , -43.6653826 , -43.6653826 , ..., -43.6653826 ,\n",
+ " -43.6653826 , -43.6653826 ],\n",
+ " [-43.33831983, -43.33831983, -43.33831983, ..., -43.33831983,\n",
+ " -43.33831983, -43.33831983],\n",
+ " [-43.00947316, -43.00947316, -43.00947316, ..., -43.00947316,\n",
+ " -43.00947316, -43.00947316],\n",
+ " ...,\n",
+ " [ 49.15978847, 49.15978847, 49.15978847, ..., 49.15978847,\n",
+ " 49.15978847, 49.15978847],\n",
+ " [ 49.45358158, 49.45358158, 49.45358158, ..., 49.45358158,\n",
+ " 49.45358158, 49.45358158],\n",
+ " [ 49.74561434, 49.74561434, 49.74561434, ..., 49.74561434,\n",
+ " 49.74561434, 49.74561434]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('y', 'x'),\n",
+ " 'units': 'degrees_north',\n",
+ " 'axis': 'Y',\n",
+ " 'long_name': 'latitude coordinate',\n",
+ " 'standard_name': 'latitude'}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy_1.lat"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n",
+ " 74.54883588, 74.99814646],\n",
+ " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n",
+ " 74.54883588, 74.99814646],\n",
+ " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n",
+ " 74.54883588, 74.99814646],\n",
+ " ...,\n",
+ " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n",
+ " 74.54883588, 74.99814646],\n",
+ " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n",
+ " 74.54883588, 74.99814646],\n",
+ " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n",
+ " 74.54883588, 74.99814646]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('y', 'x'),\n",
+ " 'units': 'degrees_east',\n",
+ " 'axis': 'X',\n",
+ " 'long_name': 'longitude coordinate',\n",
+ " 'standard_name': 'longitude'}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy_1.lon"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nessy_1.lat_bnds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nessy_1.lon_bnds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nessy_1.load()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "nessy_1.variables"
+ "## 2. Write dataset"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Creating mercator_file_1.nc\n",
+ "Rank 000: NetCDF ready to write\n",
+ "Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n"
+ ]
+ }
+ ],
"source": [
"nessy_1.to_netcdf('mercator_file_1.nc', info=True)"
]
@@ -198,34 +379,29 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Reopen with NES"
+ "## 3. Reopen dataset"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy_2 = open_netcdf('mercator_file_1.nc', info=True)\n",
"nessy_2"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Reopen with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.open_dataset('mercator_file_1.nc')"
- ]
}
],
"metadata": {
diff --git a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb
index 7e8f47811128efff6ba72b3e5a7156aa4b23ac56..7916cfd3b080df59bcd79aa7cf9b342998df1b0a 100644
--- a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb
+++ b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb
@@ -13,8 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
- "from netCDF4 import Dataset\n",
"from nes import *"
]
},
@@ -31,6292 +29,16 @@
"metadata": {},
"outputs": [],
"source": [
- "obs_path = '/gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc'"
+ "# Original path: /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc\n",
+ "# Points grid from EBAS network\n",
+ "obs_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/obs_sconco3_201804.nc'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 1.1. Read dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Open with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (station: 168, time: 720, N_flag_codes: 186, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
- "Data variables: (12/179)\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NMVOC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NOx_emissions (station) float32 ...\n",
- " ... ...\n",
- " station_timezone (station) object ...\n",
- " street_type (station) object ...\n",
- " street_width (station) float32 ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- "Attributes:\n",
- " title: Surface ozone data in the EBAS network in 2018...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.3.3\n",
- " history: Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dm...\n",
- " NCO: 4.7.2\n",
- " nco_openmp_thread_number: 1 Dimensions: station : 168time : 720N_flag_codes : 186N_qa_codes : 77
Coordinates: (1)
Data variables: (179)
ASTER_v3_altitude
(station)
float32
...
standard_name : ASTER v3 altitude long_name : ASTER v3 altitude, relative to EGM96 geoid datum units : m description : Altitude from ASTER v3 digital elevation model, relative to EGM96 geoid vertical datum, in metres. The dataset was generated using 1,880,306 Level-1A scenes (taken from the NASA TERRA spacecraft) acquired between March 1, 2000 and November 30, 2013. The ASTER GDEM was created by stacking all individual cloud-masked scene DEMs and non-cloud-masked scene DEMs, then applying various algorithms to remove abnormal data. A statistical approach is not always effective for anomaly removal in areas with a limited number of images. Several existing reference DEMs were used to replace residual anomalies caused by the insufficient number of stacked scenes. In addition to ASTER GDEM, the ASTER Global Water Body Database (ASTWBD) was generated as a by-product to correct elevation values of water body surfaces like sea, rivers, and lakes. The ASTWBD was applied to GDEM to provide proper elevation values for water body surfaces. The sea and lake have a flattened elevation value. The river has a stepped-down elevation value from the upper stream to the lower stream. Native resolution of 1 arc second ~= 30m at the equator. array([ 201., 17., 124., 1003., 234., 1021., 3075., 1158., 466., 548.,\n",
- " 574., 337., 156., 208., 881., 1816., 485., 420., 277., 159.,\n",
- " 1733., 39., 555., 3526., 494., 536., 1126., 1024., 796., 2121.,\n",
- " 0., 519., 726., 532., 1130., 13., 86., 1197., 97., 869.,\n",
- " 6., 981., 2648., nan, 11., 0., 13., 3286., 43., 52.,\n",
- " 5., 917., 684., 60., 1260., 122., 1369., 76., 374., 842.,\n",
- " 971., 483., 554., 23., 2362., 4., 9., 282., 561., 748.,\n",
- " 393., 601., 223., 776., 139., 1760., 770., 225., 2828., 602.,\n",
- " 171., 1447., 191., 122., 83., 263., 265., 358., 177., 347.,\n",
- " 122., 48., 163., 5., 260., 12., 5., 79., 76., 199.,\n",
- " 392., 118., 291., 845., 6., 11., 224., 1775., 4., 1049.,\n",
- " 10., 264., 9., 27., 39., 63., 12., 18., 190., 154.,\n",
- " 13., 13., 19., 8., 14., 202., 458., 206., 406., 136.,\n",
- " 15., 12., 285., 1275., 81., 56., 178., 361., 171., 1588.,\n",
- " 6., 195., 780., 9., 338., 397., 7., 170., 51., 181.,\n",
- " 36., 221., 234., 107., 514., 766., 1757., 1617., 819., 484.,\n",
- " 100., 9., 1691., 3401., nan, 100., 1387., 174.], dtype=float32) EDGAR_v4.3.2_annual_average_BC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average BC emissions long_name : EDGAR v4.3.2 annual average black carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average BC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 3.042509e-14, 2.185555e-15,\n",
- " 0.000000e+00, 1.992454e-13, 0.000000e+00, 4.209633e-13, 1.679097e-15,\n",
- " 4.852100e-13, 1.969001e-15, 1.340942e-13, 6.248710e-14, 2.100758e-13,\n",
- " 9.962270e-14, 0.000000e+00, 1.147561e-13, 2.141060e-13, 2.465255e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.657339e-13, 1.374218e-13,\n",
- " 4.016085e-13, 7.663287e-14, 9.962270e-14, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.513695e-13,\n",
- " 4.548921e-13, 2.977381e-16, 3.040299e-13, 2.874330e-14, 1.893871e-13,\n",
- " 1.742075e-12, 1.633923e-13, 5.822990e-17, 0.000000e+00, 4.616434e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.759273e-14, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.108332e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 2.516593e-18, 9.132636e-16, 7.187694e-14, 0.000000e+00, 5.344048e-16,\n",
- " 1.361373e-16, 1.220174e-17, 1.876187e-13, 8.860958e-17, 0.000000e+00,\n",
- " 1.844417e-13, 0.000000e+00, 0.000000e+00, 2.568060e-13, 0.000000e+00,\n",
- " 0.000000e+00, 3.067904e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.580146e-15,\n",
- " 7.427175e-14, 0.000000e+00, 2.651062e-16, 0.000000e+00, 0.000000e+00,\n",
- " 1.297408e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.663155e-13,\n",
- " 0.000000e+00, 1.152715e-16, 1.529763e-14, 6.158420e-13, 2.570045e-16,\n",
- " 1.580146e-15, 9.744980e-14, 0.000000e+00, 1.251323e-13, 3.560953e-14,\n",
- " 0.000000e+00, 1.210138e-13, 0.000000e+00, 4.598798e-14, 1.106295e-13,\n",
- " 5.161284e-16, 3.596131e-13, 0.000000e+00, 1.800758e-15, 7.103424e-14,\n",
- " 0.000000e+00, 3.291449e-13, 0.000000e+00, 1.249780e-15, 0.000000e+00,\n",
- " 0.000000e+00, 2.685832e-15, 0.000000e+00, 1.745137e-15, 4.048674e-13,\n",
- " 6.465924e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 9.721247e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.125880e-14,\n",
- " 0.000000e+00, 4.190168e-13, 2.589192e-13, 0.000000e+00, 0.000000e+00,\n",
- " 8.493474e-21, 0.000000e+00, 2.828534e-15, 1.791777e-15, 0.000000e+00,\n",
- " 2.138894e-15, 0.000000e+00, 2.088533e-18, 0.000000e+00, 2.013163e-13,\n",
- " 0.000000e+00, 4.152850e-13, 1.590138e-13, 0.000000e+00, 4.941700e-14,\n",
- " 0.000000e+00, 4.534964e-17, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 2.872648e-13, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_CO_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average CO emissions long_name : EDGAR v4.3.2 annual average carbon monoxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average CO emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 8.225822e-14, 6.012667e-14,\n",
- " 0.000000e+00, 5.386828e-13, 0.000000e+00, 1.138131e-12, 4.619353e-14,\n",
- " 1.311828e-12, 5.416911e-14, 3.759797e-13, 1.689421e-13, 5.679673e-13,\n",
- " 2.693421e-13, 0.000000e+00, 3.448982e-13, 5.788603e-13, 6.665133e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.184455e-13, 3.715371e-13,\n",
- " 1.088436e-12, 2.071862e-13, 2.693421e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.092466e-13,\n",
- " 1.237345e-12, 8.191075e-15, 8.219818e-13, 1.428169e-13, 5.120298e-13,\n",
- " 4.709930e-12, 4.417532e-13, 1.601967e-15, 0.000000e+00, 1.255242e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.016369e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 5.700131e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 2.674954e-16, 4.568149e-14, 2.298078e-11, 0.000000e+00, 1.470202e-14,\n",
- " 3.745274e-15, 3.356823e-16, 5.072486e-13, 2.437739e-15, 0.000000e+00,\n",
- " 4.986610e-13, 0.000000e+00, 0.000000e+00, 6.943062e-13, 0.000000e+00,\n",
- " 0.000000e+00, 8.294464e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.347128e-14,\n",
- " 2.008032e-13, 0.000000e+00, 7.293348e-15, 0.000000e+00, 0.000000e+00,\n",
- " 3.569300e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.200188e-13,\n",
- " 0.000000e+00, 3.171229e-15, 4.135889e-14, 2.969954e-10, 7.070461e-15,\n",
- " 4.347128e-14, 2.634675e-13, 0.000000e+00, 3.383109e-13, 9.627513e-14,\n",
- " 0.000000e+00, 3.271759e-13, 0.000000e+00, 1.267826e-13, 3.026542e-13,\n",
- " 1.419923e-14, 5.129310e-11, 0.000000e+00, 9.007420e-14, 1.920503e-13,\n",
- " 0.000000e+00, 4.694731e-11, 0.000000e+00, 3.438267e-14, 0.000000e+00,\n",
- " 0.000000e+00, 7.739928e-14, 0.000000e+00, 4.801032e-14, 5.774795e-11,\n",
- " 2.868396e-11, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.674411e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.115487e-13,\n",
- " 0.000000e+00, 1.132864e-12, 7.000194e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 9.668300e-14, 8.962475e-14, 0.000000e+00,\n",
- " 9.488507e-12, 0.000000e+00, 2.219958e-16, 0.000000e+00, 5.442847e-13,\n",
- " 0.000000e+00, 1.122772e-12, 4.299122e-13, 0.000000e+00, 1.737866e-13,\n",
- " 0.000000e+00, 5.721683e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 8.000223e-13, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_NH3_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NH3 emissions long_name : EDGAR v4.3.2 annual average ammonia emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NH3 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.375585e-16, 0.000000e+00,\n",
- " 0.000000e+00, 9.008314e-16, 0.000000e+00, 1.903272e-15, 0.000000e+00,\n",
- " 2.193740e-15, 0.000000e+00, 6.038217e-16, 2.825187e-16, 9.497996e-16,\n",
- " 4.504157e-16, 0.000000e+00, 5.125246e-16, 9.680197e-16, 1.114598e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.201439e-15, 6.213159e-16,\n",
- " 1.815286e-15, 3.464735e-16, 4.504157e-16, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.843763e-16,\n",
- " 2.055305e-15, 0.000000e+00, 1.374585e-15, 1.180892e-16, 8.562589e-16,\n",
- " 7.876276e-15, 7.387314e-16, 0.000000e+00, 0.000000e+00, 2.085885e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.699650e-16, 6.597628e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 9.532244e-16, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.711655e-14, 1.205071e-13, 1.943890e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 8.482645e-16, 0.000000e+00, 0.000000e+00,\n",
- " 8.339012e-16, 0.000000e+00, 0.000000e+00, 1.161074e-15, 0.000000e+00,\n",
- " 0.000000e+00, 1.387067e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 3.357990e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.204071e-15,\n",
- " 0.000000e+00, 0.000000e+00, 6.916365e-17, 3.522678e-11, 0.000000e+00,\n",
- " 0.000000e+00, 4.405916e-16, 0.000000e+00, 5.657517e-16, 1.609990e-16,\n",
- " 0.000000e+00, 5.471314e-16, 0.000000e+00, 2.074751e-16, 4.995342e-16,\n",
- " 0.000000e+00, 1.329593e-15, 6.251216e-14, 3.440699e-13, 3.211615e-16,\n",
- " 5.829590e-15, 1.216946e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.923823e-14, 0.000000e+00, 0.000000e+00, 1.496914e-15,\n",
- " 2.301217e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.865407e-16,\n",
- " 0.000000e+00, 1.894465e-15, 1.170628e-15, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 6.601672e-13, 0.000000e+00, 1.009839e-13, 0.000000e+00, 9.101958e-16,\n",
- " 0.000000e+00, 1.877591e-15, 7.189337e-16, 0.000000e+00, 2.161016e-16,\n",
- " 0.000000e+00, 8.178586e-17, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.294533e-15, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_NMVOC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NMVOC emissions long_name : EDGAR v4.3.2 annual average non-methane volatile organic compound emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NMVOC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 5.175291e-13, 2.733042e-14,\n",
- " 0.000000e+00, 3.389148e-12, 0.000000e+00, 7.160564e-12, 2.099715e-14,\n",
- " 8.253374e-12, 2.462239e-14, 2.278494e-12, 1.062905e-12, 3.573377e-12,\n",
- " 1.694574e-12, 0.000000e+00, 1.945705e-12, 3.641927e-12, 4.193397e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.520102e-12, 2.337537e-12,\n",
- " 6.830868e-12, 1.303518e-12, 1.694574e-12, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.574783e-12,\n",
- " 7.736342e-12, 3.723225e-15, 5.171510e-12, 4.770983e-13, 3.221458e-12,\n",
- " 2.963254e-11, 2.779294e-12, 7.281668e-16, 0.000000e+00, 7.851215e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.394503e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.586252e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.170196e-16, 2.284069e-15, 2.754316e-12, 0.000000e+00, 6.682769e-15,\n",
- " 1.702404e-15, 1.525833e-16, 3.191379e-12, 1.108067e-15, 0.000000e+00,\n",
- " 3.137339e-12, 0.000000e+00, 0.000000e+00, 4.368254e-12, 0.000000e+00,\n",
- " 0.000000e+00, 5.218467e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.975979e-14,\n",
- " 1.263358e-12, 0.000000e+00, 3.315160e-15, 0.000000e+00, 0.000000e+00,\n",
- " 1.622416e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.530011e-12,\n",
- " 0.000000e+00, 1.441471e-15, 2.602110e-13, 2.803539e-11, 3.213846e-15,\n",
- " 1.975979e-14, 1.657613e-12, 0.000000e+00, 2.128489e-12, 6.057175e-13,\n",
- " 0.000000e+00, 2.058437e-12, 0.000000e+00, 7.818060e-13, 1.881156e-12,\n",
- " 6.454220e-15, 9.438283e-12, 0.000000e+00, 4.503709e-15, 1.208288e-12,\n",
- " 0.000000e+00, 8.638583e-12, 0.000000e+00, 1.562851e-14, 0.000000e+00,\n",
- " 0.000000e+00, 2.816417e-14, 0.000000e+00, 2.182292e-14, 1.062598e-11,\n",
- " 2.605788e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.215648e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.018115e-13,\n",
- " 0.000000e+00, 7.127464e-12, 4.404200e-12, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 2.698554e-14, 4.481238e-15, 0.000000e+00,\n",
- " 8.619777e-13, 0.000000e+00, 9.711523e-17, 0.000000e+00, 3.424377e-12,\n",
- " 0.000000e+00, 7.063961e-12, 2.704809e-12, 0.000000e+00, 8.332826e-13,\n",
- " 0.000000e+00, 2.688864e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 4.882130e-12, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_NOx_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NOx emissions long_name : EDGAR v4.3.2 annual average emissions of nitrogen oxides units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NOx emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.522043e-12, 6.996554e-13,\n",
- " 0.000000e+00, 9.967411e-12, 0.000000e+00, 2.105912e-11, 5.375259e-13,\n",
- " 2.427307e-11, 6.303303e-13, 6.854504e-12, 3.125981e-12, 1.050924e-11,\n",
- " 4.983706e-12, 0.000000e+00, 6.117938e-12, 1.071083e-11, 1.233268e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.329357e-11, 6.874644e-12,\n",
- " 2.011957e-11, 3.833614e-12, 4.983706e-12, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.572397e-12,\n",
- " 2.283791e-11, 9.531410e-14, 1.520934e-11, 2.146782e-12, 9.474236e-12,\n",
- " 8.714885e-11, 8.173846e-12, 1.864103e-14, 0.000000e+00, 2.317172e-11,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.880610e-12, 2.437077e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.054711e-11, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.763506e-15, 2.389657e-13, 1.149823e-11, 0.000000e+00, 1.710780e-13,\n",
- " 4.358154e-14, 3.906132e-15, 9.385776e-12, 2.836642e-14, 0.000000e+00,\n",
- " 9.226877e-12, 0.000000e+00, 0.000000e+00, 1.284696e-11, 0.000000e+00,\n",
- " 0.000000e+00, 1.534748e-11, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 5.058475e-13,\n",
- " 3.715512e-12, 0.000000e+00, 8.486790e-14, 0.000000e+00, 0.000000e+00,\n",
- " 4.153366e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.332267e-11,\n",
- " 0.000000e+00, 3.690153e-14, 7.652783e-13, 2.254739e-11, 8.227430e-14,\n",
- " 5.058475e-13, 4.875005e-12, 0.000000e+00, 6.259855e-12, 1.781405e-12,\n",
- " 0.000000e+00, 6.053818e-12, 0.000000e+00, 2.327244e-12, 5.573037e-12,\n",
- " 1.652272e-13, 7.673363e-12, 2.381948e-15, 4.826952e-13, 3.553562e-12,\n",
- " 4.351761e-16, 7.023245e-12, 0.000000e+00, 4.000890e-13, 0.000000e+00,\n",
- " 0.000000e+00, 7.422984e-13, 0.000000e+00, 5.586654e-13, 8.639011e-12,\n",
- " 2.090962e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.112041e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.064015e-12,\n",
- " 0.000000e+00, 2.096167e-11, 1.295267e-11, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 8.530831e-13, 4.615680e-13, 0.000000e+00,\n",
- " 7.962478e-13, 0.000000e+00, 4.127878e-15, 0.000000e+00, 1.007104e-11,\n",
- " 0.000000e+00, 2.077494e-11, 7.954819e-12, 0.000000e+00, 2.909625e-12,\n",
- " 0.000000e+00, 3.651467e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.462513e-11, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_OC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average OC emissions long_name : EDGAR v4.3.2 annual average organic carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average OC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.521148e-14, 1.075611e-15,\n",
- " 0.000000e+00, 9.961557e-14, 0.000000e+00, 2.104675e-13, 8.263584e-16,\n",
- " 2.425881e-13, 9.690349e-16, 6.703846e-14, 3.124149e-14, 1.050307e-13,\n",
- " 4.980778e-14, 0.000000e+00, 5.736338e-14, 1.070454e-13, 1.232543e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.328576e-13, 6.870605e-14,\n",
- " 2.007902e-13, 3.831362e-14, 4.980778e-14, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.567948e-14,\n",
- " 2.274283e-13, 1.465305e-16, 1.520045e-13, 1.435014e-14, 9.468710e-14,\n",
- " 8.709783e-13, 8.169043e-14, 2.865768e-17, 0.000000e+00, 2.308030e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.879505e-14, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.054094e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 9.163830e-18, 4.590217e-16, 4.857565e-14, 0.000000e+00, 2.630054e-16,\n",
- " 6.699941e-17, 6.005034e-18, 9.380263e-14, 4.360905e-17, 0.000000e+00,\n",
- " 9.221455e-14, 0.000000e+00, 0.000000e+00, 1.283941e-13, 0.000000e+00,\n",
- " 0.000000e+00, 1.533846e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.776644e-16,\n",
- " 3.713333e-14, 0.000000e+00, 1.304709e-16, 0.000000e+00, 0.000000e+00,\n",
- " 6.385160e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.331484e-13,\n",
- " 0.000000e+00, 5.673017e-17, 7.648287e-15, 5.473372e-12, 1.264837e-16,\n",
- " 7.776644e-16, 4.872142e-14, 0.000000e+00, 6.256178e-14, 1.780361e-14,\n",
- " 0.000000e+00, 6.050261e-14, 0.000000e+00, 2.299157e-14, 5.530981e-14,\n",
- " 2.540110e-16, 1.798061e-13, 0.000000e+00, 9.050940e-16, 3.551475e-14,\n",
- " 0.000000e+00, 1.645722e-13, 0.000000e+00, 6.150755e-16, 0.000000e+00,\n",
- " 0.000000e+00, 1.158464e-15, 0.000000e+00, 8.588610e-16, 2.024330e-13,\n",
- " 6.811853e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.784277e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.062807e-14,\n",
- " 0.000000e+00, 2.094940e-13, 1.294506e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 1.400828e-15, 9.005805e-16, 0.000000e+00,\n",
- " 2.253324e-15, 0.000000e+00, 7.605125e-18, 0.000000e+00, 1.006512e-13,\n",
- " 0.000000e+00, 2.076277e-13, 7.950146e-14, 0.000000e+00, 2.469410e-14,\n",
- " 0.000000e+00, 1.935444e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.436156e-13, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_PM10_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average PM10 emissions long_name : EDGAR v4.3.2 annual average PM10 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average PM10 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.521823e-13, 1.093214e-14,\n",
- " 0.000000e+00, 9.965986e-13, 0.000000e+00, 2.105607e-12, 8.398846e-15,\n",
- " 2.426955e-12, 9.848946e-15, 6.707232e-13, 3.125530e-13, 1.050772e-12,\n",
- " 4.982993e-13, 0.000000e+00, 5.739947e-13, 1.070930e-12, 1.233090e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.329164e-12, 6.873672e-13,\n",
- " 2.008799e-12, 3.833089e-13, 4.982993e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.571304e-13,\n",
- " 2.275316e-12, 1.489287e-15, 1.520717e-12, 1.437705e-13, 9.472905e-13,\n",
- " 8.713610e-12, 8.172664e-13, 2.912671e-16, 0.000000e+00, 2.309072e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.880339e-13, 6.782045e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.054561e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.076963e-16, 1.506612e-14, 1.758185e-13, 0.000000e+00, 2.673100e-15,\n",
- " 6.809607e-16, 6.103339e-17, 9.384433e-13, 4.432253e-16, 0.000000e+00,\n",
- " 9.225539e-13, 0.000000e+00, 0.000000e+00, 1.284510e-12, 0.000000e+00,\n",
- " 0.000000e+00, 1.534524e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.903901e-15,\n",
- " 3.714978e-13, 0.000000e+00, 1.326064e-15, 0.000000e+00, 0.000000e+00,\n",
- " 6.489629e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.332075e-12,\n",
- " 0.000000e+00, 5.765861e-16, 7.651659e-14, 2.043820e-11, 1.285535e-15,\n",
- " 7.903901e-15, 4.874308e-13, 0.000000e+00, 6.258952e-13, 1.781150e-13,\n",
- " 0.000000e+00, 6.052961e-13, 0.000000e+00, 2.300253e-13, 5.533560e-13,\n",
- " 2.581682e-15, 1.798102e-12, 6.411004e-15, 3.661463e-14, 3.553056e-13,\n",
- " 4.276441e-16, 1.645756e-12, 0.000000e+00, 6.251414e-15, 0.000000e+00,\n",
- " 0.000000e+00, 1.972915e-14, 0.000000e+00, 8.729169e-15, 2.024374e-12,\n",
- " 2.697392e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.862578e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.063720e-13,\n",
- " 0.000000e+00, 2.095866e-12, 1.295080e-12, 0.000000e+00, 0.000000e+00,\n",
- " 1.132456e-17, 0.000000e+00, 1.414835e-14, 8.962475e-15, 0.000000e+00,\n",
- " 1.374389e-14, 0.000000e+00, 1.552330e-14, 0.000000e+00, 1.006958e-12,\n",
- " 0.000000e+00, 2.077194e-12, 7.953650e-13, 0.000000e+00, 2.471775e-13,\n",
- " 0.000000e+00, 7.172658e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.436866e-12, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_SO2_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average SO2 emissions long_name : EDGAR v4.3.2 annual average sulphur dioxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average SO2 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 9.231252e-13, 5.466069e-14,\n",
- " 0.000000e+00, 6.045283e-12, 0.000000e+00, 1.277240e-11, 4.199423e-14,\n",
- " 1.472168e-11, 4.924454e-14, 4.065654e-12, 1.895917e-12, 6.373896e-12,\n",
- " 3.022638e-12, 0.000000e+00, 3.474358e-12, 6.496169e-12, 7.479813e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 8.062605e-12, 4.169506e-12,\n",
- " 1.218464e-11, 2.325105e-12, 3.022638e-12, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.592688e-12,\n",
- " 1.380024e-11, 7.446459e-15, 9.224540e-12, 8.581082e-13, 5.746173e-12,\n",
- " 5.285586e-11, 4.957475e-12, 1.456334e-15, 0.000000e+00, 1.400510e-11,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.140598e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.396873e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.796339e-17, 2.284069e-14, 1.736909e-13, 0.000000e+00, 1.336550e-14,\n",
- " 3.404800e-15, 3.051662e-16, 5.692519e-12, 2.216131e-15, 0.000000e+00,\n",
- " 5.596131e-12, 0.000000e+00, 0.000000e+00, 7.791722e-12, 0.000000e+00,\n",
- " 0.000000e+00, 9.308299e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.951955e-14,\n",
- " 2.253470e-12, 0.000000e+00, 6.630320e-15, 0.000000e+00, 0.000000e+00,\n",
- " 3.244823e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 8.080256e-12,\n",
- " 0.000000e+00, 2.882938e-15, 4.641434e-13, 2.088297e-12, 6.427676e-15,\n",
- " 3.951955e-14, 2.956710e-12, 0.000000e+00, 3.796626e-12, 1.080426e-12,\n",
- " 0.000000e+00, 3.671671e-12, 0.000000e+00, 1.394787e-12, 3.355824e-12,\n",
- " 1.290841e-14, 5.519056e-12, 0.000000e+00, 4.503710e-14, 2.155245e-12,\n",
- " 0.000000e+00, 5.051448e-12, 0.000000e+00, 3.125702e-14, 0.000000e+00,\n",
- " 0.000000e+00, 9.761866e-14, 0.000000e+00, 4.364585e-14, 6.213544e-12,\n",
- " 2.622671e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.431287e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.251830e-12,\n",
- " 0.000000e+00, 1.271331e-11, 7.855838e-12, 0.000000e+00, 0.000000e+00,\n",
- " 3.173154e-16, 0.000000e+00, 7.074175e-14, 4.481238e-14, 0.000000e+00,\n",
- " 8.675671e-16, 0.000000e+00, 1.490791e-17, 0.000000e+00, 6.108124e-12,\n",
- " 0.000000e+00, 1.260006e-11, 4.824619e-12, 0.000000e+00, 1.490720e-12,\n",
- " 0.000000e+00, 1.239232e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 8.710870e-12, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions long_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average biogenic PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 3.868548e-17, 0.000000e+00,\n",
- " 0.000000e+00, 2.533411e-16, 0.000000e+00, 5.352590e-16, 0.000000e+00,\n",
- " 6.169483e-16, 0.000000e+00, 1.698127e-16, 7.945291e-17, 2.671126e-16,\n",
- " 1.266708e-16, 0.000000e+00, 1.441375e-16, 2.722365e-16, 3.134586e-17,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.378819e-16, 1.747324e-16,\n",
- " 5.105129e-16, 9.743910e-17, 1.266708e-16, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.924670e-16,\n",
- " 5.780155e-16, 0.000000e+00, 3.865764e-16, 3.321025e-17, 2.408063e-16,\n",
- " 2.215057e-15, 2.077544e-16, 0.000000e+00, 0.000000e+00, 5.866167e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.779914e-17, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.680754e-16, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.904895e-17, 0.000000e+00, 3.074643e-15, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 2.385578e-16, 0.000000e+00, 0.000000e+00,\n",
- " 2.345185e-16, 0.000000e+00, 0.000000e+00, 3.265298e-16, 0.000000e+00,\n",
- " 0.000000e+00, 3.900847e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 9.443683e-17, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.386200e-16,\n",
- " 0.000000e+00, 0.000000e+00, 1.945097e-17, 1.752106e-11, 0.000000e+00,\n",
- " 0.000000e+00, 1.239079e-16, 0.000000e+00, 1.591063e-16, 4.527793e-17,\n",
- " 0.000000e+00, 1.538695e-16, 0.000000e+00, 5.834836e-17, 1.404839e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 9.032056e-17,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.229276e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 5.246104e-17,\n",
- " 0.000000e+00, 5.327835e-16, 3.292167e-16, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 4.066370e-16, 0.000000e+00, 1.580880e-17, 0.000000e+00, 2.559748e-16,\n",
- " 0.000000e+00, 5.280338e-16, 2.021865e-16, 0.000000e+00, 6.077435e-17,\n",
- " 0.000000e+00, 1.893055e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 3.640635e-16, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions long_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.521014e-13, 1.093214e-14,\n",
- " 0.000000e+00, 9.960692e-13, 0.000000e+00, 2.104491e-12, 8.398846e-15,\n",
- " 2.425667e-12, 9.848946e-15, 6.703692e-13, 3.123873e-13, 1.050215e-12,\n",
- " 4.980365e-13, 0.000000e+00, 5.736932e-13, 1.070361e-12, 1.232434e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.328461e-12, 6.870007e-13,\n",
- " 2.007732e-12, 3.831024e-13, 4.980365e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.567284e-13,\n",
- " 2.274109e-12, 1.489287e-15, 1.519911e-12, 1.437014e-13, 9.467864e-13,\n",
- " 8.708992e-12, 8.168320e-13, 2.912671e-16, 0.000000e+00, 2.307849e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.879340e-13, 1.164987e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.054001e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 4.668681e-17, 6.346848e-15, 1.661733e-13, 0.000000e+00, 2.673100e-15,\n",
- " 6.809607e-16, 6.103339e-17, 9.379449e-13, 4.432253e-16, 0.000000e+00,\n",
- " 9.220624e-13, 0.000000e+00, 0.000000e+00, 1.283825e-12, 0.000000e+00,\n",
- " 0.000000e+00, 1.533710e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.903901e-15,\n",
- " 3.713005e-13, 0.000000e+00, 1.326064e-15, 0.000000e+00, 0.000000e+00,\n",
- " 6.489629e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.331367e-12,\n",
- " 0.000000e+00, 5.765861e-16, 7.647612e-14, 1.134093e-12, 1.285535e-15,\n",
- " 7.903901e-15, 4.871738e-13, 0.000000e+00, 6.255629e-13, 1.780204e-13,\n",
- " 0.000000e+00, 6.049734e-13, 0.000000e+00, 2.299037e-13, 5.530631e-13,\n",
- " 2.581682e-15, 1.798102e-12, 1.103664e-15, 1.395974e-14, 3.551158e-13,\n",
- " 8.140140e-17, 1.645756e-12, 0.000000e+00, 6.251414e-15, 0.000000e+00,\n",
- " 0.000000e+00, 1.354031e-14, 0.000000e+00, 8.729169e-15, 2.024374e-12,\n",
- " 1.767644e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.862578e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.062625e-13,\n",
- " 0.000000e+00, 2.094752e-12, 1.294390e-12, 0.000000e+00, 0.000000e+00,\n",
- " 8.493472e-18, 0.000000e+00, 1.414835e-14, 8.962475e-15, 0.000000e+00,\n",
- " 9.510742e-15, 0.000000e+00, 2.666073e-15, 0.000000e+00, 1.006424e-12,\n",
- " 0.000000e+00, 2.076092e-12, 7.949445e-13, 0.000000e+00, 2.470507e-13,\n",
- " 0.000000e+00, 3.217921e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.436104e-12, 0.000000e+00], dtype=float32) ESDAC_Iwahashi_landform_classification
(station)
object
...
standard_name : ESDAC Iwahashi landform classification long_name : ESDAC Iwahashi landform classification units : unitless description : European Soil Data Centre (ESDAC) Iwahashi landform classification. The classification presents relief classes which are classified using an unsupervised nested-means algorithms and a three part geometric signature. Slope gradient, surface texture and local convexity are calculated based on the SRTM30 digital elevation model, within a given window size and classified according to the inherent data set properties. This is a dynamic landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['nan', 'gentle - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'steep - coarse texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity', 'nan',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - high convexity',\n",
- " 'steep - coarse texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'nan',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity', 'nan',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity', 'nan',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - high convexity', 'nan',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity'], dtype=object) ESDAC_Meybeck_landform_classification
(station)
object
...
standard_name : ESDAC Meybeck landform classification long_name : ESDAC Meybeck landform classification units : unitless description : European Soil Data Centre (ESDAC) Meybeck landform classification. The classification presents relief classes which are calculated based on the relief roughness. Roughness and elevation are classified based on a digital elevation model according to static thresholds, with a given window size. This is a static landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['nan', 'lowlands', 'plains', 'mid altitude mountains',\n",
- " 'very low plateaus', 'mid altitude mountains',\n",
- " 'high altitude mountains', 'mid altitude mountains',\n",
- " 'very low plateaus', 'high altitude plains', 'low plateaus',\n",
- " 'very low plateaus', 'plains', 'mid altitude plains',\n",
- " 'low altitude mountains', 'mid altitude mountains', 'very low plateaus',\n",
- " 'mid altitude plains', 'very low plateaus', 'lowlands',\n",
- " 'mid altitude mountains', 'lowlands', 'nan', 'high altitude mountains',\n",
- " 'very low plateaus', 'low plateaus', 'mid altitude mountains',\n",
- " 'low altitude mountains', 'low plateaus', 'mid altitude mountains',\n",
- " 'water', 'very low plateaus', 'low plateaus', 'low plateaus',\n",
- " 'mid altitude plateaus', 'plains', 'plains', 'mid altitude mountains',\n",
- " 'lowlands', 'low plateaus', 'plains', 'low altitude mountains',\n",
- " 'high altitude mountains', 'nan', 'plains', 'nan', 'water', 'nan',\n",
- " 'plains', 'plains', 'plains', 'low altitude mountains', 'low plateaus',\n",
- " 'water', 'mid altitude mountains', 'rugged lowlands',\n",
- " 'mid altitude plateaus', 'water', 'very low plateaus', 'low plateaus',\n",
- " 'low plateaus', 'low altitude mountains', 'low plateaus', 'plains',\n",
- " 'high altitude mountains', 'water', 'water', 'nan', 'nan',\n",
- " 'low plateaus', 'very low plateaus', 'low plateaus',\n",
- " 'very low plateaus', 'low plateaus', 'lowlands',\n",
- " 'mid altitude mountains', 'low plateaus', 'very low plateaus',\n",
- " 'high altitude mountains', 'low altitude mountains', 'plains',\n",
- " 'mid altitude mountains', 'very low plateaus', 'lowlands',\n",
- " 'rugged lowlands', 'very low plateaus', 'very low plateaus', 'hills',\n",
- " 'plains', 'hills', 'lowlands', 'plains', 'lowlands', 'plains',\n",
- " 'mid altitude plains', 'water', 'plains', 'plains', 'lowlands',\n",
- " 'lowlands', 'hills', 'plains', 'very low plateaus',\n",
- " 'low altitude mountains', 'lowlands', 'plains', 'very low plateaus',\n",
- " 'mid altitude mountains', 'plains', 'low altitude mountains', 'nan',\n",
- " 'rugged lowlands', 'water', 'water', 'lowlands', 'water', 'plains',\n",
- " 'plains', 'plains', 'lowlands', 'plains', 'plains', 'plains', 'plains',\n",
- " 'plains', 'rugged lowlands', 'nan', 'nan', 'nan', 'lowlands', 'nan',\n",
- " 'lowlands', 'very low plateaus', 'nan', 'rugged lowlands', 'lowlands',\n",
- " 'nan', 'mid altitude plains', 'plains', 'mid altitude mountains',\n",
- " 'plains', 'plains', 'low altitude mountains', 'nan', 'nan', 'nan',\n",
- " 'water', 'plains', 'lowlands', 'lowlands', 'plains', 'lowlands', 'nan',\n",
- " 'lowlands', 'low plateaus', 'low altitude mountains',\n",
- " 'mid altitude mountains', 'mid altitude mountains', 'low plateaus',\n",
- " 'hills', 'plains', 'nan', 'mid altitude plateaus',\n",
- " 'high altitude plateaus', 'nan', 'water', 'mid altitude mountains',\n",
- " 'rugged lowlands'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 25km long_name : ESDAC modal Iwahashi landform classification in 25km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 25km around station location. array(['nan', 'steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'nan', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - coarse texture - high convexity', 'water', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water', 'water', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water', 'water',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'nan', 'water', 'water',\n",
- " 'water', 'water', 'water', 'water', 'water',\n",
- " 'medium gentle - fine texture - high convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'nan',\n",
- " 'steep - fine texture - high convexity', 'water', 'nan',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - high convexity', 'nan', 'water',\n",
- " 'steep - fine texture - high convexity', 'water'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 5km long_name : ESDAC modal Iwahashi landform classification in 5km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 5km around station location. array(['nan', 'water', 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - coarse texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - coarse texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'nan', 'water',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - high convexity',\n",
- " 'steep - coarse texture - high convexity', 'water',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity', 'water', 'water',\n",
- " 'water', 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'nan', 'water', 'water',\n",
- " 'water', 'water', 'water', 'water',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity', 'water',\n",
- " 'steep - coarse texture - low convexity', 'nan',\n",
- " 'steep - fine texture - low convexity', 'water', 'nan',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - coarse texture - high convexity', 'nan', 'water',\n",
- " 'steep - fine texture - high convexity', 'water'], dtype=object) ESDAC_modal_Meybeck_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 25km long_name : ESDAC modal Meybeck landform classification in 25km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 25km around station location. array(['nan', 'low altitude mountains', 'plains', 'mid altitude mountains',\n",
- " 'very low plateaus', 'low plateaus', 'mid altitude mountains',\n",
- " 'low altitude mountains', 'low plateaus', 'low plateaus',\n",
- " 'very low plateaus', 'very low plateaus', 'plains', 'plains',\n",
- " 'low altitude mountains', 'mid altitude mountains', 'very low plateaus',\n",
- " 'very low plateaus', 'very low plateaus', 'plains',\n",
- " 'mid altitude mountains', 'water', 'nan', 'high altitude mountains',\n",
- " 'low plateaus', 'low plateaus', 'low altitude mountains',\n",
- " 'low altitude mountains', 'low plateaus', 'mid altitude mountains',\n",
- " 'water', 'low altitude mountains', 'low plateaus', 'low plateaus',\n",
- " 'low plateaus', 'water', 'plains', 'low altitude mountains', 'plains',\n",
- " 'very low plateaus', 'water', 'low plateaus', 'mid altitude mountains',\n",
- " 'nan', 'water', 'nan', 'plains', 'nan', 'plains', 'plains', 'water',\n",
- " 'low plateaus', 'water', 'water', 'mid altitude mountains', 'water',\n",
- " 'mid altitude plateaus', 'water', 'very low plateaus', 'low plateaus',\n",
- " 'high altitude plains', 'very low plateaus', 'low plateaus', 'plains',\n",
- " 'water', 'water', 'water', 'nan', 'nan', 'low altitude mountains',\n",
- " 'very low plateaus', 'very low plateaus', 'lowlands',\n",
- " 'low altitude mountains', 'plains', 'high altitude mountains',\n",
- " 'low plateaus', 'very low plateaus', 'mid altitude mountains',\n",
- " 'low altitude mountains', 'plains', 'low plateaus', 'very low plateaus',\n",
- " 'lowlands', 'lowlands', 'lowlands', 'hills', 'very low plateaus',\n",
- " 'very low plateaus', 'very low plateaus', 'water', 'plains', 'lowlands',\n",
- " 'plains', 'very low plateaus', 'water', 'water', 'water', 'lowlands',\n",
- " 'hills', 'water', 'plains', 'very low plateaus',\n",
- " 'low altitude mountains', 'water', 'water', 'very low plateaus',\n",
- " 'low altitude mountains', 'water', 'very low plateaus', 'nan', 'water',\n",
- " 'water', 'water', 'water', 'water', 'water', 'plains', 'plains',\n",
- " 'water', 'plains', 'plains', 'plains', 'plains', 'plains',\n",
- " 'very low plateaus', 'nan', 'nan', 'nan', 'lowlands', 'nan', 'water',\n",
- " 'water', 'nan', 'lowlands', 'water', 'nan', 'low altitude mountains',\n",
- " 'plains', 'low altitude mountains', 'water', 'plains',\n",
- " 'low altitude mountains', 'nan', 'nan', 'nan', 'water',\n",
- " 'mid altitude plains', 'lowlands', 'plains', 'plains', 'lowlands',\n",
- " 'nan', 'plains', 'low altitude mountains', 'low altitude mountains',\n",
- " 'mid altitude mountains', 'low altitude mountains', 'low plateaus',\n",
- " 'low altitude mountains', 'plains', 'nan', 'high altitude plains',\n",
- " 'high altitude plateaus', 'nan', 'water', 'low altitude mountains',\n",
- " 'water'], dtype=object) ESDAC_modal_Meybeck_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 5km long_name : ESDAC modal Meybeck landform classification in 5km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 5km around station location. array(['nan', 'water', 'plains', 'mid altitude mountains',\n",
- " 'mid altitude plains', 'low altitude mountains',\n",
- " 'high altitude mountains', 'low altitude mountains',\n",
- " 'very low plateaus', 'low plateaus', 'low altitude mountains', 'hills',\n",
- " 'plains', 'plains', 'low altitude mountains', 'mid altitude mountains',\n",
- " 'very low plateaus', 'very low plateaus', 'very low plateaus',\n",
- " 'lowlands', 'mid altitude mountains', 'water', 'nan',\n",
- " 'high altitude mountains', 'mid altitude plains',\n",
- " 'low altitude mountains', 'low altitude mountains',\n",
- " 'low altitude mountains', 'low plateaus', 'mid altitude mountains',\n",
- " 'water', 'very low plateaus', 'low plateaus', 'low plateaus',\n",
- " 'mid altitude plateaus', 'water', 'plains', 'low altitude mountains',\n",
- " 'lowlands', 'low altitude mountains', 'water', 'low plateaus',\n",
- " 'mid altitude mountains', 'nan', 'water', 'nan', 'plains', 'nan',\n",
- " 'plains', 'plains', 'water', 'low plateaus', 'hills', 'water',\n",
- " 'mid altitude mountains', 'water', 'mid altitude plateaus', 'water',\n",
- " 'very low plateaus', 'low plateaus', 'high altitude plains',\n",
- " 'very low plateaus', 'low plateaus', 'plains', 'mid altitude mountains',\n",
- " 'water', 'water', 'nan', 'nan', 'low altitude mountains',\n",
- " 'very low plateaus', 'low plateaus', 'very low plateaus',\n",
- " 'low altitude mountains', 'lowlands', 'high altitude mountains',\n",
- " 'low plateaus', 'very low plateaus', 'mid altitude mountains',\n",
- " 'low altitude mountains', 'plains', 'low plateaus', 'very low plateaus',\n",
- " 'lowlands', 'lowlands', 'very low plateaus', 'very low plateaus',\n",
- " 'very low plateaus', 'lowlands', 'hills', 'lowlands', 'plains',\n",
- " 'lowlands', 'plains', 'very low plateaus', 'water', 'water', 'lowlands',\n",
- " 'lowlands', 'plains', 'hills', 'plains', 'very low plateaus',\n",
- " 'low altitude mountains', 'lowlands', 'water', 'very low plateaus',\n",
- " 'mid altitude mountains', 'water', 'low altitude mountains', 'nan',\n",
- " 'water', 'water', 'water', 'water', 'water', 'water', 'plains',\n",
- " 'plains', 'water', 'plains', 'plains', 'plains', 'plains', 'plains',\n",
- " 'very low plateaus', 'nan', 'nan', 'nan', 'lowlands', 'nan', 'plains',\n",
- " 'very low plateaus', 'nan', 'lowlands', 'water', 'nan',\n",
- " 'mid altitude plains', 'plains', 'mid altitude mountains', 'plains',\n",
- " 'plains', 'low altitude mountains', 'nan', 'nan', 'nan', 'water',\n",
- " 'very low plateaus', 'lowlands', 'lowlands', 'plains', 'lowlands',\n",
- " 'nan', 'plains', 'low altitude mountains', 'low altitude mountains',\n",
- " 'low altitude mountains', 'mid altitude mountains', 'low plateaus',\n",
- " 'hills', 'plains', 'nan', 'high altitude plains',\n",
- " 'high altitude plateaus', 'nan', 'water', 'mid altitude mountains',\n",
- " 'water'], dtype=object) ETOPO1_altitude
(station)
float32
...
standard_name : ETOPO1 altitude long_name : ETOPO1 altitude, relative to sea level datum units : m description : Altitude from ETOPO1 digital elevation model, relative to sea level vertical datum, in metres. Over Antarctica and Greenland the elevation given is on top of the ice sheets. Native resolution of 1 arc minute. A correction for coastal sites is made: if the derived altitude is <= -5 m, the maximum altitude of the neighbouring grid boxes will be used instead. If all neighbouring grid boxes have altitudes <= -5 m, the original value will be retained. array([ 1.900e+01, 3.000e+00, 1.120e+02, 9.090e+02, 2.720e+02, 9.180e+02,\n",
- " 2.589e+03, 1.038e+03, 4.450e+02, 5.490e+02, 5.390e+02, 3.020e+02,\n",
- " 1.570e+02, 2.020e+02, 6.570e+02, 1.594e+03, 4.320e+02, 4.300e+02,\n",
- " 2.640e+02, 1.890e+02, 1.589e+03, -2.000e+00, 5.840e+02, 3.314e+03,\n",
- " 4.850e+02, 5.980e+02, 9.950e+02, 7.140e+02, 7.280e+02, 1.780e+03,\n",
- " 4.100e+01, 4.650e+02, 6.870e+02, 5.250e+02, 1.071e+03, 4.000e+00,\n",
- " 7.100e+01, 1.077e+03, 8.200e+01, 8.190e+02, -2.000e+00, 8.030e+02,\n",
- " 2.379e+03, 4.100e+01, 4.000e+00, 5.000e+00, 3.000e+00, 3.203e+03,\n",
- " 3.700e+01, 7.000e+01, 1.000e+00, 9.940e+02, 5.560e+02, 6.000e+00,\n",
- " 1.284e+03, 1.900e+01, 1.378e+03, 1.000e+01, 3.800e+02, 7.470e+02,\n",
- " 9.190e+02, 5.100e+02, 4.200e+02, 3.600e+01, 2.098e+03, 0.000e+00,\n",
- " 8.000e+00, 2.960e+02, 4.760e+02, 7.400e+02, 3.670e+02, 6.190e+02,\n",
- " 1.990e+02, 7.550e+02, 1.220e+02, 1.659e+03, 7.870e+02, 2.280e+02,\n",
- " 2.258e+03, 6.290e+02, 1.760e+02, 1.122e+03, 2.450e+02, 1.470e+02,\n",
- " 1.240e+02, 2.650e+02, 2.910e+02, 2.240e+02, 1.870e+02, 3.140e+02,\n",
- " 6.800e+01, 4.100e+01, 1.550e+02, 0.000e+00, 2.610e+02, 3.000e+00,\n",
- " 2.000e+00, 3.800e+01, 7.700e+01, 1.570e+02, 2.630e+02, 1.250e+02,\n",
- " 2.940e+02, 7.710e+02, 2.200e+01, 5.000e+00, 2.350e+02, 1.720e+03,\n",
- " 8.000e+00, 8.700e+02, -4.590e+02, 7.800e+01, 5.100e+01, 1.800e+01,\n",
- " 2.000e+00, -1.000e+00, -2.000e+00, 1.500e+01, 1.810e+02, 5.400e+01,\n",
- " 1.700e+01, -1.000e+00, 2.900e+01, -1.000e+00, -1.000e+00, 1.710e+02,\n",
- " 6.310e+02, 3.890e+02, 2.510e+02, 1.780e+02, 4.400e+01, 3.000e+01,\n",
- " 2.920e+02, 1.349e+03, 7.000e+01, 1.800e+01, 6.700e+01, 3.570e+02,\n",
- " 1.770e+02, 1.230e+03, 1.000e+00, 1.980e+02, 6.470e+02, -2.000e+00,\n",
- " 3.520e+02, 4.000e+02, -2.000e+00, 1.880e+02, 8.100e+01, 1.810e+02,\n",
- " 2.900e+01, 2.040e+02, 2.690e+02, 1.250e+02, 6.000e+02, 6.700e+02,\n",
- " 1.445e+03, 1.517e+03, 8.210e+02, 4.760e+02, 1.100e+02, 1.000e+00,\n",
- " 1.672e+03, 3.298e+03, 2.745e+03, 9.500e+01, 1.276e+03, -3.000e+00],\n",
- " dtype=float32) ETOPO1_max_altitude_difference_5km
(station)
float32
...
standard_name : ETOPO1 max altitude difference 5km long_name : ETOPO1 maximum altitude difference between the ETOPO1_altitude and all ETOPO1 altitudes in 5km radius units : m description : Altitude difference between the ETOPO1_altitude, and the minimum ETOP1 altitude in a radius of 5 km around the station location, in metres. array([7.800e+01, 8.000e+00, 2.000e+00, 1.970e+02, 4.600e+01, 3.120e+02,\n",
- " 1.103e+03, 4.880e+02, 2.100e+01, 3.600e+01, 2.230e+02, 1.000e+02,\n",
- " 8.000e+00, 4.900e+01, 1.030e+02, 5.180e+02, 9.300e+01, 6.500e+01,\n",
- " 1.900e+01, 6.000e+01, 3.810e+02, 3.610e+02, 9.480e+02, 1.573e+03,\n",
- " 4.500e+01, 1.330e+02, 5.730e+02, 3.320e+02, 2.190e+02, 7.430e+02,\n",
- " 8.200e+01, 1.310e+02, 1.490e+02, 3.900e+01, 2.400e+02, 1.800e+01,\n",
- " 6.000e+00, 6.170e+02, 1.800e+01, 2.410e+02, 7.000e+00, 2.130e+02,\n",
- " 1.363e+03, 2.000e+01, 3.100e+01, 2.300e+01, 6.000e+00, 6.000e+00,\n",
- " 2.400e+01, 2.100e+01, 2.500e+01, 2.080e+02, 5.490e+02, 8.300e+01,\n",
- " 4.100e+02, 4.700e+01, 1.050e+02, 3.130e+02, 4.900e+01, 2.490e+02,\n",
- " 7.200e+01, 8.900e+01, 1.100e+01, 1.600e+01, 7.740e+02, 2.600e+01,\n",
- " 1.000e+01, 1.070e+02, 2.090e+02, 2.010e+02, 1.810e+02, 8.500e+01,\n",
- " 4.500e+01, 2.330e+02, 3.200e+01, 8.600e+01, 1.000e+02, 4.000e+00,\n",
- " 8.220e+02, 3.000e+00, 6.000e+00, 3.760e+02, 2.800e+01, 1.040e+02,\n",
- " 1.020e+02, 1.520e+02, 2.200e+01, 8.000e+01, 6.200e+01, 9.200e+01,\n",
- " 5.900e+01, 1.900e+01, 9.800e+01, 4.000e+00, 5.800e+01, 1.700e+01,\n",
- " 5.000e+00, 7.000e+01, 2.100e+01, 7.100e+01, 3.010e+02, 6.000e+00,\n",
- " 4.300e+01, 1.110e+02, 2.900e+01, 1.400e+01, 4.800e+01, 7.290e+02,\n",
- " 5.300e+01, 5.280e+02, 1.300e+02, 1.920e+02, 2.367e+03, 4.130e+02,\n",
- " 1.200e+01, 6.600e+01, 2.300e+01, 6.000e+00, 1.600e+01, 2.030e+02,\n",
- " 2.000e+00, 6.000e+00, 7.000e+00, 1.100e+01, 2.000e+00, 3.900e+01,\n",
- " 2.670e+02, 4.100e+01, 2.910e+02, 1.370e+02, 2.700e+01, 1.140e+02,\n",
- " 1.280e+02, 2.530e+02, 4.400e+01, 3.210e+02, 5.840e+02, 2.100e+01,\n",
- " 1.100e+01, 6.080e+02, 1.600e+01, 5.400e+01, 3.450e+02, 4.000e+00,\n",
- " 5.900e+01, 1.100e+02, 1.800e+01, 1.200e+01, 2.300e+01, 1.330e+02,\n",
- " 1.200e+01, 4.200e+01, 9.400e+01, 1.600e+01, 1.180e+02, 2.560e+02,\n",
- " 8.450e+02, 4.860e+02, 1.310e+02, 1.460e+02, 4.000e+00, 3.100e+01,\n",
- " 1.140e+02, 5.310e+02, 6.000e+00, 1.330e+02, 5.130e+02, 5.800e+01],\n",
- " dtype=float32) GHOST_version
(station)
object
...
standard_name : GHOST version long_name : Globally Harmonised Observational Surface Treatment (GHOST) version units : unitless description : Version of the Globally Harmonised Observational Surface Treatment (GHOST). array(['1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3'],\n",
- " dtype=object) GHSL_average_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL average built up area density 25km long_name : GHSL average built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 25km around the station location. array([0.000000e+00, 1.641689e-02, 3.451080e+00, 7.928834e-01, 2.470266e+00,\n",
- " 4.198371e+00, 2.615061e-01, 1.547885e+00, 5.053829e+00, 1.780731e+00,\n",
- " 3.675361e+00, 4.530497e+00, 7.248289e+00, 7.722292e+00, 1.335771e+00,\n",
- " 5.389296e-01, 4.493267e+00, 2.250240e+00, 9.585333e+00, 9.667427e+00,\n",
- " 5.860271e-01, 8.468797e-01, 0.000000e+00, 9.568463e-01, 7.484965e+00,\n",
- " 9.972881e+00, 6.356478e+00, 7.342062e+00, 1.081794e+01, 8.715981e-02,\n",
- " 5.796069e-02, 1.382651e+00, 2.516628e+00, 1.958444e+00, 9.287160e-01,\n",
- " 6.647419e-01, 2.530408e+00, 6.291438e+00, 1.274882e+00, 2.589620e+00,\n",
- " 9.504257e-01, 2.772359e+00, 1.763192e+00, 0.000000e+00, 5.039271e-01,\n",
- " 0.000000e+00, 1.432233e+01, 0.000000e+00, 2.171165e+00, 1.474489e-01,\n",
- " 1.316300e-02, 3.715080e-01, 5.540162e+00, 7.196336e-01, 2.372977e+00,\n",
- " 3.680726e-01, 5.416950e-02, 1.261725e+00, 4.719187e-01, 2.808142e-01,\n",
- " 3.585190e-01, 1.053950e+00, 8.513233e-01, 7.054963e-01, 8.015128e-01,\n",
- " 8.402810e-03, 2.291435e-01, 2.284175e-02, 1.433760e-02, 2.136332e+00,\n",
- " 3.735501e+00, 6.768995e-01, 9.774334e-01, 4.437283e+00, 4.211910e+00,\n",
- " 3.007562e-01, 4.923448e-01, 2.575564e+00, 8.797497e-01, 4.006590e-01,\n",
- " 1.619805e+00, 7.541102e+00, 2.941880e-01, 4.833968e-01, 5.643592e+00,\n",
- " 6.884031e-01, 3.981796e-02, 9.051574e-01, 6.854321e+00, 1.107726e+01,\n",
- " 5.144464e+00, 2.557497e+00, 1.309789e+00, 6.571144e+00, 6.101875e+00,\n",
- " 8.283137e-01, 5.297230e+00, 1.322004e-01, 5.172078e+00, 1.031147e+00,\n",
- " 5.167284e-01, 4.270855e+00, 1.153668e+00, 1.339450e+00, 1.155674e-01,\n",
- " 2.075151e-01, 1.159228e+01, 1.082785e+00, 2.005096e+00, 2.044497e+00,\n",
- " 0.000000e+00, 1.445777e+00, 0.000000e+00, 2.523717e-02, 1.025008e+00,\n",
- " 1.158988e+00, 1.880750e-01, 2.870233e-01, 4.256294e-02, 8.264276e-01,\n",
- " 6.642136e+00, 5.101707e+00, 1.218093e+01, 1.989838e+01, 1.816336e+01,\n",
- " 1.476102e+00, 5.189290e-03, 9.017465e-02, 0.000000e+00, 9.208214e-01,\n",
- " 1.412649e-01, 1.858197e+00, 1.173063e+00, 0.000000e+00, 1.958592e+00,\n",
- " 1.846698e+00, 0.000000e+00, 8.816104e-05, 1.119897e+00, 2.483717e+00,\n",
- " 6.325631e-01, 9.276118e-01, 2.028772e+00, 2.411625e-02, 6.078035e-02,\n",
- " 6.349144e-03, 2.117275e+00, 3.696595e-01, 1.844982e+00, 3.478464e+00,\n",
- " 8.611571e-01, 4.373772e-01, 6.845628e-02, 3.827053e-01, 6.236709e-01,\n",
- " 1.579185e+00, 3.834881e+00, 1.627478e+00, 1.692341e+00, 7.824045e-01,\n",
- " 5.020796e+00, 1.079307e-01, 1.158018e+01, 4.559449e-03, nan,\n",
- " 7.309250e-01, 3.054895e-02, 3.118353e-01], dtype=float32) GHSL_average_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL average built up area density 5km long_name : GHSL average built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 5km around the station location. array([0.000000e+00, 2.178124e-01, 1.762877e+00, 1.173100e+00, 3.447264e+00,\n",
- " 1.157241e+00, 0.000000e+00, 1.082312e-01, 2.730326e+00, 1.097038e+00,\n",
- " 7.343925e-01, 4.117237e+00, 7.932886e+00, 3.585407e+00, 0.000000e+00,\n",
- " 5.709864e-01, 2.330912e+01, 3.738471e+00, 1.455563e+01, 1.084787e+01,\n",
- " 2.159310e-01, 3.609302e+00, 0.000000e+00, 7.879626e-02, 1.005981e+01,\n",
- " 9.531594e+00, 8.789180e+00, 5.631969e+00, 6.667284e+00, 0.000000e+00,\n",
- " 1.582758e-02, 6.211513e-01, 1.664067e+00, 1.201386e+00, 9.626284e-02,\n",
- " 8.676528e+00, 7.920068e-01, 8.619682e-01, 3.290072e-01, 8.044924e-01,\n",
- " 2.644389e+00, 3.581728e+00, 7.409145e-01, 0.000000e+00, 1.083261e+00,\n",
- " 0.000000e+00, 6.965761e+00, 0.000000e+00, 4.894001e-01, 7.065599e-02,\n",
- " 7.999263e-04, 8.400992e-01, 2.238591e+00, 4.062458e+00, 1.188911e+00,\n",
- " 1.745365e+00, 5.404123e-02, 2.044145e+00, 2.338474e-02, 6.927220e-01,\n",
- " 1.767892e-01, 4.340597e-01, 6.455219e-01, 1.024512e+00, 7.908591e-02,\n",
- " 1.758303e-02, 7.507005e-01, 8.004932e-04, 1.600209e-03, 4.782001e-01,\n",
- " 4.639083e+00, 2.510897e-01, 2.263173e-01, 1.250228e+00, 6.538601e+00,\n",
- " 1.250346e-01, 1.827326e-01, 9.651652e-01, 3.808995e-02, 0.000000e+00,\n",
- " 3.267201e-01, 1.344514e+00, 2.406637e-01, 2.401341e-01, 2.844512e+00,\n",
- " 8.047519e-02, 1.229706e-02, 1.148596e+00, 8.515395e+00, 1.691308e-01,\n",
- " 4.246195e+00, 1.425020e+00, 1.462115e+00, 4.663021e+00, 1.962126e+00,\n",
- " 7.194607e-01, 1.059486e+01, 2.554662e+00, 1.097080e+00, 1.507694e+00,\n",
- " 3.357595e-02, 5.652899e+00, 3.537214e-01, 7.504822e-02, 5.260807e-01,\n",
- " 4.769939e-01, 9.996733e+00, 2.118837e-01, 1.207809e+00, 4.746772e-01,\n",
- " 0.000000e+00, 1.382775e+00, 0.000000e+00, 4.121612e-01, 9.242194e-01,\n",
- " 2.173033e+00, 2.112249e-01, 2.668983e-01, 2.128088e-02, 5.657371e+00,\n",
- " 5.485679e+00, 2.886175e+00, 3.586135e+00, 1.284233e+01, 7.272482e+00,\n",
- " 8.062506e-02, 3.034798e-03, 9.131713e-03, 0.000000e+00, 2.589814e-01,\n",
- " 1.616438e-02, 1.805817e+00, 3.091420e-01, 0.000000e+00, 6.698462e+00,\n",
- " 1.806687e-02, 0.000000e+00, 0.000000e+00, 1.867657e+00, 8.254259e-01,\n",
- " 4.061785e+00, 1.361787e-02, 1.437942e+00, 0.000000e+00, 2.318183e-02,\n",
- " 1.522284e-02, 1.873920e+00, 3.908790e-02, 7.051749e-01, 2.862581e-01,\n",
- " 8.917750e-01, 4.788742e-03, 2.964287e-02, 3.550355e-01, 3.375556e-01,\n",
- " 2.270696e-01, 1.752070e-01, 3.026851e-02, 1.821911e+00, 1.412027e-01,\n",
- " 9.366841e-01, 2.007703e-01, 4.072711e+00, 0.000000e+00, nan,\n",
- " 6.320625e-01, 0.000000e+00, 4.462050e-01], dtype=float32) GHSL_average_population_density_25km
(station)
float32
...
standard_name : GHSL average population density 25km long_name : GHSL average population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 25km around the station location. array([0.000000e+00, 3.568217e+01, 9.980865e+01, 3.190750e+01, 6.726781e+01,\n",
- " 1.903521e+02, 1.465043e+01, 6.950777e+01, 2.090169e+02, 4.181914e+01,\n",
- " 1.111647e+02, 1.033284e+02, 3.054646e+02, 2.914489e+02, 5.580139e+01,\n",
- " 2.613210e+01, 2.718617e+02, 4.358804e+01, 3.936721e+02, 2.216847e+02,\n",
- " 3.192635e+01, 3.153716e+01, 0.000000e+00, 3.253862e+01, 2.249072e+02,\n",
- " 3.978526e+02, 1.978509e+02, 3.428036e+02, 4.081971e+02, 1.291192e+01,\n",
- " 4.141846e+01, 5.976871e+01, 7.761426e+01, 5.375138e+01, 3.167670e+01,\n",
- " 1.127749e+01, 4.898831e+01, 2.754430e+02, 3.236520e+01, 1.153577e+02,\n",
- " 2.262887e+01, 1.059273e+02, 6.933392e+01, 0.000000e+00, 8.035113e+00,\n",
- " 0.000000e+00, 3.784261e+02, 0.000000e+00, 4.947928e+01, 6.790388e+00,\n",
- " 6.697233e-01, 1.088477e+01, 1.051814e+02, 3.138204e+01, 2.739621e+02,\n",
- " 1.373242e+01, 1.703370e+00, 3.002914e+01, 1.949632e+01, 1.805814e+01,\n",
- " 6.843007e+00, 3.621369e+01, 3.052421e+01, 1.223881e+01, 1.875040e+02,\n",
- " 1.447079e-01, 3.895106e+00, 2.392212e+00, 1.111375e+00, 6.129661e+01,\n",
- " 8.542714e+01, 1.366323e+01, 2.474633e+01, 1.228301e+02, 6.223643e+01,\n",
- " 1.781759e+01, 1.225262e+01, 5.170035e+01, 3.290307e+01, 1.588643e+01,\n",
- " 2.694235e+01, 2.114845e+02, 7.558183e+00, 2.699802e+01, 2.290650e+02,\n",
- " 4.549244e+01, 2.541747e+00, 4.061278e+01, 4.023341e+02, 5.586509e+02,\n",
- " 2.820373e+02, 7.914844e+01, 5.536787e+01, 2.179019e+02, 3.757118e+02,\n",
- " 4.958083e+01, 1.798999e+02, 8.834999e+00, 2.425554e+02, 4.248013e+01,\n",
- " 1.942521e+01, 9.634933e+01, 5.070405e+01, 2.880951e+02, 6.044481e+00,\n",
- " 6.659240e+00, 4.672126e+02, 5.658886e+01, 5.670205e+01, 1.165914e+02,\n",
- " 0.000000e+00, 4.831990e+01, 8.448791e-02, 8.890940e-01, 3.994497e+01,\n",
- " 8.159893e+01, 5.042799e+00, 1.060563e+01, 5.302037e+00, 3.427647e+01,\n",
- " 2.447528e+02, 1.906743e+02, 3.186886e+02, 7.659880e+02, 7.489997e+02,\n",
- " 3.012765e+01, 4.445325e-01, 5.833637e+00, 0.000000e+00, 2.191649e+01,\n",
- " 6.931715e+00, 3.664505e+01, 3.269740e+01, 0.000000e+00, 5.546283e+01,\n",
- " 1.344142e+02, 0.000000e+00, 3.210384e-01, 6.782119e+01, 1.451961e+02,\n",
- " 1.807269e+01, 4.862088e+01, 1.635670e+02, 4.603565e+00, 2.567884e+00,\n",
- " 1.369664e-01, 5.944976e+01, 1.119758e+01, 6.363085e+01, 7.001279e+01,\n",
- " 3.880754e+01, 1.061801e+01, 2.944844e+00, 1.101898e+01, 2.447752e+01,\n",
- " 8.707396e+01, 1.655813e+02, 7.425315e+01, 1.034081e+02, 3.454342e+01,\n",
- " 1.164009e+02, 3.855874e+00, 1.956914e+02, 5.680043e-04, nan,\n",
- " 2.559976e+01, 1.299583e+02, 9.822782e+00], dtype=float32) GHSL_average_population_density_5km
(station)
float32
...
standard_name : GHSL average population density 5km long_name : GHSL average population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 5km around the station location. array([0.000000e+00, 4.690972e+02, 3.103303e+01, 3.508097e+01, 8.357281e+01,\n",
- " 7.617443e+01, 0.000000e+00, 2.803909e+01, 9.498553e+01, 2.553344e+01,\n",
- " 2.845852e+01, 1.096564e+02, 1.904848e+02, 5.720120e+01, 0.000000e+00,\n",
- " 1.987993e+01, 1.588540e+03, 6.831900e+01, 4.451785e+02, 2.379049e+02,\n",
- " 1.170464e+01, 1.557633e+02, 0.000000e+00, 1.805310e+00, 2.232487e+02,\n",
- " 3.019567e+02, 2.615233e+02, 2.469198e+02, 2.144834e+02, 0.000000e+00,\n",
- " 1.000541e+01, 1.336399e+01, 5.976002e+01, 2.118015e+01, 2.695873e+00,\n",
- " 1.579932e+02, 1.385422e+01, 4.974121e+01, 9.051698e+00, 6.027180e+01,\n",
- " 3.681785e+01, 1.945499e+02, 2.302906e+01, 0.000000e+00, 1.170047e+01,\n",
- " 0.000000e+00, 1.907815e+02, 0.000000e+00, 7.951523e+00, 3.214146e+00,\n",
- " 0.000000e+00, 2.978236e+01, 3.532858e+01, 2.466392e+02, 1.457125e+02,\n",
- " 5.406254e+01, 9.787757e-01, 3.198607e+01, 1.136314e+00, 5.891815e+01,\n",
- " 3.874274e+00, 1.062251e+01, 2.402342e+01, 1.628610e+01, 2.137181e+01,\n",
- " 2.845254e-01, 1.005700e+01, 1.440888e-01, 1.784233e-01, 1.545241e+01,\n",
- " 1.057307e+02, 7.796998e+00, 7.325947e+00, 3.691739e+01, 8.081860e+01,\n",
- " 8.280608e+00, 4.802943e+00, 2.830708e+01, 1.852106e+00, 1.700853e+00,\n",
- " 7.315959e+00, 4.145933e+01, 1.970219e+00, 1.593568e+01, 1.091642e+02,\n",
- " 6.647443e+00, 2.362038e-01, 4.600963e+01, 4.361660e+02, 3.396060e+00,\n",
- " 2.143313e+02, 3.766386e+01, 7.087405e+01, 1.456985e+02, 1.298899e+02,\n",
- " 4.017353e+01, 3.977556e+02, 1.118931e+02, 4.404686e+01, 6.715195e+01,\n",
- " 2.973364e+00, 1.421294e+02, 1.949533e+01, 1.780552e+01, 2.974153e+01,\n",
- " 1.002674e+01, 3.728353e+02, 1.566943e+01, 6.660810e+00, 3.033878e+01,\n",
- " 0.000000e+00, 4.079543e+01, 2.098099e+00, 1.449848e+01, 2.455117e+01,\n",
- " 1.426273e+02, 2.542975e+00, 9.341696e+00, 4.373961e+00, 2.026381e+02,\n",
- " 1.797982e+02, 6.053999e+01, 8.839283e+01, 3.067233e+02, 1.869860e+02,\n",
- " 2.518704e+00, 2.775064e-01, 8.188425e-01, 0.000000e+00, 7.892782e+00,\n",
- " 1.129082e+00, 3.514456e+01, 1.014561e+01, 0.000000e+00, 2.087034e+02,\n",
- " 2.136142e-01, 0.000000e+00, 6.060986e-01, 9.335101e+01, 4.803714e+01,\n",
- " 6.946500e+01, 1.229736e+00, 7.673063e+01, 0.000000e+00, 1.034389e+00,\n",
- " 1.321983e-01, 5.870374e+01, 1.243675e+00, 2.242510e+01, 5.536726e+00,\n",
- " 2.430091e+01, 1.516435e-01, 1.447979e+00, 1.302931e+01, 1.017144e+01,\n",
- " 3.515065e+01, 1.289989e+01, 2.165792e+00, 7.235415e+01, 5.460165e+00,\n",
- " 1.734164e+01, 1.086669e+00, 4.020491e+01, 0.000000e+00, nan,\n",
- " 3.256375e+01, 0.000000e+00, 7.023504e-02], dtype=float32) GHSL_built_up_area_density
(station)
float32
...
standard_name : GHSL built up area density long_name : GHSL built up area density units : % description : Global Human Settlement Layer (GHSL) built up area density (technical label: GHS_BUILT_LDSMT_GLOBE_R2018A), in units of built-up area percent per gridcell (0-100). The product is a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). Native resolution of 0.25 x 0.25 kilometres. array([0.00000e+00, 0.00000e+00, 1.02944e+01, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 1.93600e+01,\n",
- " 0.00000e+00, 6.62080e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 5.00000e+00, 1.36528e+01, 2.13904e+01, 2.19152e+01,\n",
- " 1.63520e+00, 5.03600e+01, 0.00000e+00, 0.00000e+00, 3.84000e-01,\n",
- " 5.02400e+01, 4.01440e+00, 0.00000e+00, 2.67840e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 4.55328e+01, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 6.24640e+00,\n",
- " 0.00000e+00, 5.50912e+01, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 7.38400e+00, 8.41600e-01, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 2.39584e+01, 3.56480e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 1.21440e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 5.82000e+01,\n",
- " 0.00000e+00, 0.00000e+00, 1.13376e+01, 0.00000e+00, 0.00000e+00,\n",
- " 4.39520e+00, 5.56160e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 4.87840e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 3.56528e+01, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 2.46400e+00,\n",
- " 0.00000e+00, 2.06368e+01, 0.00000e+00, 6.36000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 1.84960e+00,\n",
- " 0.00000e+00, 2.79360e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 2.16960e+00, 0.00000e+00, 3.89840e+01, 0.00000e+00, 3.27696e+01,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 3.04000e-02, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 4.63840e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 7.16960e+00, 2.82400e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 5.60000e-02, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 1.06560e+01, 0.00000e+00,\n",
- " 5.78240e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, nan,\n",
- " 0.00000e+00, 0.00000e+00, 1.00512e+01], dtype=float32) GHSL_max_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL max built up area density 25km long_name : GHSL max built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 25km around the station location. array([ 0., 21., 100., 99., 100., 99., 100., 100., 100., 99., 100., 100.,\n",
- " 100., 100., 100., 97., 100., 100., 100., 100., 93., 100., 0., 100.,\n",
- " 100., 100., 100., 100., 100., 84., 83., 99., 100., 100., 99., 100.,\n",
- " 100., 100., 100., 100., 99., 98., 99., 0., 98., 0., 100., 0.,\n",
- " 100., 82., 22., 93., 100., 100., 99., 97., 67., 100., 99., 97.,\n",
- " 96., 100., 98., 100., 89., 14., 84., 62., 39., 100., 100., 100.,\n",
- " 98., 100., 100., 89., 98., 100., 99., 82., 100., 100., 94., 97.,\n",
- " 100., 99., 49., 100., 100., 100., 100., 100., 100., 100., 100., 99.,\n",
- " 100., 95., 100., 100., 98., 100., 96., 100., 76., 63., 100., 100.,\n",
- " 100., 95., 0., 100., 0., 77., 100., 100., 78., 92., 56., 100.,\n",
- " 100., 100., 100., 100., 100., 100., 10., 64., 0., 96., 80., 100.,\n",
- " 98., 0., 100., 100., 0., 2., 94., 100., 100., 100., 98., 43.,\n",
- " 87., 30., 100., 91., 100., 100., 99., 100., 88., 95., 99., 100.,\n",
- " 100., 100., 99., 95., 100., 71., 100., 43., nan, 93., 57., 97.],\n",
- " dtype=float32) GHSL_max_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL max built up area density 5km long_name : GHSL max built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 5km around the station location. array([ 0., 16., 95., 78., 99., 63., 0., 13., 92., 65., 67., 100.,\n",
- " 99., 98., 0., 66., 100., 100., 100., 98., 47., 99., 0., 51.,\n",
- " 100., 100., 100., 99., 99., 0., 7., 64., 79., 81., 27., 100.,\n",
- " 80., 61., 60., 77., 99., 81., 84., 0., 92., 0., 96., 0.,\n",
- " 34., 26., 1., 68., 91., 95., 58., 86., 28., 99., 18., 74.,\n",
- " 62., 43., 55., 94., 13., 5., 74., 1., 1., 52., 100., 39.,\n",
- " 29., 74., 99., 16., 22., 44., 11., 0., 42., 53., 30., 43.,\n",
- " 100., 11., 9., 88., 100., 13., 98., 54., 91., 98., 94., 57.,\n",
- " 100., 95., 61., 67., 12., 100., 37., 11., 45., 28., 97., 28.,\n",
- " 90., 81., 0., 71., 0., 77., 54., 80., 39., 50., 6., 100.,\n",
- " 100., 96., 71., 100., 99., 15., 2., 3., 0., 43., 7., 69.,\n",
- " 27., 0., 98., 6., 0., 0., 93., 83., 100., 4., 87., 0.,\n",
- " 12., 8., 56., 19., 67., 29., 80., 6., 22., 63., 37., 34.,\n",
- " 35., 23., 67., 46., 94., 25., 100., 0., nan, 48., 0., 37.],\n",
- " dtype=float32) GHSL_max_population_density_25km
(station)
float32
...
standard_name : GHSL max population density 25km long_name : GHSL max population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 25km around the station location. array([0.0000e+00, 4.2063e+04, 6.5730e+03, 6.6770e+03, 5.3320e+03, 7.0010e+03,\n",
- " 1.7676e+04, 6.3090e+03, 5.8220e+03, 2.9770e+03, 8.5300e+03, 2.9980e+03,\n",
- " 8.5300e+03, 8.5300e+03, 5.9200e+03, 4.9860e+03, 7.7380e+03, 2.7080e+03,\n",
- " 8.4580e+03, 3.2800e+03, 6.5480e+03, 7.0990e+03, 0.0000e+00, 1.1336e+04,\n",
- " 8.2710e+03, 9.1110e+03, 2.0960e+04, 9.6880e+03, 7.7890e+03, 1.1658e+04,\n",
- " 5.8795e+04, 4.8640e+03, 4.1920e+03, 4.0360e+03, 4.3620e+03, 2.1890e+03,\n",
- " 2.5860e+03, 9.3110e+03, 2.7010e+03, 6.3760e+03, 3.9580e+03, 5.3530e+03,\n",
- " 4.8140e+03, 0.0000e+00, 2.3630e+03, 0.0000e+00, 9.1000e+03, 0.0000e+00,\n",
- " 4.1640e+03, 4.8400e+03, 1.5430e+03, 2.7370e+03, 4.6690e+03, 8.5560e+03,\n",
- " 1.8067e+04, 5.0860e+03, 1.7510e+03, 3.9900e+03, 5.2520e+03, 6.4640e+03,\n",
- " 2.6000e+03, 7.3590e+03, 4.5870e+03, 2.5160e+03, 3.7168e+04, 1.6410e+03,\n",
- " 2.1120e+03, 6.1570e+03, 2.9770e+03, 3.7970e+03, 3.3730e+03, 3.5000e+03,\n",
- " 2.9770e+03, 8.9170e+03, 2.0470e+03, 7.1390e+03, 2.5260e+03, 2.7140e+03,\n",
- " 4.2490e+03, 3.3080e+03, 2.2750e+03, 5.5500e+03, 5.4230e+03, 5.7050e+03,\n",
- " 1.4285e+04, 9.0180e+03, 5.1940e+03, 6.8060e+03, 2.0982e+04, 3.7103e+04,\n",
- " 2.7409e+04, 1.0314e+04, 7.0960e+03, 1.9072e+04, 2.0982e+04, 6.7170e+03,\n",
- " 1.2793e+04, 4.4160e+03, 2.0415e+04, 5.5150e+03, 5.0310e+03, 2.5650e+03,\n",
- " 4.5340e+03, 2.2953e+04, 3.0590e+03, 1.1171e+04, 1.6010e+04, 1.0313e+04,\n",
- " 1.4621e+04, 1.6802e+04, 0.0000e+00, 4.5340e+03, 6.0000e+00, 2.6660e+03,\n",
- " 4.3400e+03, 9.0120e+03, 2.7810e+03, 4.0150e+03, 7.1200e+03, 7.9750e+03,\n",
- " 5.3290e+03, 5.8130e+03, 4.4290e+03, 8.5190e+03, 6.6160e+03, 3.1760e+03,\n",
- " 7.8400e+02, 4.9090e+03, 0.0000e+00, 2.7580e+03, 5.2170e+03, 2.6850e+03,\n",
- " 2.5930e+03, 0.0000e+00, 3.0280e+03, 2.4824e+04, 0.0000e+00, 2.7600e+02,\n",
- " 8.6670e+03, 8.3350e+03, 4.1900e+03, 6.0790e+03, 1.8005e+04, 8.8350e+03,\n",
- " 3.5390e+03, 7.6900e+02, 4.2790e+03, 3.8920e+03, 5.0420e+03, 5.2860e+03,\n",
- " 7.4390e+03, 2.9080e+03, 3.7110e+03, 2.9750e+03, 4.0250e+03, 6.5640e+03,\n",
- " 8.3180e+03, 6.0660e+03, 1.4915e+04, 5.9230e+03, 4.6900e+03, 4.4930e+03,\n",
- " 1.9853e+04, 1.7000e+01, nan, 1.0545e+04, 2.7399e+05, 4.1067e+04],\n",
- " dtype=float32) GHSL_max_population_density_5km
(station)
float32
...
standard_name : GHSL max population density 5km long_name : GHSL max population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 5km around the station location. array([0.0000e+00, 3.1821e+04, 1.6600e+03, 2.2810e+03, 2.2720e+03, 3.9410e+03,\n",
- " 0.0000e+00, 2.8490e+03, 2.8600e+03, 1.3560e+03, 2.2670e+03, 2.9980e+03,\n",
- " 3.0270e+03, 1.6240e+03, 0.0000e+00, 2.0880e+03, 6.9420e+03, 1.7380e+03,\n",
- " 3.7310e+03, 2.2750e+03, 2.0590e+03, 7.0990e+03, 0.0000e+00, 1.1460e+03,\n",
- " 2.5490e+03, 3.3070e+03, 5.5400e+03, 5.7610e+03, 3.7810e+03, 0.0000e+00,\n",
- " 5.0150e+03, 1.6240e+03, 2.6530e+03, 1.5830e+03, 9.7600e+02, 2.0890e+03,\n",
- " 1.3140e+03, 3.3330e+03, 1.4570e+03, 5.0990e+03, 1.3520e+03, 4.2180e+03,\n",
- " 2.5660e+03, 0.0000e+00, 1.0230e+03, 0.0000e+00, 3.5420e+03, 0.0000e+00,\n",
- " 6.2200e+02, 8.6700e+02, 0.0000e+00, 2.3560e+03, 1.3740e+03, 8.5560e+03,\n",
- " 7.3760e+03, 2.5930e+03, 4.5600e+02, 1.7070e+03, 9.9400e+02, 5.6600e+03,\n",
- " 1.2310e+03, 1.0900e+03, 1.7200e+03, 1.4010e+03, 7.8500e+03, 1.5300e+02,\n",
- " 9.6200e+02, 1.1100e+02, 1.4500e+02, 1.7370e+03, 2.4130e+03, 1.0500e+03,\n",
- " 8.1700e+02, 1.9330e+03, 1.6040e+03, 1.0410e+03, 5.4900e+02, 1.3980e+03,\n",
- " 5.1700e+02, 3.0000e+00, 1.1000e+03, 1.4970e+03, 2.2300e+02, 2.6030e+03,\n",
- " 6.4370e+03, 6.2500e+02, 1.3400e+02, 5.5150e+03, 6.8250e+03, 4.3600e+02,\n",
- " 7.7710e+03, 3.3120e+03, 5.5430e+03, 6.5120e+03, 5.9500e+03, 3.4840e+03,\n",
- " 1.0109e+04, 4.4160e+03, 3.4520e+03, 3.0300e+03, 1.1100e+03, 2.5650e+03,\n",
- " 1.8400e+03, 2.3620e+03, 2.5230e+03, 1.5220e+03, 8.7160e+03, 3.0470e+03,\n",
- " 1.3660e+03, 6.9540e+03, 0.0000e+00, 2.0100e+03, 6.0000e+00, 2.6660e+03,\n",
- " 1.3550e+03, 4.7080e+03, 4.8900e+02, 1.6290e+03, 1.0930e+03, 4.4960e+03,\n",
- " 3.2320e+03, 2.2730e+03, 1.7960e+03, 3.2010e+03, 4.2590e+03, 4.3500e+02,\n",
- " 1.5500e+02, 2.7100e+02, 0.0000e+00, 1.2310e+03, 7.9600e+02, 1.3900e+03,\n",
- " 8.1100e+02, 0.0000e+00, 3.0040e+03, 1.1300e+02, 0.0000e+00, 2.0000e+00,\n",
- " 4.2030e+03, 8.3350e+03, 1.7670e+03, 3.3000e+02, 4.2410e+03, 0.0000e+00,\n",
- " 5.0400e+02, 6.9000e+01, 2.2130e+03, 5.5100e+02, 1.9790e+03, 4.8300e+02,\n",
- " 2.3020e+03, 1.4000e+02, 9.2300e+02, 2.1400e+03, 1.1020e+03, 4.7590e+03,\n",
- " 2.2370e+03, 1.6070e+03, 3.7750e+03, 1.6310e+03, 1.7070e+03, 3.4600e+02,\n",
- " 2.3210e+03, 0.0000e+00, nan, 2.3540e+03, 0.0000e+00, 9.0000e+00],\n",
- " dtype=float32) GHSL_modal_settlement_model_classification_25km
(station)
object
...
standard_name : GHSL modal settlement model classification 25km long_name : GHSL modal settlement model classification in 25km radius units : unitless description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 25km around station location. array(['water', 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'water', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'water', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'water', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural', 'water',\n",
- " 'water', 'water', 'water', 'water', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'low density rural', 'very low density rural', 'low density rural',\n",
- " 'water', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'nan', 'water',\n",
- " 'very low density rural', 'water'], dtype=object) GHSL_modal_settlement_model_classification_5km
(station)
object
...
standard_name : GHSL modal settlement model classification 5km long_name : GHSL modal settlement model classification in 5km radius units : unitless description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 5km around station location. array(['water', 'water', 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'urban centre',\n",
- " 'very low density rural', 'low density rural', 'low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'water', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'water', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'suburban/peri-urban',\n",
- " 'very low density rural', 'water', 'very low density rural', 'water',\n",
- " 'water', 'water', 'water', 'very low density rural', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'low density rural', 'very low density rural', 'low density rural',\n",
- " 'suburban/peri-urban', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'nan', 'water',\n",
- " 'very low density rural', 'water'], dtype=object) GHSL_population_density
(station)
float32
...
standard_name : GHSL population density long_name : GHSL population density units : xx km–2 description : Global Human Settlement Layer (GHSL) population density (technical label: GHS_POP_MT_GLOBE_R2019A), in populus per squared kilometre. It depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the GHSL global layer per corresponding epoch. Native resolution of 0.25 x 0.25 kilometres. array([0.000000e+00, 0.000000e+00, 1.782431e+02, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.731908e+02,\n",
- " 0.000000e+00, 1.914441e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 3.471132e+02, 2.372969e+02, 4.450653e+02, 5.039089e+02,\n",
- " 7.097375e+01, 1.695632e+03, 1.898080e-05, 0.000000e+00, 9.790040e+00,\n",
- " 1.599291e+03, 1.526475e+02, 0.000000e+00, 7.273004e+01, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 9.513987e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.878987e+01,\n",
- " 0.000000e+00, 1.613998e+03, 0.000000e+00, 0.000000e+00, 9.863296e-01,\n",
- " 0.000000e+00, 0.000000e+00, 1.114266e+02, 4.753482e+01, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 4.123594e+02, 1.724864e+02, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.445650e+02,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 9.625802e+02,\n",
- " 0.000000e+00, 0.000000e+00, 4.447774e+02, 0.000000e+00, 2.500990e+00,\n",
- " 4.356402e+01, 1.567155e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.242259e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.030722e+02, 5.175986e+01, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.099533e+02,\n",
- " 0.000000e+00, 2.089355e+01, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 6.772582e+00, 0.000000e+00, 4.574234e+01,\n",
- " 0.000000e+00, 3.487929e+01, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 6.828555e+01, 0.000000e+00, 8.998964e+02, 0.000000e+00, 7.671004e+02,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.687347e-02, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 1.269492e+00, 1.126304e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 3.004128e+02, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 2.823801e+02, 7.915669e+01, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.890558e+00, 0.000000e+00,\n",
- " 0.000000e+00, 3.651308e-01, 0.000000e+00, 4.936926e+02, 0.000000e+00,\n",
- " 1.088258e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00, nan,\n",
- " 0.000000e+00, 0.000000e+00, 2.453957e+00], dtype=float32) GHSL_settlement_model_classification
(station)
object
...
standard_name : GHSL settlement model classification long_name : GHSL settlement model classification units : unitless description : Global Human Settlement Layer (GHSL) settlement model classification (technical label: GHS_SMOD_POPMT_GLOBE_R2019A). The classification delineates and classify settlement typologies via a logic of population size, population and built-up area densities as a refinement of the ‘degree of urbanization’ method described by EUROSTAT. The classification is derived by using the GHS_POP_MT_GLOBE_R2019A and GHS_BUILT_LDSMT_GLOBE_R2018A products. The GHS Settlement Model grid is an improvement of the GHS Settlement Grid (R2016A) introducing a more detailed classification of settlements in two levels, also called ‘refined degree of urbanization’. The Settlement Model is provided at detailed level (Second Level - L2). The First Level, as a porting of the Degree of Urbanization adopted by EUROSTAT can be obtained aggregating L2. Native resolution of 1.0 x 1.0 kilometres. array(['very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'low density rural', 'low density rural',\n",
- " 'low density rural', 'very low density rural', 'very low density rural',\n",
- " 'suburban/peri-urban', 'low density rural', 'low density rural',\n",
- " 'rural cluster', 'very low density rural', 'rural cluster',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'semi-dense urban cluster', 'semi-dense urban cluster',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'rural cluster',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'low density rural', 'very low density rural', 'very low density rural',\n",
- " 'rural cluster', 'very low density rural', 'very low density rural',\n",
- " 'low density rural', 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'semi-dense urban cluster', 'rural cluster', 'rural cluster',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'semi-dense urban cluster', 'very low density rural',\n",
- " 'low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'low density rural', 'very low density rural', 'low density rural',\n",
- " 'low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'nan', 'water', 'very low density rural',\n",
- " 'very low density rural'], dtype=object) GPW_average_population_density_25km
(station)
float32
...
standard_name : GPW average population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 25 km around the station location. array([0.000000e+00, 3.676580e+00, 1.043439e+02, 2.891395e+01, 6.576196e+01,\n",
- " 2.002287e+02, 1.932350e+01, 7.101602e+01, 2.097893e+02, 4.481235e+01,\n",
- " 1.687959e+02, 1.031429e+02, 3.593801e+02, 3.602792e+02, 5.492215e+01,\n",
- " 2.671996e+01, 2.724147e+02, 4.551095e+01, 3.866893e+02, 2.181801e+02,\n",
- " 3.109594e+01, 6.972549e+01, 1.213884e-05, 4.173470e+01, 2.325059e+02,\n",
- " 4.137915e+02, 2.110254e+02, 3.685287e+02, 4.179377e+02, 3.795084e+00,\n",
- " 4.906758e+01, 7.188786e+01, 8.250193e+01, 5.352773e+01, 3.476805e+01,\n",
- " 1.651753e+01, 4.945769e+01, 2.725477e+02, 3.951214e+01, 1.201239e+02,\n",
- " 2.823137e+01, 1.105656e+02, 6.960040e+01, 0.000000e+00, 1.131580e+01,\n",
- " 0.000000e+00, 4.083706e+02, 0.000000e+00, 5.340744e+01, 7.582005e+00,\n",
- " 8.339360e-01, 1.285531e+01, 1.327255e+02, 3.862384e+01, 2.897724e+02,\n",
- " 1.663684e+01, 3.359083e+00, 3.664787e+01, 2.677676e+01, 1.348262e+01,\n",
- " 7.627829e+00, 4.842535e+01, 3.060976e+01, 1.909778e+01, 2.306035e+02,\n",
- " 6.721435e-01, 1.101931e+01, 2.194751e+00, 1.015538e+00, 7.271815e+01,\n",
- " 8.780937e+01, 1.440131e+01, 2.592144e+01, 1.242082e+02, 6.269833e+01,\n",
- " 1.875869e+01, 1.258263e+01, 5.353754e+01, 2.929527e+01, 1.718634e+01,\n",
- " 2.733528e+01, 2.142651e+02, 7.796501e+00, 2.926344e+01, 2.517786e+02,\n",
- " 4.762556e+01, 3.003328e+00, 4.112241e+01, 4.263912e+02, 5.634548e+02,\n",
- " 3.181353e+02, 8.138203e+01, 6.382910e+01, 2.198636e+02, 3.920922e+02,\n",
- " 5.497407e+01, 2.054859e+02, 1.422717e+01, 2.442289e+02, 3.954022e+01,\n",
- " 2.253697e+01, 9.857957e+01, 4.844148e+01, 2.391857e+02, 7.922302e+00,\n",
- " 1.050398e+01, 5.112840e+02, 5.714964e+01, 6.447959e+01, 1.192496e+02,\n",
- " 0.000000e+00, 4.986892e+01, 1.267819e-01, 1.323066e+00, 7.507764e+01,\n",
- " 8.046577e+01, 8.533894e+00, 1.901917e+01, 1.291049e+01, 5.241627e+01,\n",
- " 2.869067e+02, 1.844621e+02, 3.418260e+02, 8.262206e+02, 7.747103e+02,\n",
- " 4.711851e+01, 2.940538e+00, 4.054120e+00, 4.309018e-02, 2.309335e+01,\n",
- " 3.564212e+00, 3.743097e+01, 3.728577e+01, 0.000000e+00, 5.972689e+01,\n",
- " 1.457091e+02, 0.000000e+00, 6.820842e-01, 6.970630e+01, 1.380541e+02,\n",
- " 2.033021e+01, 4.553246e+01, 1.686101e+02, 3.782959e-02, 1.712330e+00,\n",
- " 1.115660e+00, 7.542218e+01, 1.646836e+01, 7.729394e+01, 8.965453e+01,\n",
- " 4.288913e+01, 8.058159e+00, 3.422457e+00, 1.209941e+01, 2.527039e+01,\n",
- " 9.015112e+01, 1.792928e+02, 6.864024e+01, 1.111997e+02, 3.141101e+01,\n",
- " 1.195565e+02, 3.268207e+00, 1.872376e+02, 5.126638e-02, 0.000000e+00,\n",
- " 2.306228e+01, 8.593194e+01, 2.638384e+01], dtype=float32) GPW_average_population_density_5km
(station)
float32
...
standard_name : GPW average population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 5 km around the station location. array([0.000000e+00, 2.912720e+00, 3.400988e+01, 2.154398e+01, 6.598087e+01,\n",
- " 9.970491e+01, 1.105146e+01, 5.292439e+01, 1.186672e+02, 5.602424e+01,\n",
- " 4.912894e+01, 1.303824e+02, 1.811169e+02, 9.129450e+01, 1.785338e+01,\n",
- " 2.909961e+01, 1.471922e+03, 5.477317e+01, 1.668051e+02, 3.134350e+02,\n",
- " 2.134855e+01, 4.047465e+02, 1.638963e-05, 1.377935e+01, 2.175175e+02,\n",
- " 2.899651e+02, 3.711274e+02, 3.319088e+02, 2.799866e+02, 3.581799e+00,\n",
- " 1.348548e+02, 1.979032e+01, 5.576019e+01, 2.449666e+01, 1.818749e+01,\n",
- " 1.031549e+02, 3.602426e+01, 4.057293e+02, 2.431496e+01, 1.799521e+02,\n",
- " 3.717742e+01, 1.794494e+02, 7.255676e+01, 0.000000e+00, 1.384620e+01,\n",
- " 0.000000e+00, 3.133285e+02, 0.000000e+00, 2.374423e+01, 3.775826e+00,\n",
- " 1.640763e-01, 2.272928e+01, 1.413851e+02, 2.125484e+02, 6.483813e+02,\n",
- " 3.296441e+01, 1.688196e+00, 3.147202e+01, 2.301476e+01, 1.118226e+01,\n",
- " 5.425424e+00, 1.234548e+01, 3.023590e+01, 2.765573e+01, 1.778005e+02,\n",
- " 1.300927e+00, 8.720321e+00, 3.050449e+00, 1.218582e+00, 4.770991e+01,\n",
- " 6.724979e+01, 8.731964e+00, 7.686491e+00, 4.421979e+01, 7.920045e+01,\n",
- " 7.858858e+00, 4.791967e+00, 4.259996e+01, 3.844222e+01, 2.610172e+00,\n",
- " 1.217603e+01, 5.982760e+01, 1.467179e+00, 1.501577e+01, 1.132830e+02,\n",
- " 7.346476e+00, 6.666635e-01, 4.407020e+01, 4.410812e+02, 8.965228e+00,\n",
- " 2.337221e+02, 4.066570e+01, 7.409615e+01, 1.502415e+02, 1.244322e+02,\n",
- " 5.046038e+01, 5.054806e+02, 1.796715e+02, 4.213168e+01, 4.581692e+01,\n",
- " 3.445216e+01, 2.692736e+02, 6.864005e+01, 2.874861e+02, 3.864462e+01,\n",
- " 1.721245e+01, 4.380867e+02, 1.619647e+01, 1.605528e+01, 3.136322e+01,\n",
- " 0.000000e+00, 6.509299e+01, 3.169551e+00, 2.388034e+01, 5.419331e+01,\n",
- " 1.573701e+02, 1.121844e+01, 5.210264e+00, 1.032769e+01, 2.247625e+02,\n",
- " 1.727518e+02, 9.614613e+01, 2.416419e+02, 5.982119e+02, 2.203112e+02,\n",
- " 1.083303e+01, 3.014866e+00, 4.387572e+00, 8.884601e-02, 4.875578e+01,\n",
- " 2.958232e+00, 1.261045e+02, 2.084609e+01, 0.000000e+00, 7.042564e+01,\n",
- " 9.063771e-01, 0.000000e+00, 1.022637e+00, 5.729935e+01, 5.940437e+01,\n",
- " 7.064051e+01, 1.806690e+01, 8.454706e+01, 4.031043e-02, 2.291488e+00,\n",
- " 3.564391e-01, 1.411496e+02, 1.175669e+01, 6.427244e+01, 2.211434e+01,\n",
- " 3.437179e+01, 5.238231e+00, 1.950056e+00, 1.613721e+01, 1.364565e+01,\n",
- " 4.778874e+01, 1.887730e+01, 2.253966e+01, 7.289510e+01, 1.572427e+01,\n",
- " 8.078988e+01, 1.976923e+01, 4.117908e+01, 3.095616e-03, 0.000000e+00,\n",
- " 3.171951e+01, 8.547469e+01, 4.788679e-02], dtype=float32) GPW_max_population_density_25km
(station)
float32
...
standard_name : GPW max population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 25 km around the station location. array([0.000000e+00, 7.596221e+00, 4.196918e+02, 1.047600e+03, 5.342902e+02,\n",
- " 2.169918e+03, 1.160142e+02, 1.769506e+03, 2.238533e+03, 2.902039e+02,\n",
- " 4.399082e+03, 4.939289e+02, 4.399083e+03, 4.399083e+03, 1.466429e+03,\n",
- " 3.816585e+02, 2.170391e+03, 1.127037e+03, 2.730783e+03, 1.094270e+03,\n",
- " 9.727322e+01, 1.580782e+03, 2.069240e-05, 1.407145e+03, 3.958634e+03,\n",
- " 4.454292e+03, 4.435365e+03, 3.825571e+03, 2.990599e+03, 1.299165e+02,\n",
- " 3.957805e+02, 2.023634e+03, 5.896717e+02, 3.052775e+02, 2.376391e+02,\n",
- " 2.457559e+02, 2.404993e+02, 1.705172e+03, 1.447625e+02, 4.138253e+02,\n",
- " 1.378877e+03, 5.954080e+02, 3.425591e+02, 0.000000e+00, 1.155959e+03,\n",
- " 0.000000e+00, 7.223920e+03, 0.000000e+00, 2.850471e+03, 8.838765e+02,\n",
- " 1.353755e+02, 6.711597e+01, 8.639467e+02, 7.393783e+02, 5.347466e+03,\n",
- " 7.367470e+01, 1.805890e+01, 2.303136e+03, 3.123501e+02, 4.939915e+01,\n",
- " 1.186029e+02, 6.988399e+02, 9.666424e+01, 1.213562e+03, 3.737029e+03,\n",
- " 1.959151e+01, 5.019508e+01, 3.050450e+00, 1.307162e+00, 7.499749e+02,\n",
- " 1.456541e+03, 6.132180e+02, 2.941577e+02, 1.925251e+03, 4.256683e+02,\n",
- " 1.595856e+03, 1.874457e+02, 6.907155e+02, 1.101565e+03, 3.409009e+02,\n",
- " 5.188201e+02, 4.473265e+03, 2.796940e+03, 2.248724e+03, 9.835548e+03,\n",
- " 4.619351e+03, 2.458400e+03, 2.901922e+03, 1.710923e+04, 1.318641e+04,\n",
- " 1.943179e+04, 4.608442e+03, 4.449922e+03, 9.243162e+03, 1.710923e+04,\n",
- " 4.496920e+03, 6.915309e+03, 3.480686e+03, 8.319948e+03, 1.605188e+02,\n",
- " 6.346664e+02, 3.353836e+02, 3.970044e+02, 1.997083e+03, 9.257162e+02,\n",
- " 1.441793e+03, 8.466245e+03, 4.270815e+03, 1.068325e+04, 8.250134e+03,\n",
- " 0.000000e+00, 2.176671e+02, 4.553756e+01, 5.645988e+01, 2.169255e+02,\n",
- " 2.955543e+02, 4.439969e+01, 1.672228e+02, 1.130873e+02, 2.998557e+03,\n",
- " 1.310236e+03, 2.375627e+03, 2.493601e+03, 6.149771e+03, 4.327395e+03,\n",
- " 3.059219e+02, 8.158216e+00, 8.981381e+00, 1.098970e-01, 1.712690e+02,\n",
- " 4.330686e+00, 4.002065e+02, 1.417241e+02, 0.000000e+00, 2.002448e+02,\n",
- " 9.201427e+03, 0.000000e+00, 2.152332e+01, 8.081088e+02, 1.121058e+03,\n",
- " 2.818542e+02, 2.788280e+03, 1.012623e+04, 5.401184e-02, 2.291489e+00,\n",
- " 2.764339e+00, 1.592660e+03, 9.561663e+02, 1.536779e+03, 3.355430e+03,\n",
- " 4.683061e+03, 5.341991e+01, 1.551821e+01, 6.936597e+01, 6.386975e+02,\n",
- " 2.383873e+03, 2.744521e+03, 4.787817e+02, 1.619820e+03, 3.520659e+02,\n",
- " 7.049489e+02, 2.306286e+03, 6.311625e+03, 1.951150e+01, 0.000000e+00,\n",
- " 2.171837e+03, 1.077469e+02, 3.519015e+04], dtype=float32) GPW_max_population_density_5km
(station)
float32
...
standard_name : GPW max population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 5 km around the station location. array([0.000000e+00, 7.596220e+00, 3.872487e+02, 2.635724e+01, 9.327704e+01,\n",
- " 1.932475e+02, 2.367033e+01, 1.146238e+02, 2.466041e+02, 1.422318e+02,\n",
- " 1.127776e+02, 4.939288e+02, 7.791940e+02, 3.282423e+02, 2.455698e+01,\n",
- " 4.217877e+01, 2.170391e+03, 5.714431e+01, 3.553141e+02, 6.449427e+02,\n",
- " 4.212551e+01, 1.493271e+03, 2.069240e-05, 2.992861e+01, 3.977629e+02,\n",
- " 8.473145e+02, 1.943391e+03, 9.753057e+02, 1.682020e+03, 3.581799e+00,\n",
- " 3.561705e+02, 4.398273e+01, 1.032194e+02, 5.389030e+01, 5.282152e+01,\n",
- " 2.457559e+02, 7.466113e+01, 1.404797e+03, 3.352261e+01, 4.071289e+02,\n",
- " 2.008988e+02, 3.769590e+02, 1.208774e+02, 0.000000e+00, 2.839655e+01,\n",
- " 0.000000e+00, 1.779836e+03, 0.000000e+00, 6.502718e+01, 8.103524e+00,\n",
- " 6.088673e-01, 2.479526e+01, 3.920511e+02, 7.393783e+02, 2.861667e+03,\n",
- " 5.273864e+01, 3.175393e+00, 1.156015e+02, 3.152973e+01, 1.288622e+01,\n",
- " 9.615093e+00, 3.120769e+01, 4.944548e+01, 2.765573e+01, 2.367693e+02,\n",
- " 1.959151e+01, 9.322552e+00, 3.050450e+00, 1.307162e+00, 2.238172e+02,\n",
- " 1.719670e+02, 1.238368e+01, 2.281512e+01, 2.464978e+02, 3.300192e+02,\n",
- " 8.302430e+00, 1.228482e+01, 1.353416e+02, 6.355141e+01, 2.024619e+01,\n",
- " 7.954646e+01, 6.628364e+02, 2.064309e+00, 1.740889e+02, 2.281263e+03,\n",
- " 2.893008e+01, 6.851274e-01, 1.237620e+03, 5.616340e+03, 2.649752e+02,\n",
- " 4.507116e+03, 1.678491e+03, 1.206023e+03, 4.099714e+03, 4.305407e+03,\n",
- " 1.965852e+03, 6.848575e+03, 3.480686e+03, 4.560266e+02, 7.159418e+01,\n",
- " 8.833292e+01, 3.353835e+02, 1.201789e+02, 1.997082e+03, 8.736220e+02,\n",
- " 3.726891e+01, 2.201854e+03, 3.769270e+02, 4.654190e+02, 6.244286e+02,\n",
- " 0.000000e+00, 1.209587e+02, 4.553756e+01, 5.645988e+01, 8.943985e+01,\n",
- " 2.955542e+02, 2.718571e+01, 1.994590e+01, 1.310321e+01, 2.127132e+03,\n",
- " 2.805988e+02, 1.474622e+02, 2.736410e+02, 1.525705e+03, 1.812846e+03,\n",
- " 7.832114e+01, 3.252899e+00, 4.387607e+00, 1.098970e-01, 5.004140e+01,\n",
- " 4.330686e+00, 1.726357e+02, 5.955319e+01, 0.000000e+00, 7.122749e+01,\n",
- " 5.542551e+00, 0.000000e+00, 2.518394e+00, 9.636668e+01, 2.426768e+02,\n",
- " 2.818542e+02, 2.250613e+01, 1.958325e+02, 5.401183e-02, 2.291489e+00,\n",
- " 2.764338e+00, 2.738259e+02, 2.809345e+01, 1.990008e+02, 7.795489e+01,\n",
- " 4.181690e+01, 1.385086e+01, 1.950057e+00, 1.710931e+01, 2.055932e+02,\n",
- " 4.685620e+02, 2.013748e+02, 1.757976e+02, 4.758453e+02, 2.981873e+01,\n",
- " 1.883994e+02, 1.935876e+03, 4.163841e+02, 4.666241e-02, 0.000000e+00,\n",
- " 3.004012e+02, 1.077468e+02, 2.653221e-01], dtype=float32) GPW_population_density
(station)
float32
...
standard_name : GPW population density long_name : GPW population density units : xx km–2 description : Gridded Population of the World (GPW), population density, in populus per squared kilometre, from either version 3 and 4 of the provided gridded datasets, dependent on the data year: v3 (1990-2000), v4 (2000-2015). Native resolution of 0.04166 x 0.04166 for v3 data; native resolution of 0.0083 x 0.0083 degrees for v4 data. array([0.000000e+00, 7.596219e+00, 3.727455e+01, 2.160091e+01, 6.370982e+01,\n",
- " 7.343772e+01, 9.315965e+00, 6.090043e+01, 6.510796e+01, 6.826463e+01,\n",
- " 3.772225e+01, 3.420486e+01, 3.691249e+02, 7.935504e+01, 1.726200e+01,\n",
- " 2.718578e+01, 2.170391e+03, 5.714430e+01, 4.576423e+01, 3.144363e+02,\n",
- " 1.854863e+01, 9.635000e+02, 2.069239e-05, 1.087448e+01, 3.977628e+02,\n",
- " 4.340916e+02, 8.674380e+02, 4.810824e+02, 1.608476e+02, 3.581799e+00,\n",
- " 3.561705e+02, 2.397668e+01, 5.991482e+01, 1.817250e+01, 3.504665e+01,\n",
- " 2.413085e+02, 2.268549e+01, 9.255155e+01, 1.566007e+01, 2.357139e+02,\n",
- " 5.666391e+01, 1.830428e+02, 1.083345e+02, 0.000000e+00, 2.839654e+01,\n",
- " 0.000000e+00, 6.737329e+02, 0.000000e+00, 3.361328e+01, 1.009231e+00,\n",
- " 6.088673e-01, 2.479526e+01, 1.210394e+02, 2.683964e+02, 7.582309e+01,\n",
- " 5.273864e+01, 1.791653e+00, 1.156015e+02, 1.649900e+01, 1.040796e+01,\n",
- " 7.115889e+00, 1.366867e+01, 2.215422e+01, 2.765573e+01, 1.961870e+02,\n",
- " 1.959151e+01, 9.322549e+00, 3.050449e+00, 1.307162e+00, 1.091055e+01,\n",
- " 2.971791e+01, 9.780583e+00, 6.008892e+00, 3.013581e+01, 3.300191e+02,\n",
- " 8.302428e+00, 4.554766e+00, 3.828349e+01, 6.355141e+01, 2.558549e+00,\n",
- " 3.855883e+00, 7.174796e+01, 1.456353e+00, 1.348664e+01, 4.410024e+01,\n",
- " 6.646183e+00, 6.851272e-01, 4.985307e+01, 6.500746e+01, 1.561153e+00,\n",
- " 1.111417e+01, 2.619129e+01, 2.740698e+01, 2.270585e+01, 7.810502e+00,\n",
- " 3.342193e+01, 3.205909e+01, 3.927141e+01, 9.589882e+01, 7.159418e+01,\n",
- " 6.761034e+01, 3.353835e+02, 1.201789e+02, 2.187611e+02, 4.365664e+01,\n",
- " 3.267254e+01, 4.102968e+01, 7.405539e-01, 1.033207e+01, 5.284633e-02,\n",
- " 0.000000e+00, 1.209587e+02, 4.553756e+01, 5.645987e+01, 8.943983e+01,\n",
- " 2.955542e+02, 2.718570e+01, 4.414946e+00, 1.042509e+01, 8.647990e+01,\n",
- " 1.683762e+02, 1.143645e+02, 2.736409e+02, 7.359886e+02, 1.859560e+02,\n",
- " 7.547290e+00, 3.252898e+00, 4.387606e+00, 1.098970e-01, 5.004138e+01,\n",
- " 0.000000e+00, 1.726357e+02, 9.563196e+00, 0.000000e+00, 7.122747e+01,\n",
- " 5.542550e+00, 0.000000e+00, 1.172251e+00, 4.029773e+01, 5.022400e+01,\n",
- " 2.818542e+02, 1.703191e+01, 1.958324e+02, 5.401182e-02, 2.291488e+00,\n",
- " 3.105233e-01, 2.738258e+02, 1.088254e+01, 2.435864e+01, 1.901610e+01,\n",
- " 4.181689e+01, 1.315142e+00, 1.950056e+00, 1.710930e+01, 2.718584e+01,\n",
- " 3.195612e+01, 9.911605e+00, 1.283776e+01, 1.271799e+01, 1.500078e+01,\n",
- " 7.439497e+01, 0.000000e+00, 3.542852e+01, 0.000000e+00, 0.000000e+00,\n",
- " 1.746961e+02, 7.488651e+01, 2.653221e-01], dtype=float32) GSFC_coastline_proximity
(station)
float32
...
standard_name : GSFC coastline proximity long_name : GSFC proximity to the coastline units : km description : Proximity to the coastline provided by the NASA Goddard Space Flight Center (GSFC) Ocean Color Group, in kilometres, produced using the Generic Mapping Tools package. Native resolution of 0.01 x 0.01 degrees. Negative distances represent locations over land (including land-locked bodies of water), while positive distances represent locations over the ocean. There is an uncertainty of up to 1 km in the computed distance at any given point. array([ 1.000e+00, 0.000e+00, -3.220e+02, -1.010e+02, -3.730e+02, -2.900e+02,\n",
- " -1.430e+02, -2.460e+02, -2.450e+02, -3.610e+02, -3.140e+02, -3.250e+02,\n",
- " -3.710e+02, -3.450e+02, -2.370e+02, -1.520e+02, -2.040e+02, -1.780e+02,\n",
- " -1.470e+02, -1.070e+02, -8.100e+01, 0.000e+00, -2.000e+00, -2.430e+02,\n",
- " -3.000e+02, -3.390e+02, -3.220e+02, -2.940e+02, -3.100e+02, -5.500e+01,\n",
- " 1.000e+00, -2.100e+01, -4.420e+02, -4.360e+02, -3.640e+02, 0.000e+00,\n",
- " -1.020e+02, -3.930e+02, -9.200e+01, -3.280e+02, -1.000e+00, -2.730e+02,\n",
- " -2.350e+02, -8.000e+00, 0.000e+00, -1.600e+01, 0.000e+00, -3.450e+02,\n",
- " -1.800e+01, -9.000e+00, 0.000e+00, -3.110e+02, -4.000e+00, 0.000e+00,\n",
- " -5.500e+01, -2.000e+00, -2.270e+02, 0.000e+00, -1.310e+02, -7.400e+01,\n",
- " -2.290e+02, -4.500e+01, -8.000e+01, -6.000e+00, -1.100e+01, 1.000e+00,\n",
- " -2.000e+00, -1.420e+02, -2.120e+02, -3.790e+02, -1.640e+02, -3.630e+02,\n",
- " -1.300e+02, -3.520e+02, -4.600e+01, -1.560e+02, -2.200e+02, -6.700e+01,\n",
- " -1.480e+02, -1.280e+02, -2.900e+02, -2.600e+02, -3.800e+01, -2.300e+01,\n",
- " -1.100e+01, -2.000e+01, -2.000e+01, -6.200e+01, -1.300e+01, -6.200e+01,\n",
- " -5.000e+00, -1.200e+01, -7.000e+00, -5.500e+01, -2.000e+01, 0.000e+00,\n",
- " -1.000e+00, -1.000e+00, -2.600e+01, -1.700e+01, -3.000e+00, -4.210e+02,\n",
- " -2.250e+02, -4.500e+01, -1.000e+00, -1.000e+00, -1.520e+02, -4.900e+01,\n",
- " 0.000e+00, -9.500e+01, 2.000e+00, -1.000e+00, 0.000e+00, 0.000e+00,\n",
- " -2.000e+00, -2.000e+00, -1.000e+00, -1.000e+01, -9.200e+01, 0.000e+00,\n",
- " -7.400e+01, -1.000e+00, -8.600e+01, -3.000e+00, -3.400e+01, -1.500e+01,\n",
- " -3.200e+01, -1.000e+01, -4.000e+00, -1.900e+01, -2.700e+01, -2.000e+00,\n",
- " -5.500e+01, -2.100e+02, -7.000e+00, -1.000e+00, -1.000e+00, -9.500e+01,\n",
- " -3.200e+02, -3.280e+02, -3.000e+00, -1.030e+02, -2.620e+02, -1.000e+00,\n",
- " -1.500e+02, -1.440e+02, 0.000e+00, -1.010e+02, -3.500e+01, -2.500e+01,\n",
- " -4.800e+01, -5.100e+01, -5.800e+01, -1.260e+02, -3.800e+01, -1.280e+02,\n",
- " -9.100e+01, -5.560e+02, -5.680e+02, -6.090e+02, -3.890e+02, 1.000e+00,\n",
- " -1.218e+03, -3.700e+01, -1.278e+03, 0.000e+00, -3.130e+02, 2.000e+00],\n",
- " dtype=float32) Joly-Peuch_classification_code
(station)
float32
...
standard_name : Joly-Peuch classification code long_name : Joly-Peuch classification code units : unitless description : Joly-Peuch European classification code (range of 1-10) designed to objectively stratify stations between those diplaying rural and urban signatures (most rural == 1, most urban == 10). This classification is objectively made per species. The species that this is done for are: O3, NO2, SO2, CO, PM10, PM2.5. See reference here: https://www.sciencedirect.com/science/article/abs/pii/S1352231011012088 array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 4., 3., 1., 1.,\n",
- " nan, nan, nan, nan, nan, nan, nan, 3., nan, nan, nan, nan, 2., nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, 3., 2., 1., nan, 2., 2., 2.,\n",
- " 1., 2., 2., 1., 1., 2., 1., 2., nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 3., 4.,\n",
- " 2., 2., 3., 2., 4., 3., 3., 3., 2., 3., 3., nan, nan, nan,\n",
- " 3., nan, nan, 2., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, 3., 3., nan, nan, nan, nan, nan, nan, 3.,\n",
- " nan, 4., nan, 3., nan, 2., 3., nan, nan, nan, nan, nan, 2., nan,\n",
- " 2., 2., 2., nan, 3., 3., 3., nan, nan, 1., nan, 1., 3., 3.,\n",
- " 6., nan, nan, nan, 3., 2., 1., nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) Koppen-Geiger_classification
(station)
object
...
standard_name : Koppen-Geiger classification long_name : Koppen-Geiger classification units : unitless description : Koppen-Geiger classification, classifying the global climates into 5 main groups (30 total groups with subcategories). Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". See citation: Beck, H.E., N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Nature Scientific Data, 2018. array(['polar - frost', 'polar - tundra', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'tropical - rainforest',\n",
- " 'polar - tundra', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'arid - desert - cold',\n",
- " 'arid - desert - hot', 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra', 'water',\n",
- " 'temperate - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer', 'polar - frost',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - hot summer', 'arid - steppe - cold',\n",
- " 'arid - steppe - cold', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'polar - tundra',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'tropical - rainforest',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - hot summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'cold - no dry season - hot summer', 'tropical - savannah',\n",
- " 'tropical - rainforest', 'cold - no dry season - hot summer', 'water',\n",
- " 'water', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'polar - frost',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'polar - frost',\n",
- " 'arid - steppe - cold', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'arid - steppe - cold', 'polar - tundra', 'polar - frost', 'water',\n",
- " 'temperate - dry winter - hot summer',\n",
- " 'temperate - dry summer - warm summer'], dtype=object) Koppen-Geiger_modal_classification_25km
(station)
object
...
standard_name : Koppen-Geiger modal classification 25km long_name : Koppen-Geiger classification units : unitless description : Modal Koppen-Geiger classification in radius of 25km around station location. array(['water', 'polar - tundra', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'polar - tundra',\n",
- " 'polar - tundra', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'arid - desert - cold', 'water',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'water', 'water',\n",
- " 'cold - no dry season - warm summer', 'polar - frost',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer', 'water',\n",
- " 'temperate - dry summer - hot summer', 'water',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - dry summer - hot summer', 'arid - steppe - cold',\n",
- " 'arid - steppe - cold', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - warm summer', 'water',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'polar - tundra',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'water', 'water', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - dry summer - hot summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'tropical - rainforest', 'water',\n",
- " 'water', 'temperate - no dry season - hot summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - hot summer', 'water', 'water', 'water',\n",
- " 'water', 'water', 'water', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'water',\n",
- " 'cold - no dry season - cold summer', 'polar - frost',\n",
- " 'cold - no dry season - warm summer', 'water', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'arid - steppe - cold',\n",
- " 'temperate - no dry season - warm summer', 'polar - frost', 'water',\n",
- " 'temperate - dry winter - hot summer', 'water'], dtype=object) Koppen-Geiger_modal_classification_5km
(station)
object
...
standard_name : Koppen-Geiger modal classification 5km long_name : Koppen-Geiger classification units : unitless description : Modal Koppen-Geiger classification in radius of 5km around station location. array(['water', 'water', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'polar - tundra',\n",
- " 'polar - tundra', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'arid - desert - cold', 'water',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'water', 'water', 'water',\n",
- " 'cold - no dry season - warm summer', 'polar - frost',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer', 'water',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - dry summer - hot summer', 'arid - steppe - cold',\n",
- " 'arid - steppe - cold', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - warm summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'polar - tundra',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'tropical - rainforest',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - hot summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - hot summer', 'water', 'water', 'water',\n",
- " 'water', 'cold - no dry season - hot summer', 'water', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'polar - frost',\n",
- " 'cold - no dry season - warm summer', 'water', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'arid - steppe - cold',\n",
- " 'polar - tundra', 'polar - frost', 'water',\n",
- " 'temperate - dry winter - hot summer', 'water'], dtype=object) MODIS_MCD12C1_v6_IGBP_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP land use long_name : MODIS MCD12C1 v6 IGBP land use units : unitless description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['permanent snow and ice', 'urban and built-up lands', 'grasslands',\n",
- " 'mixed forests', 'croplands', 'woody savannas', 'grasslands',\n",
- " 'woody savannas', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'mixed forests', 'croplands', 'croplands',\n",
- " 'mixed forests', 'mixed forests', 'urban and built-up lands',\n",
- " 'savannas', 'urban and built-up lands', 'croplands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'barren',\n",
- " 'permanent snow and ice', 'croplands', 'savannas', 'woody savannas',\n",
- " 'mixed forests', 'grasslands', 'open shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'croplands',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'mixed forests', 'mixed forests',\n",
- " 'mixed forests', 'savannas', 'mixed forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'barren', 'croplands', 'permanent snow and ice', 'savannas',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'woody savannas', 'water bodies', 'savannas', 'woody savannas',\n",
- " 'grasslands', 'water bodies', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'woody savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'woody savannas', 'woody savannas', 'woody savannas',\n",
- " 'mixed forests', 'mixed forests', 'mixed forests',\n",
- " 'cropland/natural vegetation mosaics', 'mixed forests', 'croplands',\n",
- " 'grasslands', 'woody savannas', 'savannas', 'grasslands',\n",
- " 'woody savannas', 'savannas', 'woody savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'mixed forests', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'croplands', 'savannas',\n",
- " 'cropland/natural vegetation mosaics', 'savannas', 'water bodies',\n",
- " 'croplands', 'water bodies', 'croplands', 'croplands', 'grasslands',\n",
- " 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen broadleaf forests', 'savannas', 'savannas', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'grasslands', 'woody savannas',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'croplands', 'water bodies', 'croplands',\n",
- " 'woody savannas', 'croplands', 'savannas', 'croplands', 'savannas',\n",
- " 'savannas', 'grasslands', 'mixed forests', 'grasslands', 'grasslands',\n",
- " 'barren', 'evergreen needleleaf forests', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'permanent snow and ice',\n",
- " 'woody savannas', 'water bodies', 'permanent snow and ice',\n",
- " 'grasslands', 'croplands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'mixed forests', 'savannas', 'open shrublands',\n",
- " 'woody savannas', 'woody savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'woody savannas',\n",
- " 'mixed forests', 'croplands', 'deciduous broadleaf forests',\n",
- " 'croplands', 'permanent wetlands', 'grasslands', 'barren',\n",
- " 'water bodies', 'water bodies', 'savannas', 'water bodies'],\n",
- " dtype=object) MODIS_MCD12C1_v6_LAI
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI long_name : MODIS MCD12C1 v6 Leaf Area Index units : unitless description : Majority Leaf Area Index class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['non-vegetated lands', 'urban and built-up lands', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'savannas', 'grasslands',\n",
- " 'savannas', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'broadleaf croplands', 'broadleaf croplands',\n",
- " 'deciduous broadleaf forests', 'evergreen needleleaf forests',\n",
- " 'urban and built-up lands', 'savannas', 'urban and built-up lands',\n",
- " 'broadleaf croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'non-vegetated lands', 'non-vegetated lands', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'savannas', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'water bodies', 'non-vegetated lands', 'grasslands',\n",
- " 'non-vegetated lands', 'savannas', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'grasslands', 'savannas', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'water bodies', 'savannas', 'savannas',\n",
- " 'grasslands', 'savannas', 'savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'savannas', 'savannas', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'savannas', 'savannas', 'broadleaf croplands', 'grasslands',\n",
- " 'savannas', 'savannas', 'grasslands', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'evergreen needleleaf forests', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'savannas', 'savannas',\n",
- " 'savannas', 'water bodies', 'grasslands', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'grasslands', 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen broadleaf forests', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'grasslands', 'savannas', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'grasslands', 'savannas', 'broadleaf croplands',\n",
- " 'savannas', 'grasslands', 'savannas', 'savannas', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'grasslands',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'savannas',\n",
- " 'water bodies', 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'savannas', 'water bodies', 'non-vegetated lands', 'grasslands',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'deciduous broadleaf forests', 'savannas', 'shrublands', 'savannas',\n",
- " 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'savannas', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'savannas',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'deciduous broadleaf forests', 'broadleaf croplands',\n",
- " 'grasslands', 'grasslands', 'non-vegetated lands', 'water bodies',\n",
- " 'water bodies', 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_UMD_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD land use long_name : MODIS MCD12C1 v6 UMD land use units : unitless description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['non-vegetated lands', 'urban and built-up lands', 'grasslands',\n",
- " 'mixed forests', 'croplands', 'woody savannas', 'grasslands',\n",
- " 'woody savannas', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'mixed forests', 'croplands', 'croplands',\n",
- " 'mixed forests', 'mixed forests', 'urban and built-up lands',\n",
- " 'savannas', 'urban and built-up lands', 'croplands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'non-vegetated lands',\n",
- " 'non-vegetated lands', 'croplands', 'savannas', 'woody savannas',\n",
- " 'mixed forests', 'grasslands', 'open shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'croplands',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'mixed forests', 'mixed forests',\n",
- " 'mixed forests', 'savannas', 'mixed forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'non-vegetated lands', 'croplands', 'non-vegetated lands', 'savannas',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'woody savannas', 'water bodies', 'savannas', 'woody savannas',\n",
- " 'grasslands', 'water bodies', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'woody savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'woody savannas', 'woody savannas', 'woody savannas',\n",
- " 'mixed forests', 'mixed forests', 'mixed forests',\n",
- " 'cropland/natural vegetation mosaics', 'mixed forests', 'croplands',\n",
- " 'grasslands', 'woody savannas', 'savannas', 'grasslands',\n",
- " 'woody savannas', 'savannas', 'woody savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'mixed forests', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'croplands', 'savannas',\n",
- " 'cropland/natural vegetation mosaics', 'savannas', 'water bodies',\n",
- " 'croplands', 'water bodies', 'croplands', 'croplands', 'grasslands',\n",
- " 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen broadleaf forests', 'savannas', 'savannas', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'grasslands', 'woody savannas',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'croplands',\n",
- " 'woody savannas', 'croplands', 'savannas', 'croplands', 'savannas',\n",
- " 'savannas', 'grasslands', 'mixed forests', 'grasslands', 'grasslands',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'savannas',\n",
- " 'water bodies', 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'woody savannas', 'water bodies', 'non-vegetated lands', 'grasslands',\n",
- " 'croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'mixed forests', 'savannas', 'open shrublands', 'woody savannas',\n",
- " 'woody savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'mixed forests', 'woody savannas',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'mixed forests', 'woody savannas', 'mixed forests', 'croplands',\n",
- " 'deciduous broadleaf forests', 'croplands', 'permanent wetlands',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 25km long_name : MODIS MCD12C1 v6 IGBP modal land use in 25km radius units : unitless description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['water bodies', 'grasslands', 'croplands', 'mixed forests', 'croplands',\n",
- " 'mixed forests', 'grasslands', 'mixed forests', 'savannas', 'croplands',\n",
- " 'mixed forests', 'croplands', 'croplands', 'croplands', 'mixed forests',\n",
- " 'mixed forests', 'woody savannas', 'mixed forests', 'savannas',\n",
- " 'croplands', 'woody savannas', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'croplands', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'open shrublands', 'water bodies', 'grasslands',\n",
- " 'croplands', 'croplands', 'mixed forests', 'water bodies', 'croplands',\n",
- " 'mixed forests', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'mixed forests',\n",
- " 'mixed forests', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'croplands', 'permanent snow and ice', 'croplands', 'mixed forests',\n",
- " 'water bodies', 'grasslands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies', 'grasslands', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'croplands', 'woody savannas', 'grasslands',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'woody savannas',\n",
- " 'woody savannas', 'mixed forests', 'mixed forests', 'savannas',\n",
- " 'croplands', 'savannas', 'croplands', 'grasslands', 'woody savannas',\n",
- " 'savannas', 'grasslands', 'woody savannas', 'croplands', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'water bodies', 'croplands', 'savannas',\n",
- " 'croplands', 'savannas', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'croplands', 'croplands', 'water bodies', 'grasslands', 'croplands',\n",
- " 'evergreen broadleaf forests', 'water bodies', 'water bodies',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'water bodies', 'croplands', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'mixed forests', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'water bodies', 'cropland/natural vegetation mosaics',\n",
- " 'water bodies', 'grasslands', 'woody savannas', 'grasslands',\n",
- " 'grasslands', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'permanent snow and ice', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'water bodies', 'grasslands', 'croplands',\n",
- " 'mixed forests', 'water bodies', 'savannas', 'savannas', 'grasslands',\n",
- " 'woody savannas', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'croplands', 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'mixed forests',\n",
- " 'mixed forests', 'mixed forests', 'deciduous broadleaf forests',\n",
- " 'croplands', 'water bodies', 'grasslands', 'barren', 'water bodies',\n",
- " 'water bodies', 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 5km long_name : MODIS MCD12C1 v6 IGBP modal land use in 5km radius units : unitless description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['water bodies', 'water bodies', 'grasslands', 'mixed forests',\n",
- " 'croplands', 'woody savannas', 'grasslands', 'woody savannas',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'mixed forests',\n",
- " 'mixed forests', 'croplands', 'croplands', 'mixed forests',\n",
- " 'mixed forests', 'urban and built-up lands', 'savannas', 'savannas',\n",
- " 'croplands', 'evergreen needleleaf forests', 'water bodies', 'barren',\n",
- " 'permanent snow and ice', 'croplands', 'savannas', 'woody savannas',\n",
- " 'mixed forests', 'grasslands', 'open shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'croplands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'croplands',\n",
- " 'mixed forests', 'mixed forests', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'mixed forests', 'grasslands', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies',\n",
- " 'permanent snow and ice', 'savannas', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'grasslands', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'woody savannas', 'grasslands', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'savannas', 'woody savannas', 'grasslands',\n",
- " 'grasslands', 'water bodies', 'woody savannas', 'woody savannas',\n",
- " 'woody savannas', 'mixed forests', 'mixed forests', 'mixed forests',\n",
- " 'croplands', 'mixed forests', 'croplands', 'grasslands',\n",
- " 'woody savannas', 'savannas', 'grasslands', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'croplands', 'savannas',\n",
- " 'cropland/natural vegetation mosaics', 'savannas', 'water bodies',\n",
- " 'water bodies', 'savannas', 'croplands', 'croplands', 'grasslands',\n",
- " 'grasslands', 'mixed forests', 'evergreen broadleaf forests',\n",
- " 'savannas', 'water bodies', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'water bodies',\n",
- " 'cropland/natural vegetation mosaics', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'croplands',\n",
- " 'water bodies', 'savannas', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'croplands', 'savannas', 'savannas', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'grasslands', 'barren',\n",
- " 'evergreen needleleaf forests', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'permanent snow and ice',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'grasslands', 'croplands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'mixed forests', 'deciduous broadleaf forests',\n",
- " 'open shrublands', 'woody savannas', 'woody savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'croplands', 'water bodies',\n",
- " 'grasslands', 'barren', 'water bodies', 'water bodies', 'savannas',\n",
- " 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_LAI_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 25km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 25km radius units : unitless description : Modal Leaf Area Index in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['water bodies', 'grasslands', 'broadleaf croplands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'savannas', 'grasslands',\n",
- " 'savannas', 'savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'grasslands', 'broadleaf croplands',\n",
- " 'broadleaf croplands', 'deciduous broadleaf forests',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'deciduous broadleaf forests', 'savannas', 'broadleaf croplands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'grasslands', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'shrublands', 'water bodies', 'grasslands', 'grasslands',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'savannas', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'grasslands',\n",
- " 'non-vegetated lands', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'grasslands', 'savannas', 'water bodies', 'savannas',\n",
- " 'water bodies', 'grasslands', 'water bodies', 'savannas', 'savannas',\n",
- " 'grasslands', 'broadleaf croplands', 'savannas', 'grasslands',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'savannas', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'broadleaf croplands', 'savannas', 'broadleaf croplands',\n",
- " 'grasslands', 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'grasslands', 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'savannas', 'savannas',\n",
- " 'water bodies', 'grasslands', 'savannas', 'grasslands', 'savannas',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'water bodies', 'grasslands', 'savannas',\n",
- " 'evergreen broadleaf forests', 'water bodies', 'water bodies',\n",
- " 'savannas', 'deciduous broadleaf forests', 'water bodies', 'savannas',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'grasslands',\n",
- " 'savannas', 'water bodies', 'savannas', 'grasslands', 'savannas',\n",
- " 'water bodies', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'grasslands', 'grasslands', 'non-vegetated lands',\n",
- " 'evergreen needleleaf forests', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'grasslands', 'grasslands', 'savannas', 'water bodies', 'grasslands',\n",
- " 'savannas', 'grasslands', 'savannas', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'grasslands', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'deciduous broadleaf forests', 'grasslands',\n",
- " 'deciduous broadleaf forests', 'broadleaf croplands', 'water bodies',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_LAI_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 5km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 5km radius units : unitless description : Modal Leaf Area Index in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['water bodies', 'water bodies', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'savannas', 'grasslands',\n",
- " 'savannas', 'savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'broadleaf croplands', 'broadleaf croplands',\n",
- " 'deciduous broadleaf forests', 'evergreen needleleaf forests',\n",
- " 'urban and built-up lands', 'savannas', 'savannas',\n",
- " 'broadleaf croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'non-vegetated lands', 'non-vegetated lands', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'deciduous broadleaf forests', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'non-vegetated lands', 'savannas', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'grasslands', 'savannas', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'water bodies', 'savannas', 'savannas',\n",
- " 'grasslands', 'savannas', 'savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'savannas', 'savannas', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'broadleaf croplands', 'savannas', 'broadleaf croplands',\n",
- " 'grasslands', 'savannas', 'savannas', 'grasslands', 'savannas',\n",
- " 'deciduous broadleaf forests', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'savannas', 'savannas',\n",
- " 'savannas', 'water bodies', 'water bodies', 'savannas', 'grasslands',\n",
- " 'grasslands', 'grasslands', 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen broadleaf forests', 'savannas', 'water bodies', 'savannas',\n",
- " 'savannas', 'water bodies', 'savannas', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'savannas', 'savannas', 'water bodies', 'savannas',\n",
- " 'grasslands', 'savannas', 'savannas', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'grasslands',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'savannas',\n",
- " 'water bodies', 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'grasslands', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'deciduous broadleaf forests', 'savannas', 'shrublands',\n",
- " 'savannas', 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'deciduous broadleaf forests', 'savannas',\n",
- " 'deciduous broadleaf forests', 'grasslands', 'water bodies',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 25km long_name : MODIS MCD12C1 v6 UMD modal land use in 25km radius units : unitless description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['water bodies', 'water bodies', 'croplands', 'mixed forests',\n",
- " 'croplands', 'mixed forests', 'grasslands', 'mixed forests', 'savannas',\n",
- " 'croplands', 'mixed forests', 'croplands', 'croplands', 'croplands',\n",
- " 'mixed forests', 'mixed forests', 'woody savannas', 'mixed forests',\n",
- " 'savannas', 'croplands', 'woody savannas', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'croplands', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'open shrublands', 'water bodies', 'grasslands',\n",
- " 'croplands', 'croplands', 'mixed forests', 'water bodies', 'croplands',\n",
- " 'mixed forests', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'mixed forests',\n",
- " 'mixed forests', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'croplands', 'non-vegetated lands', 'croplands', 'mixed forests',\n",
- " 'water bodies', 'grasslands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies', 'grasslands', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'croplands', 'woody savannas', 'grasslands',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'woody savannas',\n",
- " 'woody savannas', 'mixed forests', 'mixed forests', 'savannas',\n",
- " 'croplands', 'savannas', 'croplands', 'grasslands', 'woody savannas',\n",
- " 'savannas', 'grasslands', 'woody savannas', 'croplands', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'water bodies', 'croplands', 'savannas',\n",
- " 'croplands', 'savannas', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'croplands', 'croplands', 'water bodies', 'grasslands', 'croplands',\n",
- " 'evergreen broadleaf forests', 'water bodies', 'water bodies',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'water bodies', 'croplands', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'mixed forests', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'water bodies', 'cropland/natural vegetation mosaics',\n",
- " 'water bodies', 'grasslands', 'woody savannas', 'grasslands',\n",
- " 'grasslands', 'non-vegetated lands', 'evergreen needleleaf forests',\n",
- " 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'water bodies', 'grasslands', 'croplands', 'mixed forests',\n",
- " 'water bodies', 'savannas', 'savannas', 'grasslands', 'woody savannas',\n",
- " 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'croplands', 'woody savannas',\n",
- " 'evergreen needleleaf forests', 'woody savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'mixed forests', 'mixed forests', 'mixed forests', 'mixed forests',\n",
- " 'deciduous broadleaf forests', 'croplands', 'water bodies',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 5km long_name : MODIS MCD12C1 v6 UMD modal land use in 5km radius units : unitless description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['water bodies', 'water bodies', 'grasslands', 'mixed forests',\n",
- " 'croplands', 'woody savannas', 'grasslands', 'woody savannas',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'mixed forests',\n",
- " 'mixed forests', 'croplands', 'croplands', 'mixed forests',\n",
- " 'mixed forests', 'urban and built-up lands', 'savannas', 'savannas',\n",
- " 'croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'non-vegetated lands', 'non-vegetated lands', 'croplands', 'savannas',\n",
- " 'woody savannas', 'mixed forests', 'grasslands', 'open shrublands',\n",
- " 'water bodies', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'croplands', 'mixed forests', 'mixed forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'mixed forests',\n",
- " 'grasslands', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'non-vegetated lands', 'savannas',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'woody savannas', 'water bodies', 'savannas', 'woody savannas',\n",
- " 'grasslands', 'water bodies', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'woody savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'woody savannas', 'woody savannas', 'woody savannas',\n",
- " 'mixed forests', 'mixed forests', 'mixed forests', 'croplands',\n",
- " 'mixed forests', 'croplands', 'grasslands', 'woody savannas',\n",
- " 'savannas', 'grasslands', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'croplands', 'savannas',\n",
- " 'cropland/natural vegetation mosaics', 'savannas', 'water bodies',\n",
- " 'water bodies', 'savannas', 'croplands', 'croplands', 'grasslands',\n",
- " 'grasslands', 'mixed forests', 'evergreen broadleaf forests',\n",
- " 'savannas', 'water bodies', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'water bodies',\n",
- " 'cropland/natural vegetation mosaics', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'savannas', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'croplands', 'savannas', 'savannas', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'grasslands',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'savannas',\n",
- " 'water bodies', 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'grasslands', 'croplands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'mixed forests', 'deciduous broadleaf forests',\n",
- " 'open shrublands', 'woody savannas', 'woody savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'croplands', 'water bodies',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 25km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 2., 10., 5., 8., 14., 3., 9., 15., 6., 12., 12., 18., 19.,\n",
- " 5., 3., 18., 10., 26., 26., 3., 4., nan, 5., 20., 28., 18., 22.,\n",
- " 28., 1., 1., 12., 10., 8., 5., 2., 5., 14., 4., 10., 3., 9.,\n",
- " 10., nan, 1., nan, 23., 0., 6., 3., 0., 2., 16., 4., 17., 4.,\n",
- " 0., 6., 3., 3., 2., 9., 4., 3., 14., 0., 6., 4., 3., 10.,\n",
- " 13., 3., 5., 14., 11., 8., 2., 7., 7., 3., 4., 20., 1., 5.,\n",
- " 12., 6., 1., 5., 23., 26., 12., 8., 6., 18., 20., 5., 12., 2.,\n",
- " 16., 8., 3., 6., 7., 2., 1., 1., 36., 11., 8., 17., nan, 4.,\n",
- " 0., 0., 9., 9., 0., 1., 1., 5., 18., 11., 27., 32., 37., 10.,\n",
- " 1., 2., nan, 6., 6., 7., 12., nan, 14., 6., nan, 0., 7., 15.,\n",
- " 2., 4., 9., 1., 2., 2., 10., 7., 13., 16., 9., 4., 4., 4.,\n",
- " 4., 8., 12., 8., 11., 1., 8., 2., 23., 0., nan, 2., 0., 1.],\n",
- " dtype=float32) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 5km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 20., 6., 7., 9., 12., 0., 5., 11., 7., 6., 14., 20., 12.,\n",
- " 0., 1., 46., 12., 36., 30., 4., 16., nan, 2., 23., 26., 23., 22.,\n",
- " 25., 0., 0., 7., 10., 6., 6., 21., 2., 9., 1., 14., 6., 14.,\n",
- " 7., nan, 1., nan, 20., 0., 1., 3., 0., 3., 11., 17., 24., 12.,\n",
- " 1., 4., 1., 7., 1., 5., 7., 10., 1., 0., 5., 0., 2., 6.,\n",
- " 12., 0., 1., 11., 16., 10., 0., 5., 5., 0., 1., 15., 0., 2.,\n",
- " 9., 1., 0., 5., 38., 8., 15., 7., 8., 13., 11., 8., 18., 17.,\n",
- " 9., 10., 2., 7., 3., 0., 6., 0., 34., 7., 7., 9., nan, 3.,\n",
- " 0., 1., 17., 12., 0., 0., 0., 25., 16., 10., 12., 30., 20., 4.,\n",
- " 0., 0., nan, 1., 14., 11., 9., nan, 37., 0., nan, 0., 7., 14.,\n",
- " 10., 0., 10., 2., 1., 4., 7., 3., 10., 8., 7., 0., 8., 1.,\n",
- " 3., 6., 5., 5., 19., 0., 1., 22., 19., 0., nan, 3., 0., 0.],\n",
- " dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 25km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 62., 57., 53., 54., 55., 42., 57., 62., 42., 58., 59., 63., 63.,\n",
- " 58., 37., 63., 45., 62., 63., 51., 62., nan, 55., 62., 63., 61., 61.,\n",
- " 61., 51., 60., 63., 54., 48., 29., 56., 53., 62., 44., 51., 61., 51.,\n",
- " 50., nan, 19., nan, 63., 0., 58., 50., 9., 32., 61., 63., 63., 44.,\n",
- " 12., 62., 52., 62., 32., 62., 47., 59., 63., 0., 58., 62., 60., 52.,\n",
- " 60., 24., 32., 62., 56., 58., 28., 59., 58., 50., 49., 63., 29., 58.,\n",
- " 63., 61., 36., 40., 63., 63., 63., 61., 56., 63., 63., 44., 63., 52.,\n",
- " 63., 57., 53., 61., 56., 57., 21., 15., 62., 51., 63., 62., nan, 56.,\n",
- " 0., 6., 59., 58., 14., 28., 18., 63., 61., 62., 61., 63., 63., 61.,\n",
- " 34., 53., nan, 63., 62., 63., 63., nan, 63., 63., nan, 0., 46., 56.,\n",
- " 43., 52., 62., 34., 50., 60., 62., 33., 60., 63., 63., 58., 53., 58.,\n",
- " 39., 51., 62., 56., 63., 29., 55., 62., 63., 5., nan, 51., 18., 50.],\n",
- " dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 5km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 62., 19., 23., 27., 21., 6., 17., 28., 18., 13., 53., 45., 23.,\n",
- " 7., 11., 63., 29., 56., 45., 21., 48., nan, 10., 44., 45., 51., 40.,\n",
- " 52., 0., 5., 12., 18., 8., 24., 56., 10., 18., 8., 50., 18., 30.,\n",
- " 31., nan, 6., nan, 61., 0., 9., 17., 0., 15., 29., 63., 62., 26.,\n",
- " 5., 23., 13., 41., 8., 10., 34., 59., 12., 0., 25., 0., 10., 26.,\n",
- " 31., 7., 8., 39., 36., 37., 6., 14., 22., 5., 7., 51., 0., 10.,\n",
- " 35., 7., 0., 12., 63., 20., 53., 19., 16., 35., 45., 25., 59., 52.,\n",
- " 23., 25., 10., 21., 28., 6., 17., 6., 51., 20., 30., 25., nan, 10.,\n",
- " 0., 6., 44., 22., 9., 0., 6., 61., 42., 15., 18., 61., 42., 41.,\n",
- " 0., 0., nan, 8., 33., 30., 29., nan, 63., 5., nan, 0., 19., 41.,\n",
- " 31., 0., 39., 26., 8., 11., 41., 8., 39., 17., 40., 0., 51., 7.,\n",
- " 14., 38., 17., 27., 29., 8., 10., 62., 60., 0., nan, 9., 0., 0.],\n",
- " dtype=float32) NOAA-DMSP-OLS_v4_nighttime_stable_lights
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 nighttime stable lights long_name : NOAA DMSP-OLS version 4 nighttime stable lights units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 nighttime stable lights. Native resolution of 0.0083 x 0.0083 degrees. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 14., 8., 14., 8., 16., 0., 5., 9., 5., 6., 10., 29., 10.,\n",
- " 0., 0., 58., 8., 46., 30., 0., 19., nan, 0., 41., 32., 14., 15.,\n",
- " 19., 0., 0., 11., 15., 6., 12., 46., 0., 8., 0., 8., 9., 18.,\n",
- " 6., nan, 0., nan, 17., 0., 0., 0., 0., 12., 6., 13., 20., 20.,\n",
- " 0., 0., 0., 5., 0., 5., 10., 6., 0., 0., 0., 0., 0., 8.,\n",
- " 14., 0., 0., 9., 35., 11., 0., 7., 0., 5., 0., 11., 0., 0.,\n",
- " 8., 0., 0., 8., 51., 7., 7., 6., 6., 10., 8., 8., 14., 26.,\n",
- " 8., 14., 0., 6., 5., 0., 14., 0., 35., 0., 13., 6., nan, 0.,\n",
- " 0., 5., 25., 16., 0., 0., 0., 33., 14., 12., 15., 26., 20., 0.,\n",
- " 0., 0., nan, 0., 23., 16., 8., nan, 46., 0., nan, 0., 7., 7.,\n",
- " 18., 0., 9., 0., 8., 9., 6., 7., 8., 7., 7., 0., 8., 0.,\n",
- " 0., 7., 7., 8., 20., 0., 0., 21., 13., 0., nan, 6., 0., 0.],\n",
- " dtype=float32) OMI_level3_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column annual average NO2 long_name : OMI level3 column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([3.473341e+15, 2.757102e+15, 7.345563e+15, 4.663894e+15, 6.847881e+15,\n",
- " 6.168189e+15, 4.584701e+15, 5.862558e+15, 6.179914e+15, 6.455329e+15,\n",
- " 7.378372e+15, 7.316141e+15, 7.611382e+15, 7.677720e+15, 5.845425e+15,\n",
- " 4.858490e+15, 6.308826e+15, 6.443415e+15, 9.380302e+15, 8.875505e+15,\n",
- " 3.809209e+15, 3.315453e+15, 4.279896e+15, 4.351421e+15, 5.403429e+15,\n",
- " 6.935566e+15, 4.956889e+15, 5.876876e+15, 5.775327e+15, 3.005828e+15,\n",
- " 2.579832e+15, 3.935034e+15, 7.049085e+15, 7.130492e+15, 6.502953e+15,\n",
- " 6.226574e+15, 8.221473e+15, 5.835075e+15, 7.717232e+15, 7.564886e+15,\n",
- " 6.954989e+15, 5.841256e+15, 5.286593e+15, 3.742470e+15, 7.381992e+15,\n",
- " 4.288764e+15, 6.681218e+15, nan, 5.106118e+15, 4.167499e+15,\n",
- " 4.139215e+15, 4.062219e+15, 4.100420e+15, 3.933322e+15, 4.104039e+15,\n",
- " 3.957674e+15, 4.119975e+15, 4.649604e+15, 3.938711e+15, 4.308088e+15,\n",
- " 3.753127e+15, 4.559792e+15, 4.047396e+15, 4.061001e+15, 3.250765e+15,\n",
- " 3.973670e+15, 4.912318e+15, 3.922882e+15, 3.888045e+15, 5.590997e+15,\n",
- " 7.136552e+15, 4.412827e+15, 4.272603e+15, 5.016980e+15, 4.066349e+15,\n",
- " 3.994068e+15, 3.910209e+15, 4.822903e+15, 3.575267e+15, 4.783221e+15,\n",
- " 4.322277e+15, 3.947022e+15, 4.579524e+15, 3.834530e+15, 4.273179e+15,\n",
- " 7.581315e+15, 3.640667e+15, 4.764857e+15, 4.699631e+15, 7.661165e+15,\n",
- " 6.513909e+15, 7.293615e+15, 3.779409e+15, 7.954559e+15, 4.699631e+15,\n",
- " 7.423154e+15, 8.594613e+15, 4.073575e+15, 5.604822e+15, 4.208290e+15,\n",
- " 3.607691e+15, 5.737488e+15, 5.957745e+15, 2.461156e+15, 2.962316e+15,\n",
- " 3.128346e+15, 8.365826e+15, 5.172941e+15, 3.949571e+15, 4.763467e+15,\n",
- " 4.153752e+15, 4.998319e+15, 2.723367e+15, 3.546172e+15, 7.692245e+15,\n",
- " 4.759712e+15, 5.936859e+15, 5.139146e+15, 4.734415e+15, 3.604704e+15,\n",
- " 1.125353e+16, 8.950532e+15, 1.122162e+16, 1.061557e+16, 1.146695e+16,\n",
- " 4.921244e+15, 3.337922e+15, 3.643524e+15, 3.962955e+15, 4.708453e+15,\n",
- " 3.591406e+15, 4.612725e+15, 4.928113e+15, nan, 4.821815e+15,\n",
- " 3.455548e+15, 3.723008e+15, 3.007410e+15, 7.015296e+15, 7.453896e+15,\n",
- " 5.797097e+15, 5.698536e+15, 4.854830e+15, 3.686678e+15, 3.459293e+15,\n",
- " 3.760629e+15, 5.946741e+15, 5.273878e+15, 5.083707e+15, 6.174996e+15,\n",
- " 4.721677e+15, 4.468787e+15, 3.710538e+15, 4.536030e+15, 4.745260e+15,\n",
- " 5.496117e+15, 5.114420e+15, 5.579634e+15, 5.180446e+15, 4.930619e+15,\n",
- " 6.805185e+15, 3.456580e+15, 5.581911e+15, 2.814228e+15, nan,\n",
- " 3.253400e+15, 3.206773e+15, 3.050493e+15], dtype=float32) OMI_level3_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column cloud screened annual average NO2 long_name : OMI level3 column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([3.532428e+15, 2.639686e+15, 5.867690e+15, 4.187000e+15, 5.588512e+15,\n",
- " 5.222007e+15, 4.145348e+15, 5.214945e+15, 4.917223e+15, 4.954742e+15,\n",
- " 6.094566e+15, 5.826555e+15, 6.110681e+15, 6.275420e+15, 4.848703e+15,\n",
- " 4.424205e+15, 5.583187e+15, 5.758002e+15, 8.526934e+15, 8.135533e+15,\n",
- " 3.795629e+15, 3.434910e+15, 4.453713e+15, 4.068646e+15, 5.065195e+15,\n",
- " 5.791190e+15, 4.857933e+15, 5.250922e+15, 5.352893e+15, 2.987894e+15,\n",
- " 2.586228e+15, 4.001106e+15, 5.342705e+15, 5.562301e+15, 5.080109e+15,\n",
- " 5.267317e+15, 6.264316e+15, 5.544388e+15, 5.676098e+15, 5.874213e+15,\n",
- " 5.218944e+15, 5.295258e+15, 4.130269e+15, 3.843294e+15, 5.849887e+15,\n",
- " nan, 5.456292e+15, nan, 4.499519e+15, 4.109295e+15,\n",
- " 4.112221e+15, 3.996820e+15, 4.212227e+15, 3.848318e+15, 4.158201e+15,\n",
- " 3.840789e+15, 3.927497e+15, 4.347146e+15, 3.960222e+15, 4.245645e+15,\n",
- " 3.773102e+15, 4.420939e+15, 3.942068e+15, 4.156779e+15, 3.303044e+15,\n",
- " 3.961144e+15, 4.489403e+15, 4.059251e+15, 3.941625e+15, 5.209817e+15,\n",
- " 5.950123e+15, 4.397264e+15, 4.193617e+15, 5.063839e+15, 4.150675e+15,\n",
- " 4.045118e+15, 3.761147e+15, 4.525007e+15, 3.482751e+15, 4.580291e+15,\n",
- " 4.417874e+15, 3.884223e+15, 3.986373e+15, 3.876703e+15, 4.612321e+15,\n",
- " 5.765426e+15, 3.758801e+15, 4.570372e+15, 4.403545e+15, 6.471081e+15,\n",
- " 6.303857e+15, 6.325613e+15, 4.056245e+15, 6.595459e+15, 4.403545e+15,\n",
- " 5.959062e+15, 7.787043e+15, 3.714682e+15, 5.685939e+15, 4.257667e+15,\n",
- " 3.688463e+15, 5.080269e+15, 5.093506e+15, 2.585899e+15, 3.306903e+15,\n",
- " 3.462402e+15, 7.005490e+15, 5.021203e+15, 3.890843e+15, 4.356960e+15,\n",
- " nan, 4.714578e+15, 2.727766e+15, 3.529483e+15, 7.606910e+15,\n",
- " 4.600654e+15, 4.509253e+15, 4.308589e+15, 4.259049e+15, 3.615048e+15,\n",
- " 9.011939e+15, 6.937365e+15, 9.850068e+15, 9.264850e+15, 9.735531e+15,\n",
- " 4.262205e+15, 4.006099e+15, 3.579393e+15, 3.895142e+15, 3.984774e+15,\n",
- " 3.835624e+15, 4.269370e+15, 4.525225e+15, nan, 4.394645e+15,\n",
- " 3.428801e+15, nan, 3.016481e+15, 5.421997e+15, 5.674910e+15,\n",
- " 4.873663e+15, 4.614278e+15, 4.703133e+15, 4.224745e+15, 3.903788e+15,\n",
- " 3.801359e+15, 5.026321e+15, 4.592180e+15, 4.592788e+15, 4.937449e+15,\n",
- " 4.424600e+15, 4.151914e+15, 3.886247e+15, 4.396008e+15, 4.517754e+15,\n",
- " 4.788532e+15, 4.789130e+15, 4.602289e+15, 4.507351e+15, 4.204522e+15,\n",
- " 5.657308e+15, 3.518051e+15, 5.169831e+15, 2.783493e+15, nan,\n",
- " 3.268930e+15, 3.306158e+15, 3.058697e+15], dtype=float32) OMI_level3_tropospheric_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column annual average NO2 long_name : OMI level3 tropospheric column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([2.089988e+14, 3.584643e+14, 4.854586e+15, 2.186984e+15, 4.350195e+15,\n",
- " 3.692631e+15, 2.309433e+15, 3.353392e+15, 3.745330e+15, 4.048057e+15,\n",
- " 4.817116e+15, 4.833554e+15, 4.980179e+15, 5.174898e+15, 3.493000e+15,\n",
- " 2.414175e+15, 3.825593e+15, 4.207652e+15, 6.867158e+15, 6.651714e+15,\n",
- " 1.295513e+15, 8.029932e+14, 2.521319e+14, 1.917871e+15, 2.904494e+15,\n",
- " 4.445739e+15, 2.502260e+15, 3.447178e+15, 3.443647e+15, 7.490186e+14,\n",
- " 4.168854e+14, 1.267978e+15, 4.634118e+15, 4.707298e+15, 4.161965e+15,\n",
- " 4.126384e+15, 5.823442e+15, 3.324731e+15, 5.469872e+15, 5.140282e+15,\n",
- " 4.567384e+15, 3.378735e+15, 2.818044e+15, 1.796697e+14, 5.129525e+15,\n",
- " 1.563328e+14, 4.324850e+15, nan, 2.778101e+15, 1.952670e+15,\n",
- " 1.826281e+15, 1.416338e+15, 1.476852e+15, 1.312767e+15, 1.383646e+15,\n",
- " 1.535568e+15, 1.708116e+15, 2.060796e+15, 1.311217e+15, 1.647795e+15,\n",
- " 1.187153e+15, 1.991316e+15, 1.594897e+15, 1.367500e+15, 7.470208e+14,\n",
- " 1.633403e+15, 2.730057e+15, 9.785123e+14, 8.111674e+14, 3.245221e+15,\n",
- " 4.758437e+15, 1.936010e+15, 1.713117e+15, 2.699113e+15, 1.623961e+15,\n",
- " 1.487158e+15, 1.514948e+15, 2.434608e+15, 1.221044e+15, 2.432322e+15,\n",
- " 1.861073e+15, 1.521955e+15, 2.244473e+15, 1.536093e+15, 1.791047e+15,\n",
- " 5.291541e+15, 1.336670e+15, 2.367100e+15, 2.402279e+15, 5.210743e+15,\n",
- " 4.007436e+15, 4.937721e+15, 1.387553e+15, 5.519484e+15, 2.402279e+15,\n",
- " 5.118559e+15, 6.268216e+15, 1.780961e+15, 3.129665e+15, 1.570732e+15,\n",
- " 9.362735e+14, 3.233052e+15, 3.402136e+15, 5.822059e+14, 5.290588e+14,\n",
- " 7.036002e+14, 5.966317e+15, 2.606465e+15, 1.351236e+15, 2.215723e+15,\n",
- " 3.067026e+14, 2.387623e+15, 3.904560e+14, 1.207140e+15, 5.069879e+15,\n",
- " 2.243110e+15, 3.781858e+15, 2.947661e+15, 2.401002e+15, 1.003628e+15,\n",
- " 8.804974e+15, 6.670742e+15, 8.657363e+15, 8.296446e+15, 8.966678e+15,\n",
- " 2.676036e+15, 7.422353e+14, 1.324518e+15, 3.595907e+14, 2.595616e+15,\n",
- " 6.079751e+14, 2.405702e+15, 2.671498e+15, nan, 2.559082e+15,\n",
- " 1.063710e+15, 1.542406e+14, 6.303014e+14, 4.712985e+15, 5.056608e+15,\n",
- " 3.595138e+15, 3.667676e+15, 2.310345e+15, 2.727649e+14, 9.364728e+14,\n",
- " 6.600575e+14, 3.644737e+15, 3.042722e+15, 2.808257e+15, 3.999318e+15,\n",
- " 2.477753e+15, 2.157872e+15, 1.211690e+15, 2.253023e+15, 2.259441e+15,\n",
- " 2.974318e+15, 2.577151e+15, 3.143127e+15, 2.789992e+15, 2.529712e+15,\n",
- " 4.251182e+15, 2.957702e+14, 3.090131e+15, 6.266068e+14, nan,\n",
- " 7.479317e+14, 1.051665e+15, 6.196790e+14], dtype=float32) OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column cloud screened annual average NO2 long_name : OMI level3 tropospheric column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([4.754073e+14, 4.502345e+14, 2.989990e+15, 1.590792e+15, 2.660002e+15,\n",
- " 2.411796e+15, 1.592236e+15, 2.567111e+15, 2.043094e+15, 2.184788e+15,\n",
- " 3.174512e+15, 2.963212e+15, 3.216767e+15, 3.312168e+15, 2.197623e+15,\n",
- " 1.668676e+15, 2.693183e+15, 3.058137e+15, 5.815727e+15, 5.277298e+15,\n",
- " 1.248434e+15, 8.502206e+14, 2.582090e+14, 1.353951e+15, 2.303014e+15,\n",
- " 2.946991e+15, 2.208910e+15, 2.459448e+15, 2.593697e+15, 7.465364e+14,\n",
- " 4.226264e+14, 1.284026e+15, 2.466927e+15, 2.666904e+15, 2.430968e+15,\n",
- " 2.764044e+15, 3.516648e+15, 2.786582e+15, 2.977559e+15, 3.145857e+15,\n",
- " 2.556432e+15, 2.419145e+15, 1.573150e+15, 1.129309e+13, 3.320597e+15,\n",
- " nan, 2.660673e+15, nan, 1.743551e+15, 1.149409e+15,\n",
- " 1.190683e+15, 1.377289e+15, 1.440652e+15, 1.158520e+15, 1.419276e+15,\n",
- " 1.510276e+15, 1.417442e+15, 1.667895e+15, 1.313956e+15, 1.555254e+15,\n",
- " 1.145179e+15, 1.748105e+15, 1.436062e+15, 1.449538e+15, 7.845993e+14,\n",
- " 1.130061e+15, 1.442196e+15, 9.003820e+14, 7.055360e+14, 2.461058e+15,\n",
- " 3.165109e+15, 1.665509e+15, 1.517896e+15, 2.341339e+15, 1.439897e+15,\n",
- " 1.304336e+15, 1.198233e+15, 1.861282e+15, 1.027340e+15, 2.106200e+15,\n",
- " 1.638015e+15, 1.238765e+15, 1.505213e+15, 1.259837e+15, 1.742799e+15,\n",
- " 3.343065e+15, 1.263338e+15, 1.913839e+15, 1.905305e+15, 3.681093e+15,\n",
- " 3.502112e+15, 3.580193e+15, 1.342023e+15, 3.891760e+15, 1.905305e+15,\n",
- " 3.323524e+15, 4.983296e+15, 1.373583e+15, 2.999636e+15, 1.520230e+15,\n",
- " 9.605778e+14, 2.321274e+15, 2.321322e+15, 6.900251e+14, 6.714803e+14,\n",
- " 8.239604e+14, 4.353938e+15, 2.347301e+15, 1.202253e+15, 1.642187e+15,\n",
- " nan, 2.086612e+15, 3.965878e+14, 1.102438e+15, 4.918048e+15,\n",
- " 2.023781e+15, 1.800709e+15, 1.613745e+15, 1.312202e+15, 9.724966e+14,\n",
- " 6.267002e+15, 4.397799e+15, 7.011783e+15, 6.574743e+15, 6.867529e+15,\n",
- " 1.505240e+15, 6.233324e+14, 1.009843e+15, 4.258189e+14, 1.360584e+15,\n",
- " 6.146184e+14, 1.763054e+15, 1.497895e+15, nan, 1.556925e+15,\n",
- " 1.028614e+15, nan, 6.977960e+14, 2.564195e+15, 2.841774e+15,\n",
- " 2.170737e+15, 2.068653e+15, 1.887385e+15, 3.482298e+14, 9.475596e+14,\n",
- " 7.613750e+14, 2.168495e+15, 1.730114e+15, 1.786381e+15, 2.239897e+15,\n",
- " 1.679060e+15, 1.381100e+15, 1.031851e+15, 1.485161e+15, 1.899351e+15,\n",
- " 2.088062e+15, 2.021358e+15, 2.007737e+15, 1.859909e+15, 1.526916e+15,\n",
- " 2.747525e+15, 4.914727e+14, 2.609755e+15, 6.267174e+14, nan,\n",
- " 6.815022e+14, 1.139593e+15, 6.288178e+14], dtype=float32) UMBC_anthrome_classification
(station)
object
...
standard_name : UMBC anthrome classification long_name : UMBC anthrome classification units : unitless description : University of Maryland Baltimore County (UMBC) anthrome classification, describing the anthropogenic land use (for the year 2000). There are 20 distinct classifications. Native resolution of 0.0833 x 0.0833 degrees. A correction for costal sites is made: if the native anthrome class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water', 'residential rangelands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'populated rangelands', 'populated croplands',\n",
- " 'pastoral villages', 'residential woodlands', 'residential woodlands',\n",
- " 'residential rangelands', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated woodlands', 'urban', 'residential rainfed croplands',\n",
- " 'mixed settlements', 'rainfed villages',\n",
- " 'inhabited treeless and barren lands', 'water',\n",
- " 'wild treeless and barren lands', 'populated rangelands',\n",
- " 'rainfed villages', 'pastoral villages', 'rainfed villages',\n",
- " 'mixed settlements', 'pastoral villages', 'populated rangelands',\n",
- " 'residential woodlands', 'populated woodlands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands', 'water',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential rangelands', 'rainfed villages', 'residential rangelands',\n",
- " 'water', 'residential rainfed croplands', 'water', 'rainfed villages',\n",
- " 'water', 'residential rainfed croplands', 'populated woodlands',\n",
- " 'water', 'inhabited treeless and barren lands', 'residential woodlands',\n",
- " 'rainfed villages', 'rainfed villages', 'residential rainfed croplands',\n",
- " 'populated croplands', 'inhabited treeless and barren lands',\n",
- " 'residential rainfed croplands', 'populated croplands',\n",
- " 'populated croplands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'water', 'populated croplands',\n",
- " 'wild woodlands', 'wild woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'wild woodlands', 'populated croplands',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'populated rangelands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated rangelands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rangelands', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'rainfed villages',\n",
- " 'residential rangelands', 'residential rainfed croplands',\n",
- " 'residential irrigated croplands', 'residential rangelands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'mixed settlements', 'residential rangelands', 'residential woodlands',\n",
- " 'rainfed villages', 'populated woodlands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'water', 'residential woodlands',\n",
- " 'water', 'water', 'populated woodlands', 'populated woodlands',\n",
- " 'inhabited treeless and barren lands', 'populated woodlands',\n",
- " 'residential woodlands', 'water', 'residential irrigated croplands',\n",
- " 'residential rainfed croplands', 'residential irrigated croplands',\n",
- " 'residential rainfed croplands', 'rainfed villages',\n",
- " 'populated woodlands', 'wild woodlands', 'populated woodlands', 'water',\n",
- " 'populated woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands', 'water',\n",
- " 'mixed settlements', 'populated croplands', 'water',\n",
- " 'residential irrigated croplands', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'populated croplands', 'rainfed villages', 'wild woodlands',\n",
- " 'populated woodlands', 'wild woodlands', 'water', 'populated woodlands',\n",
- " 'populated woodlands', 'populated croplands', 'populated croplands',\n",
- " 'populated woodlands', 'populated woodlands', 'populated woodlands',\n",
- " 'residential woodlands', 'residential woodlands',\n",
- " 'populated rangelands', 'populated woodlands', 'rainfed villages',\n",
- " 'populated woodlands', 'residential irrigated croplands',\n",
- " 'inhabited treeless and barren lands',\n",
- " 'residential irrigated croplands', 'wild woodlands', 'water',\n",
- " 'residential woodlands', 'residential woodlands', 'remote rangelands'],\n",
- " dtype=object) UMBC_modal_anthrome_classification_25km
(station)
object
...
standard_name : UMBC modal anthrome classification 25km long_name : UMBC modal anthrome classification in 25km radius units : unitless description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 25km around station location. array(['water', 'remote rangelands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'populated rangelands', 'populated woodlands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential irrigated croplands', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'populated woodlands', 'residential rangelands',\n",
- " 'residential woodlands', 'rainfed villages', 'rainfed villages',\n",
- " 'inhabited treeless and barren lands', 'water',\n",
- " 'wild treeless and barren lands', 'populated rangelands',\n",
- " 'rainfed villages', 'rainfed villages', 'rainfed villages',\n",
- " 'mixed settlements', 'rainfed villages', 'populated rangelands',\n",
- " 'water', 'populated croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential woodlands', 'water',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential woodlands', 'water',\n",
- " 'residential rangelands', 'mixed settlements', 'water', 'water',\n",
- " 'water', 'rainfed villages', 'water', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'water', 'populated croplands',\n",
- " 'residential woodlands', 'water', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'populated croplands', 'water',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'water', 'water', 'wild woodlands',\n",
- " 'populated woodlands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential irrigated croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'populated rangelands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'populated rangelands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'rainfed villages', 'water',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'water', 'water', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'water',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'water', 'water', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'water',\n",
- " 'residential rainfed croplands', 'water', 'water', 'water', 'water',\n",
- " 'water', 'water', 'water', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'water', 'rainfed villages',\n",
- " 'rainfed villages', 'irrigated villages', 'water', 'rainfed villages',\n",
- " 'populated woodlands', 'populated woodlands', 'populated woodlands',\n",
- " 'water', 'populated woodlands', 'populated woodlands', 'water',\n",
- " 'populated woodlands', 'water', 'populated woodlands', 'water', 'water',\n",
- " 'populated croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'wild woodlands', 'populated woodlands', 'wild woodlands', 'water',\n",
- " 'populated woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands',\n",
- " 'populated woodlands', 'populated woodlands', 'populated woodlands',\n",
- " 'populated woodlands', 'residential woodlands', 'populated woodlands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'residential irrigated croplands', 'water',\n",
- " 'residential irrigated croplands', 'wild woodlands', 'water', 'water',\n",
- " 'residential woodlands', 'water'], dtype=object) UMBC_modal_anthrome_classification_5km
(station)
object
...
standard_name : UMBC modal anthrome classification 5km long_name : UMBC modal anthrome classification in 5km radius units : unitless description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 5km around station location. array(['water', 'residential rangelands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'populated rangelands', 'populated croplands',\n",
- " 'pastoral villages', 'residential woodlands', 'residential woodlands',\n",
- " 'residential rangelands', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated woodlands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'pastoral villages',\n",
- " 'rainfed villages', 'inhabited treeless and barren lands', 'water',\n",
- " 'wild treeless and barren lands', 'populated rangelands',\n",
- " 'rainfed villages', 'pastoral villages', 'rainfed villages',\n",
- " 'mixed settlements', 'rainfed villages', 'populated rangelands',\n",
- " 'residential woodlands', 'populated woodlands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential woodlands', 'water',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential woodlands', 'residential woodlands',\n",
- " 'residential rangelands', 'rainfed villages', 'residential rangelands',\n",
- " 'water', 'residential rainfed croplands', 'water', 'rainfed villages',\n",
- " 'water', 'residential rainfed croplands', 'residential woodlands',\n",
- " 'water', 'inhabited treeless and barren lands', 'residential woodlands',\n",
- " 'rainfed villages', 'rainfed villages', 'residential rainfed croplands',\n",
- " 'populated croplands', 'inhabited treeless and barren lands',\n",
- " 'residential rainfed croplands', 'populated croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'water', 'populated croplands',\n",
- " 'wild woodlands', 'wild woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'wild woodlands', 'populated croplands',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'populated rangelands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'populated rangelands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rangelands', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'rainfed villages',\n",
- " 'residential rangelands', 'rainfed villages',\n",
- " 'residential irrigated croplands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'mixed settlements', 'residential rangelands', 'residential woodlands',\n",
- " 'rainfed villages', 'populated woodlands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'water', 'residential woodlands',\n",
- " 'water', 'water', 'populated woodlands', 'populated woodlands',\n",
- " 'inhabited treeless and barren lands', 'populated woodlands',\n",
- " 'residential woodlands', 'water', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'residential irrigated croplands',\n",
- " 'rainfed villages', 'rainfed villages', 'populated woodlands',\n",
- " 'populated woodlands', 'populated woodlands', 'water',\n",
- " 'populated woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands', 'water',\n",
- " 'mixed settlements', 'populated croplands', 'water',\n",
- " 'residential irrigated croplands', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'populated croplands', 'rainfed villages', 'wild woodlands',\n",
- " 'populated woodlands', 'wild woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands',\n",
- " 'populated woodlands', 'populated croplands', 'populated croplands',\n",
- " 'populated woodlands', 'wild woodlands', 'populated woodlands',\n",
- " 'residential woodlands', 'residential woodlands',\n",
- " 'populated rangelands', 'populated woodlands', 'rainfed villages',\n",
- " 'populated woodlands', 'residential irrigated croplands',\n",
- " 'inhabited treeless and barren lands',\n",
- " 'residential irrigated croplands', 'wild woodlands', 'water',\n",
- " 'residential woodlands', 'residential woodlands', 'remote rangelands'],\n",
- " dtype=object) WMO_region
(station)
object
...
standard_name : WMO region long_name : WMO region code units : unitless description : World Meteorological Organization (WMO) region of station. The available regions are: Africa, Asia, South America, "Northern America, Central America and the Caribbean", South-West Pacific, Europe and Antarctica. array(['Antarctic', 'South America', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'North America, Central America And The Caribbean',\n",
- " 'North America, Central America And The Caribbean', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'South America', 'Africa',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Antarctic', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Africa', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'South-West Pacific', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Antarctic', 'Asia', 'Asia', 'Asia',\n",
- " 'Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Antarctic', 'Europe',\n",
- " 'South-West Pacific', 'Antarctic', 'South-West Pacific', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Asia', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'North America, Central America And The Caribbean',\n",
- " 'North America, Central America And The Caribbean',\n",
- " 'South-West Pacific', 'Antarctic',\n",
- " 'North America, Central America And The Caribbean', 'Asia', 'Africa'],\n",
- " dtype=object) WWF_TEOW_biogeographical_realm
(station)
object
...
standard_name : WWF TEOW biogeographical realm long_name : WWF TEOW biogeographical realm units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 8 biogeographical realms. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['antarctic', 'neotropics', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'water', 'nearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'neotropics',\n",
- " 'water', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'water', 'water', 'nearctic',\n",
- " 'palearctic', 'water', 'palearctic', 'palearctic', 'water',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'water', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'water',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'indomalay', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'water',\n",
- " 'palearctic', 'oceania', 'water', 'water', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'antarctic', 'palearctic', 'australasia', 'water',\n",
- " 'australasia', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'nearctic',\n",
- " 'nearctic', 'oceania', 'water', 'water', 'indomalay', 'afrotropics'],\n",
- " dtype=object) WWF_TEOW_biome
(station)
object
...
standard_name : WWF TEOW biome long_name : WWF TEOW biome units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 14 biomes. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['tundra', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate conifer forests', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate conifer forests', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'water', 'tundra',\n",
- " 'temperate conifer forests', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub', 'water',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'water', 'water', 'tundra', 'temperate broadleaf and mixed forests',\n",
- " 'water', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'water',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub', 'water',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub', 'water',\n",
- " 'boreal forests/taiga', 'boreal forests/taiga', 'boreal forests/taiga',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'tropical and subtropical moist broadleaf forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub', 'water',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'tropical and subtropical moist broadleaf forests', 'water', 'water',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'tundra', 'tundra', 'tundra',\n",
- " 'temperate broadleaf and mixed forests', 'boreal forests/taiga',\n",
- " 'temperate conifer forests', 'boreal forests/taiga', 'tundra',\n",
- " 'boreal forests/taiga', 'temperate broadleaf and mixed forests',\n",
- " 'water', 'temperate grasslands, savannas and shrublands',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'tundra',\n",
- " 'boreal forests/taiga', 'boreal forests/taiga',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'boreal forests/taiga',\n",
- " 'boreal forests/taiga', 'temperate broadleaf and mixed forests',\n",
- " 'temperate conifer forests', 'temperate conifer forests',\n",
- " 'temperate conifer forests', 'temperate conifer forests',\n",
- " 'temperate conifer forests', 'temperate broadleaf and mixed forests',\n",
- " 'tundra', 'temperate grasslands, savannas and shrublands',\n",
- " 'tropical and subtropical grasslands, savannas and shrublands', 'water',\n",
- " 'water', 'tropical and subtropical moist broadleaf forests',\n",
- " 'mediterranean forests, woodlands and scrub'], dtype=object) WWF_TEOW_terrestrial_ecoregion
(station)
object
...
standard_name : WWF TEOW terrestrial ecoregion long_name : WWF TEOW terrestrial ecoregion units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 825 terrestrial ecoregions. Ecoregions are relatively large units of land containing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['marielandia antarctic tundra', 'magellanic subpolar forests',\n",
- " 'pannonian mixed forests', 'alps conifer and mixed forests',\n",
- " 'central european mixed forests', 'western european broadleaf forests',\n",
- " 'alps conifer and mixed forests', 'alps conifer and mixed forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'pannonian mixed forests',\n",
- " 'central european mixed forests', 'pannonian mixed forests',\n",
- " 'pannonian mixed forests', 'alps conifer and mixed forests',\n",
- " 'alps conifer and mixed forests', 'pannonian mixed forests',\n",
- " 'western european broadleaf forests', 'atlantic mixed forests',\n",
- " 'atlantic mixed forests', 'rodope montane mixed forests', 'water',\n",
- " 'high arctic tundra', 'alps conifer and mixed forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'chilean matorral', 'water',\n",
- " 'cyprus mediterranean forests', 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'atlantic mixed forests',\n",
- " 'atlantic mixed forests', 'western european broadleaf forests',\n",
- " 'baltic mixed forests', 'western european broadleaf forests',\n",
- " 'baltic mixed forests', 'western european broadleaf forests',\n",
- " 'alps conifer and mixed forests', 'water', 'water',\n",
- " 'kalaallit nunaat high arctic tundra', 'baltic mixed forests',\n",
- " 'rock and ice', 'atlantic mixed forests', 'sarmatic mixed forests',\n",
- " 'water', 'northwest iberian montane forests',\n",
- " 'cantabrian mixed forests',\n",
- " 'northeastern spain and southern france mediterranean forests',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'cantabrian mixed forests', 'iberian conifer forests', 'water',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'cantabrian mixed forests',\n",
- " 'southwest iberian mediterranean sclerophyllous and mixed forests',\n",
- " 'canary islands dry woodlands and forests', 'water',\n",
- " 'scandinavian and russian taiga', 'scandinavian and russian taiga',\n",
- " 'scandinavian and russian taiga', 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'atlantic mixed forests',\n",
- " 'western european broadleaf forests', 'atlantic mixed forests',\n",
- " 'alps conifer and mixed forests', 'western european broadleaf forests',\n",
- " 'atlantic mixed forests', 'pyrenees conifer and mixed forests',\n",
- " 'northeastern spain and southern france mediterranean forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'celtic broadleaf forests',\n",
- " 'north atlantic moist mixed forests', 'celtic broadleaf forests',\n",
- " 'celtic broadleaf forests', 'caledon conifer forests',\n",
- " 'celtic broadleaf forests', 'celtic broadleaf forests',\n",
- " 'celtic broadleaf forests', 'english lowlands beech forests',\n",
- " 'english lowlands beech forests', 'celtic broadleaf forests',\n",
- " 'celtic broadleaf forests', 'celtic broadleaf forests',\n",
- " 'celtic broadleaf forests', 'english lowlands beech forests',\n",
- " 'north atlantic moist mixed forests', 'english lowlands beech forests',\n",
- " 'aegean and western turkey sclerophyllous and mixed forests',\n",
- " 'crete mediterranean forests', 'pannonian mixed forests',\n",
- " 'pannonian mixed forests', 'sumatran montane rain forests',\n",
- " 'celtic broadleaf forests', 'celtic broadleaf forests',\n",
- " 'alps conifer and mixed forests', 'appenine deciduous montane forests',\n",
- " 'tyrrhenian-adriatic sclerophyllous and mixed forests',\n",
- " 'italian sclerophyllous and semi-deciduous forests', 'water',\n",
- " 'taiheiyo evergreen forests', 'ogasawara subtropical moist forests',\n",
- " 'water', 'water', 'southern korea evergreen forests',\n",
- " 'central european mixed forests', 'sarmatic mixed forests',\n",
- " 'sarmatic mixed forests',\n",
- " 'tyrrhenian-adriatic sclerophyllous and mixed forests',\n",
- " 'atlantic mixed forests', 'atlantic mixed forests',\n",
- " 'atlantic mixed forests', 'atlantic mixed forests',\n",
- " 'atlantic mixed forests', 'sarmatic mixed forests',\n",
- " 'scandinavian montane birch forest and grasslands',\n",
- " 'scandinavian montane birch forest and grasslands', 'arctic desert',\n",
- " 'sarmatic mixed forests', 'scandinavian and russian taiga',\n",
- " 'scandinavian coastal conifer forests',\n",
- " 'scandinavian and russian taiga', 'maudlandia antarctic desert',\n",
- " 'scandinavian and russian taiga', 'north island temperate forests',\n",
- " 'water', 'cantebury-otago tussock grasslands',\n",
- " 'central european mixed forests', 'western european broadleaf forests',\n",
- " 'baltic mixed forests', 'central european mixed forests',\n",
- " 'balkan mixed forests', 'taimyr-central siberian tundra',\n",
- " 'scandinavian and russian taiga', 'scandinavian and russian taiga',\n",
- " 'sarmatic mixed forests', 'sarmatic mixed forests',\n",
- " 'sarmatic mixed forests', 'baltic mixed forests',\n",
- " 'sarmatic mixed forests', 'sarmatic mixed forests',\n",
- " 'scandinavian and russian taiga', 'scandinavian and russian taiga',\n",
- " 'pannonian mixed forests', 'alps conifer and mixed forests',\n",
- " 'alps conifer and mixed forests', 'carpathian montane forests',\n",
- " 'carpathian montane forests', 'carpathian montane forests',\n",
- " 'pannonian mixed forests', 'arctic coastal tundra',\n",
- " 'western short grasslands', 'hawaii tropical high shrublands', 'water',\n",
- " 'water', 'northern indochina subtropical forests',\n",
- " 'montane fynbos and renosterveld'], dtype=object) administrative_country_division_1
(station)
object
...
standard_name : administrative country division 1 long_name : administrative country division 1 units : unitless description : Name of the first (i.e. largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['nan', 'Tierra Del Fuego', 'Burgenland', 'Carinthia', 'Lower Austria',\n",
- " 'Vorarlberg', 'Carinthia', 'Styria', 'Salzburg', 'Lower Austria',\n",
- " 'Lower Austria', 'Lower Austria', 'Lower Austria', 'Lower Austria',\n",
- " 'Upper Austria', 'Styria', 'Styria', 'Wallonia', 'Wallonia', 'Wallonia',\n",
- " 'Smolyan', 'Hamilton City', 'nan', 'Bern', 'Vaud', 'Thurgau',\n",
- " 'Neuchatel', 'Schwyz', 'Lucerne', 'nan', 'nan', 'Lefkosia', 'Vysocina',\n",
- " 'Central Bohemia', 'Jihocesky', 'Schleswig-Holstein', 'Lower Saxony',\n",
- " 'Baden-Wuerttemberg', 'nan', 'Thuringia', 'Mecklenburg-Vorpommern',\n",
- " 'Bavaria', 'Tyrol', 'nan', 'nan', 'nan', 'Zealand', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Castille-La Mancha', 'Galicia', 'Balearic Islands', 'Andalusia',\n",
- " 'Asturias', 'Castille-La Mancha', 'Catalonia', 'Extremadura',\n",
- " 'Valencia', 'Castille And Leon', 'Catalonia', 'Galicia', 'nan',\n",
- " 'Canary Islands', 'nan', 'Kymenlaakso', 'nan', 'nan', 'Alsace',\n",
- " 'Champagne-Ardenne', 'nan', 'Midi-Pyrenees', 'Franche-Comte',\n",
- " 'Pays De La Loire', "Provence-Alpes-Cote D'Azur", 'Limousin',\n",
- " 'Lower Normandy', 'Midi-Pyrenees', 'nan', 'Centre', 'Auvergne', 'nan',\n",
- " 'nan', 'England', 'England', 'nan', 'England', 'Scotland', 'England',\n",
- " 'England', 'England', 'Wales', 'England', 'Scotland', 'England',\n",
- " 'England', 'Scotland', 'England', 'Central Greece', 'Crete',\n",
- " 'Bacs-Kiskun', 'Vas', 'West Sumatra', 'Munster', 'nan', 'Lombardy',\n",
- " 'Tuscany', 'Sicily', 'Umbria', 'nan', 'nan', 'nan', 'Okinawa', 'nan',\n",
- " 'nan', 'nan', 'Rucavas', 'Vecpiebalga', 'L-Gharb', 'Gelderland',\n",
- " 'Groningen', 'North Brabant', 'South Holland', 'Utrecht', 'Aust-Agder',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'Rogaland', 'Akershus', 'nan',\n",
- " 'Telemark', 'nan', 'nan', 'nan', 'Masovian Voivodeship',\n",
- " 'Lower Silesian Voivodeship', 'Pomeranian Voivodeship',\n",
- " 'Warmian-Masurian Voivodeship', 'Central Serbia', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'Skane', 'Uppsala', 'nan', 'Vaesterbotten', 'nan',\n",
- " 'Kostel', 'Sostanj', 'Cerklje Na Gorenjskem', 'nan', 'Presovsky', 'nan',\n",
- " 'nan', 'nan', 'Colorado', 'nan', 'nan', 'California', 'Huyen Dien Bien',\n",
- " 'nan'], dtype=object) administrative_country_division_2
(station)
object
...
standard_name : administrative country division 2 long_name : administrative country division 2 units : unitless description : Name of the second (i.e. second largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['nan', 'nan', 'Politischer Bezirk Neusiedl Am See',\n",
- " 'Politischer Bezirk Spittal An Der Drau',\n",
- " 'Politischer Bezirk Hollabrunn', 'Politischer Bezirk Bregenz',\n",
- " 'Politischer Bezirk Spittal An Der Drau',\n",
- " 'Politischer Bezirk Hartberg-Fuerstenfeld',\n",
- " 'Politischer Bezirk Salzburg-Umgebung', 'Politischer Bezirk Gmund',\n",
- " 'Politischer Bezirk Sankt Polten', 'Politischer Bezirk Krems',\n",
- " 'Politischer Bezirk Ganserndorf',\n",
- " 'Politischer Bezirk Bruck An Der Leitha',\n",
- " 'Politischer Bezirk Steyr-Land', 'Politischer Bezirk Murau',\n",
- " 'Politischer Bezirk Graz-Umgebung', 'Province Du Luxembourg',\n",
- " 'Province De Liege', 'Province De Namur', 'Obshtina Chepelare', 'nan',\n",
- " 'nan', 'Interlaken-Oberhasli District', 'Broye-Vully District',\n",
- " 'Muenchwilen District', 'Val-De-Ruz District', 'Bezirk Kuessnacht',\n",
- " 'Sursee District', 'nan', 'nan', 'nan', "Okres Zd'Ar Nad Sazavou",\n",
- " 'nan', 'Okres Prachatice', 'nan', 'nan', 'Freiburg Region', 'nan',\n",
- " 'nan', 'nan', 'Upper Bavaria', 'Politischer Bezirk Reutte', 'nan',\n",
- " 'nan', 'nan', 'Roskilde Kommune', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Provincia De Ciudad Real', 'Provincia Da Coruna', 'Illes Balears',\n",
- " 'Provincia De Granada', 'Province Of Asturias',\n",
- " 'Provincia De Guadalajara', 'Provincia De Girona',\n",
- " 'Provincia De Badajoz', 'Provincia De Valencia',\n",
- " 'Provincia De Salamanca', 'Provincia De Lleida', 'Provincia De Lugo',\n",
- " 'nan', 'Provincia De Santa Cruz De Tenerife', 'nan', 'Kotka-Hamina',\n",
- " 'nan', 'nan', 'Departement Du Bas-Rhin', 'Departement Des Ardennes',\n",
- " 'nan', 'Departement Du Gers', 'Departement Du Doubs',\n",
- " 'Departement De La Vendee', 'Departement Des Hautes-Alpes',\n",
- " 'Departement De La Creuse', "Departement De L'Orne",\n",
- " 'Departement Des Hautes-Pyrenees', 'nan', 'Departement Du Cher',\n",
- " 'Departement Du Puy-De-Dome', 'nan', 'nan', 'Devon', 'North Yorkshire',\n",
- " 'nan', 'Shropshire', 'Midlothian', 'Derbyshire', 'East Sussex',\n",
- " 'Suffolk', 'Pembrokeshire', 'Cambridgeshire', 'Midlothian', 'Norfolk',\n",
- " 'Essex', 'Shetland Islands', 'Hampshire', 'Nomos Voiotias',\n",
- " 'Nomos Lasithiou', 'nan', 'nan', 'nan', 'Ciarrai', 'nan',\n",
- " 'Provincia Di Varese', 'Provincia Di Pistoia', 'Trapani',\n",
- " 'Provincia Di Perugia', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'Berkelland', 'Gemeente De Marne',\n",
- " 'Gemeente Sint Anthonis', 'Gemeente Noordwijkerhout', 'Gemeente Lopik',\n",
- " 'Birkenes', 'nan', 'nan', 'nan', 'nan', 'nan', 'Karmoy', 'Hurdal',\n",
- " 'nan', 'Skien', 'nan', 'nan', 'nan', 'Powiat Garwolinski',\n",
- " 'Powiat Jeleniogorski', 'Powiat Leborski', 'Powiat Goldapski',\n",
- " 'Nisavski Okrug', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Svalovs Kommun', 'Osthammars Kommun', 'nan', 'Vindelns Kommun', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'Okres Poprad', 'nan', 'nan', 'nan',\n",
- " 'Boulder County', 'nan', 'nan', 'Humboldt County', 'nan', 'nan'],\n",
- " dtype=object) altitude
(station)
float32
...
standard_name : altitude long_name : altitude relative to mean sea level units : m description : Altitude of the ground level at the station, relative to the stated vertical datum, in metres. array([1.995e+02, 1.900e+01, 1.170e+02, 1.020e+03, 3.150e+02, 1.020e+03,\n",
- " 3.106e+03, 1.170e+03, 7.300e+02, 5.700e+02, 5.810e+02, 3.200e+02,\n",
- " 1.610e+02, 2.400e+02, 8.990e+02, 1.648e+03, 4.810e+02, 4.300e+02,\n",
- " 2.950e+02, 1.600e+02, 1.750e+03, 1.840e+02, 6.100e+02, 3.578e+03,\n",
- " 4.890e+02, 5.390e+02, 1.137e+03, 1.031e+03, 7.970e+02, 2.225e+03,\n",
- " 1.750e+01, 5.320e+02, 7.350e+02, 5.350e+02, 1.118e+03, 1.200e+01,\n",
- " 7.400e+01, 1.205e+03, 6.200e+01, 9.370e+02, 1.000e+00, 9.900e+02,\n",
- " 2.671e+03, 4.900e+01, 1.000e+01, 2.000e+01, 3.000e+00, 3.238e+03,\n",
- " 1.000e+01, 3.200e+01, 6.000e+00, 9.170e+02, 6.830e+02, 7.800e+01,\n",
- " 1.230e+03, 1.340e+02, 1.360e+03, 7.600e+01, 3.930e+02, 8.850e+02,\n",
- " 9.850e+02, 4.700e+02, 5.060e+02, 3.500e+01, 2.400e+03, 7.000e+00,\n",
- " 4.000e+00, 3.100e+02, 5.650e+02, 7.750e+02, 3.900e+02, 6.200e+02,\n",
- " 2.000e+02, 8.360e+02, 1.330e+02, 1.750e+03, 8.100e+02, 3.090e+02,\n",
- " 2.877e+03, 6.050e+02, 1.820e+02, 1.465e+03, 2.430e+02, 1.260e+02,\n",
- " 1.190e+02, 2.670e+02, 2.700e+02, 3.700e+02, 1.800e+02, 4.200e+02,\n",
- " 1.200e+02, 4.600e+01, 1.600e+02, 5.000e+00, 2.600e+02, 1.600e+01,\n",
- " 8.000e+00, 8.500e+01, 7.800e+01, 1.100e+02, 2.500e+02, 1.250e+02,\n",
- " 3.140e+02, 8.640e+02, 1.100e+01, 5.000e+00, 2.090e+02, 2.165e+03,\n",
- " 5.000e+00, 1.092e+03, 1.850e+01, 2.640e+02, 1.000e+01, 3.400e+01,\n",
- " 8.600e+01, 8.000e+01, 5.000e+00, 1.800e+01, 1.880e+02, 1.830e+02,\n",
- " 2.000e+01, 1.000e+00, 2.800e+01, 4.000e+00, 4.000e+00, 2.190e+02,\n",
- " 4.390e+02, 2.100e+02, 4.740e+02, 1.600e+02, 3.000e+01, 1.500e+01,\n",
- " 3.000e+02, 1.553e+03, 2.000e+01, 8.600e+01, 1.840e+02, 3.700e+02,\n",
- " 1.800e+02, 1.603e+03, 2.000e+00, 1.570e+02, 8.130e+02, 8.000e+00,\n",
- " 4.040e+02, 4.750e+02, 5.000e+00, 1.800e+02, 6.500e+01, 1.900e+02,\n",
- " 4.500e+01, 2.610e+02, 2.250e+02, 1.320e+02, 5.200e+02, 7.700e+02,\n",
- " 1.754e+03, 2.008e+03, 8.080e+02, 3.450e+02, 1.130e+02, 1.100e+01,\n",
- " 1.689e+03, 3.397e+03, 2.841e+03, 1.070e+02, 1.478e+03, 2.340e+02],\n",
- " dtype=float32) annual_native_max_gap_percent
(station, time)
uint8
...
standard_name : annual native max gap percent long_name : annual native max gap percent units : % description : Percentage of the maximum data gap in the annual averaged measurement UTC window filled by native resolution data, relative to the total window length. [120960 values with dtype=uint8] annual_native_representativity_percent
(station, time)
uint8
...
standard_name : annual native representativity percent long_name : annual native representativity percent units : % description : Percentage of the annual averaged measurement UTC window represented by native resolution data. [120960 values with dtype=uint8] area_classification
(station)
object
...
standard_name : area classification long_name : standardised network provided area classification units : unitless description : Standardised network provided classification, describing type of area a measurement station is situated in. array(['rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'urban-suburban', 'urban-suburban', 'rural', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-suburban', 'rural', 'rural', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'rural',\n",
- " 'rural', 'rural', 'rural', 'urban-suburban', 'rural', 'urban-suburban',\n",
- " 'rural', 'urban-suburban', 'rural', 'rural', 'rural', 'rural',\n",
- " 'urban-suburban', 'rural', 'urban-suburban', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'urban-suburban',\n",
- " 'rural', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'urban-suburban', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'urban-suburban', 'rural', 'rural', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'urban-suburban', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'urban-suburban', 'rural', 'rural', 'urban-suburban', 'rural',\n",
- " 'urban-suburban', 'rural', 'urban-suburban', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'urban-suburban', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'urban-suburban', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'urban-suburban',\n",
- " 'rural', 'rural', 'urban-suburban', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'rural'], dtype=object) associated_networks
(station)
object
...
standard_name : other associated networks long_name : other associated networks units : unitless description : String pair of associated network name and station reference. Format: network1:station_reference1;network2:station_reference2 array(['WDCA:GAWAAR__MBI;GAW:MBI', 'GAW:USH', 'GAW:ILL', 'GAW:VOH', 'nan',\n",
- " 'nan', 'WDCA:GAWAAT__SNB;GAW:SNB;ZAMG:SNB', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'GAW:ZOE', 'nan', 'nan', 'GAW:OFF', 'GAW:EUP',\n",
- " 'GAW:VEZ', 'GAW:RJP', 'WDCA:GAWAGB__BMW;GAW:BMW', 'GAW:EUK', 'GAW:JFJ',\n",
- " 'GAW:PAY', 'GAW:TAE', 'GAW:CMT', 'GAW:RIG', 'GAW:BRM',\n",
- " 'WDCA:GAWACL__TLL;GAW:TLL', 'GAW:CVO', 'WDCA:GAWACY__CYP;GAW:CYP',\n",
- " 'GAW:SVT', 'WDCA:GAWACZ__KOS;GAW:KOS', 'nan',\n",
- " 'GAW:WES;UBA:DEUB001;WMO:10020-0', 'GAW:WAL', 'GAW:SSL', 'GAW:NGL',\n",
- " 'nan', 'GAW:ZGT;UBA:DEUB028', 'WDCA:GAWADE__HPB;GAW:HPB',\n",
- " 'WDCA:GAWADE__ZSF;GAW:ZSF', 'GAW:NMY', 'GAW:KLD', 'GAW:SNO', 'nan',\n",
- " 'WDCA:GAWADK__SUM;GAW:SUM', 'GAW:UBG', 'GAW:LAH', 'GAW:VSD', 'GAW:SPM',\n",
- " 'GAW:NIA', 'GAW:MHN', 'GAW:VIZ', 'GAW:NIE', 'GAW:CPM', 'GAW:CCR', 'nan',\n",
- " 'GAW:ZRR', 'GAW:PNS', 'GAW:ELT', 'GAW:OSV', 'GAW:DON', 'GAW:IZO',\n",
- " 'GAW:UTO', 'nan', 'GAW:OUX', 'WDCA:GAWAFI__PAL;GAW:PAL', 'nan', 'nan',\n",
- " 'nan', 'GAW:PYE', 'nan', 'GAW:TAD', 'nan', 'nan', 'nan', 'GAW:PDM',\n",
- " 'nan', 'nan', 'GAW:PUY', 'GAW:EDM', 'GAW:LNR', 'GAW:YRW', 'GAW:HMS',\n",
- " 'nan', 'GAW:ASH', 'GAW:BUS', 'GAW:LBR', 'GAW:LNH', 'GAW:SBN', 'GAW:NBH',\n",
- " 'GAW:WKF', 'GAW:AUC', 'GAW:WAO', 'nan', 'GAW:SIS', 'nan', 'GAW:ATS',\n",
- " 'GAW:FIK', 'WDCA:GAWAHU__KPS;GAW:KPS', 'nan',\n",
- " 'WDCA:GAWAID__BKT;GAW:BKT', 'GAW:VTO', 'WDCA:GAWAIE__MHD;GAW:MHD',\n",
- " 'WDCA:GAWAIT__IPR;GAW:IPR', 'GAW:CMN', 'WDCA:GAWAIT__CGR;GAW:CGR',\n",
- " 'nan', 'GAW:SYO', 'WDCA:GAWAJP__RYO;GAW:RYO',\n",
- " 'WDCA:GAWAJP__MNM;GAW:MNM', 'WDCA:GAWAJP__YON;GAW:YON',\n",
- " 'WDCA:GAWAKR__AMY;GAW:AMY', 'GAW:JGS', 'GAW:PLA', 'GAW:RCV', 'GAW:ZSN',\n",
- " 'WDCA:GAWAMT__GLH;GAW:GLH', 'GAW:EIB', 'GAW:KMW', 'nan', 'GAW:DZI',\n",
- " 'GAW:CES', 'WDCA:GAWANO__BIR;GAW:BIR', 'GAW:TUV', 'GAW:KRV',\n",
- " 'WDCA:GAWANO__ZEP;GAW:ZEP', 'GAW:PRB', 'GAW:SVV', 'GAW:SND', 'nan',\n",
- " 'nan', 'nan', 'GAW:BHD', 'WDCA:GAWANZ__ARH;GAW:ARH',\n",
- " 'WDCA:GAWANZ__LAU;GAW:LAU', 'GAW:JCZ', 'GAW:SNZ', 'GAW:LEB', 'GAW:DIG',\n",
- " 'GAW:KAM', 'WDCA:GAWARU__TIK;GAW:TIK', 'GAW:BRE', 'GAW:ESR', 'GAW:RAO',\n",
- " 'nan', 'nan', 'Airbase:SE0116A', 'nan', 'GAW:NKV', 'GAW:VDL', 'nan',\n",
- " 'GAW:IRB', 'nan', 'GAW:KVV', 'GAW:CHO', 'GAW:STL', 'GAW:STA', 'GAW:TOP',\n",
- " 'WDCA:GAWAUSAKBRW;GAW:BRW', 'GAW:BOS;BSRN:34;AERONET:Boulder',\n",
- " 'WDCA:GAWAUSHIMLO;GAW:MLO', 'WDCA:GAWAUS__SPO;GAW:SPO',\n",
- " 'WDCA:GAWAUSCATHD;GAW:THD', 'WDCA:GAWAID__PDI;GAW:PDI', 'GAW:CPT'],\n",
- " dtype=object) city
(station)
object
...
standard_name : city long_name : city units : unitless description : Name of the city the station is located in. array(['nan', 'Ushuaia', 'Illmitz', 'Oberdrauburg', 'Retz', 'Sulzberg',\n",
- " 'Grosskirchheim', 'Poellauberg', 'Nussdorf Am Haunsberg', 'Litschau',\n",
- " 'Altlengbach', 'Durnstein', 'Ganserndorf',\n",
- " 'Trautmannsdorf An Der Leitha', 'Reichraming', 'Sankt Lambrecht',\n",
- " 'Hart Bei Graz', 'Bertrix', 'Baelen', 'Noville-Les-Bois', 'Chepelare',\n",
- " 'Hamilton', 'nan', 'Lauterbrunnen', 'Payerne', 'Aadorf',\n",
- " 'Grand-Savagnier', 'Kussnacht', 'Gunzwil', 'nan', 'nan', 'Peristerona',\n",
- " 'Svratka', 'Cechtice', 'Stachy', 'Westerland', 'Soltendieck',\n",
- " 'Oberried', 'nan', 'Gehlberg', 'Zingst', 'Hohenpeissenberg', 'Ehrwald',\n",
- " 'nan', 'nan', 'nan', 'Roskilde', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Retuerta De Bullaque', 'Boiro', 'Es Castell', 'Viznar', 'Llanes',\n",
- " 'Campisabalos', 'Cadaques', 'Higuera De Vargas', 'Zarra', 'Santiz',\n",
- " 'Juncosa', 'Chantada', 'nan', 'Guimar', 'nan', 'Virolahti', 'nan',\n",
- " 'nan', 'Schirmeck', 'Revin', 'nan', 'Marciac', 'Saint-Hippolyte',\n",
- " 'La Chataigneraie', 'Le Monetier-Les-Bains', 'Banize',\n",
- " 'La Ferriere-Aux-Etangs', 'Campan', 'nan', 'Dun-Sur-Auron', 'Orcines',\n",
- " 'nan', 'nan', 'Bovey Tracey', 'Pickering', 'nan', "Bishop'S Castle",\n",
- " 'Penicuik', 'Hope Valley', 'Seaford', 'Cookley', 'Templeton', 'Soham',\n",
- " 'Penicuik', 'Sheringham', 'Saint Osyth', 'Lerwick', 'Andover',\n",
- " 'Aliartos', 'Elounda', 'Lajosmizse', 'Szentgotthard', 'Matur',\n",
- " 'Cahersiveen', 'nan', 'Cadrezzate', 'Abetone', 'Tre Fontane',\n",
- " "Giano Dell'Umbria", 'nan', 'nan', 'nan', 'Yonakuni', 'nan', 'nan',\n",
- " 'Neringa', 'Rucava', 'Vecpiebalga', 'Gharb', 'Borculo', 'Ulrum',\n",
- " 'Sint Anthonis', 'Noordwijkerhout', 'Lopik', 'Birkeland', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'Akrehamn', 'Hurdal', 'nan', 'Skien', 'nan',\n",
- " 'Mcmurdo Station', 'nan', 'Zelechow', 'Karpacz', 'Leba',\n",
- " 'Banie Mazurskie', 'Nis', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Kagerod', 'Bjorklinge', 'nan', 'Vindeln', 'nan', 'Kostel', 'Topolsica',\n",
- " 'Cerklje Na Gorenjskem', 'nan', 'Nova Lesna', 'nan', 'nan', 'nan',\n",
- " 'Niwot', 'nan', 'nan', 'Westhaven-Moonstone', 'Thi Tran Tuan Giao',\n",
- " 'nan'], dtype=object) climatology
(station)
object
...
standard_name : climatology long_name : climatology units : unitless description : Name of the climatology of which the observations pertain to. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) contact_email_address
(station)
object
...
standard_name : contact email address long_name : contact email address units : unitless description : Email address of the principal data contact for the specific reported data. array(['barlasina@smn.gov.ar', 'mcupeiro@smn.gov.ar', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'e.adriaenssens@vmm.be', 'e.adriaenssens@vmm.be',\n",
- " 'e.adriaenssens@vmm.be', 'nan', 'Audra.mcclure@noaa.gov',\n",
- " 'sara.crepinsek@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'martin.steinbacher@empa.ch', 'katie.read@ncas.ac.uk', 'nan', 'nan',\n",
- " 'dvorska.a@czechglobe.cz', 'nan', 'anke.penzlin@uba.de',\n",
- " 'anke.penzlin@uba.de', 'anke.penzlin@uba.de', 'anke.penzlin@uba.de',\n",
- " 'anke.penzlin@uba.de', 'anke.penzlin@uba.de', 'Robert.Holla@dwd.de',\n",
- " 'cedric.couret@uba.de', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Audra.mcclure@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'ctorresg@aemet.es', 'kaisa.korpi@fmi.fi', 'mika.vestenius@fmi.fi',\n",
- " 'kaisa.korpi@fmi.fi', 'mika.vestenius@fmi.fi', 'nan',\n",
- " 'therese.salameh@mines-douai.fr', 'nan',\n",
- " 'therese.salameh@mines-douai.fr', 'nan',\n",
- " 'therese.salameh@mines-douai.fr', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'labancz.k@met.hu', 'labancz.k@met.hu',\n",
- " 'martin.steinbacher@empa.ch', 'nan', 'nan', 'jean.putaud@ec.europa.eu',\n",
- " 'p.cristofanelli@isac.cnr.it', 'p.cristofanelli@isac.cnr.it',\n",
- " 'm.angelucci@arpa.umbria.it', 'antarctic@met.kishou.go.jp',\n",
- " 'ghg_obs@met.kishou.go.jp', 'ghg_obs@met.kishou.go.jp',\n",
- " 'ghg_obs@met.kishou.go.jp', 'sulla@korea.kr', 'sulla@korea.kr', 'nan',\n",
- " 'nan', 'nan', 'marvic.grima@um.edu.mt', 'rob.zwartjes@rivm.nl',\n",
- " 'rob.zwartjes@rivm.nl', 'rob.zwartjes@rivm.nl', 'rob.zwartjes@rivm.nl',\n",
- " 'rob.zwartjes@rivm.nl', 'agh@nilu.no', 'waa@nilu.no', 'agh@nilu.no',\n",
- " 'agh@nilu.no', 'agh@nilu.no', 'kt@nilu.no', 'agh@nilu.no',\n",
- " 'waa@nilu.no', 'agh@nilu.no', 'agh@nilu.no', 'sylvia.nichol@niwa.co.nz',\n",
- " 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'sara.crepinsek@noaa.gov', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'roman.kocuvan@eimv.si', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'martin.steinbacher@empa.ch',\n",
- " 'thumeka.mkololo@weathersa.co.za'], dtype=object) contact_institution
(station)
object
...
standard_name : contact institution long_name : contact institution units : unitless description : Institution of the principal data contact for the specific reported data. array(['Servicio Meteorológico Nacional', 'Servicio Meteorologico Nacional',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'Flanders Environment Agency',\n",
- " 'Flanders Environment Agency', 'Flanders Environment Agency', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Physical Sciences Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Swiss Federal Laboratories for Materials Science and Technology',\n",
- " 'National centre for Atmospheric Science', 'nan', 'nan',\n",
- " 'Global Change Research Centre AS CR v. v. i.', 'nan',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Meteorological Observatory Hohenpeissenberg',\n",
- " 'German Environment Agency', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Agencia Estatal de Meteorologia', 'nan',\n",
- " 'Finnish Meteorological Institute', 'nan',\n",
- " 'Finnish Meteorological Institute', 'nan', 'Ecole des Mines de Douai',\n",
- " 'nan', 'Ecole des Mines de Douai', 'nan', 'Ecole des Mines de Douai',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Hungarian Meteorological Service', 'Hungarian Meteorological Service',\n",
- " 'Swiss Federal Laboratories for Materials Science and Technology',\n",
- " 'nan', 'nan', 'EC Joint Research Centre',\n",
- " 'National Research Council of Italy',\n",
- " 'National Research Council of Italy',\n",
- " 'Arpa Umbria - Umbria Regional Agency for Environmental Protection',\n",
- " 'Japan Meteorological Agency', 'Japan Meteorological Agency',\n",
- " 'Japan Meteorological Agency', 'Japan Meteorological Agency',\n",
- " 'National Institute of Meteorological Sciences',\n",
- " 'National Institute of Meteorological Sciences', 'nan', 'nan', 'nan',\n",
- " 'University of Malta', 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'National Institute of Water and Atmospheric Research',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Physical Sciences Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Milan Vidmar Electric Power Research Institute', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'Swiss Federal Laboratories for Materials Science and Technology',\n",
- " 'South African Weather Service'], dtype=object) contact_name
(station)
object
...
standard_name : contact name long_name : contact name units : unitless description : Full name of the principal data contact for the specific reported data. array(['Maria Elena Barlasina', 'Manuel Cupeiro', 'Marina Froelich',\n",
- " 'Marina Froelich', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Marina Froelich',\n",
- " 'Wolfgang Spangl', 'Marina Froelich', 'Elke Adriaenssens',\n",
- " 'Elke Adriaenssens', 'Elke Adriaenssens', 'Emiliya Stoyneva',\n",
- " 'Audra Mcclure-Begley', 'Sara Crepinsek', 'Robert Gehrig',\n",
- " 'Robert Gehrig', 'Robert Gehrig', 'Robert Gehrig', 'Robert Gehrig',\n",
- " 'Thomas Seitz', 'Martin Steinbacher', 'Katie Read',\n",
- " 'Christos Papadopoulos', 'Jaroslav Pekarek', 'Jaroslav Pekarek',\n",
- " 'Jaroslav Pekarek', 'Anke Penzlin', 'Anke Penzlin', 'Anke Penzlin',\n",
- " 'Anke Penzlin', 'Anke Penzlin', 'Anke Penzlin', 'Robert Holla',\n",
- " 'Cedric Couret', 'Rolf Weller', 'Rune Keller', 'Rune Keller',\n",
- " 'Rune Keller', 'Audra Mcclure-Begley', 'Rune Keller', 'Truuts Toivo',\n",
- " 'Truuts Toivo', 'Juan Martinez', 'Juan Martinez', 'Alberto Gonzalez',\n",
- " 'Alberto Gonzalez', 'Alberto Gonzalez', 'Alberto Gonzalez',\n",
- " 'Alberto Gonzalez', 'Alberto Gonzalez', 'Abellan Maj Britt Larka',\n",
- " 'Alberto Gonzalez', 'Alberto Gonzalez', 'Alberto Gonzalez',\n",
- " 'Alberto Gonzalez', 'Carlos Torres', 'Timo Salmi', 'Mika Vestenius',\n",
- " 'Timo Salmi', 'Mika Vestenius', 'Patrice Coddeville',\n",
- " 'Patrice Coddeville', 'Patrice Coddeville', 'Patrice Coddeville',\n",
- " 'Patrice Coddeville', 'Patrice Coddeville', 'Patrice Coddeville',\n",
- " 'Patrice Coddeville', 'Patrice Coddeville', 'Patrice Coddeville',\n",
- " 'Stephane Sauvage', 'Stephane Sauvage', 'Patrice Coddeville',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Keith Vincent', 'A Adamopoulos',\n",
- " 'Nikos Mihalopoulos', 'Krisztina Labancz', 'Krisztina Labancz',\n",
- " 'Joerg Klausen', 'Barbara Oleary', 'Geoff Broughton', 'Jp Putaud',\n",
- " 'Paolo Cristofanelli', 'Paolo Cristofanelli', 'Monica Angelucci',\n",
- " 'Yoki Mori', 'Susumu Hashimoto', 'Susumu Hashimoto', 'Susumu Hashimoto',\n",
- " 'Sumin Kim', 'Sumin Kim', 'Dalia Sopauskiene', 'Frolova Marina',\n",
- " 'Frolova Marina', 'Marvic Grima', 'Jacobus P.J. Berkhout',\n",
- " 'Jacobus P.J. Berkhout', 'Jacobus P.J. Berkhout',\n",
- " 'Jacobus P.J. Berkhout', 'Ronald Spoor', 'Anne Hjellbrekke',\n",
- " 'Wenche Aas', 'Anne Hjellbrekke', 'Anne Hjellbrekke',\n",
- " 'Anne Hjellbrekke', 'Kjetil Tørseth', 'Anne Hjellbrekke', 'Wenche Aas',\n",
- " 'Anne Hjellbrekke', 'Anne Hjellbrekke', 'Sylvia Nichol',\n",
- " 'Audra Mcclure-Begley', 'Audra Mcclure-Begley', 'Magdalena Bogucka',\n",
- " 'Magdalena Bogucka', 'Magdalena Bogucka', 'Anna Degorska',\n",
- " 'Jasmina Knezevic', 'Sara Crepinsek', 'Karin Sjoberg', 'Karin Sjoberg',\n",
- " 'Karin Sjoberg', 'Karin Sjøberg', 'Karin Sjøberg', 'Karin Sjöberg',\n",
- " 'Karin Sjöberg', 'Karin Sjoberg', 'Karin Sjoberg', 'Karin Sjoberg',\n",
- " 'Murovec Marijana', 'Marijana Murovec', 'Marijana Murovec',\n",
- " 'Marta Mitosinkova', 'Marta Mitosinkova', 'Marta Mitosinkova',\n",
- " 'Marta Mitosinkova', 'Audra Mcclure-Begley', 'Audra Mcclure-Begley',\n",
- " 'Audra Mcclure-Begley', 'Audra Mcclure-Begley', 'Audra Mcclure-Begley',\n",
- " 'Martin Steinbacher', 'Thumeka Mkololo'], dtype=object) country
(station)
object
...
standard_name : country long_name : country units : unitless description : Name of the country the station is located in. array(['Argentina', 'Argentina', 'Austria', 'Austria', 'Austria', 'Austria',\n",
- " 'Austria', 'Austria', 'Austria', 'Austria', 'Austria', 'Austria',\n",
- " 'Austria', 'Austria', 'Austria', 'Austria', 'Austria', 'Belgium',\n",
- " 'Belgium', 'Belgium', 'Bulgaria', 'Bermuda', 'Canada', 'Switzerland',\n",
- " 'Switzerland', 'Switzerland', 'Switzerland', 'Switzerland',\n",
- " 'Switzerland', 'Chile', 'Cape Verde', 'Cyprus', 'Czech Republic',\n",
- " 'Czech Republic', 'Czech Republic', 'Germany', 'Germany', 'Germany',\n",
- " 'Germany', 'Germany', 'Germany', 'Germany', 'Germany', 'Germany',\n",
- " 'Denmark', 'Denmark', 'Denmark', 'Denmark', 'Denmark', 'Estonia',\n",
- " 'Estonia', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain',\n",
- " 'Spain', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain',\n",
- " 'Finland', 'Finland', 'Finland', 'Finland', 'France', 'France',\n",
- " 'France', 'France', 'France', 'France', 'France', 'France', 'France',\n",
- " 'France', 'France', 'France', 'France', 'United Kingdom',\n",
- " 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United Kingdom',\n",
- " 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United Kingdom',\n",
- " 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United Kingdom',\n",
- " 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United Kingdom',\n",
- " 'Greece', 'Greece', 'Hungary', 'Hungary', 'Indonesia', 'Ireland',\n",
- " 'Ireland', 'Italy', 'Italy', 'Italy', 'Italy', 'Japan', 'Japan',\n",
- " 'Japan', 'Japan', 'South Korea', 'South Korea', 'Lithuania', 'Latvia',\n",
- " 'Latvia', 'Malta', 'Netherlands', 'Netherlands', 'Netherlands',\n",
- " 'Netherlands', 'Netherlands', 'Norway', 'Norway', 'Norway', 'Norway',\n",
- " 'Norway', 'Norway', 'Norway', 'Norway', 'Norway', 'Norway',\n",
- " 'New Zealand', 'New Zealand', 'New Zealand', 'Poland', 'Poland',\n",
- " 'Poland', 'Poland', 'Serbia', 'Russia', 'Sweden', 'Sweden', 'Sweden',\n",
- " 'Sweden', 'Sweden', 'Sweden', 'Sweden', 'Sweden', 'Sweden', 'Sweden',\n",
- " 'Slovenia', 'Slovenia', 'Slovenia', 'Slovakia', 'Slovakia', 'Slovakia',\n",
- " 'Slovakia', 'United States', 'United States', 'United States',\n",
- " 'United States', 'United States', 'Vietnam', 'South Africa'],\n",
- " dtype=object) daily_native_max_gap_percent
(station, time)
uint8
...
standard_name : daily native max gap percent long_name : daily native max gap percent units : % description : Percentage of the maximum data gap in the daily averaged measurement UTC window filled by native resolution data, relative to the total window length. [120960 values with dtype=uint8] daily_native_representativity_percent
(station, time)
uint8
...
standard_name : daily native representativity percent long_name : daily native representativity percent units : % description : Percentage of the daily averaged measurement UTC window represented by native resolution data. [120960 values with dtype=uint8] daily_passing_vehicles
(station)
float32
...
standard_name : daily passing vehicles long_name : average daily number of passing vehicles units : unitless description : Average number of vehicles passing daily. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) data_level
(station)
object
...
standard_name : data level long_name : data level units : unitless description : Data level of data reported. This varies per network. If data level is variable per measurement, and not static per reported file, then this is set as "variable". If there is no reported data level this is set as "none" array(['2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0'], dtype=object) data_licence
(station)
object
...
standard_name : data licence long_name : data licence units : unitless description : Information pertaining to the data licence governing the redistribution/publication of the ingested network data. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) day_night_code
(station, time)
uint8
...
standard_name : day/night code long_name : day/night code per measurement units : unitless description : Binary indication if measurement was made during the day or night. Day=0, Night=1. The classification is made by calculating the solar elevation angle for a latitude/longitude/measurement height at a mid-measurement window timestamp. If the solar elevation angle is > 0, it is classed as daytime, otherwise it is nightime. Classification is 255 if cannot be made. [120960 values with dtype=uint8] daytime_traffic_speed
(station)
float32
...
standard_name : daytime traffic speed long_name : average daytime speed of passing traffic units : km hr-1 description : Average daytime speed of the passing traffic where measurements are being made (if applicable), in kilometres per hour. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) derived_uncertainty_per_measurement
(station, time)
float32
...
standard_name : derived measurement uncertainty per measurement long_name : derived measurement uncertainty per measurement units : nmol mol-1 description : Derived measurement uncertainty (±) of methodology, for a specific measurement. This is calculated through the quadratic addition of reported (or if not available, documented) accuracy and precision metrics. This is given in absolute terms in nmol mol-1. [120960 values with dtype=float32] distance_to_building
(station)
float32
...
standard_name : distance to building long_name : distance to the nearest building units : m description : Distance to the nearest building of the inlet/instrument/sampler, in metres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) distance_to_junction
(station)
float32
...
standard_name : distance to junction long_name : distance to the nearest road junction units : m description : Distance to the nearest road junction of the inlet/instrument/sampler, in metres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) distance_to_kerb
(station)
float32
...
standard_name : distance to kerb long_name : distance to the street kerb units : m description : Distance to the street kerb of the inlet/instrument/sampler, in metres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) distance_to_source
(station)
float32
...
standard_name : distance to source long_name : distance to the main emission source units : km description : Distance to the main emission source (see variable: "main_emission_source") of the inlet/instrument/sampler, in kilometres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) ellipsoid
(station)
object
...
standard_name : ellipsoid long_name : ellipsoid units : unitless description : The ellipsoidal model of the earth used as a basis for 2D and 3D geographic coordinate systems. array(['WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84'],\n",
- " dtype=object) flag
(station, time, N_flag_codes)
float32
...
standard_name : flags long_name : data reporter provided standardised flags units : unitless description : List of associated data flag codes per measurement, indicating the data quality of a specific measurement, provided by the data reporter. Fill value code of 255. [22498560 values with dtype=float32] horizontal_datum
(station)
object
...
standard_name : horizontal datum long_name : horizontal datum units : unitless description : Name of the horizontal datum used in defining geodetic latitudes and longitudes on the Earth's surface. The datum is set when positioning an ellipsoid model of the Earth to an anchor point. If not explicitely stated then this is assumed to be 'World Geodetic System 1984'. array(['WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984'],\n",
- " dtype=object) hourly_native_max_gap_percent
(station, time)
uint8
...
standard_name : hourly native max gap percent long_name : hourly native max gap percent units : % description : Percentage of the maximum data gap in the hourly averaged measurement UTC window filled by native resolution data, relative to the total window length. [120960 values with dtype=uint8] hourly_native_representativity_percent
(station, time)
uint8
...
standard_name : hourly native representativity percent long_name : hourly native representativity percent units : % description : Percentage of the hourly averaged measurement UTC window represented by native resolution data. [120960 values with dtype=uint8] land_use
(station)
object
...
standard_name : land use long_name : standardised network provided land use type units : unitless description : Standardised network provided classification, describing the dominant land use in the area of the reporting station. array(['barren-rock', 'urban-transportation', 'open', 'open',\n",
- " 'urban-agricultural', 'open', 'barren-rock', 'forest',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open',\n",
- " 'urban-agricultural', 'urban-agricultural', 'urban-agricultural',\n",
- " 'forest', 'open', 'urban-residential', 'urban-residential',\n",
- " 'urban-agricultural', 'urban-agricultural', 'forest',\n",
- " 'urban-residential', 'barren-rock', 'barren-rock', 'urban-agricultural',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open',\n",
- " 'urban-agricultural', 'barren-desert', 'barren-rock',\n",
- " 'urban-agricultural', 'urban-agricultural', 'urban-agricultural',\n",
- " 'forest', 'open', 'forest', 'open', 'forest', 'forest', 'open', 'open',\n",
- " 'barren-rock', 'snow', 'urban-agricultural', 'snow', 'urban-industrial',\n",
- " 'snow', 'forest', 'open', 'forest', 'open', 'open', 'open', 'forest',\n",
- " 'open', 'open', 'barren-rock', 'forest', 'open', 'open',\n",
- " 'urban-agricultural', 'open', 'open', 'barren-rock', 'open', 'forest',\n",
- " 'forest', 'forest', 'forest', 'forest', 'urban-agricultural',\n",
- " 'urban-agricultural', 'open', 'open', 'open', 'open', 'open',\n",
- " 'barren-rock', 'open', 'open', 'forest', 'urban-agricultural', 'forest',\n",
- " 'urban-agricultural', 'urban-agricultural', 'wetland', 'open', 'open',\n",
- " 'open', 'open', 'urban-agricultural', 'urban-agricultural',\n",
- " 'urban-agricultural', 'open', 'open', 'urban-agricultural', 'open',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open', 'forest', 'forest',\n",
- " 'forest', 'urban-agricultural', 'open', 'urban-park', 'open',\n",
- " 'urban-agricultural', 'open', 'snow', 'forest', 'barren-rock',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open', 'open',\n",
- " 'urban-agricultural', 'open', 'barren-rock', 'urban-agricultural',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open',\n",
- " 'urban-agricultural', 'forest', 'forest', 'forest', 'barren-rock',\n",
- " 'forest', 'forest', 'urban-agricultural', 'forest', 'snow', 'forest',\n",
- " 'open', 'snow', 'urban-agricultural', 'urban-agricultural',\n",
- " 'barren-rock', 'open', 'forest', 'forest', 'open', 'open', 'forest',\n",
- " 'forest', 'urban-agricultural', 'urban-agricultural', 'forest',\n",
- " 'forest', 'forest', 'forest', 'open', 'forest', 'forest', 'open',\n",
- " 'barren-rock', 'open', 'open', 'urban-agricultural', 'wetland',\n",
- " 'urban-agricultural', 'barren-rock', 'snow', 'urban-residential',\n",
- " 'forest', 'open'], dtype=object) latitude
(station)
float64
...
standard_name : latitude long_name : latitude units : decimal degrees North description : Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum. axis : Y array([-64.24006 , -54.848465, 47.766666, 46.677778, 48.721111, 47.529167,\n",
- " 47.05407 , 47.348056, 47.973056, 48.878611, 48.106111, 48.371111,\n",
- " 48.334722, 48.050833, 47.838611, 47.040277, 47.066944, 49.877778,\n",
- " 50.629421, 50.503333, 41.695833, 32.27 , 80.050003, 46.5475 ,\n",
- " 46.813056, 47.479722, 47.049722, 47.0675 , 47.189614, -30.17254 ,\n",
- " 16.86403 , 35.0381 , 49.735084, 49.573394, 49.066667, 54.925556,\n",
- " 52.802222, 47.914722, 53.166667, 50.65 , 54.4368 , 47.801498,\n",
- " 47.4165 , -70.666 , 54.746495, 81.6 , 55.693588, 72.580002,\n",
- " 56.290424, 59.5 , 58.383333, 39.54694 , 42.72056 , 39.87528 ,\n",
- " 37.23722 , 43.43917 , 41.27417 , 42.31917 , 38.47278 , 39.08278 ,\n",
- " 41.23889 , 41.39389 , 42.63472 , 37.05194 , 28.309 , 59.779167,\n",
- " 60.53002 , 66.320278, 67.973333, 48.5 , 49.9 , 47.266667,\n",
- " 43.616667, 47.3 , 46.65 , 45. , 45.8 , 48.633333,\n",
- " 42.936667, 44.569444, 46.814728, 45.772223, 55.313056, 54.443056,\n",
- " 50.596389, 54.334444, 57.734444, 52.503889, 55.858611, 53.398889,\n",
- " 50.792778, 52.293889, 51.781784, 52.298333, 55.79216 , 52.950556,\n",
- " 51.778056, 60.13922 , 51.149617, 38.366667, 35.316667, 46.966667,\n",
- " 46.91 , -0.20194 , 51.939722, 53.32583 , 45.8 , 44.183333,\n",
- " 37.571111, 42.805462, -69.005 , 39.0319 , 24.2883 , 24.466941,\n",
- " 36.538334, 33.293917, 55.376111, 56.161944, 57.135278, 36.0722 ,\n",
- " 52.083333, 53.333889, 51.541111, 52.3 , 51.974444, 58.38853 ,\n",
- " 65.833333, 62.783333, 78.90715 , 59. , 69.45 , 59.2 ,\n",
- " 60.372386, -72.0117 , 59.2 , -41.408192, -77.832001, -45.037998,\n",
- " 51.814408, 50.736444, 54.753894, 54.15 , 43.4 , 71.586166,\n",
- " 63.85 , 67.883333, 57.394 , 57.1645 , 57.9525 , 56.0429 ,\n",
- " 60.0858 , 57.816667, 64.25 , 59.728 , 45.566667, 46.428611,\n",
- " 46.299444, 48.933333, 49.15 , 49.05 , 47.96 , 71.323013,\n",
- " 40.12498 , 19.53623 , -89.996948, 41.0541 , 21.5731 , -34.35348 ]) local_time
(station, time)
datetime64[ns]
...
standard_name : local time long_name : local time description : Time in hours since 0001-01-01 00:00 local time. Time given refers to the start of the time window the measurement is representative of (temporal resolution). axis : T tz : local [120960 values with dtype=datetime64[ns]] longitude
(station)
float64
...
standard_name : longitude long_name : longitude units : decimal degrees East description : Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum. axis : X array([-5.662478e+01, -6.831069e+01, 1.676667e+01, 1.297222e+01,\n",
- " 1.594222e+01, 9.926667e+00, 1.295794e+01, 1.588222e+01,\n",
- " 1.301611e+01, 1.504667e+01, 1.591944e+01, 1.554667e+01,\n",
- " 1.673056e+01, 1.667667e+01, 1.444139e+01, 1.433000e+01,\n",
- " 1.549361e+01, 5.203611e+00, 6.001019e+00, 4.989444e+00,\n",
- " 2.473861e+01, -6.488000e+01, -8.641666e+01, 7.985000e+00,\n",
- " 6.944722e+00, 8.904722e+00, 6.979444e+00, 8.463889e+00,\n",
- " 8.175434e+00, -7.079923e+01, -2.486752e+01, 3.305780e+01,\n",
- " 1.603420e+01, 1.508028e+01, 1.360000e+01, 8.309722e+00,\n",
- " 1.075944e+01, 7.908611e+00, 1.303333e+01, 1.076667e+01,\n",
- " 1.272490e+01, 1.100962e+01, 1.097964e+01, -8.266000e+00,\n",
- " 1.073616e+01, -1.667000e+01, 1.208580e+01, -3.848000e+01,\n",
- " 8.427486e+00, 2.590000e+01, 2.181667e+01, -4.350560e+00,\n",
- " -8.923610e+00, 4.316390e+00, -3.534170e+00, -4.850000e+00,\n",
- " -3.142500e+00, 3.315830e+00, -6.923610e+00, -1.101110e+00,\n",
- " -5.897500e+00, 7.347200e-01, -7.704720e+00, -6.555280e+00,\n",
- " -1.649940e+01, 2.137722e+01, 2.766754e+01, 2.940167e+01,\n",
- " 2.411611e+01, 7.133333e+00, 4.633333e+00, 4.083333e+00,\n",
- " 1.833330e-01, 6.833333e+00, -7.500000e-01, 6.466667e+00,\n",
- " 2.066667e+00, -4.500000e-01, 1.419440e-01, 5.278972e+00,\n",
- " 2.610008e+00, 2.964886e+00, -3.204167e+00, -7.870000e+00,\n",
- " -3.713056e+00, -8.075000e-01, -4.774444e+00, -3.033056e+00,\n",
- " -3.205000e+00, -1.753333e+00, 1.794440e-01, 1.463056e+00,\n",
- " -4.691462e+00, 2.927780e-01, -3.242900e+00, 1.121944e+00,\n",
- " 1.082230e+00, -1.185319e+00, -1.438228e+00, 2.308333e+01,\n",
- " 2.566667e+01, 1.958333e+01, 1.632000e+01, 1.003181e+02,\n",
- " -1.024444e+01, -9.899440e+00, 8.633333e+00, 1.070000e+01,\n",
- " 1.265972e+01, 1.256564e+01, 3.959056e+01, 1.418222e+02,\n",
- " 1.539833e+02, 1.230109e+02, 1.263300e+02, 1.261631e+02,\n",
- " 2.103056e+01, 2.117306e+01, 2.590556e+01, 1.421840e+01,\n",
- " 6.566667e+00, 6.277222e+00, 5.853611e+00, 4.500000e+00,\n",
- " 4.923611e+00, 8.252000e+00, 1.391667e+01, 8.883333e+00,\n",
- " 1.188668e+01, 1.153333e+01, 3.003333e+01, 5.200000e+00,\n",
- " 1.107814e+01, 2.535100e+00, 9.516667e+00, 1.748708e+02,\n",
- " 1.666600e+02, 1.696840e+02, 2.197242e+01, 1.573950e+01,\n",
- " 1.753426e+01, 2.206667e+01, 2.195000e+01, 1.289188e+02,\n",
- " 1.533333e+01, 2.106667e+01, 1.191400e+01, 1.478250e+01,\n",
- " 1.240300e+01, 1.314800e+01, 1.750528e+01, 1.556667e+01,\n",
- " 1.976667e+01, 1.547200e+01, 1.486667e+01, 1.500333e+01,\n",
- " 1.453861e+01, 1.958333e+01, 2.028333e+01, 2.226667e+01,\n",
- " 1.786056e+01, -1.566115e+02, -1.052368e+02, -1.555762e+02,\n",
- " -2.480000e+01, -1.241510e+02, 1.035157e+02, 1.848968e+01]) main_emission_source
(station)
object
...
standard_name : main emission source long_name : standardised network provided main emission source units : unitless description : Standardised network provided classification, describing the main emission source influencing air measured at a station. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) mean_solar_time
(station, time)
datetime64[ns]
...
standard_name : mean solar time long_name : mean solar time description : Time in hours since 0001-01-01 00:00 mean solar time. Time given refers to the start of the time window the measurement is representative of (temporal resolution). axis : T tz : mean solar [120960 values with dtype=datetime64[ns]] measurement_altitude
(station)
float32
...
standard_name : measurement altitude long_name : measurement altitude relative to mean sea level units : m description : Altitude of the inlet/instrument/sampler, relative to the stated vertical datum, in metres. array([2.0450e+02, 2.6000e+01, 1.1700e+02, 1.0200e+03, 3.1500e+02, 1.0200e+03,\n",
- " 3.1160e+03, 1.1700e+03, 7.3000e+02, 5.7000e+02, 5.8100e+02, 3.2000e+02,\n",
- " 1.6100e+02, 2.4000e+02, 8.9900e+02, 1.6480e+03, 4.8100e+02, 4.3000e+02,\n",
- " 2.9500e+02, 1.6000e+02, 1.7500e+03, 2.1400e+02, 6.1000e+02, 3.5780e+03,\n",
- " 4.8900e+02, 5.3900e+02, 1.1370e+03, 1.0310e+03, 7.9700e+02, 2.2300e+03,\n",
- " 2.5000e+01, 5.3500e+02, 7.3800e+02, 5.8500e+02, 1.1210e+03, 2.2500e+01,\n",
- " 8.1000e+01, 1.2170e+03, 6.8200e+01, 9.5300e+02, 9.0000e+00, 1.0000e+03,\n",
- " 2.6710e+03, 5.6000e+01, 1.0000e+01, 2.0000e+01, 3.0000e+00, 3.2480e+03,\n",
- " 1.0000e+01, 3.2000e+01, 6.0000e+00, 9.1700e+02, 6.8300e+02, 7.8000e+01,\n",
- " 1.2300e+03, 1.3400e+02, 1.3600e+03, 7.6000e+01, 3.9300e+02, 8.8500e+02,\n",
- " 9.8500e+02, 4.7000e+02, 5.0600e+02, 3.5000e+01, 2.4050e+03, 1.2000e+01,\n",
- " 9.0000e+00, 3.1500e+02, 5.6900e+02, 7.7500e+02, 3.9400e+02, 6.2000e+02,\n",
- " 2.0400e+02, 8.3600e+02, 1.3700e+02, 1.7500e+03, 8.1000e+02, 3.0900e+02,\n",
- " 2.8770e+03, 6.0500e+02, 1.8200e+02, 1.4770e+03, 2.4300e+02, 1.2600e+02,\n",
- " 1.1900e+02, 2.6700e+02, 2.7000e+02, 3.7000e+02, 1.8000e+02, 4.2000e+02,\n",
- " 1.2000e+02, 4.6000e+01, 1.6000e+02, 5.0000e+00, 2.6000e+02, 1.6000e+01,\n",
- " 8.0000e+00, 8.5000e+01, 7.8000e+01, 1.1000e+02, 2.5400e+02, 1.2800e+02,\n",
- " 3.1600e+02, 8.6400e+02, 1.1000e+01, 5.0000e+00, 2.1100e+02, 2.1720e+03,\n",
- " 9.0000e+00, 1.0920e+03, 2.3500e+01, 2.7200e+02, 1.8000e+01, 4.4000e+01,\n",
- " 1.2600e+02, 8.9500e+01, 5.0000e+00, 1.8000e+01, 1.8800e+02, 1.9000e+02,\n",
- " 2.2000e+01, 3.0000e+00, 3.0000e+01, 6.0000e+00, 6.0000e+00, 2.1900e+02,\n",
- " 4.3900e+02, 2.1000e+02, 4.7400e+02, 1.6000e+02, 3.0000e+01, 1.5000e+01,\n",
- " 3.0000e+02, 1.5530e+03, 2.0000e+01, 9.6000e+01, 1.9400e+02, 3.8000e+02,\n",
- " 1.8224e+02, 1.6075e+03, 5.0000e+00, 1.6135e+02, 8.1300e+02, 1.8000e+01,\n",
- " 4.0400e+02, 4.7500e+02, 5.0000e+00, 1.8000e+02, 6.5000e+01, 1.9000e+02,\n",
- " 4.5000e+01, 2.6100e+02, 2.2500e+02, 1.3200e+02, 5.2500e+02, 7.7300e+02,\n",
- " 1.7640e+03, 2.0080e+03, 8.0800e+02, 3.4500e+02, 1.1300e+02, 2.1000e+01,\n",
- " 1.6950e+03, 3.4070e+03, 2.8510e+03, 1.1700e+02, 1.4900e+03, 2.3800e+02],\n",
- " dtype=float32) measurement_methodology
(station)
object
...
standard_name : measurement methodology long_name : standardised measurement methodology units : unitless description : Standardised name of the measurement methodology. array(['ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry'], dtype=object) measurement_scale
(station)
object
...
standard_name : measurement scale long_name : standardised network provided measurement scale units : unitless description : Standardised network provided classification, a denotation of the geographic scope of the air quality measurements made. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_calibration_scale
(station)
object
...
standard_name : measuring instrument calibration scale long_name : measuring instrument calibration scale name units : unitless description : Name of calibration scale used for the calibration of the measuring instrument. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_absorption_cross_section
(station)
object
...
standard_name : measuring instrument documented absorption cross section long_name : measuring instrument documented absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in instrumental manual/documentation. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['1.1476e-17', '1.1476e-17', 'nan', 'nan', 'nan', 'nan', '1.1476e-17',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17', 'nan', '1.1476e-17', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', 'nan', 'nan', 'nan', '1.1476e-17', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', '1.1476e-17', 'nan', '1.1476e-17', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17'], dtype=object) measuring_instrument_documented_accuracy
(station)
object
...
standard_name : measuring instrument documented measurement accuracy long_name : measuring instrument documented measurement accuracy units : nmol mol-1 description : Measurement accuracy (±), as given in the instrumental manual/documentation. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['1.0%', '1.0%', 'nan', 'nan', 'nan', 'nan', '1.0%', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', 'nan', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0%', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.0%', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0%', '1.0%', '1.0%', '1.0%', 'nan', 'nan',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '5.0', '1.0%', '1.0%', '1.0%', '1.0%',\n",
- " '1.0%', 'nan', 'nan', 'nan', '1.0%', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', 'nan', '1.0%',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%', '1.0%', '1.0%', 'nan', 'nan', 'nan', 'nan', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '1.0%'], dtype=object) measuring_instrument_documented_flow_rate
(station)
object
...
standard_name : measuring instrument documented flow rate long_name : measuring instrument documented flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['1.0-3.0', '1.0-3.0', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0',\n",
- " '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '0.5',\n",
- " '0.8', '1.0-3.0', '0.8', '0.7', '0.7', '0.7', '0.7', '0.7', '0.7',\n",
- " '1.0-3.0', '1.0-3.0', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', '1.0-3.0', '1.0-3.0',\n",
- " '1.0-3.0', '1.0-3.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', '1.0-3.0', '0.7',\n",
- " '1.0-3.0', 'nan', 'nan', '1.0-3.0', '1.0-3.0', '1.0-3.0', '0.8', '1.5',\n",
- " '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', 'nan', 'nan',\n",
- " 'nan', '1.0-3.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0-3.0',\n",
- " '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', 'nan',\n",
- " '1.0-3.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', '1.0-3.0', '0.8', '1.0-3.0', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0',\n",
- " '1.0-3.0'], dtype=object) measuring_instrument_documented_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented lower limit of detection long_name : measuring instrument documented lower limit of detection units : nmol mol-1 description : Lower limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([1. , 1. , nan, nan, nan, nan, 0.5, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
- " 0.5, 1. , 0.5, 0.5, 0.4, 0.5, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1. ,\n",
- " 0.5, nan, nan, nan, nan, 1. , nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, 1. , 0.5, 0.5, 0.5, 0.5, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, 0.5, 0.5, 0.5, 1. , nan, nan, 0.5, 0.5, 0.5, 0.6, nan, 0.5,\n",
- " 0.5, 0.5, 0.5, 0.5, nan, nan, nan, 0.5, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5, 1. , 1. , 0.5, 0.5,\n",
- " 0.5, 0.5, nan, 0.5, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " 1. , 0.4, 1. , nan, nan, nan, nan, 1. , 0.5, 1. , 1. , 1. , 0.5, 0.5],\n",
- " dtype=float32) measuring_instrument_documented_measurement_resolution
(station)
float32
...
standard_name : measuring instrument documented measurement resolution long_name : measuring instrument documented measurement resolution units : nmol mol-1 description : Measurement resolution, as given in instrumental manual/documentation. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, 0.001, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan], dtype=float32) measuring_instrument_documented_precision
(station)
object
...
standard_name : measuring instrument documented measurement precision long_name : measuring instrument documented measurement precision units : nmol mol-1 description : Measurement precision (±), as given in instrumental manual/documentation. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['1.0', '1.0', 'nan', 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0',\n",
- " '1.0', '1.0/1.0%', '0.5%>=100.0', '1.0', '0.5%>=100.0', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '1.0', '1.0', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0', '1.0',\n",
- " '1.0', '1.0', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.0', '1.0', '1.0%', '1.0', 'nan', 'nan',\n",
- " '1.0', '1.0', '1.0', '0.5%', '5.0', '1.0', '1.0', '1.0', '1.0', '1.0',\n",
- " 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0',\n",
- " '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', 'nan', '1.0', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0',\n",
- " '0.5%>=100.0', '1.0', 'nan', 'nan', 'nan', 'nan', '1.0', '1.0', '1.0',\n",
- " '1.0', '1.0', '1.0', '1.0'], dtype=object) measuring_instrument_documented_span_drift
(station)
object
...
standard_name : measuring instrument documented span drift long_name : measuring instrument documented span drift units : nmol mol-1 description : Span drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['1.0%/month', '1.0%/month', 'nan', 'nan', 'nan', 'nan', '1.0%/month',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', '0.5%/day', '1.0%/day', '1.0%/month',\n",
- " '1.0%/day', '0.5/day', '0.5/day', '0.5/day', '0.5/day', '0.5/day',\n",
- " '0.5/day', '1.0%/month', '1.0%/month', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%/month', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%/month', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%/month', '1.0%/month', '0.5/day', '1.0%/month', 'nan', 'nan',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/day', '5.0/month',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " 'nan', 'nan', 'nan', '1.0%/month', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', 'nan', '1.0%/month', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0%/month',\n",
- " '1.0%/day', '1.0%/month', 'nan', 'nan', 'nan', 'nan', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " '1.0%/month'], dtype=object) measuring_instrument_documented_uncertainty
(station)
object
...
standard_name : measuring instrument documented measurement uncertainty long_name : measuring instrument documented measurement uncertainty units : nmol mol-1 description : Measurement uncertainty (±), as given in the instrumental manual/documentation. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented upper limit of detection long_name : measuring instrument documented upper limit of detection units : nmol mol-1 description : Upper limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([200000., 200000., nan, nan, nan, nan, 200000., nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, 200000., 200000., 200000.,\n",
- " 200000., 200000., 200000., 200000., 200000., 200000., 200000., 20000.,\n",
- " 10000., 200000., 10000., 100000., 100000., 100000., 100000., 100000.,\n",
- " 100000., 200000., 200000., nan, nan, nan, nan, 200000.,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " 200000., 200000., 200000., 200000., 200000., nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, 200000., nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, 200000., 200000., 100000., 200000.,\n",
- " nan, nan, 200000., 200000., 200000., 10000., 1000., 200000.,\n",
- " 200000., 200000., 200000., 200000., nan, nan, nan, 200000.,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, 200000.,\n",
- " 200000., 200000., 200000., 200000., 200000., 200000., nan, 200000.,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, 200000., 10000., 200000., nan, nan, nan,\n",
- " nan, 200000., 200000., 200000., 200000., 200000., 200000., 200000.],\n",
- " dtype=float32) measuring_instrument_documented_zero_drift
(station)
object
...
standard_name : measuring instrument documented zero drift long_name : measuring instrument documented zero drift units : nmol mol-1 description : Zero drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['1.0/day', '1.0/day', 'nan', 'nan', 'nan', 'nan', '1.0/day', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0/day', '1.0/day', '1.0/day', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '0.5/day', '0.5/day',\n",
- " '0.5/day', '0.5/day', '0.5/day', '0.5/day', '1.0/day', '1.0/day', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0/day', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0/day', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0/day', '1.0/day', '0.5/day', '1.0/day', 'nan', 'nan',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '5.0/month', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', 'nan', 'nan', 'nan',\n",
- " '1.0/day', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0/day', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day', 'nan', '1.0/day',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0/day', '1.0/day', '1.0/day', 'nan', 'nan', 'nan', 'nan', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day'],\n",
- " dtype=object) measuring_instrument_documented_zonal_drift
(station)
object
...
standard_name : measuring instrument documented zonal drift long_name : measuring instrument documented zonal drift units : nmol mol-1 description : Zonal drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_further_details
(station)
object
...
standard_name : measuring instrument further details long_name : measuring instrument further details units : unitless description : Further associated details regarding the specifics of the measurement methodology/instrument. array(['no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:https://books.google.es/books?id=suisdwaaqbaj&pg=pa48&lpg=pa48&dq=dasibi+1108&source=bl&ots=tnxwozqtbt&sig=acfu3u0ba3efwduqenfd2g2zk-rqp7cqfg&hl=en&sa=x&ved=2ahukewjrk8bx2f7fahud4oakhsr1a5wq6aewdhoecaaqaq#v=onepage&q=dasibi%201108&f=false',\n",
- " 'nan', 'nan',\n",
- " 'specifications online:https://translate.google.com/translate?hl=en&sl=ja&u=http://www.dylec.co.jp/01ozone/m1100.html&prev=search',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here: http://www.data.jma.go.jp/gmd/env/ghg_obs/en/station/station_minamitorishima.html',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here: http://www.data.jma.go.jp/gmd/env/ghg_obs/en/station/station_minamitorishima.html',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here: http://www.data.jma.go.jp/gmd/env/ghg_obs/en/station/station_minamitorishima.html',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:https://www.rivm.nl/en/documents_and_publications/scientific/reports/1993/juni/acceptance_procedure_and_testresults_of_the_ozone_analyzers_model_49w_made_by_thermo_environmental_instruments_on_behalf_of_the_dutch_national_air_quality_monitoring_network',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan'], dtype=object) measuring_instrument_inlet_information
(station)
object
...
standard_name : measuring instrument inlet information long_name : measuring instrument measurement inlet information units : unitless description : Description of sampling inlet of the measuring instrument. array(['inlet type: downward-facing tube;description: 5 m above ground on top of the shelter,2 m above the roof * 6.35 mm ptfe tubing - length=4 m up to the instrument;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 6.0 mm;length: 4.0 m',\n",
- " 'inlet type: downward-facing tube;description: 7 m above ground on top of the station building,2 m above the roof * 19 mm pfa tubing - length=6 m up to the instrument - aditional pump to increase flow rate - residence time < 5 s',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;tube material: stainless steel;inner diameter: 150.0 mm;length: 4.0 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: inlet approx. 2 m above the building and 5 m above the ground;coarse mesh at the top of the inlet to protect against spiders etc.;47 mm teflon filter (5 micron) just upstream of the instrument inlet.;tube material: teflon;outer diameter: 6.35 mm;length: 10.0 m',\n",
- " 'inlet type: hat or hood;description: 13m heated glass sampling inlet (40mm diameter) (residence time 4 secs),then 2m of 1/4inch teflon tubing at a flow rate of 1.5 l/min,then through a particulate filter (1-2micron) before it enters the instrument.',\n",
- " 'nan', 'description: downward facing with hood',\n",
- " 'inlet type: downward-facing tube',\n",
- " 'inlet type: hat or hood;description: downward facing with hood',\n",
- " 'inlet type: hat or hood', 'inlet type: hat or hood',\n",
- " 'inlet type: hat or hood', 'inlet type: hat or hood',\n",
- " 'inlet type: hat or hood', 'inlet type: hat or hood',\n",
- " 'inlet type: hat or hood;description: upward;tube material: teflon;outer diameter: 12.8 mm;inner diameter: 8.7 mm;length: 6.0 m',\n",
- " 'nan',\n",
- " 'inlet type: hat or hood;description: monthly leak test,inlet filter checked every 6 months,ozone scrubber checked and replaced if needed once/year',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'inlet type: stainless steel',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: upstream from manifold,tube length=6.5m,flow rate=17l/min;tube material: teflon;outer diameter: 41.5 mm;inner diameter: 28.0 mm;length: 1.5 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;tube material: teflon;outer diameter: 6.35 mm;length: 4.0 m',\n",
- " 'inlet type: hat or hood;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 3.95 mm;length: 4.5 m',\n",
- " 'inlet type: hat or hood;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 3.95 mm;length: 3.5 m',\n",
- " 'inlet type: hat or hood;description: inlet approx. 2.5 m above the station and 10 m above the ground;tube material: teflon',\n",
- " 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: pvc_home_made;tube material: teflon;outer diameter: 60.0 mm;inner diameter: 27.0 mm;length: 4.0 m',\n",
- " 'inlet type: hat or hood;description: the air intake is composed by an external (outside building) steel pipe (internally covered by teflon) and the internal (inside building) pyrex pipe;tube material: teflon;outer diameter: 6.35 mm;length: 1.5 m',\n",
- " 'inlet type: hat or hood;description: the air intake is composed by an external (outside building) steel pipe (internally covered by teflon) and the internal (inside building) pyrex pipe;tube material: teflon;outer diameter: 6.35 mm;length: 1.5 m',\n",
- " 'nan',\n",
- " 'inlet type: stainless steel;description: covered with hat;tube material: teflon;outer diameter: 8.0 mm;inner diameter: 6.0 mm;length: 6.0 m',\n",
- " 'inlet type: hat or hood;description: inlet filter checked every 2 months,leak test every 3 months,ozone scrubber checked and replaced if needed once/year',\n",
- " 'inlet type: hat or hood;description: inlet filter checked every 2 months,leak test every 3 months,ozone scrubber checked and replaced if needed once/year',\n",
- " 'inlet type: hat or hood;description: inlet filter checked every 2 months,leak test every 3 months,ozone scrubber checked and replaced if needed once/year',\n",
- " 'inlet type: hat or hood;description: hat is made of pfa teflon. there is still mesh filter under the hat bottom to prevente bugs. inlet is connected to a glass manifold,from where o3 analyzer is connected by 1/4inch tubing.;outer diameter: 13.0 mm;inner diameter: 10.0 mm;length: 9.0 m',\n",
- " 'inlet type: hat or hood;description: hat is made of pfa teflon. there is still mesh filter under the hat bottom to prevente bugs. inlet is connected to a glass manifold,from where o3 analyzer is connected by 1/4inch tubing.;outer diameter: 13.0 mm;inner diameter: 10.0 mm;length: 9.5 m',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'inlet type: downward-facing tube;description: downward-facing tube;tube material: teflon;outer diameter: 38.0 mm;length: 4.5 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'description: downward facing', 'description: downward facing',\n",
- " 'description: downward facing', 'inlet type: hat or hood', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: open conductive tubing;description: air inlet to instrument through teflone tube,connected to major glassinlet tube,mounted on the roof of object;tube material: teflon;outer diameter: 6.35 mm;length: 4.0 m',\n",
- " 'inlet type: hat or hood;description: air inlet to instrument through teflone tube,connected to major glassinlet tube,mounted on the roof of object;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.5 mm;length: 1.0 m',\n",
- " 'inlet type: open conductive tubing;description: air inlet to instrument through teflone tube,connected to glassinlet tube,mounted on the adge of balcony roof.',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: inlet approx. 6 m above the building and 12 m above the ground;coarse mesh at the top of the inlet to protect against spiders etc.;47 mm teflon filter (5 micron) just upstream of the instrument inlet.;tube material: teflon;outer diameter: 6.35 mm;length: 15.0 m',\n",
- " 'inlet type: downward-facing tube;description: teflon tubing,covered with rain hat;tube material: teflon;outer diameter: 8.0 mm;inner diameter: 6.0 mm;length: 30.0 m'],\n",
- " dtype=object) measuring_instrument_manual_name
(station)
object
...
standard_name : measuring instrument manual name long_name : measuring instrument manual name units : unitless description : Path to the location in the esarchive of the manual for the specific measuring instrument. array(['Thermo_49C_Manual.pdf', 'Thermo_49C_Manual.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Ecotech_EC9810_Manual.pdf', 'Teledyne_T400_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Teledyne_T400_Manual.pdf', 'Horiba_APOA370_Specs.pdf',\n",
- " 'Horiba_APOA370_Specs.pdf', 'Horiba_APOA370_Specs.pdf',\n",
- " 'Horiba_APOA370_Specs.pdf', 'Horiba_APOA370_Specs.pdf',\n",
- " 'Horiba_APOA370_Specs.pdf', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Thermo_49C_Manual.pdf', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Horiba_APOA370_Specs.pdf', 'Thermo_49C_Manual.pdf', 'nan', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Teledyne_400A_Manual.pdf', 'Ebara_EG-3000F_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49C_Manual.pdf', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49C_Manual.pdf', 'Teledyne_T400_Manual.pdf',\n",
- " 'Thermo_49C_Manual.pdf', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49C_Manual.pdf', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49C_Manual.pdf', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49C_Manual.pdf', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf'], dtype=object) measuring_instrument_name
(station)
object
...
standard_name : measuring instrument name long_name : standardised measuring instrument name units : unitless description : Standardised name of the measuring instrument. array(['Thermo 49C', 'Thermo 49C', 'nan', 'nan', 'nan', 'nan', 'Thermo 49i',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Thermo 49i', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Thermo 49C', 'Thermo 49i', 'Ecotech EC9810', 'Teledyne T400',\n",
- " 'Thermo 49i', 'Teledyne T400', 'Horiba APOA370', 'Horiba APOA370',\n",
- " 'Horiba APOA370', 'Horiba APOA370', 'Horiba APOA370', 'Horiba APOA370',\n",
- " 'Thermo 49C', 'Thermo 49i', 'Environnement SA O341M', 'nan', 'nan',\n",
- " 'nan', 'Thermo 49C', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo 49C', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'Thermo 49i', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'Thermo 49i', 'Thermo 49i', 'Horiba APOA370',\n",
- " 'Thermo 49C', 'nan', 'nan', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Teledyne 400A', 'Ebara EG-3000F', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Thermo 49i', 'Thermo 49i', 'Thermo 49i', 'nan', 'nan', 'nan',\n",
- " 'Thermo 49i', 'Thermo 49w', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'Thermo 49i',\n",
- " 'Thermo 49C', 'Thermo 49C', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Thermo 49i', 'nan', 'Thermo 49i', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'Thermo 49C', 'Teledyne T400',\n",
- " 'Thermo 49C', 'nan', 'nan', 'nan', 'nan', 'Thermo 49C', 'Thermo 49i',\n",
- " 'Thermo 49C', 'Thermo 49C', 'Thermo 49C', 'Thermo 49i', 'Thermo 49i'],\n",
- " dtype=object) measuring_instrument_process_details
(station)
object
...
standard_name : measuring instrument process details long_name : measuring instrument process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the measurement methodology/instrument. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'make assumption measuring instrument is dasibi 1108 w/gen', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
- " dtype=object) measuring_instrument_reported_absorption_cross_section
(station)
object
...
standard_name : measuring instrument reported absorption cross section long_name : measuring instrument reported absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in metadata. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['nan', '1.1476e-17', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', 'nan', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.1476e-17', 'nan', '1.1476e-17', 'nan', 'nan', 'nan', '1.1476e-17',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', 'nan', 'nan', '1.147e-17', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', 'nan', 'nan', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17'], dtype=object) measuring_instrument_reported_accuracy
(station)
object
...
standard_name : measuring instrument reported measurement accuracy long_name : measuring instrument reported measurement accuracy units : nmol mol-1 description : Measurement accuracy (±), as given in metadata. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_flow_rate
(station)
object
...
standard_name : measuring instrument reported flow rate long_name : measuring instrument reported flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['1.5', '12.0', 'nan', 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.2', '1.1', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0',\n",
- " '1.4', 'nan', 'nan', '1.3', 'nan', '1.2', '1.2', '1.2', '1.2', '1.2',\n",
- " '1.2', '1.0', 'nan', '1.5', 'nan', 'nan', 'nan', '0.9', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.5', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '0.5', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '0.5', '0.6', '0.6', '1.0', 'nan', 'nan', '1.0', '1.5', '1.5', 'nan',\n",
- " '1.5', '1.5', '1.5', '1.5', '20.0', '20.0', 'nan', 'nan', 'nan', '1.5',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.4', '1.3', 'nan', 'nan',\n",
- " 'nan', '0.7', 'nan', '1.3', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '0.6', '0.8', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.1', '1.3', '1.2', '1.0', '1.3', '0.5', '1.0'], dtype=object) measuring_instrument_reported_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported lower limit of detection long_name : measuring instrument reported lower limit of detection units : nmol mol-1 description : Lower limit of detection of measurement methodology, as given in metadata. array([1. , 1. , nan, nan, nan, nan, 1. ,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " 1. , 1. , nan, nan, nan, nan, nan,\n",
- " nan, 1. , 0.1 , 0.3007 , 2. , 1. , 2. ,\n",
- " 1. , 1. , 1. , 1. , 1. , 1. , 0.6 ,\n",
- " 0.58 , 1. , nan, nan, nan, 1. , nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, 1. , nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, 1. , nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, 0.5 , 0.5 , 0.5 , 1. , nan,\n",
- " nan, 0.7 , 1. , 1. , 0.601 , 1. , 1. ,\n",
- " 1. , 1. , 0.5 , 0.5 , nan, nan, nan,\n",
- " 4. , nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, 1. , 1. , nan, nan,\n",
- " nan, 0.5 , 1.703967, 1. , nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " 0.5 , 0.6 , 0.5 , nan, nan, nan, nan,\n",
- " 1. , 1. , 1. , 1. , 1. , 1. , 1. ],\n",
- " dtype=float32) measuring_instrument_reported_measurement_resolution
(station)
float32
...
standard_name : measuring instrument reported measurement resolution long_name : measuring instrument reported measurement resolution units : nmol mol-1 description : Measurement resolution, as given in metadata. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) measuring_instrument_reported_precision
(station)
object
...
standard_name : measuring instrument reported measurement precision long_name : measuring instrument reported measurement precision units : nmol mol-1 description : Measurement precision (±), as given in metadata. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_span_drift
(station)
object
...
standard_name : measuring instrument reported span drift long_name : measuring instrument reported span drift units : nmol mol-1 description : Span drift of measuring instrument per unit of time, as given in metadata. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_uncertainty
(station)
object
...
standard_name : measuring instrument reported measurement uncertainty long_name : measuring instrument reported measurement uncertainty units : nmol mol-1 description : Measurement uncertainty (±), as given in metadata. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). It can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', '10.0', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0',\n",
- " '3.0', 'nan', '1.7', 'nan', '1.7', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '10.0', 'nan', 'nan', 'nan', '1.0', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '3.1', 'nan', '1.0', 'nan', 'nan', '0.5', 'nan', 'nan', 'nan',\n",
- " '1.0', '1.0', '1.0', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0', '1.0', '1.0', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', 'nan'], dtype=object) measuring_instrument_reported_units
(station)
object
...
standard_name : measuring instrument reported units long_name : measuring instrument reported measurement units units : unitless description : Units that the measured parameter are natively reported in. array(['nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'ug m-3', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'ug m-3', 'nmol mol-1',\n",
- " 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'ug m-3',\n",
- " 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'nmol mol-1', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1'], dtype=object) measuring_instrument_reported_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported upper limit of detection long_name : measuring instrument reported upper limit of detection units : nmol mol-1 description : Upper limit of detection of measurement methodology, as given in metadata. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) measuring_instrument_reported_zero_drift
(station)
object
...
standard_name : measuring instrument reported zero drift long_name : measuring instrument reported zero drift units : nmol mol-1 description : Zero drift of measuring instrument per unit of time, as given in metadata. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_zonal_drift
(station)
object
...
standard_name : measuring instrument reported zonal drift long_name : measuring instrument reported zonal drift units : nmol mol-1 description : Zonal drift of measuring instrument per unit of time, as given in metadata. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_sampling_type
(station)
object
...
standard_name : measuring instrument sampling type long_name : standardised sampling type of the measuring instrument units : unitless description : Standardised name of the measuring instrument sampling type. array(['low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous'], dtype=object) monthly_native_max_gap_percent
(station, time)
uint8
...
standard_name : monthly native max gap percent long_name : monthly native max gap percent units : % description : Percentage of the maximum data gap in the monthly averaged measurement UTC window filled by native resolution data, relative to the total window length. [120960 values with dtype=uint8] monthly_native_representativity_percent
(station, time)
uint8
...
standard_name : monthly native representativity percent long_name : monthly native representativity percent units : % description : Percentage of the monthly averaged measurement UTC window represented by native resolution data. [120960 values with dtype=uint8] network
(station)
object
...
standard_name : network long_name : data network units : unitless description : The name of the network which reports data for the specific station in question. array(['EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS'], dtype=object) network_maintenance_details
(station)
object
...
standard_name : network maintenance details long_name : network maintenance details units : unitless description : Extra details provided by the reporting network about the operational maintenance done at the station. array(['maintenance description: inlet filter checked every 1 month.;humidity/temp. control: none',\n",
- " 'maintenance description: monthly leak test and uv lamp test,inlet filter checked every 1 months,manual zero and spam check every 3 months;zero/span check type: automatic;zero/span check interval: 1d',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'maintenance description: biweekly leak test,inlet filter checked every 2 months,ozone scrubber checked and replaced if needed once/year;zero/span check type: automatic;zero/span check interval: 23h;humidity/temp. control: none',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'maintenance description: regular replacements of the inlet filters and check of all instrument parameters.;zero/span check type: manual;humidity/temp. control: none',\n",
- " 'humidity/temp. control: none', 'nan',\n",
- " 'humidity/temp. control description: unknown',\n",
- " 'humidity/temp. control: other (see metadata)',\n",
- " 'humidity/temp. control: none;humidity/temp. control description: unknown',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'maintenance description: inlet filter changed every 2 weeks,ozone scrubber checked and replaced if needed once/year,5 calibrations in 2018;zero/span check type: manual;zero/span check interval: 3mo;humidity/temp. control: temperature controlled;humidity/temp. control description: temperature controlled at 4°c above ambient air',\n",
- " 'nan', 'humidity/temp. control: none', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control description: automatic', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'humidity/temp. control: none', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'maintenance description: monthly leak test,inlet filter checked every 2 months,ozone scrubber checked and replaced if needed once/year;zero/span check type: manual;zero/span check interval: 3mo;humidity/temp. control: temperature conditioning at 20 deg. c;humidity/temp. control description: none',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control: none',\n",
- " 'maintenance description: monthly leak test,inlet filter and ozone scrubber checked and replaced if needed;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none',\n",
- " 'maintenance description: monthly leak test,inlet filter and ozone scrubber checked and replaced if needed;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none',\n",
- " 'maintenance description: inlet filter (47 mm teflon filter (5 micron)) changed every year;humidity/temp. control: other (see metadata);humidity/temp. control description: a cold trap (5 deg c) is used to reduce the humidity in the sample and to avoid downstream condensation. lab temp is set to 25 degc.',\n",
- " 'nan', 'nan',\n",
- " 'maintenance description: full cleaning twice a year;zero/span check type: automatic;zero/span check interval: 23h;humidity/temp. control: nafion dryer;humidity/temp. control description: 180cm',\n",
- " 'zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none',\n",
- " 'zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none',\n",
- " 'nan',\n",
- " 'zero/span check type: manual;zero/span check interval: 1y;humidity/temp. control: none',\n",
- " 'humidity/temp. control: other (see metadata);humidity/temp. control description: the air is dried with an electronic cooler unit at a dew-point temperature about 5 degrees centigrade.',\n",
- " 'humidity/temp. control: other (see metadata);humidity/temp. control description: the air is dried with an electronic cooler unit at a dew-point temperature about 5 degrees centigrade.',\n",
- " 'humidity/temp. control: other (see metadata);humidity/temp. control description: the air is dried with an electronic cooler unit at a dew-point temperature about 5 degrees centigrade.',\n",
- " 'maintenance description: inlet filter checked every 1 month,ozone scrubber checked and replaced if needed once/year,calibration every 6 months;zero/span check type: manual;zero/span check interval: 1mo;humidity/temp. control description: humidity control using silica gel',\n",
- " 'maintenance description: inlet filter checked every 1 month,ozone scrubber checked and replaced if needed once/year,calibration every 6 months;zero/span check type: manual;zero/span check interval: 1mo;humidity/temp. control description: humidity control using silica gel',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'maintenance description: zero and three points span check calibration with a primary standard every six months,leak test every six months,inlet filter replaced every 2 weeks,ozone scrubber replaced every two years;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: nafion dryer',\n",
- " 'blank correction: not blank corrected',\n",
- " 'blank correction: not blank corrected',\n",
- " 'blank correction: not blank corrected',\n",
- " 'blank correction: not blank corrected',\n",
- " 'blank correction: not blank corrected', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control: none', 'nan',\n",
- " 'humidity/temp. control description: automatic', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'maintenance description: flow check every 3 month,inlet filter replaced every 2 months,ozone scrubber checked and replaced if needed once/year;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: temperature conditioning at 20 deg. c;humidity/temp. control description: <airconditioning of room>',\n",
- " 'zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none;humidity/temp. control description: implemented air conditioner is removing heat from a station,thus cooling the air,and removing humidity',\n",
- " 'humidity/temp. control: temperature conditioning at 20 deg. c;humidity/temp. control description: airconditioning of room with instrument.',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'maintenance description: inlet filter (47 mm teflon filter (5 micron)) changed every 2 months. coarse filter is visually inspected twice a year. zero and span checks are irregularly performed with an external zero air unit and the internal ozonator.;zero/span check type: manual;humidity/temp. control: none',\n",
- " 'maintenance description: monthly leak test,inlet filter checked every 2 months,ozone scrubber checked and replaced if needed once/year;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none;humidity/temp. control description: instrument house in air conditioned room'],\n",
- " dtype=object) network_miscellaneous_details
(station)
object
...
standard_name : network miscellaneous details long_name : network miscellaneous details units : unitless description : Extra miscellanous details provided by the reporting network. array(["data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data from september 24,2001 until november 17,2003,were flagged as erroneous due to problems with ozone loss in the air inlet. -data/det.limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50117. measurement uncertainty converted from '19.9534 ug/m3' at 293.15 k,1013.25 hpa to '10.0 nmol/mol',conversion factor 0.50117. detection limit converted from '1.9953 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50117.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " 'nan',\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'ppb' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on export from ebas from 'ug/m3' at 265.15 k,653.0 hpa to 'nmol/mol',conversion factor 0.703.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan',\n",
- " "prior to shipment to and deployment in chile,the instrument was calibrated at wcc-empa against a primary ozone standard (standard reference photometer (srp),no 15,in 2010. -- data converted from 'nmol/mol' to 'ug/m3',conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '0.1 ug/m3' at 293.15 k,1013.25 hpa to '0.1 nmol/mol',conversion factor 0.501.",\n",
- " 'nan',\n",
- " "no water vapor,temperature and ozone correction -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '3.392 ug/m3' at 293.15 k,1013.25 hpa to '1.7 nmol/mol',conversion factor 0.5012. detection limit converted from '3.981 ug/m3' at 293.15 k,1013.25 hpa to '2.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "no water vapor,temperature and ozone correction -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '3.392 ug/m3' at 293.15 k,1013.25 hpa to '1.7 nmol/mol',conversion factor 0.5012. detection limit converted from '3.981 ug/m3' at 293.15 k,1013.25 hpa to '2.0 nmol/mol',conversion factor 0.5012.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50.",\n",
- " "detection limit is 3 sigma scattering during zero air measurements referring to 1 min integration -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995342748. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50116703. detection limit converted from '1.1972056 ug/m3' at 293.15 k,1013.25 hpa to '0.6 nmol/mol',conversion factor 0.50116703.",\n",
- " "detection limit is 3 standard deviation during zero air measurements. -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50117. detection limit converted from '1.1573 ug/m3' at 293.15 k,1013.25 hpa to '0.58 nmol/mol',conversion factor 0.50117.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (demore et al.,jpl,pasadena,calif.,1994) -- data converted on import into ebas from nmol/mol to ug/m3 at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '19.95 ug/m3' at 293.15 k,1013.25 hpa to '10.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "data processed according to standard method -- data and detection limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan',\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501167.",\n",
- " 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. detection limit converted from '0.5 nmol/mol' to '0.9977 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50117. detection limit converted from '0.9977 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.50117.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. measurement uncertainty converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '0.998 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.5012. detection limit converted from '1.397 ug/m3' at 293.15 k,1013.25 hpa to '0.7 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "nothing -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. detection limit converted from '0.601 nmol/mol' to '1.1992 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50117. detection limit converted from '1.1992 ug/m3' at 293.15 k,1013.25 hpa to '0.601 nmol/mol',conversion factor 0.50117.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (hearn,1961). -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (hearn,1961). -- data,measurement uncertainty,detection limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (hearn,1961). -- data,measurement uncertainty,detection limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (hearn,1961). -- data,measurement uncertainty,detection limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'ppb' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '0.5 ppb' to '1.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'ppb' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '0.5 ppb' to '1.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. detection limit converted from '4.0 nmol/mol' to '7.981 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. detection limit converted from '7.981 ug/m3' at 293.15 k,1013.25 hpa to '4.0 nmol/mol',conversion factor 0.5012.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,no conversion factor (no nonzero,valid values). -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,no conversion factor (no nonzero,valid values). -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at standard conditions (293.15 k,1013.25 hpa),conversion factor 1.9953. variable metadata uncertainty converted. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 2.00. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 2.00. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 2.00. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "on site calibration performet every four months,in acredited laboratory once a year. -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '0.6 nmol/mol' to '1.2 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.2 ug/m3' at 293.15 k,1013.25 hpa to '0.6 nmol/mol',conversion factor 0.501.",\n",
- " "on site callibration of instrument 3 times a year and once a year calibration in acredited laboratory. -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "prior to shipment to and deployment in vietnam,the instrument was calibrated at wcc-empa against a primary ozone standard (standard reference photometer (srp),no 15,in april 2012. -- data converted from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "filtered data valid data -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501."],\n",
- " dtype=object) network_provided_volume_standard_pressure
(station)
float64
...
standard_name : network provided volume standard pressure long_name : network provided volume standard pressure units : hPa description : The pressure (in hPa) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typially normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported pressure should be reported as the internal pressure of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas pressure is assumed to be the known network standard pressure for the measured component. array([1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25]) network_provided_volume_standard_temperature
(station)
float64
...
standard_name : network provided volume standard temperature long_name : network provided volume standard temperature units : K description : The temperature (in Kelvin) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typially normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported temperature should be reported as the internal temperature of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas temperature is assumed to be the known network standard temperature for the measured component. array([293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15]) network_qa_details
(station)
object
...
standard_name : network qa details long_name : network qa details units : unitless description : Extra details provided by the reporting network about the in-network quality assurance of measurements. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) network_sampling_details
(station)
object
...
standard_name : network sampling details long_name : network sampling details units : unitless description : Extra details provided by the reporting network about the sampling methods employed. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) network_uncertainty_details
(station)
object
...
standard_name : network uncertainty details long_name : network uncertainty details units : unitless description : Extra details provided by the reporting network about the uncertainties involved with the measurement methods employed. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan',\n",
- " 'zero/negative values: zero and neg. values higher than -lod are measured at low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;detection limit explained: determined by instrument counting statistics,no detection limit flag used;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;detection limit explained: determined by instrument counting statistics,no detection limit flag used;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes only calibration uncertainty',\n",
- " 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan',\n",
- " 'measurement uncertainty explained: actually max(0.5;7%) to include statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan',\n",
- " 'zero/negative values: zero and neg. values may not appear',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'nan',\n",
- " 'zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan',\n",
- " 'detection limit explained: determined by instrument counting statistics;zero/negative values: zero and neg. values reported as measured if they are inside 0 ± detection limit,flagged with 781;other negative values flaged 456',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan'], dtype=object) population
(station)
float32
...
standard_name : population long_name : population units : unitless description : Population size of the nearest urban settlement. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) primary_sampling_further_details
(station)
object
...
standard_name : primary sampling further details long_name : primary sampling further details units : unitless description : Further associated details regarding the specifics of the primary sampling instrument/type. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_documented_flow_rate
(station)
object
...
standard_name : primary sampling instrument documented flow rate long_name : primary sampling instrument documented sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_manual_name
(station)
object
...
standard_name : primary sampling instrument manual name long_name : primary sampling instrument manual name units : unitless description : Path to the location in the esarchive of the manual for the specific primary sampling instrument. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_name
(station)
object
...
standard_name : primary sampling instrument name long_name : standardised primary sampling instrument name units : unitless description : Standardised name of the primary sampling instrument (if no specific instrument is used, or known, this is the standardised primary sampling type). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_reported_flow_rate
(station)
object
...
standard_name : primary sampling instrument reported flow rate long_name : primary sampling instrument reported sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_process_details
(station)
object
...
standard_name : primary sampling process details long_name : primary sampling process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the primary sampling type/instrument. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_type
(station)
object
...
standard_name : primary sampling type long_name : standardised primary sampling type units : unitless description : Standardised primary sampling type. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) principal_investigator_email_address
(station)
object
...
standard_name : principal investigator email address long_name : principal investigator email address units : unitless description : Email address of the principal scientific investigator for the specific reported data. array(['barlasina@smn.gov.ar', 'mcupeiro@smn.gov.ar', 'nan', 'nan', 'nan',\n",
- " 'nan', 'iris.buxbaum@umweltbundesamt.at', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'e.adriaenssens@vmm.be',\n",
- " 'e.adriaenssens@vmm.be', 'e.adriaenssens@vmm.be', 'nan',\n",
- " 'Audra.mcclure@noaa.gov', 'sara.crepinsek@noaa.gov', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'katie.read@ncas.ac.uk', 'nan',\n",
- " 'jan.komarek@chmi.cz', 'kominkova.k@czechglobe.cz', 'hadinger@chmi.cz',\n",
- " 'bryan.hellack@uba.de', 'bryan.hellack@uba.de', 'bryan.hellack@uba.de',\n",
- " 'bryan.hellack@uba.de', 'bryan.hellack@uba.de', 'bryan.hellack@uba.de',\n",
- " 'Dagmar.Kubistin@dwd.de', 'cedric.couret@uba.de', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Audra.mcclure@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'ctorresg@aemet.es', 'heidi.hellen@fmi.fi',\n",
- " 'heidi.hellen@fmi.fi', 'heidi.hellen@fmi.fi', 'mika.vestenius@fmi.fi',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'ferenczi.z@met.hu', 'ferenczi.z@met.hu',\n",
- " 'reza_elka@yahoo.com', 'nan', 'nan', 'nan',\n",
- " 'p.cristofanelli@isac.cnr.it', 'p.cristofanelli@isac.cnr.it',\n",
- " 'm.angelucci@arpa.umbria.it', 'antarctic@met.kishou.go.jp',\n",
- " 'ghg_obs@met.kishou.go.jp', 'ghg_obs@met.kishou.go.jp',\n",
- " 'ghg_obs@met.kishou.go.jp', 'sulla@korea.kr', 'sulla@korea.kr', 'nan',\n",
- " 'nan', 'nan', 'ray.ellul@um.edu.mt', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'waa@nilu.no', 'nan', 'waa@nilu.no', 'waa@nilu.no', 'waa@nilu.no',\n",
- " 'kt@nilu.no', 'waa@nilu.no', 'nan', 'waa@nilu.no', 'waa@nilu.no',\n",
- " 'sylvia.nichol@niwa.co.nz', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'sara.crepinsek@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'rudi.voncina@eimv.si', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'anhnnhn@gmail.com',\n",
- " 'thumeka.mkololo@weathersa.co.za'], dtype=object) principal_investigator_institution
(station)
object
...
standard_name : principal investigator institution long_name : principal investigator institution units : unitless description : Institution of the principal scientific investigator for the specific reported data. array(['Servicio Meteorológico Nacional', 'Servicio Meteorologico Nacional',\n",
- " 'nan', 'nan', 'nan', 'nan', 'Umweltbundesamt Wien', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Flanders Environment Agency', 'Flanders Environment Agency',\n",
- " 'Flanders Environment Agency', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Physical Sciences Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National centre for Atmospheric Science', 'nan',\n",
- " 'Czech Hydrometeorological Instute',\n",
- " 'Global Change Research Centre AS CR v. v. i.',\n",
- " 'Czech Hydrometeorological Instute', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Meteorological Observatory Hohenpeissenberg',\n",
- " 'German Environment Agency', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Agencia Estatal de Meteorologia', 'nan', 'nan', 'nan',\n",
- " 'Finnish Meteorological Institute', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Hungarian Meteorological Service', 'Hungarian Meteorological Service',\n",
- " 'Badan Meteorologi Klimatologi dan Geofisika', 'nan', 'nan', 'nan',\n",
- " 'National Research Council of Italy',\n",
- " 'National Research Council of Italy',\n",
- " 'Arpa Umbria - Umbria Regional Agency for Environmental Protection',\n",
- " 'Japan Meteorological Agency', 'Japan Meteorological Agency',\n",
- " 'Japan Meteorological Agency', 'Japan Meteorological Agency',\n",
- " 'National Institute of Meteorological Sciences',\n",
- " 'National Institute of Meteorological Sciences', 'nan', 'nan', 'nan',\n",
- " 'University of Malta', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'National Institute of Water and Atmospheric Research',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Physical Sciences Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Milan Vidmar Electric Power Research Institute', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'Hydro-Meteorological and Environmental Station Network Center',\n",
- " 'South African Weather Service'], dtype=object) principal_investigator_name
(station)
object
...
standard_name : principal investigator name long_name : principal investigator name units : unitless description : Full name of the principal scientific investigator for the specific reported data. array(['Maria Elena Barlasina', 'Manuel Cupeiro', 'Marina Froelich',\n",
- " 'Marina Froelich', 'Wolfgang Spangl', 'Wolfgang Spangl', 'Iris Buxbaum',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Marina Froelich', 'Wolfgang Spangl',\n",
- " 'Marina Froelich', 'Elke Adriaenssens', 'Elke Adriaenssens',\n",
- " 'Elke Adriaenssens', 'Emiliya Stoyneva', 'Audra Mcclure-Begley',\n",
- " 'Sara Crepinsek', 'Robert Gehrig', 'Robert Gehrig', 'Robert Gehrig',\n",
- " 'Robert Gehrig', 'Robert Gehrig', 'Christoph Hueglin', 'Gaston Torres',\n",
- " 'Katie Read', 'Chrysanthos Savvides', 'Milan Vana', 'Jaroslav Pekarek',\n",
- " 'Milan Vana', 'Markus Wallasch', 'Markus Wallasch', 'Markus Wallasch',\n",
- " 'Markus Wallasch', 'Markus Wallasch', 'Markus Wallasch',\n",
- " 'Dagmar Kubistin', 'Cedric Couret', 'Rolf Weller', 'Rune Keller',\n",
- " 'Rune Keller', 'Rune Keller', 'Audra Mcclure-Begley', 'Rune Keller',\n",
- " 'Truuts Toivo', 'Usin Eve', 'Juan Martinez', 'Juan Martinez',\n",
- " 'Fermin Elizaga', 'Fermin Elizaga', 'Maj-Britt Larka', 'Fermin Elizaga',\n",
- " 'Fermin Elizaga', 'Fermin Elizaga', 'Bartolome Maria Lopez',\n",
- " 'Fermin Elizaga', 'Fermin Elizaga', 'Fermin Elizaga', 'Fermin Elizaga',\n",
- " 'Carlos Torres', 'Hannele Hakola', 'Hannele Hakola', 'Hannele Hakola',\n",
- " 'Mika Vestenius', 'Patrice Coddeville', 'Stephane Sauvage',\n",
- " 'Stephane Sauvage', 'Stephane Sauvage', 'Stephane Sauvage',\n",
- " 'Stephane Sauvage', 'Stephane Sauvage', 'Stephane Sauvage',\n",
- " 'Patrice Coddeville', 'Stephane Sauvage', 'Stephane Sauvage',\n",
- " 'Stephane Sauvage', 'Stephane Sauvage', 'Keith Vincent',\n",
- " 'Keith Vincent', 'Keith Vincent', 'Keith Vincent', 'Keith Vincent',\n",
- " 'Keith Vincent', 'Keith Vincent', 'Keith Vincent', 'Keith Vincent',\n",
- " 'Keith Vincent', 'Keith Vincent', 'Keith Vincent', 'Keith Vincent',\n",
- " 'Keith Vincent', 'Keith Vincent', 'Keith Vincent', 'Willis Paul',\n",
- " 'A Adamopoulos', 'Nikos Mihalopoulos', 'Zita Ferenczi', 'Zita Ferenczi',\n",
- " 'Joerg Klausen', 'Barbara Oleary', 'Keith Vincent', 'F Lagler',\n",
- " 'Paolo Cristofanelli', 'Paolo Cristofanelli', 'Monica Angelucci',\n",
- " 'Yoshinobu Tanaka', 'Atsushi Takizawa', 'Atsushi Takizawa',\n",
- " 'Atsushi Takizawa', 'Sumin Kim', 'Sumin Kim', 'Rasa Girgzdiene',\n",
- " 'Dubakova Iveta', 'Dubakova Iveta', 'Raymond Ellul',\n",
- " 'Jacobus P.J. Berkhout', 'Housz Floris C Ingen',\n",
- " 'Jacobus P.J. Berkhout', 'Jacobus P.J. Berkhout', 'Hans Berkhout',\n",
- " 'Wenche Aas', 'Erik Andresen', 'Wenche Aas', 'Wenche Aas', 'Wenche Aas',\n",
- " 'Kjetil Tørseth', 'Wenche Aas', 'Erik Andresen', 'Wenche Aas',\n",
- " 'Wenche Aas', 'Sylvia Nichol', 'Audra Mcclure-Begley',\n",
- " 'Audra Mcclure-Begley', 'Magdalena Bogucka', 'Magdalena Bogucka',\n",
- " 'Magdalena Bogucka', 'Zdzislaw Przadka', 'Jasmina Knezevic',\n",
- " 'Sara Crepinsek', 'Karin Sjoberg', 'Karin Sjoberg', 'Karin Sjoberg',\n",
- " 'Karin Sjøberg', 'Karin Sjøberg', 'Karin Sjöberg', 'Karin Sjöberg',\n",
- " 'Karin Sjoberg', 'Karin Sjoberg', 'Karin Sjoberg', 'Mateja Gjerek',\n",
- " 'Rudi Voncina', 'Mateja Gjerek', 'Marta Mitosinkova',\n",
- " 'Marta Mitosinkova', 'Marta Mitosinkova', 'Marta Mitosinkova',\n",
- " 'Audra Mcclure-Begley', 'Audra Mcclure-Begley', 'Audra Mcclure-Begley',\n",
- " 'Audra Mcclure-Begley', 'Audra Mcclure-Begley', 'Nhat Anh Nguyen',\n",
- " 'Thumeka Mkololo'], dtype=object) process_warnings
(station)
object
...
standard_name : process warnings long_name : process warnings units : unitless description : Warnings accumulated through GHOST processing regarding the data that should be considered. array(['Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ', '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ', '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " '', 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Area Classification Metadata - Took Most Applicable Next Value. No Station Classification Metadata - Took Most Applicable Next Value. No Land Use Metadata - Took Most Applicable Next Value. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " '', 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " '',\n",
- " 'No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " '',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Recent Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. '],\n",
- " dtype=object) projection
(station)
object
...
standard_name : projection long_name : projection units : unitless description : Name of the projected coordinate system of the original provided station position x, y coordinates. If the original coordinates are not projected, then this is set as 'geographic'. array(['GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC'], dtype=object) qa
(station, time, N_qa_codes)
float32
...
standard_name : qa long_name : qa standardised data flags units : unitless description : List of derived quality assurance flag codes per measurement, informing on the quality associated with each observation, determined by multiple scientific quality control/assurance checks. Fill value code of 255. [9313920 values with dtype=float32] reported_uncertainty_per_measurement
(station, time)
float32
...
standard_name : reported measurement uncertainty per measurement long_name : reported measurement uncertainty per measurement units : nmol mol-1 description : Reported measurement uncertainty (±) of methodology, for a specific measurement. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This is given in absolute terms in nmol mol-1. [120960 values with dtype=float32] representative_radius
(station)
float32
...
standard_name : representative_radius long_name : representative_radius units : km description : Radius of representativity of the measurements made (i.e. for what distance scale around the sampling point would the measurements be very similar?), given in kilometres. A quantitative version of the "measurement_scale" classification. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) sample_preparation_further_details
(station)
object
...
standard_name : sample preparation further details long_name : sample preparation further details units : unitless description : Further associated details regarding the specifics of the sample preparation types/instruments. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) sample_preparation_process_details
(station)
object
...
standard_name : sample preparation process details long_name : sample preparation process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the sample preparation types/techniques. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) sample_preparation_techniques
(station)
object
...
standard_name : sample preparation techniques long_name : standardised specific preparation techniques units : unitless description : Standardised sample preparation techniques utilised in the measurement process. Mutiple names are separated by ";". array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) sample_preparation_types
(station)
object
...
standard_name : sample preparation types long_name : standardised sample preparation types units : unitless description : Standardised sample preparation types utilised in the measurement process. Mutiple types are separated by ";". array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) sampling_height
(station)
float32
...
standard_name : sampling height long_name : sampling height relative to ground units : m description : Height above the ground of the inlet/instrument/sampler, in metres. array([ 5. , 7. , 0. , 0. , 0. , 0. ,\n",
- " 10. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 30. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 5. ,\n",
- " 7.5 , 3. , 3. , 50. , 3. , 10.5 ,\n",
- " 7. , 12. , 6.2 , 16. , 8. , 10. ,\n",
- " 0. , 7. , 0. , 0. , 0. , 10. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 5. , 5. ,\n",
- " 5. , 5. , 4. , 0. , 4. , 0. ,\n",
- " 4. , 0. , 4. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 12. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 4. , 3. ,\n",
- " 2. , 0. , 0. , 0. , 2. , 7. ,\n",
- " 4. , 0. , 5. , 8. , 8. , 10. ,\n",
- " 40. , 9.5 , 0. , 0. , 0. , 7. ,\n",
- " 2. , 2. , 2. , 2. , 2. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 10. , 10. , 10. ,\n",
- " 2.240005, 4.5 , 3. , 4.35 , 0. , 10. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 5. , 3. ,\n",
- " 10. , 0. , 0. , 0. , 0. , 10. ,\n",
- " 6. , 10. , 10. , 10. , 12. , 4. ],\n",
- " dtype=float32) sconco3
(station, time)
float32
...
standard_name : ozone long_name : ozone units : nmol mol-1 description : Measured value of surface ozone for the stated temporal resolution. [120960 values with dtype=float32] season_code
(station, time)
uint8
...
standard_name : season code long_name : season code per measurement units : unitless description : Code decreeing if measurement was made during the Spring, Summer, Autumn or Winter Seasons. Spring=0, Summer=1, Autumn=2, Winter=3. The classification is made by evaluating which season the local-time of a mid-measurement window timestamp falls in. [120960 values with dtype=uint8] station_classification
(station)
object
...
standard_name : station classification long_name : standardised network provided station classification units : unitless description : Standardised network provided classification, categorising the type of air measured by a station. array(['background', 'point_source-industrial', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'nan', 'nan',\n",
- " 'background', 'nan', 'nan', 'nan', 'background', 'background', 'nan',\n",
- " 'nan', 'nan', 'nan', 'background', 'background', 'background',\n",
- " 'background', 'nan', 'nan', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'nan', 'background',\n",
- " 'background', 'background', 'background', 'nan', 'nan', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'nan', 'background', 'background', 'background',\n",
- " 'background', 'nan', 'background', 'nan', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'nan',\n",
- " 'background', 'nan', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'nan', 'nan',\n",
- " 'background', 'nan', 'nan', 'background', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'nan', 'background', 'background', 'background', 'nan', 'nan',\n",
- " 'background', 'background', 'nan', 'background', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'nan', 'background', 'background', 'nan',\n",
- " 'background', 'nan', 'background', 'nan', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'nan', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'nan', 'nan', 'background', 'background',\n",
- " 'nan', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background'],\n",
- " dtype=object) station_name
(station)
object
...
standard_name : station name long_name : station name units : unitless description : Name of station where the measurement was conducted. array(['Marambio', 'Ushuaia', 'Illmitz', 'Vorhegg', 'Pillersdorf Bei Retz',\n",
- " 'Sulzberg', 'Sonnblick', 'Masenberg', 'Haunsberg', 'Heidenreichstein',\n",
- " 'Forsthof', 'Dunkelsteinerwald', 'Gänserndorf', 'Stixneusiedl',\n",
- " 'Zoebelboden', 'Grebenzen Bei St. Lamprecht', 'Graz Lustbuehel',\n",
- " 'Offagne', 'Eupen', 'Vezin', 'Rojen Peak', 'Tudor Hill (Bermuda)',\n",
- " 'Eureka', 'Jungfraujoch', 'Payerne', 'Tänikon', 'Chaumont', 'Rigi',\n",
- " 'Beromünster', 'El Tololo', 'Cape Verde Atmospheric Observatory',\n",
- " 'Agia Marina Xyliatou / Cyprus Atmospheric Observatory', 'Svratouch',\n",
- " 'Kosetice (Noak)', 'Churanov', 'Westerland', 'Waldhof', 'Schauinsland',\n",
- " 'Neuglobsow', 'Schmücke', 'Zingst', 'Hohenpeissenberg',\n",
- " 'Zugspitze-Schneefernerhaus', 'Neumayer', 'Keldsnor',\n",
- " 'Villum Research Station,Station Nord', 'Risoe', 'Summit', 'Ulborg',\n",
- " 'Lahemaa', 'Vilsandi', 'San Pablo De Los Montes', 'Noia', 'Mahón',\n",
- " 'Víznar', 'Niembro', 'Campisabalos', 'Cabo De Creus', 'Barcarrota',\n",
- " 'Zarra', 'Penausende', 'Els Torms', 'O Saviñao', 'Doñana', 'Izana',\n",
- " 'Utö', 'Virolahti Iii', 'Oulanka', 'Pallas (Sammaltunturi)', 'Donon',\n",
- " 'Revin', 'Morvan', 'Peyrusse Vieille', 'Montandon', 'La Tardière',\n",
- " 'Le Casset', 'Montfranc', 'La Coulonche', 'Pic Du Midi',\n",
- " 'Saint-Nazaire-Le-Désert', 'Verneuil', 'Puy De Dôme', 'Eskdalemuir',\n",
- " 'Lough Navar', 'Yarner Wood', 'High Muffles', 'Strath Vaich Dam',\n",
- " 'Aston Hill', 'Bush', 'Ladybower Res.', 'Lullington Heath', 'Sibton',\n",
- " 'Narberth', 'Wicken Fen', 'Auchencorth Moss', 'Weybourne', 'St. Osyth',\n",
- " 'Lerwick', 'Chilbolton Observatory', 'Aliartos', 'Finokalia',\n",
- " 'K-Puszta', 'Farkasfa', 'Bukit Kototabang', 'Valentia Observatory',\n",
- " 'Mace Head', 'Ispra', 'Mt Cimone', 'Capo Granitola', 'Monte Martano',\n",
- " 'Syowa', 'Ryori', 'Minamitorishima', 'Yonaguni', 'Anmyeon-Do', 'Gosan',\n",
- " 'Preila', 'Rucava', 'Zoseni', 'Giordan Lighthouse', 'Eibergen',\n",
- " 'Kollumerwaard', 'Vredepeel', 'De Zilk', 'Cabauw Wielsekade',\n",
- " 'Birkenes Ii', 'Tustervatn', 'Kårvatn',\n",
- " 'Zeppelin Mountain (Ny-Ålesund)', 'Prestebakke', 'Svanvik', 'Sandve',\n",
- " 'Hurdal', 'Trollhaugen', 'Haukenes', 'Baring Head', 'Arrival Heights',\n",
- " 'Lauder', 'Jarczew', 'Sniezka', 'Leba', 'Diabla Gora', 'Kamenicki Vis',\n",
- " 'Tiksi', 'Bredkälen', 'Esrange', 'Råö', 'Asa', 'Östad', 'Hallahus',\n",
- " 'Norunda Stenen', 'Norra-Kvill', 'Vindeln', 'Grimsö', 'Iskrba',\n",
- " 'Zarodnje', 'Krvavec', 'Chopok', 'Stará Lesná', 'Starina', 'Topolniky',\n",
- " 'Barrow', 'Boulder Table Mountain', 'Mauna Loa', 'South Pole',\n",
- " 'Trinidad Head', 'Pha Din', 'Cape Point'], dtype=object) station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : unitless description : reference ID for station. array(['AR0001R_UVP', 'AR0002G_UVP', 'AT0002R_UVP', 'AT0005R_UVP',\n",
- " 'AT0030R_UVP', 'AT0032R_UVP', 'AT0034G_UVP', 'AT0040R_UVP',\n",
- " 'AT0041R_UVP', 'AT0042R_UVP', 'AT0043R_UVP', 'AT0045R_UVP',\n",
- " 'AT0046R_UVP', 'AT0047R_UVP', 'AT0048R_UVP', 'AT0049R_UVP',\n",
- " 'AT0050R_UVP', 'BE0001R_UVP', 'BE0032R_UVP', 'BE0035R_UVP',\n",
- " 'BG0053R_UVP', 'BM0001R_UVP', 'CA0103R_UVP', 'CH0001G_UVP',\n",
- " 'CH0002R_UVP', 'CH0003R_UVP', 'CH0004R_UVP', 'CH0005R_UVP',\n",
- " 'CH0053R_UVP', 'CL0001R_UVP', 'CV0001G_UVP', 'CY0002R_UVP',\n",
- " 'CZ0001R_UVP', 'CZ0003R_UVP', 'CZ0005R_UVP', 'DE0001R_UVP',\n",
- " 'DE0002R_UVP', 'DE0003R_UVP', 'DE0007R_UVP', 'DE0008R_UVP',\n",
- " 'DE0009R_UVP', 'DE0043G_UVP', 'DE0054R_UVP', 'DE0060G_UVP',\n",
- " 'DK0005R_UVP', 'DK0010G_UVP', 'DK0012R_UVP', 'DK0025G_UVP',\n",
- " 'DK0031R_UVP', 'EE0009R_UVP', 'EE0011R_UVP', 'ES0001R_UVP',\n",
- " 'ES0005R_UVP', 'ES0006R_UVP', 'ES0007R_UVP', 'ES0008R_UVP',\n",
- " 'ES0009R_UVP', 'ES0010R_UVP', 'ES0011R_UVP', 'ES0012R_UVP',\n",
- " 'ES0013R_UVP', 'ES0014R_UVP', 'ES0016R_UVP', 'ES0017R_UVP',\n",
- " 'ES0018G_UVP', 'FI0009R_UVP', 'FI0018R_UVP', 'FI0022R_UVP',\n",
- " 'FI0096G_UVP', 'FR0008R_UVP', 'FR0009R_UVP', 'FR0010R_UVP',\n",
- " 'FR0013R_UVP', 'FR0014R_UVP', 'FR0015R_UVP', 'FR0016R_UVP',\n",
- " 'FR0017R_UVP', 'FR0018R_UVP', 'FR0019R_UVP', 'FR0023R_UVP',\n",
- " 'FR0025R_UVP', 'FR0030R_UVP', 'GB0002R_UVP', 'GB0006R_UVP',\n",
- " 'GB0013R_UVP', 'GB0014R_UVP', 'GB0015R_UVP', 'GB0031R_UVP',\n",
- " 'GB0033R_UVP', 'GB0037R_UVP', 'GB0038R_UVP', 'GB0039R_UVP',\n",
- " 'GB0043R_UVP', 'GB0045R_UVP', 'GB0048R_UVP', 'GB0049R_UVP',\n",
- " 'GB0050R_UVP', 'GB0052R_UVP', 'GB1055R_UVP', 'GR0001R_UVP',\n",
- " 'GR0002R_UVP', 'HU0002R_UVP', 'HU0003R_UVP', 'ID1013R_UVP',\n",
- " 'IE0001R_UVP', 'IE0031R_UVP', 'IT0004R_UVP', 'IT0009R_UVP',\n",
- " 'IT0014R_UVP', 'IT0019R_UVP', 'JP0002G_UVP', 'JP1020R_UVP--S1',\n",
- " 'JP1028G_UVP', 'JP1029R_UVP', 'KR0100R_UVP', 'KR0101R_UVP',\n",
- " 'LT0015R_UVP', 'LV0010R_UVP', 'LV0016R_UVP', 'MT0001R_UVP',\n",
- " 'NL0007R_UVP', 'NL0009R_UVP', 'NL0010R_UVP', 'NL0091R_UVP',\n",
- " 'NL0644R_UVP', 'NO0002R_UVP', 'NO0015R_UVP', 'NO0039R_UVP',\n",
- " 'NO0042G_UVP', 'NO0043R_UVP', 'NO0047R_UVP', 'NO0052R_UVP',\n",
- " 'NO0056R_UVP', 'NO0059G_UVP', 'NO0489R_UVP', 'NZ0001R_UVP',\n",
- " 'NZ0002R_UVP', 'NZ0003G_UVP', 'PL0002R_UVP', 'PL0003R_UVP',\n",
- " 'PL0004R_UVP', 'PL0005R_UVP', 'RS0005R_UVP', 'RU0100R_UVP',\n",
- " 'SE0005R_UVP', 'SE0013R_UVP', 'SE0014R_UVP', 'SE0018R_UVP',\n",
- " 'SE0019R_UVP', 'SE0020R_UVP', 'SE0022R_UVP', 'SE0032R_UVP',\n",
- " 'SE0035R_UVP', 'SE0039R_UVP', 'SI0008R_UVP', 'SI0031R_UVP',\n",
- " 'SI0032R_UVP', 'SK0002R_UVP', 'SK0004R_UVP', 'SK0006R_UVP',\n",
- " 'SK0007R_UVP', 'US0008R_UVP', 'US0901R_UVP', 'US1200R_UVP',\n",
- " 'US6004G_UVP', 'US6005G_UVP', 'VN0001R_UVP', 'ZA0001G_UVP'],\n",
- " dtype=object) station_timezone
(station)
object
...
standard_name : station timezone long_name : station timezone units : unitless description : Name of the local timezone that the measuring station is located in. This is automatically generated using Timezone Finder Python package (taking longitude and latitude as inputs). array(['America/Argentina/Ushuaia', 'America/Argentina/Ushuaia',\n",
- " 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna',\n",
- " 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna',\n",
- " 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna',\n",
- " 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna', 'Europe/Brussels',\n",
- " 'Europe/Brussels', 'Europe/Brussels', 'Europe/Sofia',\n",
- " 'Atlantic/Bermuda', 'America/Rankin_Inlet', 'Europe/Zurich',\n",
- " 'Europe/Zurich', 'Europe/Zurich', 'Europe/Zurich', 'Europe/Zurich',\n",
- " 'Europe/Zurich', 'America/Santiago', 'Atlantic/Cape_Verde',\n",
- " 'Asia/Nicosia', 'Europe/Prague', 'Europe/Prague', 'Europe/Prague',\n",
- " 'Europe/Berlin', 'Europe/Berlin', 'Europe/Berlin', 'Europe/Berlin',\n",
- " 'Europe/Berlin', 'Europe/Berlin', 'Europe/Berlin', 'Europe/Berlin',\n",
- " 'Etc/UTC', 'Europe/Copenhagen', 'America/Godthab', 'Europe/Copenhagen',\n",
- " 'America/Godthab', 'Europe/Copenhagen', 'Europe/Tallinn',\n",
- " 'Europe/Tallinn', 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid',\n",
- " 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid',\n",
- " 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid',\n",
- " 'Europe/Madrid', 'Europe/Madrid', 'Atlantic/Canary', 'Europe/Helsinki',\n",
- " 'Europe/Helsinki', 'Europe/Helsinki', 'Europe/Helsinki', 'Europe/Paris',\n",
- " 'Europe/Paris', 'Europe/Paris', 'Europe/Paris', 'Europe/Paris',\n",
- " 'Europe/Paris', 'Europe/Paris', 'Europe/Paris', 'Europe/Paris',\n",
- " 'Europe/Paris', 'Europe/Paris', 'Europe/Paris', 'Europe/Paris',\n",
- " 'Europe/London', 'Europe/London', 'Europe/London', 'Europe/London',\n",
- " 'Europe/London', 'Europe/London', 'Europe/London', 'Europe/London',\n",
- " 'Europe/London', 'Europe/London', 'Europe/London', 'Europe/London',\n",
- " 'Europe/London', 'Europe/London', 'Europe/London', 'Europe/London',\n",
- " 'Europe/London', 'Europe/Athens', 'Europe/Athens', 'Europe/Budapest',\n",
- " 'Europe/Budapest', 'Asia/Jakarta', 'Europe/Dublin', 'Europe/Dublin',\n",
- " 'Europe/Rome', 'Europe/Rome', 'Europe/Rome', 'Europe/Rome',\n",
- " 'Antarctica/Syowa', 'Asia/Tokyo', 'Asia/Tokyo', 'Asia/Tokyo',\n",
- " 'Asia/Seoul', 'Asia/Seoul', 'Europe/Vilnius', 'Europe/Riga',\n",
- " 'Europe/Riga', 'Europe/Malta', 'Europe/Amsterdam', 'Europe/Amsterdam',\n",
- " 'Europe/Amsterdam', 'Europe/Amsterdam', 'Europe/Amsterdam',\n",
- " 'Europe/Oslo', 'Europe/Oslo', 'Europe/Oslo', 'Arctic/Longyearbyen',\n",
- " 'Europe/Oslo', 'Europe/Oslo', 'Europe/Oslo', 'Europe/Oslo',\n",
- " 'Antarctica/Troll', 'Europe/Oslo', 'Pacific/Auckland',\n",
- " 'Antarctica/McMurdo', 'Pacific/Auckland', 'Europe/Warsaw',\n",
- " 'Europe/Warsaw', 'Europe/Warsaw', 'Europe/Warsaw', 'Europe/Belgrade',\n",
- " 'Asia/Yakutsk', 'Europe/Stockholm', 'Europe/Stockholm',\n",
- " 'Europe/Stockholm', 'Europe/Stockholm', 'Europe/Stockholm',\n",
- " 'Europe/Stockholm', 'Europe/Stockholm', 'Europe/Stockholm',\n",
- " 'Europe/Stockholm', 'Europe/Stockholm', 'Europe/Ljubljana',\n",
- " 'Europe/Ljubljana', 'Europe/Ljubljana', 'Europe/Bratislava',\n",
- " 'Europe/Bratislava', 'Europe/Bratislava', 'Europe/Bratislava',\n",
- " 'America/Anchorage', 'America/Denver', 'Pacific/Honolulu',\n",
- " 'Antarctica/McMurdo', 'America/Los_Angeles', 'Asia/Bangkok',\n",
- " 'Africa/Johannesburg'], dtype=object) street_type
(station)
object
...
standard_name : street type long_name : type of street units : unitless description : Type of street where measurements are being made (if applicable). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) street_width
(station)
float32
...
standard_name : street width long_name : width of the street units : m description : Width of the street where measurements are being made (if applicable), in metres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) terrain
(station)
object
...
standard_name : terrain long_name : standardised network provided terrain type units : unitless description : Standardised network provided classification, describing the dominant terrain in the area of the reporting station. array(['nan', 'coastal', 'nan', 'nan', 'nan', 'nan', 'mountain', 'nan',\n",
- " 'complex', 'complex', 'nan', 'complex', 'complex', 'complex', 'nan',\n",
- " 'mountain', 'complex', 'complex', 'complex', 'complex', 'mountain',\n",
- " 'coastal', 'nan', 'mountain', 'complex', 'complex', 'nan', 'nan', 'nan',\n",
- " 'mountain', 'coastal', 'complex', 'complex', 'nan', 'nan', 'coastal',\n",
- " 'nan', 'complex', 'complex', 'nan', 'coastal', 'mountain', 'mountain',\n",
- " 'nan', 'coastal', 'nan', 'complex', 'mountain', 'nan', 'nan', 'coastal',\n",
- " 'complex', 'nan', 'complex', 'nan', 'complex', 'nan', 'coastal', 'nan',\n",
- " 'nan', 'complex', 'nan', 'complex', 'nan', 'mountain', 'coastal',\n",
- " 'coastal', 'nan', 'nan', 'nan', 'complex', 'complex', 'nan', 'complex',\n",
- " 'complex', 'mountain', 'nan', 'complex', 'mountain', 'nan', 'nan',\n",
- " 'mountain', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'complex', 'nan',\n",
- " 'nan', 'nan', 'complex', 'complex', 'nan', 'coastal', 'complex',\n",
- " 'coastal', 'nan', 'complex', 'coastal', 'complex', 'nan', 'mountain',\n",
- " 'complex', 'coastal', 'complex', 'mountain', 'coastal', 'mountain',\n",
- " 'nan', 'coastal', 'coastal', 'coastal', 'coastal', 'coastal', 'coastal',\n",
- " 'complex', 'nan', 'coastal', 'complex', 'nan', 'complex', 'coastal',\n",
- " 'complex', 'nan', 'nan', 'nan', 'mountain', 'nan', 'nan', 'complex',\n",
- " 'nan', 'nan', 'nan', 'coastal', 'nan', 'nan', 'complex', 'mountain',\n",
- " 'coastal', 'nan', 'nan', 'coastal', 'complex', 'nan', 'coastal', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'complex', 'complex',\n",
- " 'mountain', 'mountain', 'complex', 'nan', 'nan', 'coastal', 'nan',\n",
- " 'mountain', 'nan', 'coastal', 'mountain', 'coastal'], dtype=object) vertical_datum
(station)
object
...
standard_name : vertical datum long_name : vertical datum units : unitless description : Name of the vertical datum used to define vertical elevation on the Earth. The datum is a surface of zero elevation to which other heights can be reference against. If not explicitely stated then this is assumed to be 'tidal - mean sea level'. array(['TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL'], dtype=object) weekday_weekend_code
(station, time)
uint8
...
standard_name : weekday/weekend code long_name : weekday/weekend code per measurement units : unitless description : Binary indication if measurement was made during the weekday or weekend. Weekday=0, Weekend=1. The classification is made by evaluating if the local-time of a mid-measurement window timestamp falls on a weekday or on the weekend. Classification is 255 if cannot be made. [120960 values with dtype=uint8] Attributes: (10)
title : Surface ozone data in the EBAS network in 2018-04. institution : Barcelona Supercomputing Center source : Surface observations creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es conventions : CF-1.7 data_version : 1.3.3 history : Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dmn station /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc\n",
- "Tue Mar 30 12:37:03 2021: ncrcat -L 1 /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AR0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AR0002G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0030R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0032R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0034G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0040R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0041R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0042R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0043R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0045R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0046R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0047R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0048R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0049R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0050R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BE0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BE0032R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BE0035R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BG0053R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BM0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CA0103R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0001G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0004R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0053R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CL0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CV0001G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CY0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CZ0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CZ0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CZ0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0007R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0043G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0054R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0060G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0010G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0012R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0025G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0031R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/EE0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/EE0011R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0006R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0007R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0010R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0011R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0012R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0016R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0017R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0018G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FI0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FI0018R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FI0022R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FI0096G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0010R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0015R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0016R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0017R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0018R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0019R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0023R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0025R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0030R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0006R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0015R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0031R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0033R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0037R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0038R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0039R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0043R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0045R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0048R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0049R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0050R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0052R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB1055R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GR0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GR0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/HU0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/HU0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ID1013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IE0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IE0031R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IT0004R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IT0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IT0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IT0019R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/JP0002G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/JP1020R_UVP--S1.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/JP1028G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/JP1029R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/KR0100R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/KR0101R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/LT0015R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/LV0010R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/LV0016R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/MT0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0007R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0010R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0091R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0644R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0015R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0039R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0042G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0043R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0047R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0052R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0056R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0059G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0489R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NZ0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NZ0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NZ0003G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/PL0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/PL0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/PL0004R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/PL0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/RS0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/RU0100R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0018R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0019R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0020R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0022R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0032R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0035R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0039R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SI0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SI0031R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SI0032R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SK0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SK0004R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SK0006R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SK0007R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US0901R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US1200R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US6004G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US6005G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/VN0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ZA0001G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc NCO : 4.7.2 nco_openmp_thread_number : 1 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (station: 168, time: 720, N_flag_codes: 186, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
- "Data variables: (12/179)\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NMVOC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NOx_emissions (station) float32 ...\n",
- " ... ...\n",
- " station_timezone (station) object ...\n",
- " street_type (station) object ...\n",
- " street_width (station) float32 ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- "Attributes:\n",
- " title: Surface ozone data in the EBAS network in 2018...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.3.3\n",
- " history: Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dm...\n",
- " NCO: 4.7.2\n",
- " nco_openmp_thread_number: 1"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset(obs_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Open with NES"
+ "### 1.1 Read dataset"
]
},
{
@@ -6327,7 +49,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 4,
@@ -7624,13 +1346,6 @@
"### 1.2. Write dataset"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Write with NES"
- ]
- },
{
"cell_type": "code",
"execution_count": 10,
@@ -7967,15 +1682,14 @@
"Rank 000: Var country created (81/175)\n",
"Rank 000: Var country data (81/175)\n",
"Rank 000: Var country completed (81/175)\n",
- "Rank 000: Writing daily_native_max_gap_percent var (82/175)\n",
- "Rank 000: Var daily_native_max_gap_percent created (82/175)\n"
+ "Rank 000: Writing daily_native_max_gap_percent var (82/175)"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:592: UserWarning: WARNING!!! GHOST datasets cannot be written in parallel yet. Changing to serial mode.\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:593: UserWarning: WARNING!!! GHOST datasets cannot be written in parallel yet. Changing to serial mode.\n",
" warnings.warn(msg)\n"
]
},
@@ -7983,6 +1697,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
+ "\n",
+ "Rank 000: Var daily_native_max_gap_percent created (82/175)\n",
"Rank 000: Var daily_native_max_gap_percent data (82/175)\n",
"Rank 000: Var daily_native_max_gap_percent completed (82/175)\n",
"Rank 000: Writing daily_native_representativity_percent var (83/175)\n",
@@ -8364,6294 +2080,6 @@
"obs_nes.to_netcdf('prv_obs_file.nc', info=True)"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Reopen with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 720, station: 168, N_flag_codes: 186, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- " * station (station) float64 ...\n",
- "Dimensions without coordinates: N_flag_codes, N_qa_codes\n",
- "Data variables: (12/179)\n",
- " latitude (station) float64 ...\n",
- " longitude (station) float64 ...\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " ... ...\n",
- " street_width (station) float32 ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- " flag (station, time, N_flag_codes) int64 ...\n",
- " qa (station, time, N_qa_codes) int64 ...\n",
- "Attributes:\n",
- " title: Surface ozone data in the EBAS network in 2018...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.3.3\n",
- " history: Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dm...\n",
- " NCO: 4.7.2\n",
- " nco_openmp_thread_number: 1\n",
- " Conventions: CF-1.7 Dimensions: time : 720station : 168N_flag_codes : 186N_qa_codes : 77
Coordinates: (2)
Data variables: (179)
latitude
(station)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([-64.24006 , -54.848465, 47.766666, 46.677778, 48.721111, 47.529167,\n",
- " 47.05407 , 47.348056, 47.973056, 48.878611, 48.106111, 48.371111,\n",
- " 48.334722, 48.050833, 47.838611, 47.040277, 47.066944, 49.877778,\n",
- " 50.629421, 50.503333, 41.695833, 32.27 , 80.050003, 46.5475 ,\n",
- " 46.813056, 47.479722, 47.049722, 47.0675 , 47.189614, -30.17254 ,\n",
- " 16.86403 , 35.0381 , 49.735084, 49.573394, 49.066667, 54.925556,\n",
- " 52.802222, 47.914722, 53.166667, 50.65 , 54.4368 , 47.801498,\n",
- " 47.4165 , -70.666 , 54.746495, 81.6 , 55.693588, 72.580002,\n",
- " 56.290424, 59.5 , 58.383333, 39.54694 , 42.72056 , 39.87528 ,\n",
- " 37.23722 , 43.43917 , 41.27417 , 42.31917 , 38.47278 , 39.08278 ,\n",
- " 41.23889 , 41.39389 , 42.63472 , 37.05194 , 28.309 , 59.779167,\n",
- " 60.53002 , 66.320278, 67.973333, 48.5 , 49.9 , 47.266667,\n",
- " 43.616667, 47.3 , 46.65 , 45. , 45.8 , 48.633333,\n",
- " 42.936667, 44.569444, 46.814728, 45.772223, 55.313056, 54.443056,\n",
- " 50.596389, 54.334444, 57.734444, 52.503889, 55.858611, 53.398889,\n",
- " 50.792778, 52.293889, 51.781784, 52.298333, 55.79216 , 52.950556,\n",
- " 51.778056, 60.13922 , 51.149617, 38.366667, 35.316667, 46.966667,\n",
- " 46.91 , -0.20194 , 51.939722, 53.32583 , 45.8 , 44.183333,\n",
- " 37.571111, 42.805462, -69.005 , 39.0319 , 24.2883 , 24.466941,\n",
- " 36.538334, 33.293917, 55.376111, 56.161944, 57.135278, 36.0722 ,\n",
- " 52.083333, 53.333889, 51.541111, 52.3 , 51.974444, 58.38853 ,\n",
- " 65.833333, 62.783333, 78.90715 , 59. , 69.45 , 59.2 ,\n",
- " 60.372386, -72.0117 , 59.2 , -41.408192, -77.832001, -45.037998,\n",
- " 51.814408, 50.736444, 54.753894, 54.15 , 43.4 , 71.586166,\n",
- " 63.85 , 67.883333, 57.394 , 57.1645 , 57.9525 , 56.0429 ,\n",
- " 60.0858 , 57.816667, 64.25 , 59.728 , 45.566667, 46.428611,\n",
- " 46.299444, 48.933333, 49.15 , 49.05 , 47.96 , 71.323013,\n",
- " 40.12498 , 19.53623 , -89.996948, 41.0541 , 21.5731 , -34.35348 ]) longitude
(station)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([-5.662478e+01, -6.831069e+01, 1.676667e+01, 1.297222e+01,\n",
- " 1.594222e+01, 9.926667e+00, 1.295794e+01, 1.588222e+01,\n",
- " 1.301611e+01, 1.504667e+01, 1.591944e+01, 1.554667e+01,\n",
- " 1.673056e+01, 1.667667e+01, 1.444139e+01, 1.433000e+01,\n",
- " 1.549361e+01, 5.203611e+00, 6.001019e+00, 4.989444e+00,\n",
- " 2.473861e+01, -6.488000e+01, -8.641666e+01, 7.985000e+00,\n",
- " 6.944722e+00, 8.904722e+00, 6.979444e+00, 8.463889e+00,\n",
- " 8.175434e+00, -7.079923e+01, -2.486752e+01, 3.305780e+01,\n",
- " 1.603420e+01, 1.508028e+01, 1.360000e+01, 8.309722e+00,\n",
- " 1.075944e+01, 7.908611e+00, 1.303333e+01, 1.076667e+01,\n",
- " 1.272490e+01, 1.100962e+01, 1.097964e+01, -8.266000e+00,\n",
- " 1.073616e+01, -1.667000e+01, 1.208580e+01, -3.848000e+01,\n",
- " 8.427486e+00, 2.590000e+01, 2.181667e+01, -4.350560e+00,\n",
- " -8.923610e+00, 4.316390e+00, -3.534170e+00, -4.850000e+00,\n",
- " -3.142500e+00, 3.315830e+00, -6.923610e+00, -1.101110e+00,\n",
- " -5.897500e+00, 7.347200e-01, -7.704720e+00, -6.555280e+00,\n",
- " -1.649940e+01, 2.137722e+01, 2.766754e+01, 2.940167e+01,\n",
- " 2.411611e+01, 7.133333e+00, 4.633333e+00, 4.083333e+00,\n",
- " 1.833330e-01, 6.833333e+00, -7.500000e-01, 6.466667e+00,\n",
- " 2.066667e+00, -4.500000e-01, 1.419440e-01, 5.278972e+00,\n",
- " 2.610008e+00, 2.964886e+00, -3.204167e+00, -7.870000e+00,\n",
- " -3.713056e+00, -8.075000e-01, -4.774444e+00, -3.033056e+00,\n",
- " -3.205000e+00, -1.753333e+00, 1.794440e-01, 1.463056e+00,\n",
- " -4.691462e+00, 2.927780e-01, -3.242900e+00, 1.121944e+00,\n",
- " 1.082230e+00, -1.185319e+00, -1.438228e+00, 2.308333e+01,\n",
- " 2.566667e+01, 1.958333e+01, 1.632000e+01, 1.003181e+02,\n",
- " -1.024444e+01, -9.899440e+00, 8.633333e+00, 1.070000e+01,\n",
- " 1.265972e+01, 1.256564e+01, 3.959056e+01, 1.418222e+02,\n",
- " 1.539833e+02, 1.230109e+02, 1.263300e+02, 1.261631e+02,\n",
- " 2.103056e+01, 2.117306e+01, 2.590556e+01, 1.421840e+01,\n",
- " 6.566667e+00, 6.277222e+00, 5.853611e+00, 4.500000e+00,\n",
- " 4.923611e+00, 8.252000e+00, 1.391667e+01, 8.883333e+00,\n",
- " 1.188668e+01, 1.153333e+01, 3.003333e+01, 5.200000e+00,\n",
- " 1.107814e+01, 2.535100e+00, 9.516667e+00, 1.748708e+02,\n",
- " 1.666600e+02, 1.696840e+02, 2.197242e+01, 1.573950e+01,\n",
- " 1.753426e+01, 2.206667e+01, 2.195000e+01, 1.289188e+02,\n",
- " 1.533333e+01, 2.106667e+01, 1.191400e+01, 1.478250e+01,\n",
- " 1.240300e+01, 1.314800e+01, 1.750528e+01, 1.556667e+01,\n",
- " 1.976667e+01, 1.547200e+01, 1.486667e+01, 1.500333e+01,\n",
- " 1.453861e+01, 1.958333e+01, 2.028333e+01, 2.226667e+01,\n",
- " 1.786056e+01, -1.566115e+02, -1.052368e+02, -1.555762e+02,\n",
- " -2.480000e+01, -1.241510e+02, 1.035157e+02, 1.848968e+01]) ASTER_v3_altitude
(station)
float32
...
standard_name : ASTER v3 altitude long_name : ASTER v3 altitude, relative to EGM96 geoid datum units : m description : Altitude from ASTER v3 digital elevation model, relative to EGM96 geoid vertical datum, in metres. The dataset was generated using 1,880,306 Level-1A scenes (taken from the NASA TERRA spacecraft) acquired between March 1, 2000 and November 30, 2013. The ASTER GDEM was created by stacking all individual cloud-masked scene DEMs and non-cloud-masked scene DEMs, then applying various algorithms to remove abnormal data. A statistical approach is not always effective for anomaly removal in areas with a limited number of images. Several existing reference DEMs were used to replace residual anomalies caused by the insufficient number of stacked scenes. In addition to ASTER GDEM, the ASTER Global Water Body Database (ASTWBD) was generated as a by-product to correct elevation values of water body surfaces like sea, rivers, and lakes. The ASTWBD was applied to GDEM to provide proper elevation values for water body surfaces. The sea and lake have a flattened elevation value. The river has a stepped-down elevation value from the upper stream to the lower stream. Native resolution of 1 arc second ~= 30m at the equator. array([ 201., 17., 124., 1003., 234., 1021., 3075., 1158., 466., 548.,\n",
- " 574., 337., 156., 208., 881., 1816., 485., 420., 277., 159.,\n",
- " 1733., 39., 555., 3526., 494., 536., 1126., 1024., 796., 2121.,\n",
- " 0., 519., 726., 532., 1130., 13., 86., 1197., 97., 869.,\n",
- " 6., 981., 2648., nan, 11., 0., 13., 3286., 43., 52.,\n",
- " 5., 917., 684., 60., 1260., 122., 1369., 76., 374., 842.,\n",
- " 971., 483., 554., 23., 2362., 4., 9., 282., 561., 748.,\n",
- " 393., 601., 223., 776., 139., 1760., 770., 225., 2828., 602.,\n",
- " 171., 1447., 191., 122., 83., 263., 265., 358., 177., 347.,\n",
- " 122., 48., 163., 5., 260., 12., 5., 79., 76., 199.,\n",
- " 392., 118., 291., 845., 6., 11., 224., 1775., 4., 1049.,\n",
- " 10., 264., 9., 27., 39., 63., 12., 18., 190., 154.,\n",
- " 13., 13., 19., 8., 14., 202., 458., 206., 406., 136.,\n",
- " 15., 12., 285., 1275., 81., 56., 178., 361., 171., 1588.,\n",
- " 6., 195., 780., 9., 338., 397., 7., 170., 51., 181.,\n",
- " 36., 221., 234., 107., 514., 766., 1757., 1617., 819., 484.,\n",
- " 100., 9., 1691., 3401., nan, 100., 1387., 174.], dtype=float32) EDGAR_v4.3.2_annual_average_BC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average BC emissions long_name : EDGAR v4.3.2 annual average black carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average BC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 3.042509e-14, 2.185555e-15,\n",
- " 0.000000e+00, 1.992454e-13, 0.000000e+00, 4.209633e-13, 1.679097e-15,\n",
- " 4.852100e-13, 1.969001e-15, 1.340942e-13, 6.248710e-14, 2.100758e-13,\n",
- " 9.962270e-14, 0.000000e+00, 1.147561e-13, 2.141060e-13, 2.465255e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.657339e-13, 1.374218e-13,\n",
- " 4.016085e-13, 7.663287e-14, 9.962270e-14, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.513695e-13,\n",
- " 4.548921e-13, 2.977381e-16, 3.040299e-13, 2.874330e-14, 1.893871e-13,\n",
- " 1.742075e-12, 1.633923e-13, 5.822990e-17, 0.000000e+00, 4.616434e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.759273e-14, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.108332e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 2.516593e-18, 9.132636e-16, 7.187694e-14, 0.000000e+00, 5.344048e-16,\n",
- " 1.361373e-16, 1.220174e-17, 1.876187e-13, 8.860958e-17, 0.000000e+00,\n",
- " 1.844417e-13, 0.000000e+00, 0.000000e+00, 2.568060e-13, 0.000000e+00,\n",
- " 0.000000e+00, 3.067904e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.580146e-15,\n",
- " 7.427175e-14, 0.000000e+00, 2.651062e-16, 0.000000e+00, 0.000000e+00,\n",
- " 1.297408e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.663155e-13,\n",
- " 0.000000e+00, 1.152715e-16, 1.529763e-14, 6.158420e-13, 2.570045e-16,\n",
- " 1.580146e-15, 9.744980e-14, 0.000000e+00, 1.251323e-13, 3.560953e-14,\n",
- " 0.000000e+00, 1.210138e-13, 0.000000e+00, 4.598798e-14, 1.106295e-13,\n",
- " 5.161284e-16, 3.596131e-13, 0.000000e+00, 1.800758e-15, 7.103424e-14,\n",
- " 0.000000e+00, 3.291449e-13, 0.000000e+00, 1.249780e-15, 0.000000e+00,\n",
- " 0.000000e+00, 2.685832e-15, 0.000000e+00, 1.745137e-15, 4.048674e-13,\n",
- " 6.465924e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 9.721247e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.125880e-14,\n",
- " 0.000000e+00, 4.190168e-13, 2.589192e-13, 0.000000e+00, 0.000000e+00,\n",
- " 8.493474e-21, 0.000000e+00, 2.828534e-15, 1.791777e-15, 0.000000e+00,\n",
- " 2.138894e-15, 0.000000e+00, 2.088533e-18, 0.000000e+00, 2.013163e-13,\n",
- " 0.000000e+00, 4.152850e-13, 1.590138e-13, 0.000000e+00, 4.941700e-14,\n",
- " 0.000000e+00, 4.534964e-17, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 2.872648e-13, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_CO_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average CO emissions long_name : EDGAR v4.3.2 annual average carbon monoxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average CO emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 8.225822e-14, 6.012667e-14,\n",
- " 0.000000e+00, 5.386828e-13, 0.000000e+00, 1.138131e-12, 4.619353e-14,\n",
- " 1.311828e-12, 5.416911e-14, 3.759797e-13, 1.689421e-13, 5.679673e-13,\n",
- " 2.693421e-13, 0.000000e+00, 3.448982e-13, 5.788603e-13, 6.665133e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.184455e-13, 3.715371e-13,\n",
- " 1.088436e-12, 2.071862e-13, 2.693421e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.092466e-13,\n",
- " 1.237345e-12, 8.191075e-15, 8.219818e-13, 1.428169e-13, 5.120298e-13,\n",
- " 4.709930e-12, 4.417532e-13, 1.601967e-15, 0.000000e+00, 1.255242e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.016369e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 5.700131e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 2.674954e-16, 4.568149e-14, 2.298078e-11, 0.000000e+00, 1.470202e-14,\n",
- " 3.745274e-15, 3.356823e-16, 5.072486e-13, 2.437739e-15, 0.000000e+00,\n",
- " 4.986610e-13, 0.000000e+00, 0.000000e+00, 6.943062e-13, 0.000000e+00,\n",
- " 0.000000e+00, 8.294464e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.347128e-14,\n",
- " 2.008032e-13, 0.000000e+00, 7.293348e-15, 0.000000e+00, 0.000000e+00,\n",
- " 3.569300e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.200188e-13,\n",
- " 0.000000e+00, 3.171229e-15, 4.135889e-14, 2.969954e-10, 7.070461e-15,\n",
- " 4.347128e-14, 2.634675e-13, 0.000000e+00, 3.383109e-13, 9.627513e-14,\n",
- " 0.000000e+00, 3.271759e-13, 0.000000e+00, 1.267826e-13, 3.026542e-13,\n",
- " 1.419923e-14, 5.129310e-11, 0.000000e+00, 9.007420e-14, 1.920503e-13,\n",
- " 0.000000e+00, 4.694731e-11, 0.000000e+00, 3.438267e-14, 0.000000e+00,\n",
- " 0.000000e+00, 7.739928e-14, 0.000000e+00, 4.801032e-14, 5.774795e-11,\n",
- " 2.868396e-11, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.674411e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.115487e-13,\n",
- " 0.000000e+00, 1.132864e-12, 7.000194e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 9.668300e-14, 8.962475e-14, 0.000000e+00,\n",
- " 9.488507e-12, 0.000000e+00, 2.219958e-16, 0.000000e+00, 5.442847e-13,\n",
- " 0.000000e+00, 1.122772e-12, 4.299122e-13, 0.000000e+00, 1.737866e-13,\n",
- " 0.000000e+00, 5.721683e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 8.000223e-13, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_NH3_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NH3 emissions long_name : EDGAR v4.3.2 annual average ammonia emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NH3 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.375585e-16, 0.000000e+00,\n",
- " 0.000000e+00, 9.008314e-16, 0.000000e+00, 1.903272e-15, 0.000000e+00,\n",
- " 2.193740e-15, 0.000000e+00, 6.038217e-16, 2.825187e-16, 9.497996e-16,\n",
- " 4.504157e-16, 0.000000e+00, 5.125246e-16, 9.680197e-16, 1.114598e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.201439e-15, 6.213159e-16,\n",
- " 1.815286e-15, 3.464735e-16, 4.504157e-16, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.843763e-16,\n",
- " 2.055305e-15, 0.000000e+00, 1.374585e-15, 1.180892e-16, 8.562589e-16,\n",
- " 7.876276e-15, 7.387314e-16, 0.000000e+00, 0.000000e+00, 2.085885e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.699650e-16, 6.597628e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 9.532244e-16, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.711655e-14, 1.205071e-13, 1.943890e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 8.482645e-16, 0.000000e+00, 0.000000e+00,\n",
- " 8.339012e-16, 0.000000e+00, 0.000000e+00, 1.161074e-15, 0.000000e+00,\n",
- " 0.000000e+00, 1.387067e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 3.357990e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.204071e-15,\n",
- " 0.000000e+00, 0.000000e+00, 6.916365e-17, 3.522678e-11, 0.000000e+00,\n",
- " 0.000000e+00, 4.405916e-16, 0.000000e+00, 5.657517e-16, 1.609990e-16,\n",
- " 0.000000e+00, 5.471314e-16, 0.000000e+00, 2.074751e-16, 4.995342e-16,\n",
- " 0.000000e+00, 1.329593e-15, 6.251216e-14, 3.440699e-13, 3.211615e-16,\n",
- " 5.829590e-15, 1.216946e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.923823e-14, 0.000000e+00, 0.000000e+00, 1.496914e-15,\n",
- " 2.301217e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.865407e-16,\n",
- " 0.000000e+00, 1.894465e-15, 1.170628e-15, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 6.601672e-13, 0.000000e+00, 1.009839e-13, 0.000000e+00, 9.101958e-16,\n",
- " 0.000000e+00, 1.877591e-15, 7.189337e-16, 0.000000e+00, 2.161016e-16,\n",
- " 0.000000e+00, 8.178586e-17, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.294533e-15, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_NMVOC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NMVOC emissions long_name : EDGAR v4.3.2 annual average non-methane volatile organic compound emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NMVOC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 5.175291e-13, 2.733042e-14,\n",
- " 0.000000e+00, 3.389148e-12, 0.000000e+00, 7.160564e-12, 2.099715e-14,\n",
- " 8.253374e-12, 2.462239e-14, 2.278494e-12, 1.062905e-12, 3.573377e-12,\n",
- " 1.694574e-12, 0.000000e+00, 1.945705e-12, 3.641927e-12, 4.193397e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.520102e-12, 2.337537e-12,\n",
- " 6.830868e-12, 1.303518e-12, 1.694574e-12, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.574783e-12,\n",
- " 7.736342e-12, 3.723225e-15, 5.171510e-12, 4.770983e-13, 3.221458e-12,\n",
- " 2.963254e-11, 2.779294e-12, 7.281668e-16, 0.000000e+00, 7.851215e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.394503e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.586252e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.170196e-16, 2.284069e-15, 2.754316e-12, 0.000000e+00, 6.682769e-15,\n",
- " 1.702404e-15, 1.525833e-16, 3.191379e-12, 1.108067e-15, 0.000000e+00,\n",
- " 3.137339e-12, 0.000000e+00, 0.000000e+00, 4.368254e-12, 0.000000e+00,\n",
- " 0.000000e+00, 5.218467e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.975979e-14,\n",
- " 1.263358e-12, 0.000000e+00, 3.315160e-15, 0.000000e+00, 0.000000e+00,\n",
- " 1.622416e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.530011e-12,\n",
- " 0.000000e+00, 1.441471e-15, 2.602110e-13, 2.803539e-11, 3.213846e-15,\n",
- " 1.975979e-14, 1.657613e-12, 0.000000e+00, 2.128489e-12, 6.057175e-13,\n",
- " 0.000000e+00, 2.058437e-12, 0.000000e+00, 7.818060e-13, 1.881156e-12,\n",
- " 6.454220e-15, 9.438283e-12, 0.000000e+00, 4.503709e-15, 1.208288e-12,\n",
- " 0.000000e+00, 8.638583e-12, 0.000000e+00, 1.562851e-14, 0.000000e+00,\n",
- " 0.000000e+00, 2.816417e-14, 0.000000e+00, 2.182292e-14, 1.062598e-11,\n",
- " 2.605788e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.215648e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.018115e-13,\n",
- " 0.000000e+00, 7.127464e-12, 4.404200e-12, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 2.698554e-14, 4.481238e-15, 0.000000e+00,\n",
- " 8.619777e-13, 0.000000e+00, 9.711523e-17, 0.000000e+00, 3.424377e-12,\n",
- " 0.000000e+00, 7.063961e-12, 2.704809e-12, 0.000000e+00, 8.332826e-13,\n",
- " 0.000000e+00, 2.688864e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 4.882130e-12, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_NOx_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NOx emissions long_name : EDGAR v4.3.2 annual average emissions of nitrogen oxides units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NOx emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.522043e-12, 6.996554e-13,\n",
- " 0.000000e+00, 9.967411e-12, 0.000000e+00, 2.105912e-11, 5.375259e-13,\n",
- " 2.427307e-11, 6.303303e-13, 6.854504e-12, 3.125981e-12, 1.050924e-11,\n",
- " 4.983706e-12, 0.000000e+00, 6.117938e-12, 1.071083e-11, 1.233268e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.329357e-11, 6.874644e-12,\n",
- " 2.011957e-11, 3.833614e-12, 4.983706e-12, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.572397e-12,\n",
- " 2.283791e-11, 9.531410e-14, 1.520934e-11, 2.146782e-12, 9.474236e-12,\n",
- " 8.714885e-11, 8.173846e-12, 1.864103e-14, 0.000000e+00, 2.317172e-11,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.880610e-12, 2.437077e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.054711e-11, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.763506e-15, 2.389657e-13, 1.149823e-11, 0.000000e+00, 1.710780e-13,\n",
- " 4.358154e-14, 3.906132e-15, 9.385776e-12, 2.836642e-14, 0.000000e+00,\n",
- " 9.226877e-12, 0.000000e+00, 0.000000e+00, 1.284696e-11, 0.000000e+00,\n",
- " 0.000000e+00, 1.534748e-11, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 5.058475e-13,\n",
- " 3.715512e-12, 0.000000e+00, 8.486790e-14, 0.000000e+00, 0.000000e+00,\n",
- " 4.153366e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.332267e-11,\n",
- " 0.000000e+00, 3.690153e-14, 7.652783e-13, 2.254739e-11, 8.227430e-14,\n",
- " 5.058475e-13, 4.875005e-12, 0.000000e+00, 6.259855e-12, 1.781405e-12,\n",
- " 0.000000e+00, 6.053818e-12, 0.000000e+00, 2.327244e-12, 5.573037e-12,\n",
- " 1.652272e-13, 7.673363e-12, 2.381948e-15, 4.826952e-13, 3.553562e-12,\n",
- " 4.351761e-16, 7.023245e-12, 0.000000e+00, 4.000890e-13, 0.000000e+00,\n",
- " 0.000000e+00, 7.422984e-13, 0.000000e+00, 5.586654e-13, 8.639011e-12,\n",
- " 2.090962e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.112041e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.064015e-12,\n",
- " 0.000000e+00, 2.096167e-11, 1.295267e-11, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 8.530831e-13, 4.615680e-13, 0.000000e+00,\n",
- " 7.962478e-13, 0.000000e+00, 4.127878e-15, 0.000000e+00, 1.007104e-11,\n",
- " 0.000000e+00, 2.077494e-11, 7.954819e-12, 0.000000e+00, 2.909625e-12,\n",
- " 0.000000e+00, 3.651467e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.462513e-11, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_OC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average OC emissions long_name : EDGAR v4.3.2 annual average organic carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average OC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.521148e-14, 1.075611e-15,\n",
- " 0.000000e+00, 9.961557e-14, 0.000000e+00, 2.104675e-13, 8.263584e-16,\n",
- " 2.425881e-13, 9.690349e-16, 6.703846e-14, 3.124149e-14, 1.050307e-13,\n",
- " 4.980778e-14, 0.000000e+00, 5.736338e-14, 1.070454e-13, 1.232543e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.328576e-13, 6.870605e-14,\n",
- " 2.007902e-13, 3.831362e-14, 4.980778e-14, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.567948e-14,\n",
- " 2.274283e-13, 1.465305e-16, 1.520045e-13, 1.435014e-14, 9.468710e-14,\n",
- " 8.709783e-13, 8.169043e-14, 2.865768e-17, 0.000000e+00, 2.308030e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.879505e-14, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.054094e-13, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 9.163830e-18, 4.590217e-16, 4.857565e-14, 0.000000e+00, 2.630054e-16,\n",
- " 6.699941e-17, 6.005034e-18, 9.380263e-14, 4.360905e-17, 0.000000e+00,\n",
- " 9.221455e-14, 0.000000e+00, 0.000000e+00, 1.283941e-13, 0.000000e+00,\n",
- " 0.000000e+00, 1.533846e-13, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.776644e-16,\n",
- " 3.713333e-14, 0.000000e+00, 1.304709e-16, 0.000000e+00, 0.000000e+00,\n",
- " 6.385160e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.331484e-13,\n",
- " 0.000000e+00, 5.673017e-17, 7.648287e-15, 5.473372e-12, 1.264837e-16,\n",
- " 7.776644e-16, 4.872142e-14, 0.000000e+00, 6.256178e-14, 1.780361e-14,\n",
- " 0.000000e+00, 6.050261e-14, 0.000000e+00, 2.299157e-14, 5.530981e-14,\n",
- " 2.540110e-16, 1.798061e-13, 0.000000e+00, 9.050940e-16, 3.551475e-14,\n",
- " 0.000000e+00, 1.645722e-13, 0.000000e+00, 6.150755e-16, 0.000000e+00,\n",
- " 0.000000e+00, 1.158464e-15, 0.000000e+00, 8.588610e-16, 2.024330e-13,\n",
- " 6.811853e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.784277e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.062807e-14,\n",
- " 0.000000e+00, 2.094940e-13, 1.294506e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 1.400828e-15, 9.005805e-16, 0.000000e+00,\n",
- " 2.253324e-15, 0.000000e+00, 7.605125e-18, 0.000000e+00, 1.006512e-13,\n",
- " 0.000000e+00, 2.076277e-13, 7.950146e-14, 0.000000e+00, 2.469410e-14,\n",
- " 0.000000e+00, 1.935444e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.436156e-13, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_PM10_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average PM10 emissions long_name : EDGAR v4.3.2 annual average PM10 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average PM10 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.521823e-13, 1.093214e-14,\n",
- " 0.000000e+00, 9.965986e-13, 0.000000e+00, 2.105607e-12, 8.398846e-15,\n",
- " 2.426955e-12, 9.848946e-15, 6.707232e-13, 3.125530e-13, 1.050772e-12,\n",
- " 4.982993e-13, 0.000000e+00, 5.739947e-13, 1.070930e-12, 1.233090e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.329164e-12, 6.873672e-13,\n",
- " 2.008799e-12, 3.833089e-13, 4.982993e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.571304e-13,\n",
- " 2.275316e-12, 1.489287e-15, 1.520717e-12, 1.437705e-13, 9.472905e-13,\n",
- " 8.713610e-12, 8.172664e-13, 2.912671e-16, 0.000000e+00, 2.309072e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.880339e-13, 6.782045e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.054561e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.076963e-16, 1.506612e-14, 1.758185e-13, 0.000000e+00, 2.673100e-15,\n",
- " 6.809607e-16, 6.103339e-17, 9.384433e-13, 4.432253e-16, 0.000000e+00,\n",
- " 9.225539e-13, 0.000000e+00, 0.000000e+00, 1.284510e-12, 0.000000e+00,\n",
- " 0.000000e+00, 1.534524e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.903901e-15,\n",
- " 3.714978e-13, 0.000000e+00, 1.326064e-15, 0.000000e+00, 0.000000e+00,\n",
- " 6.489629e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.332075e-12,\n",
- " 0.000000e+00, 5.765861e-16, 7.651659e-14, 2.043820e-11, 1.285535e-15,\n",
- " 7.903901e-15, 4.874308e-13, 0.000000e+00, 6.258952e-13, 1.781150e-13,\n",
- " 0.000000e+00, 6.052961e-13, 0.000000e+00, 2.300253e-13, 5.533560e-13,\n",
- " 2.581682e-15, 1.798102e-12, 6.411004e-15, 3.661463e-14, 3.553056e-13,\n",
- " 4.276441e-16, 1.645756e-12, 0.000000e+00, 6.251414e-15, 0.000000e+00,\n",
- " 0.000000e+00, 1.972915e-14, 0.000000e+00, 8.729169e-15, 2.024374e-12,\n",
- " 2.697392e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.862578e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.063720e-13,\n",
- " 0.000000e+00, 2.095866e-12, 1.295080e-12, 0.000000e+00, 0.000000e+00,\n",
- " 1.132456e-17, 0.000000e+00, 1.414835e-14, 8.962475e-15, 0.000000e+00,\n",
- " 1.374389e-14, 0.000000e+00, 1.552330e-14, 0.000000e+00, 1.006958e-12,\n",
- " 0.000000e+00, 2.077194e-12, 7.953650e-13, 0.000000e+00, 2.471775e-13,\n",
- " 0.000000e+00, 7.172658e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.436866e-12, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_SO2_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average SO2 emissions long_name : EDGAR v4.3.2 annual average sulphur dioxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average SO2 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 9.231252e-13, 5.466069e-14,\n",
- " 0.000000e+00, 6.045283e-12, 0.000000e+00, 1.277240e-11, 4.199423e-14,\n",
- " 1.472168e-11, 4.924454e-14, 4.065654e-12, 1.895917e-12, 6.373896e-12,\n",
- " 3.022638e-12, 0.000000e+00, 3.474358e-12, 6.496169e-12, 7.479813e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 8.062605e-12, 4.169506e-12,\n",
- " 1.218464e-11, 2.325105e-12, 3.022638e-12, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.592688e-12,\n",
- " 1.380024e-11, 7.446459e-15, 9.224540e-12, 8.581082e-13, 5.746173e-12,\n",
- " 5.285586e-11, 4.957475e-12, 1.456334e-15, 0.000000e+00, 1.400510e-11,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.140598e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.396873e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.796339e-17, 2.284069e-14, 1.736909e-13, 0.000000e+00, 1.336550e-14,\n",
- " 3.404800e-15, 3.051662e-16, 5.692519e-12, 2.216131e-15, 0.000000e+00,\n",
- " 5.596131e-12, 0.000000e+00, 0.000000e+00, 7.791722e-12, 0.000000e+00,\n",
- " 0.000000e+00, 9.308299e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.951955e-14,\n",
- " 2.253470e-12, 0.000000e+00, 6.630320e-15, 0.000000e+00, 0.000000e+00,\n",
- " 3.244823e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 8.080256e-12,\n",
- " 0.000000e+00, 2.882938e-15, 4.641434e-13, 2.088297e-12, 6.427676e-15,\n",
- " 3.951955e-14, 2.956710e-12, 0.000000e+00, 3.796626e-12, 1.080426e-12,\n",
- " 0.000000e+00, 3.671671e-12, 0.000000e+00, 1.394787e-12, 3.355824e-12,\n",
- " 1.290841e-14, 5.519056e-12, 0.000000e+00, 4.503710e-14, 2.155245e-12,\n",
- " 0.000000e+00, 5.051448e-12, 0.000000e+00, 3.125702e-14, 0.000000e+00,\n",
- " 0.000000e+00, 9.761866e-14, 0.000000e+00, 4.364585e-14, 6.213544e-12,\n",
- " 2.622671e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.431287e-14,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.251830e-12,\n",
- " 0.000000e+00, 1.271331e-11, 7.855838e-12, 0.000000e+00, 0.000000e+00,\n",
- " 3.173154e-16, 0.000000e+00, 7.074175e-14, 4.481238e-14, 0.000000e+00,\n",
- " 8.675671e-16, 0.000000e+00, 1.490791e-17, 0.000000e+00, 6.108124e-12,\n",
- " 0.000000e+00, 1.260006e-11, 4.824619e-12, 0.000000e+00, 1.490720e-12,\n",
- " 0.000000e+00, 1.239232e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 8.710870e-12, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions long_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average biogenic PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 3.868548e-17, 0.000000e+00,\n",
- " 0.000000e+00, 2.533411e-16, 0.000000e+00, 5.352590e-16, 0.000000e+00,\n",
- " 6.169483e-16, 0.000000e+00, 1.698127e-16, 7.945291e-17, 2.671126e-16,\n",
- " 1.266708e-16, 0.000000e+00, 1.441375e-16, 2.722365e-16, 3.134586e-17,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.378819e-16, 1.747324e-16,\n",
- " 5.105129e-16, 9.743910e-17, 1.266708e-16, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.924670e-16,\n",
- " 5.780155e-16, 0.000000e+00, 3.865764e-16, 3.321025e-17, 2.408063e-16,\n",
- " 2.215057e-15, 2.077544e-16, 0.000000e+00, 0.000000e+00, 5.866167e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.779914e-17, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.680754e-16, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.904895e-17, 0.000000e+00, 3.074643e-15, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 2.385578e-16, 0.000000e+00, 0.000000e+00,\n",
- " 2.345185e-16, 0.000000e+00, 0.000000e+00, 3.265298e-16, 0.000000e+00,\n",
- " 0.000000e+00, 3.900847e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 9.443683e-17, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.386200e-16,\n",
- " 0.000000e+00, 0.000000e+00, 1.945097e-17, 1.752106e-11, 0.000000e+00,\n",
- " 0.000000e+00, 1.239079e-16, 0.000000e+00, 1.591063e-16, 4.527793e-17,\n",
- " 0.000000e+00, 1.538695e-16, 0.000000e+00, 5.834836e-17, 1.404839e-16,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 9.032056e-17,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 1.229276e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 5.246104e-17,\n",
- " 0.000000e+00, 5.327835e-16, 3.292167e-16, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 4.066370e-16, 0.000000e+00, 1.580880e-17, 0.000000e+00, 2.559748e-16,\n",
- " 0.000000e+00, 5.280338e-16, 2.021865e-16, 0.000000e+00, 6.077435e-17,\n",
- " 0.000000e+00, 1.893055e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 3.640635e-16, 0.000000e+00], dtype=float32) EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions long_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([0.000000e+00, 0.000000e+00, 0.000000e+00, 1.521014e-13, 1.093214e-14,\n",
- " 0.000000e+00, 9.960692e-13, 0.000000e+00, 2.104491e-12, 8.398846e-15,\n",
- " 2.425667e-12, 9.848946e-15, 6.703692e-13, 3.123873e-13, 1.050215e-12,\n",
- " 4.980365e-13, 0.000000e+00, 5.736932e-13, 1.070361e-12, 1.232434e-13,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.328461e-12, 6.870007e-13,\n",
- " 2.007732e-12, 3.831024e-13, 4.980365e-13, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.567284e-13,\n",
- " 2.274109e-12, 1.489287e-15, 1.519911e-12, 1.437014e-13, 9.467864e-13,\n",
- " 8.708992e-12, 8.168320e-13, 2.912671e-16, 0.000000e+00, 2.307849e-12,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.879340e-13, 1.164987e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.054001e-12, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 4.668681e-17, 6.346848e-15, 1.661733e-13, 0.000000e+00, 2.673100e-15,\n",
- " 6.809607e-16, 6.103339e-17, 9.379449e-13, 4.432253e-16, 0.000000e+00,\n",
- " 9.220624e-13, 0.000000e+00, 0.000000e+00, 1.283825e-12, 0.000000e+00,\n",
- " 0.000000e+00, 1.533710e-12, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 7.903901e-15,\n",
- " 3.713005e-13, 0.000000e+00, 1.326064e-15, 0.000000e+00, 0.000000e+00,\n",
- " 6.489629e-15, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.331367e-12,\n",
- " 0.000000e+00, 5.765861e-16, 7.647612e-14, 1.134093e-12, 1.285535e-15,\n",
- " 7.903901e-15, 4.871738e-13, 0.000000e+00, 6.255629e-13, 1.780204e-13,\n",
- " 0.000000e+00, 6.049734e-13, 0.000000e+00, 2.299037e-13, 5.530631e-13,\n",
- " 2.581682e-15, 1.798102e-12, 1.103664e-15, 1.395974e-14, 3.551158e-13,\n",
- " 8.140140e-17, 1.645756e-12, 0.000000e+00, 6.251414e-15, 0.000000e+00,\n",
- " 0.000000e+00, 1.354031e-14, 0.000000e+00, 8.729169e-15, 2.024374e-12,\n",
- " 1.767644e-14, 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.862578e-15,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 2.062625e-13,\n",
- " 0.000000e+00, 2.094752e-12, 1.294390e-12, 0.000000e+00, 0.000000e+00,\n",
- " 8.493472e-18, 0.000000e+00, 1.414835e-14, 8.962475e-15, 0.000000e+00,\n",
- " 9.510742e-15, 0.000000e+00, 2.666073e-15, 0.000000e+00, 1.006424e-12,\n",
- " 0.000000e+00, 2.076092e-12, 7.949445e-13, 0.000000e+00, 2.470507e-13,\n",
- " 0.000000e+00, 3.217921e-16, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.436104e-12, 0.000000e+00], dtype=float32) ESDAC_Iwahashi_landform_classification
(station)
object
...
standard_name : ESDAC Iwahashi landform classification long_name : ESDAC Iwahashi landform classification units : description : European Soil Data Centre (ESDAC) Iwahashi landform classification. The classification presents relief classes which are classified using an unsupervised nested-means algorithms and a three part geometric signature. Slope gradient, surface texture and local convexity are calculated based on the SRTM30 digital elevation model, within a given window size and classified according to the inherent data set properties. This is a dynamic landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['nan', 'gentle - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'steep - coarse texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity', 'nan',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - high convexity',\n",
- " 'steep - coarse texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'nan',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity', 'nan',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity', 'nan',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - high convexity', 'nan',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity'], dtype=object) ESDAC_Meybeck_landform_classification
(station)
object
...
standard_name : ESDAC Meybeck landform classification long_name : ESDAC Meybeck landform classification units : description : European Soil Data Centre (ESDAC) Meybeck landform classification. The classification presents relief classes which are calculated based on the relief roughness. Roughness and elevation are classified based on a digital elevation model according to static thresholds, with a given window size. This is a static landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['nan', 'lowlands', 'plains', 'mid altitude mountains',\n",
- " 'very low plateaus', 'mid altitude mountains',\n",
- " 'high altitude mountains', 'mid altitude mountains',\n",
- " 'very low plateaus', 'high altitude plains', 'low plateaus',\n",
- " 'very low plateaus', 'plains', 'mid altitude plains',\n",
- " 'low altitude mountains', 'mid altitude mountains', 'very low plateaus',\n",
- " 'mid altitude plains', 'very low plateaus', 'lowlands',\n",
- " 'mid altitude mountains', 'lowlands', 'nan', 'high altitude mountains',\n",
- " 'very low plateaus', 'low plateaus', 'mid altitude mountains',\n",
- " 'low altitude mountains', 'low plateaus', 'mid altitude mountains',\n",
- " 'water', 'very low plateaus', 'low plateaus', 'low plateaus',\n",
- " 'mid altitude plateaus', 'plains', 'plains', 'mid altitude mountains',\n",
- " 'lowlands', 'low plateaus', 'plains', 'low altitude mountains',\n",
- " 'high altitude mountains', 'nan', 'plains', 'nan', 'water', 'nan',\n",
- " 'plains', 'plains', 'plains', 'low altitude mountains', 'low plateaus',\n",
- " 'water', 'mid altitude mountains', 'rugged lowlands',\n",
- " 'mid altitude plateaus', 'water', 'very low plateaus', 'low plateaus',\n",
- " 'low plateaus', 'low altitude mountains', 'low plateaus', 'plains',\n",
- " 'high altitude mountains', 'water', 'water', 'nan', 'nan',\n",
- " 'low plateaus', 'very low plateaus', 'low plateaus',\n",
- " 'very low plateaus', 'low plateaus', 'lowlands',\n",
- " 'mid altitude mountains', 'low plateaus', 'very low plateaus',\n",
- " 'high altitude mountains', 'low altitude mountains', 'plains',\n",
- " 'mid altitude mountains', 'very low plateaus', 'lowlands',\n",
- " 'rugged lowlands', 'very low plateaus', 'very low plateaus', 'hills',\n",
- " 'plains', 'hills', 'lowlands', 'plains', 'lowlands', 'plains',\n",
- " 'mid altitude plains', 'water', 'plains', 'plains', 'lowlands',\n",
- " 'lowlands', 'hills', 'plains', 'very low plateaus',\n",
- " 'low altitude mountains', 'lowlands', 'plains', 'very low plateaus',\n",
- " 'mid altitude mountains', 'plains', 'low altitude mountains', 'nan',\n",
- " 'rugged lowlands', 'water', 'water', 'lowlands', 'water', 'plains',\n",
- " 'plains', 'plains', 'lowlands', 'plains', 'plains', 'plains', 'plains',\n",
- " 'plains', 'rugged lowlands', 'nan', 'nan', 'nan', 'lowlands', 'nan',\n",
- " 'lowlands', 'very low plateaus', 'nan', 'rugged lowlands', 'lowlands',\n",
- " 'nan', 'mid altitude plains', 'plains', 'mid altitude mountains',\n",
- " 'plains', 'plains', 'low altitude mountains', 'nan', 'nan', 'nan',\n",
- " 'water', 'plains', 'lowlands', 'lowlands', 'plains', 'lowlands', 'nan',\n",
- " 'lowlands', 'low plateaus', 'low altitude mountains',\n",
- " 'mid altitude mountains', 'mid altitude mountains', 'low plateaus',\n",
- " 'hills', 'plains', 'nan', 'mid altitude plateaus',\n",
- " 'high altitude plateaus', 'nan', 'water', 'mid altitude mountains',\n",
- " 'rugged lowlands'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 25km long_name : ESDAC modal Iwahashi landform classification in 25km radius units : description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 25km around station location. array(['nan', 'steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'nan', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - coarse texture - high convexity', 'water', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water', 'water', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water', 'water',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'nan', 'water', 'water',\n",
- " 'water', 'water', 'water', 'water', 'water',\n",
- " 'medium gentle - fine texture - high convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'nan',\n",
- " 'steep - fine texture - high convexity', 'water', 'nan',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - high convexity', 'nan', 'water',\n",
- " 'steep - fine texture - high convexity', 'water'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 5km long_name : ESDAC modal Iwahashi landform classification in 5km radius units : description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 5km around station location. array(['nan', 'water', 'gentle - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - coarse texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - coarse texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'nan', 'water',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'gentle - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - high convexity',\n",
- " 'steep - coarse texture - high convexity', 'water',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity', 'water', 'water',\n",
- " 'water', 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity', 'water',\n",
- " 'steep - fine texture - high convexity', 'nan', 'water', 'water',\n",
- " 'water', 'water', 'water', 'water',\n",
- " 'gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'gentle - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - low convexity', 'water',\n",
- " 'steep - coarse texture - low convexity', 'nan',\n",
- " 'steep - fine texture - low convexity', 'water', 'nan',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'steep - fine texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'steep - coarse texture - low convexity',\n",
- " 'steep - fine texture - high convexity',\n",
- " 'gentle - coarse texture - low convexity', 'water',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'steep - coarse texture - high convexity', 'nan', 'water',\n",
- " 'steep - fine texture - high convexity', 'water'], dtype=object) ESDAC_modal_Meybeck_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 25km long_name : ESDAC modal Meybeck landform classification in 25km radius units : description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 25km around station location. array(['nan', 'low altitude mountains', 'plains', 'mid altitude mountains',\n",
- " 'very low plateaus', 'low plateaus', 'mid altitude mountains',\n",
- " 'low altitude mountains', 'low plateaus', 'low plateaus',\n",
- " 'very low plateaus', 'very low plateaus', 'plains', 'plains',\n",
- " 'low altitude mountains', 'mid altitude mountains', 'very low plateaus',\n",
- " 'very low plateaus', 'very low plateaus', 'plains',\n",
- " 'mid altitude mountains', 'water', 'nan', 'high altitude mountains',\n",
- " 'low plateaus', 'low plateaus', 'low altitude mountains',\n",
- " 'low altitude mountains', 'low plateaus', 'mid altitude mountains',\n",
- " 'water', 'low altitude mountains', 'low plateaus', 'low plateaus',\n",
- " 'low plateaus', 'water', 'plains', 'low altitude mountains', 'plains',\n",
- " 'very low plateaus', 'water', 'low plateaus', 'mid altitude mountains',\n",
- " 'nan', 'water', 'nan', 'plains', 'nan', 'plains', 'plains', 'water',\n",
- " 'low plateaus', 'water', 'water', 'mid altitude mountains', 'water',\n",
- " 'mid altitude plateaus', 'water', 'very low plateaus', 'low plateaus',\n",
- " 'high altitude plains', 'very low plateaus', 'low plateaus', 'plains',\n",
- " 'water', 'water', 'water', 'nan', 'nan', 'low altitude mountains',\n",
- " 'very low plateaus', 'very low plateaus', 'lowlands',\n",
- " 'low altitude mountains', 'plains', 'high altitude mountains',\n",
- " 'low plateaus', 'very low plateaus', 'mid altitude mountains',\n",
- " 'low altitude mountains', 'plains', 'low plateaus', 'very low plateaus',\n",
- " 'lowlands', 'lowlands', 'lowlands', 'hills', 'very low plateaus',\n",
- " 'very low plateaus', 'very low plateaus', 'water', 'plains', 'lowlands',\n",
- " 'plains', 'very low plateaus', 'water', 'water', 'water', 'lowlands',\n",
- " 'hills', 'water', 'plains', 'very low plateaus',\n",
- " 'low altitude mountains', 'water', 'water', 'very low plateaus',\n",
- " 'low altitude mountains', 'water', 'very low plateaus', 'nan', 'water',\n",
- " 'water', 'water', 'water', 'water', 'water', 'plains', 'plains',\n",
- " 'water', 'plains', 'plains', 'plains', 'plains', 'plains',\n",
- " 'very low plateaus', 'nan', 'nan', 'nan', 'lowlands', 'nan', 'water',\n",
- " 'water', 'nan', 'lowlands', 'water', 'nan', 'low altitude mountains',\n",
- " 'plains', 'low altitude mountains', 'water', 'plains',\n",
- " 'low altitude mountains', 'nan', 'nan', 'nan', 'water',\n",
- " 'mid altitude plains', 'lowlands', 'plains', 'plains', 'lowlands',\n",
- " 'nan', 'plains', 'low altitude mountains', 'low altitude mountains',\n",
- " 'mid altitude mountains', 'low altitude mountains', 'low plateaus',\n",
- " 'low altitude mountains', 'plains', 'nan', 'high altitude plains',\n",
- " 'high altitude plateaus', 'nan', 'water', 'low altitude mountains',\n",
- " 'water'], dtype=object) ESDAC_modal_Meybeck_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 5km long_name : ESDAC modal Meybeck landform classification in 5km radius units : description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 5km around station location. array(['nan', 'water', 'plains', 'mid altitude mountains',\n",
- " 'mid altitude plains', 'low altitude mountains',\n",
- " 'high altitude mountains', 'low altitude mountains',\n",
- " 'very low plateaus', 'low plateaus', 'low altitude mountains', 'hills',\n",
- " 'plains', 'plains', 'low altitude mountains', 'mid altitude mountains',\n",
- " 'very low plateaus', 'very low plateaus', 'very low plateaus',\n",
- " 'lowlands', 'mid altitude mountains', 'water', 'nan',\n",
- " 'high altitude mountains', 'mid altitude plains',\n",
- " 'low altitude mountains', 'low altitude mountains',\n",
- " 'low altitude mountains', 'low plateaus', 'mid altitude mountains',\n",
- " 'water', 'very low plateaus', 'low plateaus', 'low plateaus',\n",
- " 'mid altitude plateaus', 'water', 'plains', 'low altitude mountains',\n",
- " 'lowlands', 'low altitude mountains', 'water', 'low plateaus',\n",
- " 'mid altitude mountains', 'nan', 'water', 'nan', 'plains', 'nan',\n",
- " 'plains', 'plains', 'water', 'low plateaus', 'hills', 'water',\n",
- " 'mid altitude mountains', 'water', 'mid altitude plateaus', 'water',\n",
- " 'very low plateaus', 'low plateaus', 'high altitude plains',\n",
- " 'very low plateaus', 'low plateaus', 'plains', 'mid altitude mountains',\n",
- " 'water', 'water', 'nan', 'nan', 'low altitude mountains',\n",
- " 'very low plateaus', 'low plateaus', 'very low plateaus',\n",
- " 'low altitude mountains', 'lowlands', 'high altitude mountains',\n",
- " 'low plateaus', 'very low plateaus', 'mid altitude mountains',\n",
- " 'low altitude mountains', 'plains', 'low plateaus', 'very low plateaus',\n",
- " 'lowlands', 'lowlands', 'very low plateaus', 'very low plateaus',\n",
- " 'very low plateaus', 'lowlands', 'hills', 'lowlands', 'plains',\n",
- " 'lowlands', 'plains', 'very low plateaus', 'water', 'water', 'lowlands',\n",
- " 'lowlands', 'plains', 'hills', 'plains', 'very low plateaus',\n",
- " 'low altitude mountains', 'lowlands', 'water', 'very low plateaus',\n",
- " 'mid altitude mountains', 'water', 'low altitude mountains', 'nan',\n",
- " 'water', 'water', 'water', 'water', 'water', 'water', 'plains',\n",
- " 'plains', 'water', 'plains', 'plains', 'plains', 'plains', 'plains',\n",
- " 'very low plateaus', 'nan', 'nan', 'nan', 'lowlands', 'nan', 'plains',\n",
- " 'very low plateaus', 'nan', 'lowlands', 'water', 'nan',\n",
- " 'mid altitude plains', 'plains', 'mid altitude mountains', 'plains',\n",
- " 'plains', 'low altitude mountains', 'nan', 'nan', 'nan', 'water',\n",
- " 'very low plateaus', 'lowlands', 'lowlands', 'plains', 'lowlands',\n",
- " 'nan', 'plains', 'low altitude mountains', 'low altitude mountains',\n",
- " 'low altitude mountains', 'mid altitude mountains', 'low plateaus',\n",
- " 'hills', 'plains', 'nan', 'high altitude plains',\n",
- " 'high altitude plateaus', 'nan', 'water', 'mid altitude mountains',\n",
- " 'water'], dtype=object) ETOPO1_altitude
(station)
float32
...
standard_name : ETOPO1 altitude long_name : ETOPO1 altitude, relative to sea level datum units : m description : Altitude from ETOPO1 digital elevation model, relative to sea level vertical datum, in metres. Over Antarctica and Greenland the elevation given is on top of the ice sheets. Native resolution of 1 arc minute. A correction for coastal sites is made: if the derived altitude is <= -5 m, the maximum altitude of the neighbouring grid boxes will be used instead. If all neighbouring grid boxes have altitudes <= -5 m, the original value will be retained. array([ 1.900e+01, 3.000e+00, 1.120e+02, 9.090e+02, 2.720e+02, 9.180e+02,\n",
- " 2.589e+03, 1.038e+03, 4.450e+02, 5.490e+02, 5.390e+02, 3.020e+02,\n",
- " 1.570e+02, 2.020e+02, 6.570e+02, 1.594e+03, 4.320e+02, 4.300e+02,\n",
- " 2.640e+02, 1.890e+02, 1.589e+03, -2.000e+00, 5.840e+02, 3.314e+03,\n",
- " 4.850e+02, 5.980e+02, 9.950e+02, 7.140e+02, 7.280e+02, 1.780e+03,\n",
- " 4.100e+01, 4.650e+02, 6.870e+02, 5.250e+02, 1.071e+03, 4.000e+00,\n",
- " 7.100e+01, 1.077e+03, 8.200e+01, 8.190e+02, -2.000e+00, 8.030e+02,\n",
- " 2.379e+03, 4.100e+01, 4.000e+00, 5.000e+00, 3.000e+00, 3.203e+03,\n",
- " 3.700e+01, 7.000e+01, 1.000e+00, 9.940e+02, 5.560e+02, 6.000e+00,\n",
- " 1.284e+03, 1.900e+01, 1.378e+03, 1.000e+01, 3.800e+02, 7.470e+02,\n",
- " 9.190e+02, 5.100e+02, 4.200e+02, 3.600e+01, 2.098e+03, 0.000e+00,\n",
- " 8.000e+00, 2.960e+02, 4.760e+02, 7.400e+02, 3.670e+02, 6.190e+02,\n",
- " 1.990e+02, 7.550e+02, 1.220e+02, 1.659e+03, 7.870e+02, 2.280e+02,\n",
- " 2.258e+03, 6.290e+02, 1.760e+02, 1.122e+03, 2.450e+02, 1.470e+02,\n",
- " 1.240e+02, 2.650e+02, 2.910e+02, 2.240e+02, 1.870e+02, 3.140e+02,\n",
- " 6.800e+01, 4.100e+01, 1.550e+02, 0.000e+00, 2.610e+02, 3.000e+00,\n",
- " 2.000e+00, 3.800e+01, 7.700e+01, 1.570e+02, 2.630e+02, 1.250e+02,\n",
- " 2.940e+02, 7.710e+02, 2.200e+01, 5.000e+00, 2.350e+02, 1.720e+03,\n",
- " 8.000e+00, 8.700e+02, -4.590e+02, 7.800e+01, 5.100e+01, 1.800e+01,\n",
- " 2.000e+00, -1.000e+00, -2.000e+00, 1.500e+01, 1.810e+02, 5.400e+01,\n",
- " 1.700e+01, -1.000e+00, 2.900e+01, -1.000e+00, -1.000e+00, 1.710e+02,\n",
- " 6.310e+02, 3.890e+02, 2.510e+02, 1.780e+02, 4.400e+01, 3.000e+01,\n",
- " 2.920e+02, 1.349e+03, 7.000e+01, 1.800e+01, 6.700e+01, 3.570e+02,\n",
- " 1.770e+02, 1.230e+03, 1.000e+00, 1.980e+02, 6.470e+02, -2.000e+00,\n",
- " 3.520e+02, 4.000e+02, -2.000e+00, 1.880e+02, 8.100e+01, 1.810e+02,\n",
- " 2.900e+01, 2.040e+02, 2.690e+02, 1.250e+02, 6.000e+02, 6.700e+02,\n",
- " 1.445e+03, 1.517e+03, 8.210e+02, 4.760e+02, 1.100e+02, 1.000e+00,\n",
- " 1.672e+03, 3.298e+03, 2.745e+03, 9.500e+01, 1.276e+03, -3.000e+00],\n",
- " dtype=float32) ETOPO1_max_altitude_difference_5km
(station)
float32
...
standard_name : ETOPO1 max altitude difference 5km long_name : ETOPO1 maximum altitude difference between the ETOPO1_altitude and all ETOPO1 altitudes in 5km radius units : m description : Altitude difference between the ETOPO1_altitude, and the minimum ETOP1 altitude in a radius of 5 km around the station location, in metres. array([7.800e+01, 8.000e+00, 2.000e+00, 1.970e+02, 4.600e+01, 3.120e+02,\n",
- " 1.103e+03, 4.880e+02, 2.100e+01, 3.600e+01, 2.230e+02, 1.000e+02,\n",
- " 8.000e+00, 4.900e+01, 1.030e+02, 5.180e+02, 9.300e+01, 6.500e+01,\n",
- " 1.900e+01, 6.000e+01, 3.810e+02, 3.610e+02, 9.480e+02, 1.573e+03,\n",
- " 4.500e+01, 1.330e+02, 5.730e+02, 3.320e+02, 2.190e+02, 7.430e+02,\n",
- " 8.200e+01, 1.310e+02, 1.490e+02, 3.900e+01, 2.400e+02, 1.800e+01,\n",
- " 6.000e+00, 6.170e+02, 1.800e+01, 2.410e+02, 7.000e+00, 2.130e+02,\n",
- " 1.363e+03, 2.000e+01, 3.100e+01, 2.300e+01, 6.000e+00, 6.000e+00,\n",
- " 2.400e+01, 2.100e+01, 2.500e+01, 2.080e+02, 5.490e+02, 8.300e+01,\n",
- " 4.100e+02, 4.700e+01, 1.050e+02, 3.130e+02, 4.900e+01, 2.490e+02,\n",
- " 7.200e+01, 8.900e+01, 1.100e+01, 1.600e+01, 7.740e+02, 2.600e+01,\n",
- " 1.000e+01, 1.070e+02, 2.090e+02, 2.010e+02, 1.810e+02, 8.500e+01,\n",
- " 4.500e+01, 2.330e+02, 3.200e+01, 8.600e+01, 1.000e+02, 4.000e+00,\n",
- " 8.220e+02, 3.000e+00, 6.000e+00, 3.760e+02, 2.800e+01, 1.040e+02,\n",
- " 1.020e+02, 1.520e+02, 2.200e+01, 8.000e+01, 6.200e+01, 9.200e+01,\n",
- " 5.900e+01, 1.900e+01, 9.800e+01, 4.000e+00, 5.800e+01, 1.700e+01,\n",
- " 5.000e+00, 7.000e+01, 2.100e+01, 7.100e+01, 3.010e+02, 6.000e+00,\n",
- " 4.300e+01, 1.110e+02, 2.900e+01, 1.400e+01, 4.800e+01, 7.290e+02,\n",
- " 5.300e+01, 5.280e+02, 1.300e+02, 1.920e+02, 2.367e+03, 4.130e+02,\n",
- " 1.200e+01, 6.600e+01, 2.300e+01, 6.000e+00, 1.600e+01, 2.030e+02,\n",
- " 2.000e+00, 6.000e+00, 7.000e+00, 1.100e+01, 2.000e+00, 3.900e+01,\n",
- " 2.670e+02, 4.100e+01, 2.910e+02, 1.370e+02, 2.700e+01, 1.140e+02,\n",
- " 1.280e+02, 2.530e+02, 4.400e+01, 3.210e+02, 5.840e+02, 2.100e+01,\n",
- " 1.100e+01, 6.080e+02, 1.600e+01, 5.400e+01, 3.450e+02, 4.000e+00,\n",
- " 5.900e+01, 1.100e+02, 1.800e+01, 1.200e+01, 2.300e+01, 1.330e+02,\n",
- " 1.200e+01, 4.200e+01, 9.400e+01, 1.600e+01, 1.180e+02, 2.560e+02,\n",
- " 8.450e+02, 4.860e+02, 1.310e+02, 1.460e+02, 4.000e+00, 3.100e+01,\n",
- " 1.140e+02, 5.310e+02, 6.000e+00, 1.330e+02, 5.130e+02, 5.800e+01],\n",
- " dtype=float32) GHOST_version
(station)
object
...
standard_name : GHOST version long_name : Globally Harmonised Observational Surface Treatment (GHOST) version units : description : Version of the Globally Harmonised Observational Surface Treatment (GHOST). array(['1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3',\n",
- " '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3', '1.3.3'],\n",
- " dtype=object) GHSL_average_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL average built up area density 25km long_name : GHSL average built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 25km around the station location. array([0.000000e+00, 1.641689e-02, 3.451080e+00, 7.928834e-01, 2.470266e+00,\n",
- " 4.198371e+00, 2.615061e-01, 1.547885e+00, 5.053829e+00, 1.780731e+00,\n",
- " 3.675361e+00, 4.530497e+00, 7.248289e+00, 7.722292e+00, 1.335771e+00,\n",
- " 5.389296e-01, 4.493267e+00, 2.250240e+00, 9.585333e+00, 9.667427e+00,\n",
- " 5.860271e-01, 8.468797e-01, 0.000000e+00, 9.568463e-01, 7.484965e+00,\n",
- " 9.972881e+00, 6.356478e+00, 7.342062e+00, 1.081794e+01, 8.715981e-02,\n",
- " 5.796069e-02, 1.382651e+00, 2.516628e+00, 1.958444e+00, 9.287160e-01,\n",
- " 6.647419e-01, 2.530408e+00, 6.291438e+00, 1.274882e+00, 2.589620e+00,\n",
- " 9.504257e-01, 2.772359e+00, 1.763192e+00, 0.000000e+00, 5.039271e-01,\n",
- " 0.000000e+00, 1.432233e+01, 0.000000e+00, 2.171165e+00, 1.474489e-01,\n",
- " 1.316300e-02, 3.715080e-01, 5.540162e+00, 7.196336e-01, 2.372977e+00,\n",
- " 3.680726e-01, 5.416950e-02, 1.261725e+00, 4.719187e-01, 2.808142e-01,\n",
- " 3.585190e-01, 1.053950e+00, 8.513233e-01, 7.054963e-01, 8.015128e-01,\n",
- " 8.402810e-03, 2.291435e-01, 2.284175e-02, 1.433760e-02, 2.136332e+00,\n",
- " 3.735501e+00, 6.768995e-01, 9.774334e-01, 4.437283e+00, 4.211910e+00,\n",
- " 3.007562e-01, 4.923448e-01, 2.575564e+00, 8.797497e-01, 4.006590e-01,\n",
- " 1.619805e+00, 7.541102e+00, 2.941880e-01, 4.833968e-01, 5.643592e+00,\n",
- " 6.884031e-01, 3.981796e-02, 9.051574e-01, 6.854321e+00, 1.107726e+01,\n",
- " 5.144464e+00, 2.557497e+00, 1.309789e+00, 6.571144e+00, 6.101875e+00,\n",
- " 8.283137e-01, 5.297230e+00, 1.322004e-01, 5.172078e+00, 1.031147e+00,\n",
- " 5.167284e-01, 4.270855e+00, 1.153668e+00, 1.339450e+00, 1.155674e-01,\n",
- " 2.075151e-01, 1.159228e+01, 1.082785e+00, 2.005096e+00, 2.044497e+00,\n",
- " 0.000000e+00, 1.445777e+00, 0.000000e+00, 2.523717e-02, 1.025008e+00,\n",
- " 1.158988e+00, 1.880750e-01, 2.870233e-01, 4.256294e-02, 8.264276e-01,\n",
- " 6.642136e+00, 5.101707e+00, 1.218093e+01, 1.989838e+01, 1.816336e+01,\n",
- " 1.476102e+00, 5.189290e-03, 9.017465e-02, 0.000000e+00, 9.208214e-01,\n",
- " 1.412649e-01, 1.858197e+00, 1.173063e+00, 0.000000e+00, 1.958592e+00,\n",
- " 1.846698e+00, 0.000000e+00, 8.816104e-05, 1.119897e+00, 2.483717e+00,\n",
- " 6.325631e-01, 9.276118e-01, 2.028772e+00, 2.411625e-02, 6.078035e-02,\n",
- " 6.349144e-03, 2.117275e+00, 3.696595e-01, 1.844982e+00, 3.478464e+00,\n",
- " 8.611571e-01, 4.373772e-01, 6.845628e-02, 3.827053e-01, 6.236709e-01,\n",
- " 1.579185e+00, 3.834881e+00, 1.627478e+00, 1.692341e+00, 7.824045e-01,\n",
- " 5.020796e+00, 1.079307e-01, 1.158018e+01, 4.559449e-03, nan,\n",
- " 7.309250e-01, 3.054895e-02, 3.118353e-01], dtype=float32) GHSL_average_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL average built up area density 5km long_name : GHSL average built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 5km around the station location. array([0.000000e+00, 2.178124e-01, 1.762877e+00, 1.173100e+00, 3.447264e+00,\n",
- " 1.157241e+00, 0.000000e+00, 1.082312e-01, 2.730326e+00, 1.097038e+00,\n",
- " 7.343925e-01, 4.117237e+00, 7.932886e+00, 3.585407e+00, 0.000000e+00,\n",
- " 5.709864e-01, 2.330912e+01, 3.738471e+00, 1.455563e+01, 1.084787e+01,\n",
- " 2.159310e-01, 3.609302e+00, 0.000000e+00, 7.879626e-02, 1.005981e+01,\n",
- " 9.531594e+00, 8.789180e+00, 5.631969e+00, 6.667284e+00, 0.000000e+00,\n",
- " 1.582758e-02, 6.211513e-01, 1.664067e+00, 1.201386e+00, 9.626284e-02,\n",
- " 8.676528e+00, 7.920068e-01, 8.619682e-01, 3.290072e-01, 8.044924e-01,\n",
- " 2.644389e+00, 3.581728e+00, 7.409145e-01, 0.000000e+00, 1.083261e+00,\n",
- " 0.000000e+00, 6.965761e+00, 0.000000e+00, 4.894001e-01, 7.065599e-02,\n",
- " 7.999263e-04, 8.400992e-01, 2.238591e+00, 4.062458e+00, 1.188911e+00,\n",
- " 1.745365e+00, 5.404123e-02, 2.044145e+00, 2.338474e-02, 6.927220e-01,\n",
- " 1.767892e-01, 4.340597e-01, 6.455219e-01, 1.024512e+00, 7.908591e-02,\n",
- " 1.758303e-02, 7.507005e-01, 8.004932e-04, 1.600209e-03, 4.782001e-01,\n",
- " 4.639083e+00, 2.510897e-01, 2.263173e-01, 1.250228e+00, 6.538601e+00,\n",
- " 1.250346e-01, 1.827326e-01, 9.651652e-01, 3.808995e-02, 0.000000e+00,\n",
- " 3.267201e-01, 1.344514e+00, 2.406637e-01, 2.401341e-01, 2.844512e+00,\n",
- " 8.047519e-02, 1.229706e-02, 1.148596e+00, 8.515395e+00, 1.691308e-01,\n",
- " 4.246195e+00, 1.425020e+00, 1.462115e+00, 4.663021e+00, 1.962126e+00,\n",
- " 7.194607e-01, 1.059486e+01, 2.554662e+00, 1.097080e+00, 1.507694e+00,\n",
- " 3.357595e-02, 5.652899e+00, 3.537214e-01, 7.504822e-02, 5.260807e-01,\n",
- " 4.769939e-01, 9.996733e+00, 2.118837e-01, 1.207809e+00, 4.746772e-01,\n",
- " 0.000000e+00, 1.382775e+00, 0.000000e+00, 4.121612e-01, 9.242194e-01,\n",
- " 2.173033e+00, 2.112249e-01, 2.668983e-01, 2.128088e-02, 5.657371e+00,\n",
- " 5.485679e+00, 2.886175e+00, 3.586135e+00, 1.284233e+01, 7.272482e+00,\n",
- " 8.062506e-02, 3.034798e-03, 9.131713e-03, 0.000000e+00, 2.589814e-01,\n",
- " 1.616438e-02, 1.805817e+00, 3.091420e-01, 0.000000e+00, 6.698462e+00,\n",
- " 1.806687e-02, 0.000000e+00, 0.000000e+00, 1.867657e+00, 8.254259e-01,\n",
- " 4.061785e+00, 1.361787e-02, 1.437942e+00, 0.000000e+00, 2.318183e-02,\n",
- " 1.522284e-02, 1.873920e+00, 3.908790e-02, 7.051749e-01, 2.862581e-01,\n",
- " 8.917750e-01, 4.788742e-03, 2.964287e-02, 3.550355e-01, 3.375556e-01,\n",
- " 2.270696e-01, 1.752070e-01, 3.026851e-02, 1.821911e+00, 1.412027e-01,\n",
- " 9.366841e-01, 2.007703e-01, 4.072711e+00, 0.000000e+00, nan,\n",
- " 6.320625e-01, 0.000000e+00, 4.462050e-01], dtype=float32) GHSL_average_population_density_25km
(station)
float32
...
standard_name : GHSL average population density 25km long_name : GHSL average population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 25km around the station location. array([0.000000e+00, 3.568217e+01, 9.980865e+01, 3.190750e+01, 6.726781e+01,\n",
- " 1.903521e+02, 1.465043e+01, 6.950777e+01, 2.090169e+02, 4.181914e+01,\n",
- " 1.111647e+02, 1.033284e+02, 3.054646e+02, 2.914489e+02, 5.580139e+01,\n",
- " 2.613210e+01, 2.718617e+02, 4.358804e+01, 3.936721e+02, 2.216847e+02,\n",
- " 3.192635e+01, 3.153716e+01, 0.000000e+00, 3.253862e+01, 2.249072e+02,\n",
- " 3.978526e+02, 1.978509e+02, 3.428036e+02, 4.081971e+02, 1.291192e+01,\n",
- " 4.141846e+01, 5.976871e+01, 7.761426e+01, 5.375138e+01, 3.167670e+01,\n",
- " 1.127749e+01, 4.898831e+01, 2.754430e+02, 3.236520e+01, 1.153577e+02,\n",
- " 2.262887e+01, 1.059273e+02, 6.933392e+01, 0.000000e+00, 8.035113e+00,\n",
- " 0.000000e+00, 3.784261e+02, 0.000000e+00, 4.947928e+01, 6.790388e+00,\n",
- " 6.697233e-01, 1.088477e+01, 1.051814e+02, 3.138204e+01, 2.739621e+02,\n",
- " 1.373242e+01, 1.703370e+00, 3.002914e+01, 1.949632e+01, 1.805814e+01,\n",
- " 6.843007e+00, 3.621369e+01, 3.052421e+01, 1.223881e+01, 1.875040e+02,\n",
- " 1.447079e-01, 3.895106e+00, 2.392212e+00, 1.111375e+00, 6.129661e+01,\n",
- " 8.542714e+01, 1.366323e+01, 2.474633e+01, 1.228301e+02, 6.223643e+01,\n",
- " 1.781759e+01, 1.225262e+01, 5.170035e+01, 3.290307e+01, 1.588643e+01,\n",
- " 2.694235e+01, 2.114845e+02, 7.558183e+00, 2.699802e+01, 2.290650e+02,\n",
- " 4.549244e+01, 2.541747e+00, 4.061278e+01, 4.023341e+02, 5.586509e+02,\n",
- " 2.820373e+02, 7.914844e+01, 5.536787e+01, 2.179019e+02, 3.757118e+02,\n",
- " 4.958083e+01, 1.798999e+02, 8.834999e+00, 2.425554e+02, 4.248013e+01,\n",
- " 1.942521e+01, 9.634933e+01, 5.070405e+01, 2.880951e+02, 6.044481e+00,\n",
- " 6.659240e+00, 4.672126e+02, 5.658886e+01, 5.670205e+01, 1.165914e+02,\n",
- " 0.000000e+00, 4.831990e+01, 8.448791e-02, 8.890940e-01, 3.994497e+01,\n",
- " 8.159893e+01, 5.042799e+00, 1.060563e+01, 5.302037e+00, 3.427647e+01,\n",
- " 2.447528e+02, 1.906743e+02, 3.186886e+02, 7.659880e+02, 7.489997e+02,\n",
- " 3.012765e+01, 4.445325e-01, 5.833637e+00, 0.000000e+00, 2.191649e+01,\n",
- " 6.931715e+00, 3.664505e+01, 3.269740e+01, 0.000000e+00, 5.546283e+01,\n",
- " 1.344142e+02, 0.000000e+00, 3.210384e-01, 6.782119e+01, 1.451961e+02,\n",
- " 1.807269e+01, 4.862088e+01, 1.635670e+02, 4.603565e+00, 2.567884e+00,\n",
- " 1.369664e-01, 5.944976e+01, 1.119758e+01, 6.363085e+01, 7.001279e+01,\n",
- " 3.880754e+01, 1.061801e+01, 2.944844e+00, 1.101898e+01, 2.447752e+01,\n",
- " 8.707396e+01, 1.655813e+02, 7.425315e+01, 1.034081e+02, 3.454342e+01,\n",
- " 1.164009e+02, 3.855874e+00, 1.956914e+02, 5.680043e-04, nan,\n",
- " 2.559976e+01, 1.299583e+02, 9.822782e+00], dtype=float32) GHSL_average_population_density_5km
(station)
float32
...
standard_name : GHSL average population density 5km long_name : GHSL average population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 5km around the station location. array([0.000000e+00, 4.690972e+02, 3.103303e+01, 3.508097e+01, 8.357281e+01,\n",
- " 7.617443e+01, 0.000000e+00, 2.803909e+01, 9.498553e+01, 2.553344e+01,\n",
- " 2.845852e+01, 1.096564e+02, 1.904848e+02, 5.720120e+01, 0.000000e+00,\n",
- " 1.987993e+01, 1.588540e+03, 6.831900e+01, 4.451785e+02, 2.379049e+02,\n",
- " 1.170464e+01, 1.557633e+02, 0.000000e+00, 1.805310e+00, 2.232487e+02,\n",
- " 3.019567e+02, 2.615233e+02, 2.469198e+02, 2.144834e+02, 0.000000e+00,\n",
- " 1.000541e+01, 1.336399e+01, 5.976002e+01, 2.118015e+01, 2.695873e+00,\n",
- " 1.579932e+02, 1.385422e+01, 4.974121e+01, 9.051698e+00, 6.027180e+01,\n",
- " 3.681785e+01, 1.945499e+02, 2.302906e+01, 0.000000e+00, 1.170047e+01,\n",
- " 0.000000e+00, 1.907815e+02, 0.000000e+00, 7.951523e+00, 3.214146e+00,\n",
- " 0.000000e+00, 2.978236e+01, 3.532858e+01, 2.466392e+02, 1.457125e+02,\n",
- " 5.406254e+01, 9.787757e-01, 3.198607e+01, 1.136314e+00, 5.891815e+01,\n",
- " 3.874274e+00, 1.062251e+01, 2.402342e+01, 1.628610e+01, 2.137181e+01,\n",
- " 2.845254e-01, 1.005700e+01, 1.440888e-01, 1.784233e-01, 1.545241e+01,\n",
- " 1.057307e+02, 7.796998e+00, 7.325947e+00, 3.691739e+01, 8.081860e+01,\n",
- " 8.280608e+00, 4.802943e+00, 2.830708e+01, 1.852106e+00, 1.700853e+00,\n",
- " 7.315959e+00, 4.145933e+01, 1.970219e+00, 1.593568e+01, 1.091642e+02,\n",
- " 6.647443e+00, 2.362038e-01, 4.600963e+01, 4.361660e+02, 3.396060e+00,\n",
- " 2.143313e+02, 3.766386e+01, 7.087405e+01, 1.456985e+02, 1.298899e+02,\n",
- " 4.017353e+01, 3.977556e+02, 1.118931e+02, 4.404686e+01, 6.715195e+01,\n",
- " 2.973364e+00, 1.421294e+02, 1.949533e+01, 1.780552e+01, 2.974153e+01,\n",
- " 1.002674e+01, 3.728353e+02, 1.566943e+01, 6.660810e+00, 3.033878e+01,\n",
- " 0.000000e+00, 4.079543e+01, 2.098099e+00, 1.449848e+01, 2.455117e+01,\n",
- " 1.426273e+02, 2.542975e+00, 9.341696e+00, 4.373961e+00, 2.026381e+02,\n",
- " 1.797982e+02, 6.053999e+01, 8.839283e+01, 3.067233e+02, 1.869860e+02,\n",
- " 2.518704e+00, 2.775064e-01, 8.188425e-01, 0.000000e+00, 7.892782e+00,\n",
- " 1.129082e+00, 3.514456e+01, 1.014561e+01, 0.000000e+00, 2.087034e+02,\n",
- " 2.136142e-01, 0.000000e+00, 6.060986e-01, 9.335101e+01, 4.803714e+01,\n",
- " 6.946500e+01, 1.229736e+00, 7.673063e+01, 0.000000e+00, 1.034389e+00,\n",
- " 1.321983e-01, 5.870374e+01, 1.243675e+00, 2.242510e+01, 5.536726e+00,\n",
- " 2.430091e+01, 1.516435e-01, 1.447979e+00, 1.302931e+01, 1.017144e+01,\n",
- " 3.515065e+01, 1.289989e+01, 2.165792e+00, 7.235415e+01, 5.460165e+00,\n",
- " 1.734164e+01, 1.086669e+00, 4.020491e+01, 0.000000e+00, nan,\n",
- " 3.256375e+01, 0.000000e+00, 7.023504e-02], dtype=float32) GHSL_built_up_area_density
(station)
float32
...
standard_name : GHSL built up area density long_name : GHSL built up area density units : % description : Global Human Settlement Layer (GHSL) built up area density (technical label: GHS_BUILT_LDSMT_GLOBE_R2018A), in units of built-up area percent per gridcell (0-100). The product is a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). Native resolution of 0.25 x 0.25 kilometres. array([0.00000e+00, 0.00000e+00, 1.02944e+01, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 1.93600e+01,\n",
- " 0.00000e+00, 6.62080e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 5.00000e+00, 1.36528e+01, 2.13904e+01, 2.19152e+01,\n",
- " 1.63520e+00, 5.03600e+01, 0.00000e+00, 0.00000e+00, 3.84000e-01,\n",
- " 5.02400e+01, 4.01440e+00, 0.00000e+00, 2.67840e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 4.55328e+01, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 6.24640e+00,\n",
- " 0.00000e+00, 5.50912e+01, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 7.38400e+00, 8.41600e-01, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 2.39584e+01, 3.56480e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 1.21440e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 5.82000e+01,\n",
- " 0.00000e+00, 0.00000e+00, 1.13376e+01, 0.00000e+00, 0.00000e+00,\n",
- " 4.39520e+00, 5.56160e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 4.87840e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 3.56528e+01, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 2.46400e+00,\n",
- " 0.00000e+00, 2.06368e+01, 0.00000e+00, 6.36000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 1.84960e+00,\n",
- " 0.00000e+00, 2.79360e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 2.16960e+00, 0.00000e+00, 3.89840e+01, 0.00000e+00, 3.27696e+01,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 3.04000e-02, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 4.63840e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 7.16960e+00, 2.82400e+00, 0.00000e+00, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 5.60000e-02, 0.00000e+00,\n",
- " 0.00000e+00, 0.00000e+00, 0.00000e+00, 1.06560e+01, 0.00000e+00,\n",
- " 5.78240e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, nan,\n",
- " 0.00000e+00, 0.00000e+00, 1.00512e+01], dtype=float32) GHSL_max_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL max built up area density 25km long_name : GHSL max built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 25km around the station location. array([ 0., 21., 100., 99., 100., 99., 100., 100., 100., 99., 100., 100.,\n",
- " 100., 100., 100., 97., 100., 100., 100., 100., 93., 100., 0., 100.,\n",
- " 100., 100., 100., 100., 100., 84., 83., 99., 100., 100., 99., 100.,\n",
- " 100., 100., 100., 100., 99., 98., 99., 0., 98., 0., 100., 0.,\n",
- " 100., 82., 22., 93., 100., 100., 99., 97., 67., 100., 99., 97.,\n",
- " 96., 100., 98., 100., 89., 14., 84., 62., 39., 100., 100., 100.,\n",
- " 98., 100., 100., 89., 98., 100., 99., 82., 100., 100., 94., 97.,\n",
- " 100., 99., 49., 100., 100., 100., 100., 100., 100., 100., 100., 99.,\n",
- " 100., 95., 100., 100., 98., 100., 96., 100., 76., 63., 100., 100.,\n",
- " 100., 95., 0., 100., 0., 77., 100., 100., 78., 92., 56., 100.,\n",
- " 100., 100., 100., 100., 100., 100., 10., 64., 0., 96., 80., 100.,\n",
- " 98., 0., 100., 100., 0., 2., 94., 100., 100., 100., 98., 43.,\n",
- " 87., 30., 100., 91., 100., 100., 99., 100., 88., 95., 99., 100.,\n",
- " 100., 100., 99., 95., 100., 71., 100., 43., nan, 93., 57., 97.],\n",
- " dtype=float32) GHSL_max_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL max built up area density 5km long_name : GHSL max built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 5km around the station location. array([ 0., 16., 95., 78., 99., 63., 0., 13., 92., 65., 67., 100.,\n",
- " 99., 98., 0., 66., 100., 100., 100., 98., 47., 99., 0., 51.,\n",
- " 100., 100., 100., 99., 99., 0., 7., 64., 79., 81., 27., 100.,\n",
- " 80., 61., 60., 77., 99., 81., 84., 0., 92., 0., 96., 0.,\n",
- " 34., 26., 1., 68., 91., 95., 58., 86., 28., 99., 18., 74.,\n",
- " 62., 43., 55., 94., 13., 5., 74., 1., 1., 52., 100., 39.,\n",
- " 29., 74., 99., 16., 22., 44., 11., 0., 42., 53., 30., 43.,\n",
- " 100., 11., 9., 88., 100., 13., 98., 54., 91., 98., 94., 57.,\n",
- " 100., 95., 61., 67., 12., 100., 37., 11., 45., 28., 97., 28.,\n",
- " 90., 81., 0., 71., 0., 77., 54., 80., 39., 50., 6., 100.,\n",
- " 100., 96., 71., 100., 99., 15., 2., 3., 0., 43., 7., 69.,\n",
- " 27., 0., 98., 6., 0., 0., 93., 83., 100., 4., 87., 0.,\n",
- " 12., 8., 56., 19., 67., 29., 80., 6., 22., 63., 37., 34.,\n",
- " 35., 23., 67., 46., 94., 25., 100., 0., nan, 48., 0., 37.],\n",
- " dtype=float32) GHSL_max_population_density_25km
(station)
float32
...
standard_name : GHSL max population density 25km long_name : GHSL max population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 25km around the station location. array([0.0000e+00, 4.2063e+04, 6.5730e+03, 6.6770e+03, 5.3320e+03, 7.0010e+03,\n",
- " 1.7676e+04, 6.3090e+03, 5.8220e+03, 2.9770e+03, 8.5300e+03, 2.9980e+03,\n",
- " 8.5300e+03, 8.5300e+03, 5.9200e+03, 4.9860e+03, 7.7380e+03, 2.7080e+03,\n",
- " 8.4580e+03, 3.2800e+03, 6.5480e+03, 7.0990e+03, 0.0000e+00, 1.1336e+04,\n",
- " 8.2710e+03, 9.1110e+03, 2.0960e+04, 9.6880e+03, 7.7890e+03, 1.1658e+04,\n",
- " 5.8795e+04, 4.8640e+03, 4.1920e+03, 4.0360e+03, 4.3620e+03, 2.1890e+03,\n",
- " 2.5860e+03, 9.3110e+03, 2.7010e+03, 6.3760e+03, 3.9580e+03, 5.3530e+03,\n",
- " 4.8140e+03, 0.0000e+00, 2.3630e+03, 0.0000e+00, 9.1000e+03, 0.0000e+00,\n",
- " 4.1640e+03, 4.8400e+03, 1.5430e+03, 2.7370e+03, 4.6690e+03, 8.5560e+03,\n",
- " 1.8067e+04, 5.0860e+03, 1.7510e+03, 3.9900e+03, 5.2520e+03, 6.4640e+03,\n",
- " 2.6000e+03, 7.3590e+03, 4.5870e+03, 2.5160e+03, 3.7168e+04, 1.6410e+03,\n",
- " 2.1120e+03, 6.1570e+03, 2.9770e+03, 3.7970e+03, 3.3730e+03, 3.5000e+03,\n",
- " 2.9770e+03, 8.9170e+03, 2.0470e+03, 7.1390e+03, 2.5260e+03, 2.7140e+03,\n",
- " 4.2490e+03, 3.3080e+03, 2.2750e+03, 5.5500e+03, 5.4230e+03, 5.7050e+03,\n",
- " 1.4285e+04, 9.0180e+03, 5.1940e+03, 6.8060e+03, 2.0982e+04, 3.7103e+04,\n",
- " 2.7409e+04, 1.0314e+04, 7.0960e+03, 1.9072e+04, 2.0982e+04, 6.7170e+03,\n",
- " 1.2793e+04, 4.4160e+03, 2.0415e+04, 5.5150e+03, 5.0310e+03, 2.5650e+03,\n",
- " 4.5340e+03, 2.2953e+04, 3.0590e+03, 1.1171e+04, 1.6010e+04, 1.0313e+04,\n",
- " 1.4621e+04, 1.6802e+04, 0.0000e+00, 4.5340e+03, 6.0000e+00, 2.6660e+03,\n",
- " 4.3400e+03, 9.0120e+03, 2.7810e+03, 4.0150e+03, 7.1200e+03, 7.9750e+03,\n",
- " 5.3290e+03, 5.8130e+03, 4.4290e+03, 8.5190e+03, 6.6160e+03, 3.1760e+03,\n",
- " 7.8400e+02, 4.9090e+03, 0.0000e+00, 2.7580e+03, 5.2170e+03, 2.6850e+03,\n",
- " 2.5930e+03, 0.0000e+00, 3.0280e+03, 2.4824e+04, 0.0000e+00, 2.7600e+02,\n",
- " 8.6670e+03, 8.3350e+03, 4.1900e+03, 6.0790e+03, 1.8005e+04, 8.8350e+03,\n",
- " 3.5390e+03, 7.6900e+02, 4.2790e+03, 3.8920e+03, 5.0420e+03, 5.2860e+03,\n",
- " 7.4390e+03, 2.9080e+03, 3.7110e+03, 2.9750e+03, 4.0250e+03, 6.5640e+03,\n",
- " 8.3180e+03, 6.0660e+03, 1.4915e+04, 5.9230e+03, 4.6900e+03, 4.4930e+03,\n",
- " 1.9853e+04, 1.7000e+01, nan, 1.0545e+04, 2.7399e+05, 4.1067e+04],\n",
- " dtype=float32) GHSL_max_population_density_5km
(station)
float32
...
standard_name : GHSL max population density 5km long_name : GHSL max population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 5km around the station location. array([0.0000e+00, 3.1821e+04, 1.6600e+03, 2.2810e+03, 2.2720e+03, 3.9410e+03,\n",
- " 0.0000e+00, 2.8490e+03, 2.8600e+03, 1.3560e+03, 2.2670e+03, 2.9980e+03,\n",
- " 3.0270e+03, 1.6240e+03, 0.0000e+00, 2.0880e+03, 6.9420e+03, 1.7380e+03,\n",
- " 3.7310e+03, 2.2750e+03, 2.0590e+03, 7.0990e+03, 0.0000e+00, 1.1460e+03,\n",
- " 2.5490e+03, 3.3070e+03, 5.5400e+03, 5.7610e+03, 3.7810e+03, 0.0000e+00,\n",
- " 5.0150e+03, 1.6240e+03, 2.6530e+03, 1.5830e+03, 9.7600e+02, 2.0890e+03,\n",
- " 1.3140e+03, 3.3330e+03, 1.4570e+03, 5.0990e+03, 1.3520e+03, 4.2180e+03,\n",
- " 2.5660e+03, 0.0000e+00, 1.0230e+03, 0.0000e+00, 3.5420e+03, 0.0000e+00,\n",
- " 6.2200e+02, 8.6700e+02, 0.0000e+00, 2.3560e+03, 1.3740e+03, 8.5560e+03,\n",
- " 7.3760e+03, 2.5930e+03, 4.5600e+02, 1.7070e+03, 9.9400e+02, 5.6600e+03,\n",
- " 1.2310e+03, 1.0900e+03, 1.7200e+03, 1.4010e+03, 7.8500e+03, 1.5300e+02,\n",
- " 9.6200e+02, 1.1100e+02, 1.4500e+02, 1.7370e+03, 2.4130e+03, 1.0500e+03,\n",
- " 8.1700e+02, 1.9330e+03, 1.6040e+03, 1.0410e+03, 5.4900e+02, 1.3980e+03,\n",
- " 5.1700e+02, 3.0000e+00, 1.1000e+03, 1.4970e+03, 2.2300e+02, 2.6030e+03,\n",
- " 6.4370e+03, 6.2500e+02, 1.3400e+02, 5.5150e+03, 6.8250e+03, 4.3600e+02,\n",
- " 7.7710e+03, 3.3120e+03, 5.5430e+03, 6.5120e+03, 5.9500e+03, 3.4840e+03,\n",
- " 1.0109e+04, 4.4160e+03, 3.4520e+03, 3.0300e+03, 1.1100e+03, 2.5650e+03,\n",
- " 1.8400e+03, 2.3620e+03, 2.5230e+03, 1.5220e+03, 8.7160e+03, 3.0470e+03,\n",
- " 1.3660e+03, 6.9540e+03, 0.0000e+00, 2.0100e+03, 6.0000e+00, 2.6660e+03,\n",
- " 1.3550e+03, 4.7080e+03, 4.8900e+02, 1.6290e+03, 1.0930e+03, 4.4960e+03,\n",
- " 3.2320e+03, 2.2730e+03, 1.7960e+03, 3.2010e+03, 4.2590e+03, 4.3500e+02,\n",
- " 1.5500e+02, 2.7100e+02, 0.0000e+00, 1.2310e+03, 7.9600e+02, 1.3900e+03,\n",
- " 8.1100e+02, 0.0000e+00, 3.0040e+03, 1.1300e+02, 0.0000e+00, 2.0000e+00,\n",
- " 4.2030e+03, 8.3350e+03, 1.7670e+03, 3.3000e+02, 4.2410e+03, 0.0000e+00,\n",
- " 5.0400e+02, 6.9000e+01, 2.2130e+03, 5.5100e+02, 1.9790e+03, 4.8300e+02,\n",
- " 2.3020e+03, 1.4000e+02, 9.2300e+02, 2.1400e+03, 1.1020e+03, 4.7590e+03,\n",
- " 2.2370e+03, 1.6070e+03, 3.7750e+03, 1.6310e+03, 1.7070e+03, 3.4600e+02,\n",
- " 2.3210e+03, 0.0000e+00, nan, 2.3540e+03, 0.0000e+00, 9.0000e+00],\n",
- " dtype=float32) GHSL_modal_settlement_model_classification_25km
(station)
object
...
standard_name : GHSL modal settlement model classification 25km long_name : GHSL modal settlement model classification in 25km radius units : description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 25km around station location. array(['water', 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'water', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'water', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'water', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural', 'water',\n",
- " 'water', 'water', 'water', 'water', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'low density rural', 'very low density rural', 'low density rural',\n",
- " 'water', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'nan', 'water',\n",
- " 'very low density rural', 'water'], dtype=object) GHSL_modal_settlement_model_classification_5km
(station)
object
...
standard_name : GHSL modal settlement model classification 5km long_name : GHSL modal settlement model classification in 5km radius units : description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 5km around station location. array(['water', 'water', 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'urban centre',\n",
- " 'very low density rural', 'low density rural', 'low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'water', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'water', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'suburban/peri-urban',\n",
- " 'very low density rural', 'water', 'very low density rural', 'water',\n",
- " 'water', 'water', 'water', 'very low density rural', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'low density rural', 'very low density rural', 'low density rural',\n",
- " 'suburban/peri-urban', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'nan', 'water',\n",
- " 'very low density rural', 'water'], dtype=object) GHSL_population_density
(station)
float32
...
standard_name : GHSL population density long_name : GHSL population density units : xx km–2 description : Global Human Settlement Layer (GHSL) population density (technical label: GHS_POP_MT_GLOBE_R2019A), in populus per squared kilometre. It depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the GHSL global layer per corresponding epoch. Native resolution of 0.25 x 0.25 kilometres. array([0.000000e+00, 0.000000e+00, 1.782431e+02, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.731908e+02,\n",
- " 0.000000e+00, 1.914441e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 3.471132e+02, 2.372969e+02, 4.450653e+02, 5.039089e+02,\n",
- " 7.097375e+01, 1.695632e+03, 1.898080e-05, 0.000000e+00, 9.790040e+00,\n",
- " 1.599291e+03, 1.526475e+02, 0.000000e+00, 7.273004e+01, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 9.513987e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.878987e+01,\n",
- " 0.000000e+00, 1.613998e+03, 0.000000e+00, 0.000000e+00, 9.863296e-01,\n",
- " 0.000000e+00, 0.000000e+00, 1.114266e+02, 4.753482e+01, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 4.123594e+02, 1.724864e+02, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.445650e+02,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 9.625802e+02,\n",
- " 0.000000e+00, 0.000000e+00, 4.447774e+02, 0.000000e+00, 2.500990e+00,\n",
- " 4.356402e+01, 1.567155e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.242259e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 1.030722e+02, 5.175986e+01, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.099533e+02,\n",
- " 0.000000e+00, 2.089355e+01, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 6.772582e+00, 0.000000e+00, 4.574234e+01,\n",
- " 0.000000e+00, 3.487929e+01, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 6.828555e+01, 0.000000e+00, 8.998964e+02, 0.000000e+00, 7.671004e+02,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 4.687347e-02, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 1.269492e+00, 1.126304e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 3.004128e+02, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 2.823801e+02, 7.915669e+01, 0.000000e+00, 0.000000e+00,\n",
- " 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.890558e+00, 0.000000e+00,\n",
- " 0.000000e+00, 3.651308e-01, 0.000000e+00, 4.936926e+02, 0.000000e+00,\n",
- " 1.088258e+02, 0.000000e+00, 0.000000e+00, 0.000000e+00, nan,\n",
- " 0.000000e+00, 0.000000e+00, 2.453957e+00], dtype=float32) GHSL_settlement_model_classification
(station)
object
...
standard_name : GHSL settlement model classification long_name : GHSL settlement model classification units : description : Global Human Settlement Layer (GHSL) settlement model classification (technical label: GHS_SMOD_POPMT_GLOBE_R2019A). The classification delineates and classify settlement typologies via a logic of population size, population and built-up area densities as a refinement of the ‘degree of urbanization’ method described by EUROSTAT. The classification is derived by using the GHS_POP_MT_GLOBE_R2019A and GHS_BUILT_LDSMT_GLOBE_R2018A products. The GHS Settlement Model grid is an improvement of the GHS Settlement Grid (R2016A) introducing a more detailed classification of settlements in two levels, also called ‘refined degree of urbanization’. The Settlement Model is provided at detailed level (Second Level - L2). The First Level, as a porting of the Degree of Urbanization adopted by EUROSTAT can be obtained aggregating L2. Native resolution of 1.0 x 1.0 kilometres. array(['very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'low density rural', 'low density rural',\n",
- " 'low density rural', 'very low density rural', 'very low density rural',\n",
- " 'suburban/peri-urban', 'low density rural', 'low density rural',\n",
- " 'rural cluster', 'very low density rural', 'rural cluster',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'semi-dense urban cluster', 'semi-dense urban cluster',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'rural cluster',\n",
- " 'very low density rural', 'water', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'low density rural', 'very low density rural', 'very low density rural',\n",
- " 'rural cluster', 'very low density rural', 'very low density rural',\n",
- " 'low density rural', 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'semi-dense urban cluster', 'rural cluster', 'rural cluster',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'semi-dense urban cluster', 'very low density rural',\n",
- " 'low density rural', 'very low density rural', 'water',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'low density rural', 'very low density rural', 'low density rural',\n",
- " 'low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural', 'low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural', 'nan', 'water', 'very low density rural',\n",
- " 'very low density rural'], dtype=object) GPW_average_population_density_25km
(station)
float32
...
standard_name : GPW average population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 25 km around the station location. array([0.000000e+00, 3.676580e+00, 1.043439e+02, 2.891395e+01, 6.576196e+01,\n",
- " 2.002287e+02, 1.932350e+01, 7.101602e+01, 2.097893e+02, 4.481235e+01,\n",
- " 1.687959e+02, 1.031429e+02, 3.593801e+02, 3.602792e+02, 5.492215e+01,\n",
- " 2.671996e+01, 2.724147e+02, 4.551095e+01, 3.866893e+02, 2.181801e+02,\n",
- " 3.109594e+01, 6.972549e+01, 1.213884e-05, 4.173470e+01, 2.325059e+02,\n",
- " 4.137915e+02, 2.110254e+02, 3.685287e+02, 4.179377e+02, 3.795084e+00,\n",
- " 4.906758e+01, 7.188786e+01, 8.250193e+01, 5.352773e+01, 3.476805e+01,\n",
- " 1.651753e+01, 4.945769e+01, 2.725477e+02, 3.951214e+01, 1.201239e+02,\n",
- " 2.823137e+01, 1.105656e+02, 6.960040e+01, 0.000000e+00, 1.131580e+01,\n",
- " 0.000000e+00, 4.083706e+02, 0.000000e+00, 5.340744e+01, 7.582005e+00,\n",
- " 8.339360e-01, 1.285531e+01, 1.327255e+02, 3.862384e+01, 2.897724e+02,\n",
- " 1.663684e+01, 3.359083e+00, 3.664787e+01, 2.677676e+01, 1.348262e+01,\n",
- " 7.627829e+00, 4.842535e+01, 3.060976e+01, 1.909778e+01, 2.306035e+02,\n",
- " 6.721435e-01, 1.101931e+01, 2.194751e+00, 1.015538e+00, 7.271815e+01,\n",
- " 8.780937e+01, 1.440131e+01, 2.592144e+01, 1.242082e+02, 6.269833e+01,\n",
- " 1.875869e+01, 1.258263e+01, 5.353754e+01, 2.929527e+01, 1.718634e+01,\n",
- " 2.733528e+01, 2.142651e+02, 7.796501e+00, 2.926344e+01, 2.517786e+02,\n",
- " 4.762556e+01, 3.003328e+00, 4.112241e+01, 4.263912e+02, 5.634548e+02,\n",
- " 3.181353e+02, 8.138203e+01, 6.382910e+01, 2.198636e+02, 3.920922e+02,\n",
- " 5.497407e+01, 2.054859e+02, 1.422717e+01, 2.442289e+02, 3.954022e+01,\n",
- " 2.253697e+01, 9.857957e+01, 4.844148e+01, 2.391857e+02, 7.922302e+00,\n",
- " 1.050398e+01, 5.112840e+02, 5.714964e+01, 6.447959e+01, 1.192496e+02,\n",
- " 0.000000e+00, 4.986892e+01, 1.267819e-01, 1.323066e+00, 7.507764e+01,\n",
- " 8.046577e+01, 8.533894e+00, 1.901917e+01, 1.291049e+01, 5.241627e+01,\n",
- " 2.869067e+02, 1.844621e+02, 3.418260e+02, 8.262206e+02, 7.747103e+02,\n",
- " 4.711851e+01, 2.940538e+00, 4.054120e+00, 4.309018e-02, 2.309335e+01,\n",
- " 3.564212e+00, 3.743097e+01, 3.728577e+01, 0.000000e+00, 5.972689e+01,\n",
- " 1.457091e+02, 0.000000e+00, 6.820842e-01, 6.970630e+01, 1.380541e+02,\n",
- " 2.033021e+01, 4.553246e+01, 1.686101e+02, 3.782959e-02, 1.712330e+00,\n",
- " 1.115660e+00, 7.542218e+01, 1.646836e+01, 7.729394e+01, 8.965453e+01,\n",
- " 4.288913e+01, 8.058159e+00, 3.422457e+00, 1.209941e+01, 2.527039e+01,\n",
- " 9.015112e+01, 1.792928e+02, 6.864024e+01, 1.111997e+02, 3.141101e+01,\n",
- " 1.195565e+02, 3.268207e+00, 1.872376e+02, 5.126638e-02, 0.000000e+00,\n",
- " 2.306228e+01, 8.593194e+01, 2.638384e+01], dtype=float32) GPW_average_population_density_5km
(station)
float32
...
standard_name : GPW average population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 5 km around the station location. array([0.000000e+00, 2.912720e+00, 3.400988e+01, 2.154398e+01, 6.598087e+01,\n",
- " 9.970491e+01, 1.105146e+01, 5.292439e+01, 1.186672e+02, 5.602424e+01,\n",
- " 4.912894e+01, 1.303824e+02, 1.811169e+02, 9.129450e+01, 1.785338e+01,\n",
- " 2.909961e+01, 1.471922e+03, 5.477317e+01, 1.668051e+02, 3.134350e+02,\n",
- " 2.134855e+01, 4.047465e+02, 1.638963e-05, 1.377935e+01, 2.175175e+02,\n",
- " 2.899651e+02, 3.711274e+02, 3.319088e+02, 2.799866e+02, 3.581799e+00,\n",
- " 1.348548e+02, 1.979032e+01, 5.576019e+01, 2.449666e+01, 1.818749e+01,\n",
- " 1.031549e+02, 3.602426e+01, 4.057293e+02, 2.431496e+01, 1.799521e+02,\n",
- " 3.717742e+01, 1.794494e+02, 7.255676e+01, 0.000000e+00, 1.384620e+01,\n",
- " 0.000000e+00, 3.133285e+02, 0.000000e+00, 2.374423e+01, 3.775826e+00,\n",
- " 1.640763e-01, 2.272928e+01, 1.413851e+02, 2.125484e+02, 6.483813e+02,\n",
- " 3.296441e+01, 1.688196e+00, 3.147202e+01, 2.301476e+01, 1.118226e+01,\n",
- " 5.425424e+00, 1.234548e+01, 3.023590e+01, 2.765573e+01, 1.778005e+02,\n",
- " 1.300927e+00, 8.720321e+00, 3.050449e+00, 1.218582e+00, 4.770991e+01,\n",
- " 6.724979e+01, 8.731964e+00, 7.686491e+00, 4.421979e+01, 7.920045e+01,\n",
- " 7.858858e+00, 4.791967e+00, 4.259996e+01, 3.844222e+01, 2.610172e+00,\n",
- " 1.217603e+01, 5.982760e+01, 1.467179e+00, 1.501577e+01, 1.132830e+02,\n",
- " 7.346476e+00, 6.666635e-01, 4.407020e+01, 4.410812e+02, 8.965228e+00,\n",
- " 2.337221e+02, 4.066570e+01, 7.409615e+01, 1.502415e+02, 1.244322e+02,\n",
- " 5.046038e+01, 5.054806e+02, 1.796715e+02, 4.213168e+01, 4.581692e+01,\n",
- " 3.445216e+01, 2.692736e+02, 6.864005e+01, 2.874861e+02, 3.864462e+01,\n",
- " 1.721245e+01, 4.380867e+02, 1.619647e+01, 1.605528e+01, 3.136322e+01,\n",
- " 0.000000e+00, 6.509299e+01, 3.169551e+00, 2.388034e+01, 5.419331e+01,\n",
- " 1.573701e+02, 1.121844e+01, 5.210264e+00, 1.032769e+01, 2.247625e+02,\n",
- " 1.727518e+02, 9.614613e+01, 2.416419e+02, 5.982119e+02, 2.203112e+02,\n",
- " 1.083303e+01, 3.014866e+00, 4.387572e+00, 8.884601e-02, 4.875578e+01,\n",
- " 2.958232e+00, 1.261045e+02, 2.084609e+01, 0.000000e+00, 7.042564e+01,\n",
- " 9.063771e-01, 0.000000e+00, 1.022637e+00, 5.729935e+01, 5.940437e+01,\n",
- " 7.064051e+01, 1.806690e+01, 8.454706e+01, 4.031043e-02, 2.291488e+00,\n",
- " 3.564391e-01, 1.411496e+02, 1.175669e+01, 6.427244e+01, 2.211434e+01,\n",
- " 3.437179e+01, 5.238231e+00, 1.950056e+00, 1.613721e+01, 1.364565e+01,\n",
- " 4.778874e+01, 1.887730e+01, 2.253966e+01, 7.289510e+01, 1.572427e+01,\n",
- " 8.078988e+01, 1.976923e+01, 4.117908e+01, 3.095616e-03, 0.000000e+00,\n",
- " 3.171951e+01, 8.547469e+01, 4.788679e-02], dtype=float32) GPW_max_population_density_25km
(station)
float32
...
standard_name : GPW max population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 25 km around the station location. array([0.000000e+00, 7.596221e+00, 4.196918e+02, 1.047600e+03, 5.342902e+02,\n",
- " 2.169918e+03, 1.160142e+02, 1.769506e+03, 2.238533e+03, 2.902039e+02,\n",
- " 4.399082e+03, 4.939289e+02, 4.399083e+03, 4.399083e+03, 1.466429e+03,\n",
- " 3.816585e+02, 2.170391e+03, 1.127037e+03, 2.730783e+03, 1.094270e+03,\n",
- " 9.727322e+01, 1.580782e+03, 2.069240e-05, 1.407145e+03, 3.958634e+03,\n",
- " 4.454292e+03, 4.435365e+03, 3.825571e+03, 2.990599e+03, 1.299165e+02,\n",
- " 3.957805e+02, 2.023634e+03, 5.896717e+02, 3.052775e+02, 2.376391e+02,\n",
- " 2.457559e+02, 2.404993e+02, 1.705172e+03, 1.447625e+02, 4.138253e+02,\n",
- " 1.378877e+03, 5.954080e+02, 3.425591e+02, 0.000000e+00, 1.155959e+03,\n",
- " 0.000000e+00, 7.223920e+03, 0.000000e+00, 2.850471e+03, 8.838765e+02,\n",
- " 1.353755e+02, 6.711597e+01, 8.639467e+02, 7.393783e+02, 5.347466e+03,\n",
- " 7.367470e+01, 1.805890e+01, 2.303136e+03, 3.123501e+02, 4.939915e+01,\n",
- " 1.186029e+02, 6.988399e+02, 9.666424e+01, 1.213562e+03, 3.737029e+03,\n",
- " 1.959151e+01, 5.019508e+01, 3.050450e+00, 1.307162e+00, 7.499749e+02,\n",
- " 1.456541e+03, 6.132180e+02, 2.941577e+02, 1.925251e+03, 4.256683e+02,\n",
- " 1.595856e+03, 1.874457e+02, 6.907155e+02, 1.101565e+03, 3.409009e+02,\n",
- " 5.188201e+02, 4.473265e+03, 2.796940e+03, 2.248724e+03, 9.835548e+03,\n",
- " 4.619351e+03, 2.458400e+03, 2.901922e+03, 1.710923e+04, 1.318641e+04,\n",
- " 1.943179e+04, 4.608442e+03, 4.449922e+03, 9.243162e+03, 1.710923e+04,\n",
- " 4.496920e+03, 6.915309e+03, 3.480686e+03, 8.319948e+03, 1.605188e+02,\n",
- " 6.346664e+02, 3.353836e+02, 3.970044e+02, 1.997083e+03, 9.257162e+02,\n",
- " 1.441793e+03, 8.466245e+03, 4.270815e+03, 1.068325e+04, 8.250134e+03,\n",
- " 0.000000e+00, 2.176671e+02, 4.553756e+01, 5.645988e+01, 2.169255e+02,\n",
- " 2.955543e+02, 4.439969e+01, 1.672228e+02, 1.130873e+02, 2.998557e+03,\n",
- " 1.310236e+03, 2.375627e+03, 2.493601e+03, 6.149771e+03, 4.327395e+03,\n",
- " 3.059219e+02, 8.158216e+00, 8.981381e+00, 1.098970e-01, 1.712690e+02,\n",
- " 4.330686e+00, 4.002065e+02, 1.417241e+02, 0.000000e+00, 2.002448e+02,\n",
- " 9.201427e+03, 0.000000e+00, 2.152332e+01, 8.081088e+02, 1.121058e+03,\n",
- " 2.818542e+02, 2.788280e+03, 1.012623e+04, 5.401184e-02, 2.291489e+00,\n",
- " 2.764339e+00, 1.592660e+03, 9.561663e+02, 1.536779e+03, 3.355430e+03,\n",
- " 4.683061e+03, 5.341991e+01, 1.551821e+01, 6.936597e+01, 6.386975e+02,\n",
- " 2.383873e+03, 2.744521e+03, 4.787817e+02, 1.619820e+03, 3.520659e+02,\n",
- " 7.049489e+02, 2.306286e+03, 6.311625e+03, 1.951150e+01, 0.000000e+00,\n",
- " 2.171837e+03, 1.077469e+02, 3.519015e+04], dtype=float32) GPW_max_population_density_5km
(station)
float32
...
standard_name : GPW max population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 5 km around the station location. array([0.000000e+00, 7.596220e+00, 3.872487e+02, 2.635724e+01, 9.327704e+01,\n",
- " 1.932475e+02, 2.367033e+01, 1.146238e+02, 2.466041e+02, 1.422318e+02,\n",
- " 1.127776e+02, 4.939288e+02, 7.791940e+02, 3.282423e+02, 2.455698e+01,\n",
- " 4.217877e+01, 2.170391e+03, 5.714431e+01, 3.553141e+02, 6.449427e+02,\n",
- " 4.212551e+01, 1.493271e+03, 2.069240e-05, 2.992861e+01, 3.977629e+02,\n",
- " 8.473145e+02, 1.943391e+03, 9.753057e+02, 1.682020e+03, 3.581799e+00,\n",
- " 3.561705e+02, 4.398273e+01, 1.032194e+02, 5.389030e+01, 5.282152e+01,\n",
- " 2.457559e+02, 7.466113e+01, 1.404797e+03, 3.352261e+01, 4.071289e+02,\n",
- " 2.008988e+02, 3.769590e+02, 1.208774e+02, 0.000000e+00, 2.839655e+01,\n",
- " 0.000000e+00, 1.779836e+03, 0.000000e+00, 6.502718e+01, 8.103524e+00,\n",
- " 6.088673e-01, 2.479526e+01, 3.920511e+02, 7.393783e+02, 2.861667e+03,\n",
- " 5.273864e+01, 3.175393e+00, 1.156015e+02, 3.152973e+01, 1.288622e+01,\n",
- " 9.615093e+00, 3.120769e+01, 4.944548e+01, 2.765573e+01, 2.367693e+02,\n",
- " 1.959151e+01, 9.322552e+00, 3.050450e+00, 1.307162e+00, 2.238172e+02,\n",
- " 1.719670e+02, 1.238368e+01, 2.281512e+01, 2.464978e+02, 3.300192e+02,\n",
- " 8.302430e+00, 1.228482e+01, 1.353416e+02, 6.355141e+01, 2.024619e+01,\n",
- " 7.954646e+01, 6.628364e+02, 2.064309e+00, 1.740889e+02, 2.281263e+03,\n",
- " 2.893008e+01, 6.851274e-01, 1.237620e+03, 5.616340e+03, 2.649752e+02,\n",
- " 4.507116e+03, 1.678491e+03, 1.206023e+03, 4.099714e+03, 4.305407e+03,\n",
- " 1.965852e+03, 6.848575e+03, 3.480686e+03, 4.560266e+02, 7.159418e+01,\n",
- " 8.833292e+01, 3.353835e+02, 1.201789e+02, 1.997082e+03, 8.736220e+02,\n",
- " 3.726891e+01, 2.201854e+03, 3.769270e+02, 4.654190e+02, 6.244286e+02,\n",
- " 0.000000e+00, 1.209587e+02, 4.553756e+01, 5.645988e+01, 8.943985e+01,\n",
- " 2.955542e+02, 2.718571e+01, 1.994590e+01, 1.310321e+01, 2.127132e+03,\n",
- " 2.805988e+02, 1.474622e+02, 2.736410e+02, 1.525705e+03, 1.812846e+03,\n",
- " 7.832114e+01, 3.252899e+00, 4.387607e+00, 1.098970e-01, 5.004140e+01,\n",
- " 4.330686e+00, 1.726357e+02, 5.955319e+01, 0.000000e+00, 7.122749e+01,\n",
- " 5.542551e+00, 0.000000e+00, 2.518394e+00, 9.636668e+01, 2.426768e+02,\n",
- " 2.818542e+02, 2.250613e+01, 1.958325e+02, 5.401183e-02, 2.291489e+00,\n",
- " 2.764338e+00, 2.738259e+02, 2.809345e+01, 1.990008e+02, 7.795489e+01,\n",
- " 4.181690e+01, 1.385086e+01, 1.950057e+00, 1.710931e+01, 2.055932e+02,\n",
- " 4.685620e+02, 2.013748e+02, 1.757976e+02, 4.758453e+02, 2.981873e+01,\n",
- " 1.883994e+02, 1.935876e+03, 4.163841e+02, 4.666241e-02, 0.000000e+00,\n",
- " 3.004012e+02, 1.077468e+02, 2.653221e-01], dtype=float32) GPW_population_density
(station)
float32
...
standard_name : GPW population density long_name : GPW population density units : xx km–2 description : Gridded Population of the World (GPW), population density, in populus per squared kilometre, from either version 3 and 4 of the provided gridded datasets, dependent on the data year: v3 (1990-2000), v4 (2000-2015). Native resolution of 0.04166 x 0.04166 for v3 data; native resolution of 0.0083 x 0.0083 degrees for v4 data. array([0.000000e+00, 7.596219e+00, 3.727455e+01, 2.160091e+01, 6.370982e+01,\n",
- " 7.343772e+01, 9.315965e+00, 6.090043e+01, 6.510796e+01, 6.826463e+01,\n",
- " 3.772225e+01, 3.420486e+01, 3.691249e+02, 7.935504e+01, 1.726200e+01,\n",
- " 2.718578e+01, 2.170391e+03, 5.714430e+01, 4.576423e+01, 3.144363e+02,\n",
- " 1.854863e+01, 9.635000e+02, 2.069239e-05, 1.087448e+01, 3.977628e+02,\n",
- " 4.340916e+02, 8.674380e+02, 4.810824e+02, 1.608476e+02, 3.581799e+00,\n",
- " 3.561705e+02, 2.397668e+01, 5.991482e+01, 1.817250e+01, 3.504665e+01,\n",
- " 2.413085e+02, 2.268549e+01, 9.255155e+01, 1.566007e+01, 2.357139e+02,\n",
- " 5.666391e+01, 1.830428e+02, 1.083345e+02, 0.000000e+00, 2.839654e+01,\n",
- " 0.000000e+00, 6.737329e+02, 0.000000e+00, 3.361328e+01, 1.009231e+00,\n",
- " 6.088673e-01, 2.479526e+01, 1.210394e+02, 2.683964e+02, 7.582309e+01,\n",
- " 5.273864e+01, 1.791653e+00, 1.156015e+02, 1.649900e+01, 1.040796e+01,\n",
- " 7.115889e+00, 1.366867e+01, 2.215422e+01, 2.765573e+01, 1.961870e+02,\n",
- " 1.959151e+01, 9.322549e+00, 3.050449e+00, 1.307162e+00, 1.091055e+01,\n",
- " 2.971791e+01, 9.780583e+00, 6.008892e+00, 3.013581e+01, 3.300191e+02,\n",
- " 8.302428e+00, 4.554766e+00, 3.828349e+01, 6.355141e+01, 2.558549e+00,\n",
- " 3.855883e+00, 7.174796e+01, 1.456353e+00, 1.348664e+01, 4.410024e+01,\n",
- " 6.646183e+00, 6.851272e-01, 4.985307e+01, 6.500746e+01, 1.561153e+00,\n",
- " 1.111417e+01, 2.619129e+01, 2.740698e+01, 2.270585e+01, 7.810502e+00,\n",
- " 3.342193e+01, 3.205909e+01, 3.927141e+01, 9.589882e+01, 7.159418e+01,\n",
- " 6.761034e+01, 3.353835e+02, 1.201789e+02, 2.187611e+02, 4.365664e+01,\n",
- " 3.267254e+01, 4.102968e+01, 7.405539e-01, 1.033207e+01, 5.284633e-02,\n",
- " 0.000000e+00, 1.209587e+02, 4.553756e+01, 5.645987e+01, 8.943983e+01,\n",
- " 2.955542e+02, 2.718570e+01, 4.414946e+00, 1.042509e+01, 8.647990e+01,\n",
- " 1.683762e+02, 1.143645e+02, 2.736409e+02, 7.359886e+02, 1.859560e+02,\n",
- " 7.547290e+00, 3.252898e+00, 4.387606e+00, 1.098970e-01, 5.004138e+01,\n",
- " 0.000000e+00, 1.726357e+02, 9.563196e+00, 0.000000e+00, 7.122747e+01,\n",
- " 5.542550e+00, 0.000000e+00, 1.172251e+00, 4.029773e+01, 5.022400e+01,\n",
- " 2.818542e+02, 1.703191e+01, 1.958324e+02, 5.401182e-02, 2.291488e+00,\n",
- " 3.105233e-01, 2.738258e+02, 1.088254e+01, 2.435864e+01, 1.901610e+01,\n",
- " 4.181689e+01, 1.315142e+00, 1.950056e+00, 1.710930e+01, 2.718584e+01,\n",
- " 3.195612e+01, 9.911605e+00, 1.283776e+01, 1.271799e+01, 1.500078e+01,\n",
- " 7.439497e+01, 0.000000e+00, 3.542852e+01, 0.000000e+00, 0.000000e+00,\n",
- " 1.746961e+02, 7.488651e+01, 2.653221e-01], dtype=float32) GSFC_coastline_proximity
(station)
float32
...
standard_name : GSFC coastline proximity long_name : GSFC proximity to the coastline units : km description : Proximity to the coastline provided by the NASA Goddard Space Flight Center (GSFC) Ocean Color Group, in kilometres, produced using the Generic Mapping Tools package. Native resolution of 0.01 x 0.01 degrees. Negative distances represent locations over land (including land-locked bodies of water), while positive distances represent locations over the ocean. There is an uncertainty of up to 1 km in the computed distance at any given point. array([ 1.000e+00, 0.000e+00, -3.220e+02, -1.010e+02, -3.730e+02, -2.900e+02,\n",
- " -1.430e+02, -2.460e+02, -2.450e+02, -3.610e+02, -3.140e+02, -3.250e+02,\n",
- " -3.710e+02, -3.450e+02, -2.370e+02, -1.520e+02, -2.040e+02, -1.780e+02,\n",
- " -1.470e+02, -1.070e+02, -8.100e+01, 0.000e+00, -2.000e+00, -2.430e+02,\n",
- " -3.000e+02, -3.390e+02, -3.220e+02, -2.940e+02, -3.100e+02, -5.500e+01,\n",
- " 1.000e+00, -2.100e+01, -4.420e+02, -4.360e+02, -3.640e+02, 0.000e+00,\n",
- " -1.020e+02, -3.930e+02, -9.200e+01, -3.280e+02, -1.000e+00, -2.730e+02,\n",
- " -2.350e+02, -8.000e+00, 0.000e+00, -1.600e+01, 0.000e+00, -3.450e+02,\n",
- " -1.800e+01, -9.000e+00, 0.000e+00, -3.110e+02, -4.000e+00, 0.000e+00,\n",
- " -5.500e+01, -2.000e+00, -2.270e+02, 0.000e+00, -1.310e+02, -7.400e+01,\n",
- " -2.290e+02, -4.500e+01, -8.000e+01, -6.000e+00, -1.100e+01, 1.000e+00,\n",
- " -2.000e+00, -1.420e+02, -2.120e+02, -3.790e+02, -1.640e+02, -3.630e+02,\n",
- " -1.300e+02, -3.520e+02, -4.600e+01, -1.560e+02, -2.200e+02, -6.700e+01,\n",
- " -1.480e+02, -1.280e+02, -2.900e+02, -2.600e+02, -3.800e+01, -2.300e+01,\n",
- " -1.100e+01, -2.000e+01, -2.000e+01, -6.200e+01, -1.300e+01, -6.200e+01,\n",
- " -5.000e+00, -1.200e+01, -7.000e+00, -5.500e+01, -2.000e+01, 0.000e+00,\n",
- " -1.000e+00, -1.000e+00, -2.600e+01, -1.700e+01, -3.000e+00, -4.210e+02,\n",
- " -2.250e+02, -4.500e+01, -1.000e+00, -1.000e+00, -1.520e+02, -4.900e+01,\n",
- " 0.000e+00, -9.500e+01, 2.000e+00, -1.000e+00, 0.000e+00, 0.000e+00,\n",
- " -2.000e+00, -2.000e+00, -1.000e+00, -1.000e+01, -9.200e+01, 0.000e+00,\n",
- " -7.400e+01, -1.000e+00, -8.600e+01, -3.000e+00, -3.400e+01, -1.500e+01,\n",
- " -3.200e+01, -1.000e+01, -4.000e+00, -1.900e+01, -2.700e+01, -2.000e+00,\n",
- " -5.500e+01, -2.100e+02, -7.000e+00, -1.000e+00, -1.000e+00, -9.500e+01,\n",
- " -3.200e+02, -3.280e+02, -3.000e+00, -1.030e+02, -2.620e+02, -1.000e+00,\n",
- " -1.500e+02, -1.440e+02, 0.000e+00, -1.010e+02, -3.500e+01, -2.500e+01,\n",
- " -4.800e+01, -5.100e+01, -5.800e+01, -1.260e+02, -3.800e+01, -1.280e+02,\n",
- " -9.100e+01, -5.560e+02, -5.680e+02, -6.090e+02, -3.890e+02, 1.000e+00,\n",
- " -1.218e+03, -3.700e+01, -1.278e+03, 0.000e+00, -3.130e+02, 2.000e+00],\n",
- " dtype=float32) Joly-Peuch_classification_code
(station)
float32
...
standard_name : Joly-Peuch classification code long_name : Joly-Peuch classification code units : description : Joly-Peuch European classification code (range of 1-10) designed to objectively stratify stations between those diplaying rural and urban signatures (most rural == 1, most urban == 10). This classification is objectively made per species. The species that this is done for are: O3, NO2, SO2, CO, PM10, PM2.5. See reference here: https://www.sciencedirect.com/science/article/abs/pii/S1352231011012088 array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 4., 3., 1., 1.,\n",
- " nan, nan, nan, nan, nan, nan, nan, 3., nan, nan, nan, nan, 2., nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, 3., 2., 1., nan, 2., 2., 2.,\n",
- " 1., 2., 2., 1., 1., 2., 1., 2., nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 3., 4.,\n",
- " 2., 2., 3., 2., 4., 3., 3., 3., 2., 3., 3., nan, nan, nan,\n",
- " 3., nan, nan, 2., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, 3., 3., nan, nan, nan, nan, nan, nan, 3.,\n",
- " nan, 4., nan, 3., nan, 2., 3., nan, nan, nan, nan, nan, 2., nan,\n",
- " 2., 2., 2., nan, 3., 3., 3., nan, nan, 1., nan, 1., 3., 3.,\n",
- " 6., nan, nan, nan, 3., 2., 1., nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) Koppen-Geiger_classification
(station)
object
...
standard_name : Koppen-Geiger classification long_name : Koppen-Geiger classification units : description : Koppen-Geiger classification, classifying the global climates into 5 main groups (30 total groups with subcategories). Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". See citation: Beck, H.E., N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Nature Scientific Data, 2018. array(['polar - frost', 'polar - tundra', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'tropical - rainforest',\n",
- " 'polar - tundra', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'arid - desert - cold',\n",
- " 'arid - desert - hot', 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra', 'water',\n",
- " 'temperate - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer', 'polar - frost',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - hot summer', 'arid - steppe - cold',\n",
- " 'arid - steppe - cold', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'polar - tundra',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'tropical - rainforest',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - hot summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'cold - no dry season - hot summer', 'tropical - savannah',\n",
- " 'tropical - rainforest', 'cold - no dry season - hot summer', 'water',\n",
- " 'water', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'polar - frost',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'polar - frost',\n",
- " 'arid - steppe - cold', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'arid - steppe - cold', 'polar - tundra', 'polar - frost', 'water',\n",
- " 'temperate - dry winter - hot summer',\n",
- " 'temperate - dry summer - warm summer'], dtype=object) Koppen-Geiger_modal_classification_25km
(station)
object
...
standard_name : Koppen-Geiger modal classification 25km long_name : Koppen-Geiger classification units : description : Modal Koppen-Geiger classification in radius of 25km around station location. array(['water', 'polar - tundra', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'polar - tundra',\n",
- " 'polar - tundra', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'arid - desert - cold', 'water',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'water', 'water',\n",
- " 'cold - no dry season - warm summer', 'polar - frost',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer', 'water',\n",
- " 'temperate - dry summer - hot summer', 'water',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - dry summer - hot summer', 'arid - steppe - cold',\n",
- " 'arid - steppe - cold', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - warm summer', 'water',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'polar - tundra',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'water', 'water', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - dry summer - hot summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'tropical - rainforest', 'water',\n",
- " 'water', 'temperate - no dry season - hot summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - hot summer', 'water', 'water', 'water',\n",
- " 'water', 'water', 'water', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'water',\n",
- " 'cold - no dry season - cold summer', 'polar - frost',\n",
- " 'cold - no dry season - warm summer', 'water', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'arid - steppe - cold',\n",
- " 'temperate - no dry season - warm summer', 'polar - frost', 'water',\n",
- " 'temperate - dry winter - hot summer', 'water'], dtype=object) Koppen-Geiger_modal_classification_5km
(station)
object
...
standard_name : Koppen-Geiger modal classification 5km long_name : Koppen-Geiger classification units : description : Modal Koppen-Geiger classification in radius of 5km around station location. array(['water', 'water', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'polar - tundra',\n",
- " 'polar - tundra', 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'arid - desert - cold', 'water',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'water', 'water', 'water',\n",
- " 'cold - no dry season - warm summer', 'polar - frost',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer', 'water',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - dry summer - hot summer', 'arid - steppe - cold',\n",
- " 'arid - steppe - cold', 'arid - steppe - cold',\n",
- " 'temperate - dry summer - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - warm summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'polar - tundra',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'temperate - dry summer - hot summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'tropical - rainforest',\n",
- " 'temperate - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - hot summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - hot summer', 'water', 'water', 'water',\n",
- " 'water', 'cold - no dry season - hot summer', 'water', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'polar - tundra',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer', 'polar - frost',\n",
- " 'cold - no dry season - warm summer', 'water', 'water',\n",
- " 'temperate - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'polar - tundra',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - cold summer', 'water',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - cold summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer',\n",
- " 'cold - no dry season - warm summer', 'water', 'arid - steppe - cold',\n",
- " 'polar - tundra', 'polar - frost', 'water',\n",
- " 'temperate - dry winter - hot summer', 'water'], dtype=object) MODIS_MCD12C1_v6_IGBP_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP land use long_name : MODIS MCD12C1 v6 IGBP land use units : description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['permanent snow and ice', 'urban and built-up lands', 'grasslands',\n",
- " 'mixed forests', 'croplands', 'woody savannas', 'grasslands',\n",
- " 'woody savannas', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'mixed forests', 'croplands', 'croplands',\n",
- " 'mixed forests', 'mixed forests', 'urban and built-up lands',\n",
- " 'savannas', 'urban and built-up lands', 'croplands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'barren',\n",
- " 'permanent snow and ice', 'croplands', 'savannas', 'woody savannas',\n",
- " 'mixed forests', 'grasslands', 'open shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'croplands',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'mixed forests', 'mixed forests',\n",
- " 'mixed forests', 'savannas', 'mixed forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'barren', 'croplands', 'permanent snow and ice', 'savannas',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'woody savannas', 'water bodies', 'savannas', 'woody savannas',\n",
- " 'grasslands', 'water bodies', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'woody savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'woody savannas', 'woody savannas', 'woody savannas',\n",
- " 'mixed forests', 'mixed forests', 'mixed forests',\n",
- " 'cropland/natural vegetation mosaics', 'mixed forests', 'croplands',\n",
- " 'grasslands', 'woody savannas', 'savannas', 'grasslands',\n",
- " 'woody savannas', 'savannas', 'woody savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'mixed forests', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'croplands', 'savannas',\n",
- " 'cropland/natural vegetation mosaics', 'savannas', 'water bodies',\n",
- " 'croplands', 'water bodies', 'croplands', 'croplands', 'grasslands',\n",
- " 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen broadleaf forests', 'savannas', 'savannas', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'grasslands', 'woody savannas',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'croplands', 'water bodies', 'croplands',\n",
- " 'woody savannas', 'croplands', 'savannas', 'croplands', 'savannas',\n",
- " 'savannas', 'grasslands', 'mixed forests', 'grasslands', 'grasslands',\n",
- " 'barren', 'evergreen needleleaf forests', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'permanent snow and ice',\n",
- " 'woody savannas', 'water bodies', 'permanent snow and ice',\n",
- " 'grasslands', 'croplands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'mixed forests', 'savannas', 'open shrublands',\n",
- " 'woody savannas', 'woody savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'woody savannas',\n",
- " 'mixed forests', 'croplands', 'deciduous broadleaf forests',\n",
- " 'croplands', 'permanent wetlands', 'grasslands', 'barren',\n",
- " 'water bodies', 'water bodies', 'savannas', 'water bodies'],\n",
- " dtype=object) MODIS_MCD12C1_v6_LAI
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI long_name : MODIS MCD12C1 v6 Leaf Area Index units : description : Majority Leaf Area Index class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['non-vegetated lands', 'urban and built-up lands', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'savannas', 'grasslands',\n",
- " 'savannas', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'broadleaf croplands', 'broadleaf croplands',\n",
- " 'deciduous broadleaf forests', 'evergreen needleleaf forests',\n",
- " 'urban and built-up lands', 'savannas', 'urban and built-up lands',\n",
- " 'broadleaf croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'non-vegetated lands', 'non-vegetated lands', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'savannas', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'water bodies', 'non-vegetated lands', 'grasslands',\n",
- " 'non-vegetated lands', 'savannas', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'grasslands', 'savannas', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'water bodies', 'savannas', 'savannas',\n",
- " 'grasslands', 'savannas', 'savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'savannas', 'savannas', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'savannas', 'savannas', 'broadleaf croplands', 'grasslands',\n",
- " 'savannas', 'savannas', 'grasslands', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'evergreen needleleaf forests', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'savannas', 'savannas',\n",
- " 'savannas', 'water bodies', 'grasslands', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'grasslands', 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen broadleaf forests', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'grasslands', 'savannas', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'grasslands', 'savannas', 'broadleaf croplands',\n",
- " 'savannas', 'grasslands', 'savannas', 'savannas', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'grasslands',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'savannas',\n",
- " 'water bodies', 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'savannas', 'water bodies', 'non-vegetated lands', 'grasslands',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'deciduous broadleaf forests', 'savannas', 'shrublands', 'savannas',\n",
- " 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'savannas', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'savannas',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'deciduous broadleaf forests', 'broadleaf croplands',\n",
- " 'grasslands', 'grasslands', 'non-vegetated lands', 'water bodies',\n",
- " 'water bodies', 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_UMD_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD land use long_name : MODIS MCD12C1 v6 UMD land use units : description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['non-vegetated lands', 'urban and built-up lands', 'grasslands',\n",
- " 'mixed forests', 'croplands', 'woody savannas', 'grasslands',\n",
- " 'woody savannas', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'mixed forests', 'croplands', 'croplands',\n",
- " 'mixed forests', 'mixed forests', 'urban and built-up lands',\n",
- " 'savannas', 'urban and built-up lands', 'croplands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'non-vegetated lands',\n",
- " 'non-vegetated lands', 'croplands', 'savannas', 'woody savannas',\n",
- " 'mixed forests', 'grasslands', 'open shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'croplands',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'mixed forests', 'mixed forests',\n",
- " 'mixed forests', 'savannas', 'mixed forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'non-vegetated lands', 'croplands', 'non-vegetated lands', 'savannas',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'woody savannas', 'water bodies', 'savannas', 'woody savannas',\n",
- " 'grasslands', 'water bodies', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'woody savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'woody savannas', 'woody savannas', 'woody savannas',\n",
- " 'mixed forests', 'mixed forests', 'mixed forests',\n",
- " 'cropland/natural vegetation mosaics', 'mixed forests', 'croplands',\n",
- " 'grasslands', 'woody savannas', 'savannas', 'grasslands',\n",
- " 'woody savannas', 'savannas', 'woody savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'mixed forests', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'croplands', 'savannas',\n",
- " 'cropland/natural vegetation mosaics', 'savannas', 'water bodies',\n",
- " 'croplands', 'water bodies', 'croplands', 'croplands', 'grasslands',\n",
- " 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen broadleaf forests', 'savannas', 'savannas', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'grasslands', 'woody savannas',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'croplands',\n",
- " 'woody savannas', 'croplands', 'savannas', 'croplands', 'savannas',\n",
- " 'savannas', 'grasslands', 'mixed forests', 'grasslands', 'grasslands',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'savannas',\n",
- " 'water bodies', 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'woody savannas', 'water bodies', 'non-vegetated lands', 'grasslands',\n",
- " 'croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'mixed forests', 'savannas', 'open shrublands', 'woody savannas',\n",
- " 'woody savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'mixed forests', 'woody savannas',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'mixed forests', 'woody savannas', 'mixed forests', 'croplands',\n",
- " 'deciduous broadleaf forests', 'croplands', 'permanent wetlands',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 25km long_name : MODIS MCD12C1 v6 IGBP modal land use in 25km radius units : description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['water bodies', 'grasslands', 'croplands', 'mixed forests', 'croplands',\n",
- " 'mixed forests', 'grasslands', 'mixed forests', 'savannas', 'croplands',\n",
- " 'mixed forests', 'croplands', 'croplands', 'croplands', 'mixed forests',\n",
- " 'mixed forests', 'woody savannas', 'mixed forests', 'savannas',\n",
- " 'croplands', 'woody savannas', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'croplands', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'open shrublands', 'water bodies', 'grasslands',\n",
- " 'croplands', 'croplands', 'mixed forests', 'water bodies', 'croplands',\n",
- " 'mixed forests', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'mixed forests',\n",
- " 'mixed forests', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'croplands', 'permanent snow and ice', 'croplands', 'mixed forests',\n",
- " 'water bodies', 'grasslands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies', 'grasslands', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'croplands', 'woody savannas', 'grasslands',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'woody savannas',\n",
- " 'woody savannas', 'mixed forests', 'mixed forests', 'savannas',\n",
- " 'croplands', 'savannas', 'croplands', 'grasslands', 'woody savannas',\n",
- " 'savannas', 'grasslands', 'woody savannas', 'croplands', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'water bodies', 'croplands', 'savannas',\n",
- " 'croplands', 'savannas', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'croplands', 'croplands', 'water bodies', 'grasslands', 'croplands',\n",
- " 'evergreen broadleaf forests', 'water bodies', 'water bodies',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'water bodies', 'croplands', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'mixed forests', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'water bodies', 'cropland/natural vegetation mosaics',\n",
- " 'water bodies', 'grasslands', 'woody savannas', 'grasslands',\n",
- " 'grasslands', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'permanent snow and ice', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'water bodies', 'grasslands', 'croplands',\n",
- " 'mixed forests', 'water bodies', 'savannas', 'savannas', 'grasslands',\n",
- " 'woody savannas', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'croplands', 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'mixed forests',\n",
- " 'mixed forests', 'mixed forests', 'deciduous broadleaf forests',\n",
- " 'croplands', 'water bodies', 'grasslands', 'barren', 'water bodies',\n",
- " 'water bodies', 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 5km long_name : MODIS MCD12C1 v6 IGBP modal land use in 5km radius units : description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['water bodies', 'water bodies', 'grasslands', 'mixed forests',\n",
- " 'croplands', 'woody savannas', 'grasslands', 'woody savannas',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'mixed forests',\n",
- " 'mixed forests', 'croplands', 'croplands', 'mixed forests',\n",
- " 'mixed forests', 'urban and built-up lands', 'savannas', 'savannas',\n",
- " 'croplands', 'evergreen needleleaf forests', 'water bodies', 'barren',\n",
- " 'permanent snow and ice', 'croplands', 'savannas', 'woody savannas',\n",
- " 'mixed forests', 'grasslands', 'open shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'croplands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'croplands',\n",
- " 'mixed forests', 'mixed forests', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'mixed forests', 'grasslands', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies',\n",
- " 'permanent snow and ice', 'savannas', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'grasslands', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'woody savannas', 'grasslands', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'savannas', 'woody savannas', 'grasslands',\n",
- " 'grasslands', 'water bodies', 'woody savannas', 'woody savannas',\n",
- " 'woody savannas', 'mixed forests', 'mixed forests', 'mixed forests',\n",
- " 'croplands', 'mixed forests', 'croplands', 'grasslands',\n",
- " 'woody savannas', 'savannas', 'grasslands', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'croplands', 'savannas',\n",
- " 'cropland/natural vegetation mosaics', 'savannas', 'water bodies',\n",
- " 'water bodies', 'savannas', 'croplands', 'croplands', 'grasslands',\n",
- " 'grasslands', 'mixed forests', 'evergreen broadleaf forests',\n",
- " 'savannas', 'water bodies', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'water bodies',\n",
- " 'cropland/natural vegetation mosaics', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'croplands',\n",
- " 'water bodies', 'savannas', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'croplands', 'savannas', 'savannas', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'grasslands', 'barren',\n",
- " 'evergreen needleleaf forests', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'permanent snow and ice',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'grasslands', 'croplands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'mixed forests', 'deciduous broadleaf forests',\n",
- " 'open shrublands', 'woody savannas', 'woody savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'croplands', 'water bodies',\n",
- " 'grasslands', 'barren', 'water bodies', 'water bodies', 'savannas',\n",
- " 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_LAI_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 25km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 25km radius units : description : Modal Leaf Area Index in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['water bodies', 'grasslands', 'broadleaf croplands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'savannas', 'grasslands',\n",
- " 'savannas', 'savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'grasslands', 'broadleaf croplands',\n",
- " 'broadleaf croplands', 'deciduous broadleaf forests',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'deciduous broadleaf forests', 'savannas', 'broadleaf croplands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'grasslands', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'shrublands', 'water bodies', 'grasslands', 'grasslands',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'savannas', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'grasslands',\n",
- " 'non-vegetated lands', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'grasslands', 'savannas', 'water bodies', 'savannas',\n",
- " 'water bodies', 'grasslands', 'water bodies', 'savannas', 'savannas',\n",
- " 'grasslands', 'broadleaf croplands', 'savannas', 'grasslands',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'savannas', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'broadleaf croplands', 'savannas', 'broadleaf croplands',\n",
- " 'grasslands', 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'grasslands', 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'savannas', 'savannas',\n",
- " 'water bodies', 'grasslands', 'savannas', 'grasslands', 'savannas',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'water bodies', 'grasslands', 'savannas',\n",
- " 'evergreen broadleaf forests', 'water bodies', 'water bodies',\n",
- " 'savannas', 'deciduous broadleaf forests', 'water bodies', 'savannas',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'grasslands',\n",
- " 'savannas', 'water bodies', 'savannas', 'grasslands', 'savannas',\n",
- " 'water bodies', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'grasslands', 'grasslands', 'non-vegetated lands',\n",
- " 'evergreen needleleaf forests', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'grasslands', 'grasslands', 'savannas', 'water bodies', 'grasslands',\n",
- " 'savannas', 'grasslands', 'savannas', 'savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'grasslands', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'deciduous broadleaf forests', 'grasslands',\n",
- " 'deciduous broadleaf forests', 'broadleaf croplands', 'water bodies',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_LAI_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 5km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 5km radius units : description : Modal Leaf Area Index in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['water bodies', 'water bodies', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'savannas', 'grasslands',\n",
- " 'savannas', 'savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'broadleaf croplands', 'broadleaf croplands',\n",
- " 'deciduous broadleaf forests', 'evergreen needleleaf forests',\n",
- " 'urban and built-up lands', 'savannas', 'savannas',\n",
- " 'broadleaf croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'non-vegetated lands', 'non-vegetated lands', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'shrublands', 'water bodies',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'deciduous broadleaf forests', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'non-vegetated lands', 'savannas', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'grasslands', 'savannas', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'water bodies', 'savannas', 'savannas',\n",
- " 'grasslands', 'savannas', 'savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'savannas', 'savannas', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'broadleaf croplands', 'savannas', 'broadleaf croplands',\n",
- " 'grasslands', 'savannas', 'savannas', 'grasslands', 'savannas',\n",
- " 'deciduous broadleaf forests', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'grasslands', 'savannas', 'savannas',\n",
- " 'savannas', 'water bodies', 'water bodies', 'savannas', 'grasslands',\n",
- " 'grasslands', 'grasslands', 'grasslands', 'deciduous broadleaf forests',\n",
- " 'evergreen broadleaf forests', 'savannas', 'water bodies', 'savannas',\n",
- " 'savannas', 'water bodies', 'savannas', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'savannas', 'savannas', 'water bodies', 'savannas',\n",
- " 'grasslands', 'savannas', 'savannas', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'grasslands',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'savannas',\n",
- " 'water bodies', 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'grasslands', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'deciduous broadleaf forests', 'savannas', 'shrublands',\n",
- " 'savannas', 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'savannas', 'evergreen needleleaf forests', 'savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'deciduous broadleaf forests', 'savannas',\n",
- " 'deciduous broadleaf forests', 'grasslands', 'water bodies',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 25km long_name : MODIS MCD12C1 v6 UMD modal land use in 25km radius units : description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['water bodies', 'water bodies', 'croplands', 'mixed forests',\n",
- " 'croplands', 'mixed forests', 'grasslands', 'mixed forests', 'savannas',\n",
- " 'croplands', 'mixed forests', 'croplands', 'croplands', 'croplands',\n",
- " 'mixed forests', 'mixed forests', 'woody savannas', 'mixed forests',\n",
- " 'savannas', 'croplands', 'woody savannas', 'water bodies', 'grasslands',\n",
- " 'grasslands', 'croplands', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'open shrublands', 'water bodies', 'grasslands',\n",
- " 'croplands', 'croplands', 'mixed forests', 'water bodies', 'croplands',\n",
- " 'mixed forests', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'mixed forests',\n",
- " 'mixed forests', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'croplands', 'non-vegetated lands', 'croplands', 'mixed forests',\n",
- " 'water bodies', 'grasslands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies', 'grasslands', 'water bodies', 'savannas',\n",
- " 'savannas', 'grasslands', 'croplands', 'woody savannas', 'grasslands',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'woody savannas',\n",
- " 'woody savannas', 'mixed forests', 'mixed forests', 'savannas',\n",
- " 'croplands', 'savannas', 'croplands', 'grasslands', 'woody savannas',\n",
- " 'savannas', 'grasslands', 'woody savannas', 'croplands', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'savannas', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'water bodies', 'croplands', 'savannas',\n",
- " 'croplands', 'savannas', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'croplands', 'croplands', 'water bodies', 'grasslands', 'croplands',\n",
- " 'evergreen broadleaf forests', 'water bodies', 'water bodies',\n",
- " 'deciduous broadleaf forests', 'deciduous broadleaf forests',\n",
- " 'water bodies', 'croplands', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'mixed forests', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'water bodies', 'cropland/natural vegetation mosaics',\n",
- " 'water bodies', 'grasslands', 'woody savannas', 'grasslands',\n",
- " 'grasslands', 'non-vegetated lands', 'evergreen needleleaf forests',\n",
- " 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'water bodies', 'grasslands', 'croplands', 'mixed forests',\n",
- " 'water bodies', 'savannas', 'savannas', 'grasslands', 'woody savannas',\n",
- " 'savannas', 'water bodies', 'evergreen needleleaf forests',\n",
- " 'evergreen needleleaf forests', 'croplands', 'woody savannas',\n",
- " 'evergreen needleleaf forests', 'woody savannas',\n",
- " 'evergreen needleleaf forests', 'deciduous broadleaf forests',\n",
- " 'mixed forests', 'mixed forests', 'mixed forests', 'mixed forests',\n",
- " 'deciduous broadleaf forests', 'croplands', 'water bodies',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 5km long_name : MODIS MCD12C1 v6 UMD modal land use in 5km radius units : description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['water bodies', 'water bodies', 'grasslands', 'mixed forests',\n",
- " 'croplands', 'woody savannas', 'grasslands', 'woody savannas',\n",
- " 'grasslands', 'evergreen needleleaf forests', 'mixed forests',\n",
- " 'mixed forests', 'croplands', 'croplands', 'mixed forests',\n",
- " 'mixed forests', 'urban and built-up lands', 'savannas', 'savannas',\n",
- " 'croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'non-vegetated lands', 'non-vegetated lands', 'croplands', 'savannas',\n",
- " 'woody savannas', 'mixed forests', 'grasslands', 'open shrublands',\n",
- " 'water bodies', 'grasslands', 'evergreen needleleaf forests',\n",
- " 'croplands', 'evergreen needleleaf forests', 'water bodies',\n",
- " 'croplands', 'mixed forests', 'mixed forests',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'mixed forests',\n",
- " 'grasslands', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'non-vegetated lands', 'savannas',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'grasslands',\n",
- " 'woody savannas', 'water bodies', 'savannas', 'woody savannas',\n",
- " 'grasslands', 'water bodies', 'savannas', 'savannas', 'grasslands',\n",
- " 'savannas', 'woody savannas', 'grasslands', 'grasslands',\n",
- " 'water bodies', 'woody savannas', 'woody savannas', 'woody savannas',\n",
- " 'mixed forests', 'mixed forests', 'mixed forests', 'croplands',\n",
- " 'mixed forests', 'croplands', 'grasslands', 'woody savannas',\n",
- " 'savannas', 'grasslands', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'savannas', 'savannas',\n",
- " 'savannas', 'savannas', 'savannas', 'grasslands', 'savannas',\n",
- " 'savannas', 'savannas', 'croplands', 'savannas',\n",
- " 'cropland/natural vegetation mosaics', 'savannas', 'water bodies',\n",
- " 'water bodies', 'savannas', 'croplands', 'croplands', 'grasslands',\n",
- " 'grasslands', 'mixed forests', 'evergreen broadleaf forests',\n",
- " 'savannas', 'water bodies', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'water bodies',\n",
- " 'cropland/natural vegetation mosaics', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'water bodies', 'water bodies', 'water bodies',\n",
- " 'water bodies', 'savannas', 'woody savannas', 'water bodies',\n",
- " 'savannas', 'croplands', 'savannas', 'savannas', 'grasslands',\n",
- " 'evergreen needleleaf forests', 'grasslands', 'grasslands',\n",
- " 'non-vegetated lands', 'evergreen needleleaf forests', 'savannas',\n",
- " 'water bodies', 'evergreen needleleaf forests', 'non-vegetated lands',\n",
- " 'evergreen needleleaf forests', 'water bodies', 'water bodies',\n",
- " 'grasslands', 'croplands', 'evergreen needleleaf forests',\n",
- " 'water bodies', 'mixed forests', 'deciduous broadleaf forests',\n",
- " 'open shrublands', 'woody savannas', 'woody savannas', 'water bodies',\n",
- " 'evergreen needleleaf forests', 'evergreen needleleaf forests',\n",
- " 'mixed forests', 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'woody savannas', 'evergreen needleleaf forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests',\n",
- " 'deciduous broadleaf forests', 'mixed forests', 'woody savannas',\n",
- " 'deciduous broadleaf forests', 'croplands', 'water bodies',\n",
- " 'grasslands', 'non-vegetated lands', 'water bodies', 'water bodies',\n",
- " 'savannas', 'water bodies'], dtype=object) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 25km radius units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 2., 10., 5., 8., 14., 3., 9., 15., 6., 12., 12., 18., 19.,\n",
- " 5., 3., 18., 10., 26., 26., 3., 4., nan, 5., 20., 28., 18., 22.,\n",
- " 28., 1., 1., 12., 10., 8., 5., 2., 5., 14., 4., 10., 3., 9.,\n",
- " 10., nan, 1., nan, 23., 0., 6., 3., 0., 2., 16., 4., 17., 4.,\n",
- " 0., 6., 3., 3., 2., 9., 4., 3., 14., 0., 6., 4., 3., 10.,\n",
- " 13., 3., 5., 14., 11., 8., 2., 7., 7., 3., 4., 20., 1., 5.,\n",
- " 12., 6., 1., 5., 23., 26., 12., 8., 6., 18., 20., 5., 12., 2.,\n",
- " 16., 8., 3., 6., 7., 2., 1., 1., 36., 11., 8., 17., nan, 4.,\n",
- " 0., 0., 9., 9., 0., 1., 1., 5., 18., 11., 27., 32., 37., 10.,\n",
- " 1., 2., nan, 6., 6., 7., 12., nan, 14., 6., nan, 0., 7., 15.,\n",
- " 2., 4., 9., 1., 2., 2., 10., 7., 13., 16., 9., 4., 4., 4.,\n",
- " 4., 8., 12., 8., 11., 1., 8., 2., 23., 0., nan, 2., 0., 1.],\n",
- " dtype=float32) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 5km radius units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 20., 6., 7., 9., 12., 0., 5., 11., 7., 6., 14., 20., 12.,\n",
- " 0., 1., 46., 12., 36., 30., 4., 16., nan, 2., 23., 26., 23., 22.,\n",
- " 25., 0., 0., 7., 10., 6., 6., 21., 2., 9., 1., 14., 6., 14.,\n",
- " 7., nan, 1., nan, 20., 0., 1., 3., 0., 3., 11., 17., 24., 12.,\n",
- " 1., 4., 1., 7., 1., 5., 7., 10., 1., 0., 5., 0., 2., 6.,\n",
- " 12., 0., 1., 11., 16., 10., 0., 5., 5., 0., 1., 15., 0., 2.,\n",
- " 9., 1., 0., 5., 38., 8., 15., 7., 8., 13., 11., 8., 18., 17.,\n",
- " 9., 10., 2., 7., 3., 0., 6., 0., 34., 7., 7., 9., nan, 3.,\n",
- " 0., 1., 17., 12., 0., 0., 0., 25., 16., 10., 12., 30., 20., 4.,\n",
- " 0., 0., nan, 1., 14., 11., 9., nan, 37., 0., nan, 0., 7., 14.,\n",
- " 10., 0., 10., 2., 1., 4., 7., 3., 10., 8., 7., 0., 8., 1.,\n",
- " 3., 6., 5., 5., 19., 0., 1., 22., 19., 0., nan, 3., 0., 0.],\n",
- " dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 25km radius units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 62., 57., 53., 54., 55., 42., 57., 62., 42., 58., 59., 63., 63.,\n",
- " 58., 37., 63., 45., 62., 63., 51., 62., nan, 55., 62., 63., 61., 61.,\n",
- " 61., 51., 60., 63., 54., 48., 29., 56., 53., 62., 44., 51., 61., 51.,\n",
- " 50., nan, 19., nan, 63., 0., 58., 50., 9., 32., 61., 63., 63., 44.,\n",
- " 12., 62., 52., 62., 32., 62., 47., 59., 63., 0., 58., 62., 60., 52.,\n",
- " 60., 24., 32., 62., 56., 58., 28., 59., 58., 50., 49., 63., 29., 58.,\n",
- " 63., 61., 36., 40., 63., 63., 63., 61., 56., 63., 63., 44., 63., 52.,\n",
- " 63., 57., 53., 61., 56., 57., 21., 15., 62., 51., 63., 62., nan, 56.,\n",
- " 0., 6., 59., 58., 14., 28., 18., 63., 61., 62., 61., 63., 63., 61.,\n",
- " 34., 53., nan, 63., 62., 63., 63., nan, 63., 63., nan, 0., 46., 56.,\n",
- " 43., 52., 62., 34., 50., 60., 62., 33., 60., 63., 63., 58., 53., 58.,\n",
- " 39., 51., 62., 56., 63., 29., 55., 62., 63., 5., nan, 51., 18., 50.],\n",
- " dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 5km radius units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 62., 19., 23., 27., 21., 6., 17., 28., 18., 13., 53., 45., 23.,\n",
- " 7., 11., 63., 29., 56., 45., 21., 48., nan, 10., 44., 45., 51., 40.,\n",
- " 52., 0., 5., 12., 18., 8., 24., 56., 10., 18., 8., 50., 18., 30.,\n",
- " 31., nan, 6., nan, 61., 0., 9., 17., 0., 15., 29., 63., 62., 26.,\n",
- " 5., 23., 13., 41., 8., 10., 34., 59., 12., 0., 25., 0., 10., 26.,\n",
- " 31., 7., 8., 39., 36., 37., 6., 14., 22., 5., 7., 51., 0., 10.,\n",
- " 35., 7., 0., 12., 63., 20., 53., 19., 16., 35., 45., 25., 59., 52.,\n",
- " 23., 25., 10., 21., 28., 6., 17., 6., 51., 20., 30., 25., nan, 10.,\n",
- " 0., 6., 44., 22., 9., 0., 6., 61., 42., 15., 18., 61., 42., 41.,\n",
- " 0., 0., nan, 8., 33., 30., 29., nan, 63., 5., nan, 0., 19., 41.,\n",
- " 31., 0., 39., 26., 8., 11., 41., 8., 39., 17., 40., 0., 51., 7.,\n",
- " 14., 38., 17., 27., 29., 8., 10., 62., 60., 0., nan, 9., 0., 0.],\n",
- " dtype=float32) NOAA-DMSP-OLS_v4_nighttime_stable_lights
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 nighttime stable lights long_name : NOAA DMSP-OLS version 4 nighttime stable lights units : description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 nighttime stable lights. Native resolution of 0.0083 x 0.0083 degrees. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 0., 14., 8., 14., 8., 16., 0., 5., 9., 5., 6., 10., 29., 10.,\n",
- " 0., 0., 58., 8., 46., 30., 0., 19., nan, 0., 41., 32., 14., 15.,\n",
- " 19., 0., 0., 11., 15., 6., 12., 46., 0., 8., 0., 8., 9., 18.,\n",
- " 6., nan, 0., nan, 17., 0., 0., 0., 0., 12., 6., 13., 20., 20.,\n",
- " 0., 0., 0., 5., 0., 5., 10., 6., 0., 0., 0., 0., 0., 8.,\n",
- " 14., 0., 0., 9., 35., 11., 0., 7., 0., 5., 0., 11., 0., 0.,\n",
- " 8., 0., 0., 8., 51., 7., 7., 6., 6., 10., 8., 8., 14., 26.,\n",
- " 8., 14., 0., 6., 5., 0., 14., 0., 35., 0., 13., 6., nan, 0.,\n",
- " 0., 5., 25., 16., 0., 0., 0., 33., 14., 12., 15., 26., 20., 0.,\n",
- " 0., 0., nan, 0., 23., 16., 8., nan, 46., 0., nan, 0., 7., 7.,\n",
- " 18., 0., 9., 0., 8., 9., 6., 7., 8., 7., 7., 0., 8., 0.,\n",
- " 0., 7., 7., 8., 20., 0., 0., 21., 13., 0., nan, 6., 0., 0.],\n",
- " dtype=float32) OMI_level3_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column annual average NO2 long_name : OMI level3 column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([3.473341e+15, 2.757102e+15, 7.345563e+15, 4.663894e+15, 6.847881e+15,\n",
- " 6.168189e+15, 4.584701e+15, 5.862558e+15, 6.179914e+15, 6.455329e+15,\n",
- " 7.378372e+15, 7.316141e+15, 7.611382e+15, 7.677720e+15, 5.845425e+15,\n",
- " 4.858490e+15, 6.308826e+15, 6.443415e+15, 9.380302e+15, 8.875505e+15,\n",
- " 3.809209e+15, 3.315453e+15, 4.279896e+15, 4.351421e+15, 5.403429e+15,\n",
- " 6.935566e+15, 4.956889e+15, 5.876876e+15, 5.775327e+15, 3.005828e+15,\n",
- " 2.579832e+15, 3.935034e+15, 7.049085e+15, 7.130492e+15, 6.502953e+15,\n",
- " 6.226574e+15, 8.221473e+15, 5.835075e+15, 7.717232e+15, 7.564886e+15,\n",
- " 6.954989e+15, 5.841256e+15, 5.286593e+15, 3.742470e+15, 7.381992e+15,\n",
- " 4.288764e+15, 6.681218e+15, nan, 5.106118e+15, 4.167499e+15,\n",
- " 4.139215e+15, 4.062219e+15, 4.100420e+15, 3.933322e+15, 4.104039e+15,\n",
- " 3.957674e+15, 4.119975e+15, 4.649604e+15, 3.938711e+15, 4.308088e+15,\n",
- " 3.753127e+15, 4.559792e+15, 4.047396e+15, 4.061001e+15, 3.250765e+15,\n",
- " 3.973670e+15, 4.912318e+15, 3.922882e+15, 3.888045e+15, 5.590997e+15,\n",
- " 7.136552e+15, 4.412827e+15, 4.272603e+15, 5.016980e+15, 4.066349e+15,\n",
- " 3.994068e+15, 3.910209e+15, 4.822903e+15, 3.575267e+15, 4.783221e+15,\n",
- " 4.322277e+15, 3.947022e+15, 4.579524e+15, 3.834530e+15, 4.273179e+15,\n",
- " 7.581315e+15, 3.640667e+15, 4.764857e+15, 4.699631e+15, 7.661165e+15,\n",
- " 6.513909e+15, 7.293615e+15, 3.779409e+15, 7.954559e+15, 4.699631e+15,\n",
- " 7.423154e+15, 8.594613e+15, 4.073575e+15, 5.604822e+15, 4.208290e+15,\n",
- " 3.607691e+15, 5.737488e+15, 5.957745e+15, 2.461156e+15, 2.962316e+15,\n",
- " 3.128346e+15, 8.365826e+15, 5.172941e+15, 3.949571e+15, 4.763467e+15,\n",
- " 4.153752e+15, 4.998319e+15, 2.723367e+15, 3.546172e+15, 7.692245e+15,\n",
- " 4.759712e+15, 5.936859e+15, 5.139146e+15, 4.734415e+15, 3.604704e+15,\n",
- " 1.125353e+16, 8.950532e+15, 1.122162e+16, 1.061557e+16, 1.146695e+16,\n",
- " 4.921244e+15, 3.337922e+15, 3.643524e+15, 3.962955e+15, 4.708453e+15,\n",
- " 3.591406e+15, 4.612725e+15, 4.928113e+15, nan, 4.821815e+15,\n",
- " 3.455548e+15, 3.723008e+15, 3.007410e+15, 7.015296e+15, 7.453896e+15,\n",
- " 5.797097e+15, 5.698536e+15, 4.854830e+15, 3.686678e+15, 3.459293e+15,\n",
- " 3.760629e+15, 5.946741e+15, 5.273878e+15, 5.083707e+15, 6.174996e+15,\n",
- " 4.721677e+15, 4.468787e+15, 3.710538e+15, 4.536030e+15, 4.745260e+15,\n",
- " 5.496117e+15, 5.114420e+15, 5.579634e+15, 5.180446e+15, 4.930619e+15,\n",
- " 6.805185e+15, 3.456580e+15, 5.581911e+15, 2.814228e+15, nan,\n",
- " 3.253400e+15, 3.206773e+15, 3.050493e+15], dtype=float32) OMI_level3_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column cloud screened annual average NO2 long_name : OMI level3 column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([3.532428e+15, 2.639686e+15, 5.867690e+15, 4.187000e+15, 5.588512e+15,\n",
- " 5.222007e+15, 4.145348e+15, 5.214945e+15, 4.917223e+15, 4.954742e+15,\n",
- " 6.094566e+15, 5.826555e+15, 6.110681e+15, 6.275420e+15, 4.848703e+15,\n",
- " 4.424205e+15, 5.583187e+15, 5.758002e+15, 8.526934e+15, 8.135533e+15,\n",
- " 3.795629e+15, 3.434910e+15, 4.453713e+15, 4.068646e+15, 5.065195e+15,\n",
- " 5.791190e+15, 4.857933e+15, 5.250922e+15, 5.352893e+15, 2.987894e+15,\n",
- " 2.586228e+15, 4.001106e+15, 5.342705e+15, 5.562301e+15, 5.080109e+15,\n",
- " 5.267317e+15, 6.264316e+15, 5.544388e+15, 5.676098e+15, 5.874213e+15,\n",
- " 5.218944e+15, 5.295258e+15, 4.130269e+15, 3.843294e+15, 5.849887e+15,\n",
- " nan, 5.456292e+15, nan, 4.499519e+15, 4.109295e+15,\n",
- " 4.112221e+15, 3.996820e+15, 4.212227e+15, 3.848318e+15, 4.158201e+15,\n",
- " 3.840789e+15, 3.927497e+15, 4.347146e+15, 3.960222e+15, 4.245645e+15,\n",
- " 3.773102e+15, 4.420939e+15, 3.942068e+15, 4.156779e+15, 3.303044e+15,\n",
- " 3.961144e+15, 4.489403e+15, 4.059251e+15, 3.941625e+15, 5.209817e+15,\n",
- " 5.950123e+15, 4.397264e+15, 4.193617e+15, 5.063839e+15, 4.150675e+15,\n",
- " 4.045118e+15, 3.761147e+15, 4.525007e+15, 3.482751e+15, 4.580291e+15,\n",
- " 4.417874e+15, 3.884223e+15, 3.986373e+15, 3.876703e+15, 4.612321e+15,\n",
- " 5.765426e+15, 3.758801e+15, 4.570372e+15, 4.403545e+15, 6.471081e+15,\n",
- " 6.303857e+15, 6.325613e+15, 4.056245e+15, 6.595459e+15, 4.403545e+15,\n",
- " 5.959062e+15, 7.787043e+15, 3.714682e+15, 5.685939e+15, 4.257667e+15,\n",
- " 3.688463e+15, 5.080269e+15, 5.093506e+15, 2.585899e+15, 3.306903e+15,\n",
- " 3.462402e+15, 7.005490e+15, 5.021203e+15, 3.890843e+15, 4.356960e+15,\n",
- " nan, 4.714578e+15, 2.727766e+15, 3.529483e+15, 7.606910e+15,\n",
- " 4.600654e+15, 4.509253e+15, 4.308589e+15, 4.259049e+15, 3.615048e+15,\n",
- " 9.011939e+15, 6.937365e+15, 9.850068e+15, 9.264850e+15, 9.735531e+15,\n",
- " 4.262205e+15, 4.006099e+15, 3.579393e+15, 3.895142e+15, 3.984774e+15,\n",
- " 3.835624e+15, 4.269370e+15, 4.525225e+15, nan, 4.394645e+15,\n",
- " 3.428801e+15, nan, 3.016481e+15, 5.421997e+15, 5.674910e+15,\n",
- " 4.873663e+15, 4.614278e+15, 4.703133e+15, 4.224745e+15, 3.903788e+15,\n",
- " 3.801359e+15, 5.026321e+15, 4.592180e+15, 4.592788e+15, 4.937449e+15,\n",
- " 4.424600e+15, 4.151914e+15, 3.886247e+15, 4.396008e+15, 4.517754e+15,\n",
- " 4.788532e+15, 4.789130e+15, 4.602289e+15, 4.507351e+15, 4.204522e+15,\n",
- " 5.657308e+15, 3.518051e+15, 5.169831e+15, 2.783493e+15, nan,\n",
- " 3.268930e+15, 3.306158e+15, 3.058697e+15], dtype=float32) OMI_level3_tropospheric_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column annual average NO2 long_name : OMI level3 tropospheric column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([2.089988e+14, 3.584643e+14, 4.854586e+15, 2.186984e+15, 4.350195e+15,\n",
- " 3.692631e+15, 2.309433e+15, 3.353392e+15, 3.745330e+15, 4.048057e+15,\n",
- " 4.817116e+15, 4.833554e+15, 4.980179e+15, 5.174898e+15, 3.493000e+15,\n",
- " 2.414175e+15, 3.825593e+15, 4.207652e+15, 6.867158e+15, 6.651714e+15,\n",
- " 1.295513e+15, 8.029932e+14, 2.521319e+14, 1.917871e+15, 2.904494e+15,\n",
- " 4.445739e+15, 2.502260e+15, 3.447178e+15, 3.443647e+15, 7.490186e+14,\n",
- " 4.168854e+14, 1.267978e+15, 4.634118e+15, 4.707298e+15, 4.161965e+15,\n",
- " 4.126384e+15, 5.823442e+15, 3.324731e+15, 5.469872e+15, 5.140282e+15,\n",
- " 4.567384e+15, 3.378735e+15, 2.818044e+15, 1.796697e+14, 5.129525e+15,\n",
- " 1.563328e+14, 4.324850e+15, nan, 2.778101e+15, 1.952670e+15,\n",
- " 1.826281e+15, 1.416338e+15, 1.476852e+15, 1.312767e+15, 1.383646e+15,\n",
- " 1.535568e+15, 1.708116e+15, 2.060796e+15, 1.311217e+15, 1.647795e+15,\n",
- " 1.187153e+15, 1.991316e+15, 1.594897e+15, 1.367500e+15, 7.470208e+14,\n",
- " 1.633403e+15, 2.730057e+15, 9.785123e+14, 8.111674e+14, 3.245221e+15,\n",
- " 4.758437e+15, 1.936010e+15, 1.713117e+15, 2.699113e+15, 1.623961e+15,\n",
- " 1.487158e+15, 1.514948e+15, 2.434608e+15, 1.221044e+15, 2.432322e+15,\n",
- " 1.861073e+15, 1.521955e+15, 2.244473e+15, 1.536093e+15, 1.791047e+15,\n",
- " 5.291541e+15, 1.336670e+15, 2.367100e+15, 2.402279e+15, 5.210743e+15,\n",
- " 4.007436e+15, 4.937721e+15, 1.387553e+15, 5.519484e+15, 2.402279e+15,\n",
- " 5.118559e+15, 6.268216e+15, 1.780961e+15, 3.129665e+15, 1.570732e+15,\n",
- " 9.362735e+14, 3.233052e+15, 3.402136e+15, 5.822059e+14, 5.290588e+14,\n",
- " 7.036002e+14, 5.966317e+15, 2.606465e+15, 1.351236e+15, 2.215723e+15,\n",
- " 3.067026e+14, 2.387623e+15, 3.904560e+14, 1.207140e+15, 5.069879e+15,\n",
- " 2.243110e+15, 3.781858e+15, 2.947661e+15, 2.401002e+15, 1.003628e+15,\n",
- " 8.804974e+15, 6.670742e+15, 8.657363e+15, 8.296446e+15, 8.966678e+15,\n",
- " 2.676036e+15, 7.422353e+14, 1.324518e+15, 3.595907e+14, 2.595616e+15,\n",
- " 6.079751e+14, 2.405702e+15, 2.671498e+15, nan, 2.559082e+15,\n",
- " 1.063710e+15, 1.542406e+14, 6.303014e+14, 4.712985e+15, 5.056608e+15,\n",
- " 3.595138e+15, 3.667676e+15, 2.310345e+15, 2.727649e+14, 9.364728e+14,\n",
- " 6.600575e+14, 3.644737e+15, 3.042722e+15, 2.808257e+15, 3.999318e+15,\n",
- " 2.477753e+15, 2.157872e+15, 1.211690e+15, 2.253023e+15, 2.259441e+15,\n",
- " 2.974318e+15, 2.577151e+15, 3.143127e+15, 2.789992e+15, 2.529712e+15,\n",
- " 4.251182e+15, 2.957702e+14, 3.090131e+15, 6.266068e+14, nan,\n",
- " 7.479317e+14, 1.051665e+15, 6.196790e+14], dtype=float32) OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column cloud screened annual average NO2 long_name : OMI level3 tropospheric column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([4.754073e+14, 4.502345e+14, 2.989990e+15, 1.590792e+15, 2.660002e+15,\n",
- " 2.411796e+15, 1.592236e+15, 2.567111e+15, 2.043094e+15, 2.184788e+15,\n",
- " 3.174512e+15, 2.963212e+15, 3.216767e+15, 3.312168e+15, 2.197623e+15,\n",
- " 1.668676e+15, 2.693183e+15, 3.058137e+15, 5.815727e+15, 5.277298e+15,\n",
- " 1.248434e+15, 8.502206e+14, 2.582090e+14, 1.353951e+15, 2.303014e+15,\n",
- " 2.946991e+15, 2.208910e+15, 2.459448e+15, 2.593697e+15, 7.465364e+14,\n",
- " 4.226264e+14, 1.284026e+15, 2.466927e+15, 2.666904e+15, 2.430968e+15,\n",
- " 2.764044e+15, 3.516648e+15, 2.786582e+15, 2.977559e+15, 3.145857e+15,\n",
- " 2.556432e+15, 2.419145e+15, 1.573150e+15, 1.129309e+13, 3.320597e+15,\n",
- " nan, 2.660673e+15, nan, 1.743551e+15, 1.149409e+15,\n",
- " 1.190683e+15, 1.377289e+15, 1.440652e+15, 1.158520e+15, 1.419276e+15,\n",
- " 1.510276e+15, 1.417442e+15, 1.667895e+15, 1.313956e+15, 1.555254e+15,\n",
- " 1.145179e+15, 1.748105e+15, 1.436062e+15, 1.449538e+15, 7.845993e+14,\n",
- " 1.130061e+15, 1.442196e+15, 9.003820e+14, 7.055360e+14, 2.461058e+15,\n",
- " 3.165109e+15, 1.665509e+15, 1.517896e+15, 2.341339e+15, 1.439897e+15,\n",
- " 1.304336e+15, 1.198233e+15, 1.861282e+15, 1.027340e+15, 2.106200e+15,\n",
- " 1.638015e+15, 1.238765e+15, 1.505213e+15, 1.259837e+15, 1.742799e+15,\n",
- " 3.343065e+15, 1.263338e+15, 1.913839e+15, 1.905305e+15, 3.681093e+15,\n",
- " 3.502112e+15, 3.580193e+15, 1.342023e+15, 3.891760e+15, 1.905305e+15,\n",
- " 3.323524e+15, 4.983296e+15, 1.373583e+15, 2.999636e+15, 1.520230e+15,\n",
- " 9.605778e+14, 2.321274e+15, 2.321322e+15, 6.900251e+14, 6.714803e+14,\n",
- " 8.239604e+14, 4.353938e+15, 2.347301e+15, 1.202253e+15, 1.642187e+15,\n",
- " nan, 2.086612e+15, 3.965878e+14, 1.102438e+15, 4.918048e+15,\n",
- " 2.023781e+15, 1.800709e+15, 1.613745e+15, 1.312202e+15, 9.724966e+14,\n",
- " 6.267002e+15, 4.397799e+15, 7.011783e+15, 6.574743e+15, 6.867529e+15,\n",
- " 1.505240e+15, 6.233324e+14, 1.009843e+15, 4.258189e+14, 1.360584e+15,\n",
- " 6.146184e+14, 1.763054e+15, 1.497895e+15, nan, 1.556925e+15,\n",
- " 1.028614e+15, nan, 6.977960e+14, 2.564195e+15, 2.841774e+15,\n",
- " 2.170737e+15, 2.068653e+15, 1.887385e+15, 3.482298e+14, 9.475596e+14,\n",
- " 7.613750e+14, 2.168495e+15, 1.730114e+15, 1.786381e+15, 2.239897e+15,\n",
- " 1.679060e+15, 1.381100e+15, 1.031851e+15, 1.485161e+15, 1.899351e+15,\n",
- " 2.088062e+15, 2.021358e+15, 2.007737e+15, 1.859909e+15, 1.526916e+15,\n",
- " 2.747525e+15, 4.914727e+14, 2.609755e+15, 6.267174e+14, nan,\n",
- " 6.815022e+14, 1.139593e+15, 6.288178e+14], dtype=float32) UMBC_anthrome_classification
(station)
object
...
standard_name : UMBC anthrome classification long_name : UMBC anthrome classification units : description : University of Maryland Baltimore County (UMBC) anthrome classification, describing the anthropogenic land use (for the year 2000). There are 20 distinct classifications. Native resolution of 0.0833 x 0.0833 degrees. A correction for costal sites is made: if the native anthrome class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['water', 'residential rangelands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'populated rangelands', 'populated croplands',\n",
- " 'pastoral villages', 'residential woodlands', 'residential woodlands',\n",
- " 'residential rangelands', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated woodlands', 'urban', 'residential rainfed croplands',\n",
- " 'mixed settlements', 'rainfed villages',\n",
- " 'inhabited treeless and barren lands', 'water',\n",
- " 'wild treeless and barren lands', 'populated rangelands',\n",
- " 'rainfed villages', 'pastoral villages', 'rainfed villages',\n",
- " 'mixed settlements', 'pastoral villages', 'populated rangelands',\n",
- " 'residential woodlands', 'populated woodlands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands', 'water',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential rangelands', 'rainfed villages', 'residential rangelands',\n",
- " 'water', 'residential rainfed croplands', 'water', 'rainfed villages',\n",
- " 'water', 'residential rainfed croplands', 'populated woodlands',\n",
- " 'water', 'inhabited treeless and barren lands', 'residential woodlands',\n",
- " 'rainfed villages', 'rainfed villages', 'residential rainfed croplands',\n",
- " 'populated croplands', 'inhabited treeless and barren lands',\n",
- " 'residential rainfed croplands', 'populated croplands',\n",
- " 'populated croplands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'water', 'populated croplands',\n",
- " 'wild woodlands', 'wild woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'wild woodlands', 'populated croplands',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'populated rangelands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated rangelands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rangelands', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'rainfed villages',\n",
- " 'residential rangelands', 'residential rainfed croplands',\n",
- " 'residential irrigated croplands', 'residential rangelands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'mixed settlements', 'residential rangelands', 'residential woodlands',\n",
- " 'rainfed villages', 'populated woodlands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'water', 'residential woodlands',\n",
- " 'water', 'water', 'populated woodlands', 'populated woodlands',\n",
- " 'inhabited treeless and barren lands', 'populated woodlands',\n",
- " 'residential woodlands', 'water', 'residential irrigated croplands',\n",
- " 'residential rainfed croplands', 'residential irrigated croplands',\n",
- " 'residential rainfed croplands', 'rainfed villages',\n",
- " 'populated woodlands', 'wild woodlands', 'populated woodlands', 'water',\n",
- " 'populated woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands', 'water',\n",
- " 'mixed settlements', 'populated croplands', 'water',\n",
- " 'residential irrigated croplands', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'populated croplands', 'rainfed villages', 'wild woodlands',\n",
- " 'populated woodlands', 'wild woodlands', 'water', 'populated woodlands',\n",
- " 'populated woodlands', 'populated croplands', 'populated croplands',\n",
- " 'populated woodlands', 'populated woodlands', 'populated woodlands',\n",
- " 'residential woodlands', 'residential woodlands',\n",
- " 'populated rangelands', 'populated woodlands', 'rainfed villages',\n",
- " 'populated woodlands', 'residential irrigated croplands',\n",
- " 'inhabited treeless and barren lands',\n",
- " 'residential irrigated croplands', 'wild woodlands', 'water',\n",
- " 'residential woodlands', 'residential woodlands', 'remote rangelands'],\n",
- " dtype=object) UMBC_modal_anthrome_classification_25km
(station)
object
...
standard_name : UMBC modal anthrome classification 25km long_name : UMBC modal anthrome classification in 25km radius units : description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 25km around station location. array(['water', 'remote rangelands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'populated rangelands', 'populated woodlands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential irrigated croplands', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'populated woodlands', 'residential rangelands',\n",
- " 'residential woodlands', 'rainfed villages', 'rainfed villages',\n",
- " 'inhabited treeless and barren lands', 'water',\n",
- " 'wild treeless and barren lands', 'populated rangelands',\n",
- " 'rainfed villages', 'rainfed villages', 'rainfed villages',\n",
- " 'mixed settlements', 'rainfed villages', 'populated rangelands',\n",
- " 'water', 'populated croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential woodlands', 'water',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential woodlands', 'water',\n",
- " 'residential rangelands', 'mixed settlements', 'water', 'water',\n",
- " 'water', 'rainfed villages', 'water', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'water', 'populated croplands',\n",
- " 'residential woodlands', 'water', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'populated croplands', 'water',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'water', 'water', 'wild woodlands',\n",
- " 'populated woodlands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential irrigated croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'populated rangelands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'populated rangelands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'rainfed villages', 'water',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'water', 'water', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'water',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'water', 'water', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'water',\n",
- " 'residential rainfed croplands', 'water', 'water', 'water', 'water',\n",
- " 'water', 'water', 'water', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'water', 'rainfed villages',\n",
- " 'rainfed villages', 'irrigated villages', 'water', 'rainfed villages',\n",
- " 'populated woodlands', 'populated woodlands', 'populated woodlands',\n",
- " 'water', 'populated woodlands', 'populated woodlands', 'water',\n",
- " 'populated woodlands', 'water', 'populated woodlands', 'water', 'water',\n",
- " 'populated croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'wild woodlands', 'populated woodlands', 'wild woodlands', 'water',\n",
- " 'populated woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands',\n",
- " 'populated woodlands', 'populated woodlands', 'populated woodlands',\n",
- " 'populated woodlands', 'residential woodlands', 'populated woodlands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'residential irrigated croplands', 'water',\n",
- " 'residential irrigated croplands', 'wild woodlands', 'water', 'water',\n",
- " 'residential woodlands', 'water'], dtype=object) UMBC_modal_anthrome_classification_5km
(station)
object
...
standard_name : UMBC modal anthrome classification 5km long_name : UMBC modal anthrome classification in 5km radius units : description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 5km around station location. array(['water', 'residential rangelands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'populated rangelands', 'populated croplands',\n",
- " 'pastoral villages', 'residential woodlands', 'residential woodlands',\n",
- " 'residential rangelands', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated woodlands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'pastoral villages',\n",
- " 'rainfed villages', 'inhabited treeless and barren lands', 'water',\n",
- " 'wild treeless and barren lands', 'populated rangelands',\n",
- " 'rainfed villages', 'pastoral villages', 'rainfed villages',\n",
- " 'mixed settlements', 'rainfed villages', 'populated rangelands',\n",
- " 'residential woodlands', 'populated woodlands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential woodlands', 'water',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential woodlands', 'residential woodlands',\n",
- " 'residential rangelands', 'rainfed villages', 'residential rangelands',\n",
- " 'water', 'residential rainfed croplands', 'water', 'rainfed villages',\n",
- " 'water', 'residential rainfed croplands', 'residential woodlands',\n",
- " 'water', 'inhabited treeless and barren lands', 'residential woodlands',\n",
- " 'rainfed villages', 'rainfed villages', 'residential rainfed croplands',\n",
- " 'populated croplands', 'inhabited treeless and barren lands',\n",
- " 'residential rainfed croplands', 'populated croplands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential woodlands', 'residential rainfed croplands',\n",
- " 'residential rangelands', 'water', 'populated croplands',\n",
- " 'wild woodlands', 'wild woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'populated rangelands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'wild woodlands', 'populated croplands',\n",
- " 'residential rainfed croplands', 'residential woodlands',\n",
- " 'populated rangelands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'populated rangelands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rangelands', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'rainfed villages', 'residential rainfed croplands',\n",
- " 'residential rainfed croplands', 'rainfed villages',\n",
- " 'residential rangelands', 'rainfed villages',\n",
- " 'residential irrigated croplands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'residential rainfed croplands',\n",
- " 'mixed settlements', 'residential rangelands', 'residential woodlands',\n",
- " 'rainfed villages', 'populated woodlands', 'residential rangelands',\n",
- " 'residential rainfed croplands', 'water', 'residential woodlands',\n",
- " 'water', 'water', 'populated woodlands', 'populated woodlands',\n",
- " 'inhabited treeless and barren lands', 'populated woodlands',\n",
- " 'residential woodlands', 'water', 'rainfed villages',\n",
- " 'residential rainfed croplands', 'residential irrigated croplands',\n",
- " 'rainfed villages', 'rainfed villages', 'populated woodlands',\n",
- " 'populated woodlands', 'populated woodlands', 'water',\n",
- " 'populated woodlands', 'populated woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands', 'water',\n",
- " 'mixed settlements', 'populated croplands', 'water',\n",
- " 'residential irrigated croplands', 'residential rainfed croplands',\n",
- " 'populated woodlands', 'residential rainfed croplands',\n",
- " 'populated croplands', 'rainfed villages', 'wild woodlands',\n",
- " 'populated woodlands', 'wild woodlands',\n",
- " 'residential rainfed croplands', 'populated woodlands',\n",
- " 'populated woodlands', 'populated croplands', 'populated croplands',\n",
- " 'populated woodlands', 'wild woodlands', 'populated woodlands',\n",
- " 'residential woodlands', 'residential woodlands',\n",
- " 'populated rangelands', 'populated woodlands', 'rainfed villages',\n",
- " 'populated woodlands', 'residential irrigated croplands',\n",
- " 'inhabited treeless and barren lands',\n",
- " 'residential irrigated croplands', 'wild woodlands', 'water',\n",
- " 'residential woodlands', 'residential woodlands', 'remote rangelands'],\n",
- " dtype=object) WMO_region
(station)
object
...
standard_name : WMO region long_name : WMO region code units : description : World Meteorological Organization (WMO) region of station. The available regions are: Africa, Asia, South America, "Northern America, Central America and the Caribbean", South-West Pacific, Europe and Antarctica. array(['Antarctic', 'South America', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'North America, Central America And The Caribbean',\n",
- " 'North America, Central America And The Caribbean', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'South America', 'Africa',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Antarctic', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Africa', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'South-West Pacific', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Antarctic', 'Asia', 'Asia', 'Asia',\n",
- " 'Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Antarctic', 'Europe',\n",
- " 'South-West Pacific', 'Antarctic', 'South-West Pacific', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Asia', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe',\n",
- " 'Europe', 'North America, Central America And The Caribbean',\n",
- " 'North America, Central America And The Caribbean',\n",
- " 'South-West Pacific', 'Antarctic',\n",
- " 'North America, Central America And The Caribbean', 'Asia', 'Africa'],\n",
- " dtype=object) WWF_TEOW_biogeographical_realm
(station)
object
...
standard_name : WWF TEOW biogeographical realm long_name : WWF TEOW biogeographical realm units : description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 8 biogeographical realms. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['antarctic', 'neotropics', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'water', 'nearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'neotropics',\n",
- " 'water', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'water', 'water', 'nearctic',\n",
- " 'palearctic', 'water', 'palearctic', 'palearctic', 'water',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'water', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'water',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'indomalay', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'water',\n",
- " 'palearctic', 'oceania', 'water', 'water', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'antarctic', 'palearctic', 'australasia', 'water',\n",
- " 'australasia', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'palearctic',\n",
- " 'palearctic', 'palearctic', 'palearctic', 'palearctic', 'nearctic',\n",
- " 'nearctic', 'oceania', 'water', 'water', 'indomalay', 'afrotropics'],\n",
- " dtype=object) WWF_TEOW_biome
(station)
object
...
standard_name : WWF TEOW biome long_name : WWF TEOW biome units : description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 14 biomes. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['tundra', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate conifer forests', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate conifer forests', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'water', 'tundra',\n",
- " 'temperate conifer forests', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub', 'water',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'water', 'water', 'tundra', 'temperate broadleaf and mixed forests',\n",
- " 'water', 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'water',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub', 'water',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub', 'water',\n",
- " 'boreal forests/taiga', 'boreal forests/taiga', 'boreal forests/taiga',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'tropical and subtropical moist broadleaf forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'temperate conifer forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'mediterranean forests, woodlands and scrub', 'water',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'tropical and subtropical moist broadleaf forests', 'water', 'water',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'tundra', 'tundra', 'tundra',\n",
- " 'temperate broadleaf and mixed forests', 'boreal forests/taiga',\n",
- " 'temperate conifer forests', 'boreal forests/taiga', 'tundra',\n",
- " 'boreal forests/taiga', 'temperate broadleaf and mixed forests',\n",
- " 'water', 'temperate grasslands, savannas and shrublands',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'tundra',\n",
- " 'boreal forests/taiga', 'boreal forests/taiga',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests',\n",
- " 'temperate broadleaf and mixed forests', 'boreal forests/taiga',\n",
- " 'boreal forests/taiga', 'temperate broadleaf and mixed forests',\n",
- " 'temperate conifer forests', 'temperate conifer forests',\n",
- " 'temperate conifer forests', 'temperate conifer forests',\n",
- " 'temperate conifer forests', 'temperate broadleaf and mixed forests',\n",
- " 'tundra', 'temperate grasslands, savannas and shrublands',\n",
- " 'tropical and subtropical grasslands, savannas and shrublands', 'water',\n",
- " 'water', 'tropical and subtropical moist broadleaf forests',\n",
- " 'mediterranean forests, woodlands and scrub'], dtype=object) WWF_TEOW_terrestrial_ecoregion
(station)
object
...
standard_name : WWF TEOW terrestrial ecoregion long_name : WWF TEOW terrestrial ecoregion units : description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 825 terrestrial ecoregions. Ecoregions are relatively large units of land containing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['marielandia antarctic tundra', 'magellanic subpolar forests',\n",
- " 'pannonian mixed forests', 'alps conifer and mixed forests',\n",
- " 'central european mixed forests', 'western european broadleaf forests',\n",
- " 'alps conifer and mixed forests', 'alps conifer and mixed forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'pannonian mixed forests',\n",
- " 'central european mixed forests', 'pannonian mixed forests',\n",
- " 'pannonian mixed forests', 'alps conifer and mixed forests',\n",
- " 'alps conifer and mixed forests', 'pannonian mixed forests',\n",
- " 'western european broadleaf forests', 'atlantic mixed forests',\n",
- " 'atlantic mixed forests', 'rodope montane mixed forests', 'water',\n",
- " 'high arctic tundra', 'alps conifer and mixed forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'chilean matorral', 'water',\n",
- " 'cyprus mediterranean forests', 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'atlantic mixed forests',\n",
- " 'atlantic mixed forests', 'western european broadleaf forests',\n",
- " 'baltic mixed forests', 'western european broadleaf forests',\n",
- " 'baltic mixed forests', 'western european broadleaf forests',\n",
- " 'alps conifer and mixed forests', 'water', 'water',\n",
- " 'kalaallit nunaat high arctic tundra', 'baltic mixed forests',\n",
- " 'rock and ice', 'atlantic mixed forests', 'sarmatic mixed forests',\n",
- " 'water', 'northwest iberian montane forests',\n",
- " 'cantabrian mixed forests',\n",
- " 'northeastern spain and southern france mediterranean forests',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'cantabrian mixed forests', 'iberian conifer forests', 'water',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'cantabrian mixed forests',\n",
- " 'southwest iberian mediterranean sclerophyllous and mixed forests',\n",
- " 'canary islands dry woodlands and forests', 'water',\n",
- " 'scandinavian and russian taiga', 'scandinavian and russian taiga',\n",
- " 'scandinavian and russian taiga', 'western european broadleaf forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'atlantic mixed forests',\n",
- " 'western european broadleaf forests', 'atlantic mixed forests',\n",
- " 'alps conifer and mixed forests', 'western european broadleaf forests',\n",
- " 'atlantic mixed forests', 'pyrenees conifer and mixed forests',\n",
- " 'northeastern spain and southern france mediterranean forests',\n",
- " 'western european broadleaf forests',\n",
- " 'western european broadleaf forests', 'celtic broadleaf forests',\n",
- " 'north atlantic moist mixed forests', 'celtic broadleaf forests',\n",
- " 'celtic broadleaf forests', 'caledon conifer forests',\n",
- " 'celtic broadleaf forests', 'celtic broadleaf forests',\n",
- " 'celtic broadleaf forests', 'english lowlands beech forests',\n",
- " 'english lowlands beech forests', 'celtic broadleaf forests',\n",
- " 'celtic broadleaf forests', 'celtic broadleaf forests',\n",
- " 'celtic broadleaf forests', 'english lowlands beech forests',\n",
- " 'north atlantic moist mixed forests', 'english lowlands beech forests',\n",
- " 'aegean and western turkey sclerophyllous and mixed forests',\n",
- " 'crete mediterranean forests', 'pannonian mixed forests',\n",
- " 'pannonian mixed forests', 'sumatran montane rain forests',\n",
- " 'celtic broadleaf forests', 'celtic broadleaf forests',\n",
- " 'alps conifer and mixed forests', 'appenine deciduous montane forests',\n",
- " 'tyrrhenian-adriatic sclerophyllous and mixed forests',\n",
- " 'italian sclerophyllous and semi-deciduous forests', 'water',\n",
- " 'taiheiyo evergreen forests', 'ogasawara subtropical moist forests',\n",
- " 'water', 'water', 'southern korea evergreen forests',\n",
- " 'central european mixed forests', 'sarmatic mixed forests',\n",
- " 'sarmatic mixed forests',\n",
- " 'tyrrhenian-adriatic sclerophyllous and mixed forests',\n",
- " 'atlantic mixed forests', 'atlantic mixed forests',\n",
- " 'atlantic mixed forests', 'atlantic mixed forests',\n",
- " 'atlantic mixed forests', 'sarmatic mixed forests',\n",
- " 'scandinavian montane birch forest and grasslands',\n",
- " 'scandinavian montane birch forest and grasslands', 'arctic desert',\n",
- " 'sarmatic mixed forests', 'scandinavian and russian taiga',\n",
- " 'scandinavian coastal conifer forests',\n",
- " 'scandinavian and russian taiga', 'maudlandia antarctic desert',\n",
- " 'scandinavian and russian taiga', 'north island temperate forests',\n",
- " 'water', 'cantebury-otago tussock grasslands',\n",
- " 'central european mixed forests', 'western european broadleaf forests',\n",
- " 'baltic mixed forests', 'central european mixed forests',\n",
- " 'balkan mixed forests', 'taimyr-central siberian tundra',\n",
- " 'scandinavian and russian taiga', 'scandinavian and russian taiga',\n",
- " 'sarmatic mixed forests', 'sarmatic mixed forests',\n",
- " 'sarmatic mixed forests', 'baltic mixed forests',\n",
- " 'sarmatic mixed forests', 'sarmatic mixed forests',\n",
- " 'scandinavian and russian taiga', 'scandinavian and russian taiga',\n",
- " 'pannonian mixed forests', 'alps conifer and mixed forests',\n",
- " 'alps conifer and mixed forests', 'carpathian montane forests',\n",
- " 'carpathian montane forests', 'carpathian montane forests',\n",
- " 'pannonian mixed forests', 'arctic coastal tundra',\n",
- " 'western short grasslands', 'hawaii tropical high shrublands', 'water',\n",
- " 'water', 'northern indochina subtropical forests',\n",
- " 'montane fynbos and renosterveld'], dtype=object) administrative_country_division_1
(station)
object
...
standard_name : administrative country division 1 long_name : administrative country division 1 units : description : Name of the first (i.e. largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['nan', 'Tierra Del Fuego', 'Burgenland', 'Carinthia', 'Lower Austria',\n",
- " 'Vorarlberg', 'Carinthia', 'Styria', 'Salzburg', 'Lower Austria',\n",
- " 'Lower Austria', 'Lower Austria', 'Lower Austria', 'Lower Austria',\n",
- " 'Upper Austria', 'Styria', 'Styria', 'Wallonia', 'Wallonia', 'Wallonia',\n",
- " 'Smolyan', 'Hamilton City', 'nan', 'Bern', 'Vaud', 'Thurgau',\n",
- " 'Neuchatel', 'Schwyz', 'Lucerne', 'nan', 'nan', 'Lefkosia', 'Vysocina',\n",
- " 'Central Bohemia', 'Jihocesky', 'Schleswig-Holstein', 'Lower Saxony',\n",
- " 'Baden-Wuerttemberg', 'nan', 'Thuringia', 'Mecklenburg-Vorpommern',\n",
- " 'Bavaria', 'Tyrol', 'nan', 'nan', 'nan', 'Zealand', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Castille-La Mancha', 'Galicia', 'Balearic Islands', 'Andalusia',\n",
- " 'Asturias', 'Castille-La Mancha', 'Catalonia', 'Extremadura',\n",
- " 'Valencia', 'Castille And Leon', 'Catalonia', 'Galicia', 'nan',\n",
- " 'Canary Islands', 'nan', 'Kymenlaakso', 'nan', 'nan', 'Alsace',\n",
- " 'Champagne-Ardenne', 'nan', 'Midi-Pyrenees', 'Franche-Comte',\n",
- " 'Pays De La Loire', "Provence-Alpes-Cote D'Azur", 'Limousin',\n",
- " 'Lower Normandy', 'Midi-Pyrenees', 'nan', 'Centre', 'Auvergne', 'nan',\n",
- " 'nan', 'England', 'England', 'nan', 'England', 'Scotland', 'England',\n",
- " 'England', 'England', 'Wales', 'England', 'Scotland', 'England',\n",
- " 'England', 'Scotland', 'England', 'Central Greece', 'Crete',\n",
- " 'Bacs-Kiskun', 'Vas', 'West Sumatra', 'Munster', 'nan', 'Lombardy',\n",
- " 'Tuscany', 'Sicily', 'Umbria', 'nan', 'nan', 'nan', 'Okinawa', 'nan',\n",
- " 'nan', 'nan', 'Rucavas', 'Vecpiebalga', 'L-Gharb', 'Gelderland',\n",
- " 'Groningen', 'North Brabant', 'South Holland', 'Utrecht', 'Aust-Agder',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'Rogaland', 'Akershus', 'nan',\n",
- " 'Telemark', 'nan', 'nan', 'nan', 'Masovian Voivodeship',\n",
- " 'Lower Silesian Voivodeship', 'Pomeranian Voivodeship',\n",
- " 'Warmian-Masurian Voivodeship', 'Central Serbia', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'Skane', 'Uppsala', 'nan', 'Vaesterbotten', 'nan',\n",
- " 'Kostel', 'Sostanj', 'Cerklje Na Gorenjskem', 'nan', 'Presovsky', 'nan',\n",
- " 'nan', 'nan', 'Colorado', 'nan', 'nan', 'California', 'Huyen Dien Bien',\n",
- " 'nan'], dtype=object) administrative_country_division_2
(station)
object
...
standard_name : administrative country division 2 long_name : administrative country division 2 units : description : Name of the second (i.e. second largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['nan', 'nan', 'Politischer Bezirk Neusiedl Am See',\n",
- " 'Politischer Bezirk Spittal An Der Drau',\n",
- " 'Politischer Bezirk Hollabrunn', 'Politischer Bezirk Bregenz',\n",
- " 'Politischer Bezirk Spittal An Der Drau',\n",
- " 'Politischer Bezirk Hartberg-Fuerstenfeld',\n",
- " 'Politischer Bezirk Salzburg-Umgebung', 'Politischer Bezirk Gmund',\n",
- " 'Politischer Bezirk Sankt Polten', 'Politischer Bezirk Krems',\n",
- " 'Politischer Bezirk Ganserndorf',\n",
- " 'Politischer Bezirk Bruck An Der Leitha',\n",
- " 'Politischer Bezirk Steyr-Land', 'Politischer Bezirk Murau',\n",
- " 'Politischer Bezirk Graz-Umgebung', 'Province Du Luxembourg',\n",
- " 'Province De Liege', 'Province De Namur', 'Obshtina Chepelare', 'nan',\n",
- " 'nan', 'Interlaken-Oberhasli District', 'Broye-Vully District',\n",
- " 'Muenchwilen District', 'Val-De-Ruz District', 'Bezirk Kuessnacht',\n",
- " 'Sursee District', 'nan', 'nan', 'nan', "Okres Zd'Ar Nad Sazavou",\n",
- " 'nan', 'Okres Prachatice', 'nan', 'nan', 'Freiburg Region', 'nan',\n",
- " 'nan', 'nan', 'Upper Bavaria', 'Politischer Bezirk Reutte', 'nan',\n",
- " 'nan', 'nan', 'Roskilde Kommune', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Provincia De Ciudad Real', 'Provincia Da Coruna', 'Illes Balears',\n",
- " 'Provincia De Granada', 'Province Of Asturias',\n",
- " 'Provincia De Guadalajara', 'Provincia De Girona',\n",
- " 'Provincia De Badajoz', 'Provincia De Valencia',\n",
- " 'Provincia De Salamanca', 'Provincia De Lleida', 'Provincia De Lugo',\n",
- " 'nan', 'Provincia De Santa Cruz De Tenerife', 'nan', 'Kotka-Hamina',\n",
- " 'nan', 'nan', 'Departement Du Bas-Rhin', 'Departement Des Ardennes',\n",
- " 'nan', 'Departement Du Gers', 'Departement Du Doubs',\n",
- " 'Departement De La Vendee', 'Departement Des Hautes-Alpes',\n",
- " 'Departement De La Creuse', "Departement De L'Orne",\n",
- " 'Departement Des Hautes-Pyrenees', 'nan', 'Departement Du Cher',\n",
- " 'Departement Du Puy-De-Dome', 'nan', 'nan', 'Devon', 'North Yorkshire',\n",
- " 'nan', 'Shropshire', 'Midlothian', 'Derbyshire', 'East Sussex',\n",
- " 'Suffolk', 'Pembrokeshire', 'Cambridgeshire', 'Midlothian', 'Norfolk',\n",
- " 'Essex', 'Shetland Islands', 'Hampshire', 'Nomos Voiotias',\n",
- " 'Nomos Lasithiou', 'nan', 'nan', 'nan', 'Ciarrai', 'nan',\n",
- " 'Provincia Di Varese', 'Provincia Di Pistoia', 'Trapani',\n",
- " 'Provincia Di Perugia', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'Berkelland', 'Gemeente De Marne',\n",
- " 'Gemeente Sint Anthonis', 'Gemeente Noordwijkerhout', 'Gemeente Lopik',\n",
- " 'Birkenes', 'nan', 'nan', 'nan', 'nan', 'nan', 'Karmoy', 'Hurdal',\n",
- " 'nan', 'Skien', 'nan', 'nan', 'nan', 'Powiat Garwolinski',\n",
- " 'Powiat Jeleniogorski', 'Powiat Leborski', 'Powiat Goldapski',\n",
- " 'Nisavski Okrug', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Svalovs Kommun', 'Osthammars Kommun', 'nan', 'Vindelns Kommun', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'Okres Poprad', 'nan', 'nan', 'nan',\n",
- " 'Boulder County', 'nan', 'nan', 'Humboldt County', 'nan', 'nan'],\n",
- " dtype=object) altitude
(station)
float32
...
standard_name : altitude long_name : altitude relative to mean sea level units : m description : Altitude of the ground level at the station, relative to the stated vertical datum, in metres. array([1.995e+02, 1.900e+01, 1.170e+02, 1.020e+03, 3.150e+02, 1.020e+03,\n",
- " 3.106e+03, 1.170e+03, 7.300e+02, 5.700e+02, 5.810e+02, 3.200e+02,\n",
- " 1.610e+02, 2.400e+02, 8.990e+02, 1.648e+03, 4.810e+02, 4.300e+02,\n",
- " 2.950e+02, 1.600e+02, 1.750e+03, 1.840e+02, 6.100e+02, 3.578e+03,\n",
- " 4.890e+02, 5.390e+02, 1.137e+03, 1.031e+03, 7.970e+02, 2.225e+03,\n",
- " 1.750e+01, 5.320e+02, 7.350e+02, 5.350e+02, 1.118e+03, 1.200e+01,\n",
- " 7.400e+01, 1.205e+03, 6.200e+01, 9.370e+02, 1.000e+00, 9.900e+02,\n",
- " 2.671e+03, 4.900e+01, 1.000e+01, 2.000e+01, 3.000e+00, 3.238e+03,\n",
- " 1.000e+01, 3.200e+01, 6.000e+00, 9.170e+02, 6.830e+02, 7.800e+01,\n",
- " 1.230e+03, 1.340e+02, 1.360e+03, 7.600e+01, 3.930e+02, 8.850e+02,\n",
- " 9.850e+02, 4.700e+02, 5.060e+02, 3.500e+01, 2.400e+03, 7.000e+00,\n",
- " 4.000e+00, 3.100e+02, 5.650e+02, 7.750e+02, 3.900e+02, 6.200e+02,\n",
- " 2.000e+02, 8.360e+02, 1.330e+02, 1.750e+03, 8.100e+02, 3.090e+02,\n",
- " 2.877e+03, 6.050e+02, 1.820e+02, 1.465e+03, 2.430e+02, 1.260e+02,\n",
- " 1.190e+02, 2.670e+02, 2.700e+02, 3.700e+02, 1.800e+02, 4.200e+02,\n",
- " 1.200e+02, 4.600e+01, 1.600e+02, 5.000e+00, 2.600e+02, 1.600e+01,\n",
- " 8.000e+00, 8.500e+01, 7.800e+01, 1.100e+02, 2.500e+02, 1.250e+02,\n",
- " 3.140e+02, 8.640e+02, 1.100e+01, 5.000e+00, 2.090e+02, 2.165e+03,\n",
- " 5.000e+00, 1.092e+03, 1.850e+01, 2.640e+02, 1.000e+01, 3.400e+01,\n",
- " 8.600e+01, 8.000e+01, 5.000e+00, 1.800e+01, 1.880e+02, 1.830e+02,\n",
- " 2.000e+01, 1.000e+00, 2.800e+01, 4.000e+00, 4.000e+00, 2.190e+02,\n",
- " 4.390e+02, 2.100e+02, 4.740e+02, 1.600e+02, 3.000e+01, 1.500e+01,\n",
- " 3.000e+02, 1.553e+03, 2.000e+01, 8.600e+01, 1.840e+02, 3.700e+02,\n",
- " 1.800e+02, 1.603e+03, 2.000e+00, 1.570e+02, 8.130e+02, 8.000e+00,\n",
- " 4.040e+02, 4.750e+02, 5.000e+00, 1.800e+02, 6.500e+01, 1.900e+02,\n",
- " 4.500e+01, 2.610e+02, 2.250e+02, 1.320e+02, 5.200e+02, 7.700e+02,\n",
- " 1.754e+03, 2.008e+03, 8.080e+02, 3.450e+02, 1.130e+02, 1.100e+01,\n",
- " 1.689e+03, 3.397e+03, 2.841e+03, 1.070e+02, 1.478e+03, 2.340e+02],\n",
- " dtype=float32) annual_native_max_gap_percent
(station, time)
uint8
...
standard_name : annual native max gap percent long_name : annual native max gap percent units : % description : Percentage of the maximum data gap in the annual averaged measurement UTC window filled by native resolution data, relative to the total window length. [120960 values with dtype=uint8] annual_native_representativity_percent
(station, time)
uint8
...
standard_name : annual native representativity percent long_name : annual native representativity percent units : % description : Percentage of the annual averaged measurement UTC window represented by native resolution data. [120960 values with dtype=uint8] area_classification
(station)
object
...
standard_name : area classification long_name : standardised network provided area classification units : description : Standardised network provided classification, describing type of area a measurement station is situated in. array(['rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'urban-suburban', 'urban-suburban', 'rural', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-suburban', 'rural', 'rural', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'rural',\n",
- " 'rural', 'rural', 'rural', 'urban-suburban', 'rural', 'urban-suburban',\n",
- " 'rural', 'urban-suburban', 'rural', 'rural', 'rural', 'rural',\n",
- " 'urban-suburban', 'rural', 'urban-suburban', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'urban-suburban',\n",
- " 'rural', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'urban-suburban', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'urban-suburban', 'urban-suburban', 'rural', 'rural',\n",
- " 'urban-suburban', 'rural', 'rural', 'urban-suburban', 'rural', 'rural',\n",
- " 'rural', 'rural', 'urban-suburban', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'urban-suburban', 'rural', 'rural', 'urban-suburban', 'rural',\n",
- " 'urban-suburban', 'rural', 'urban-suburban', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'urban-suburban', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'urban-suburban', 'rural', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'urban-suburban', 'urban-suburban',\n",
- " 'rural', 'rural', 'urban-suburban', 'rural', 'rural', 'rural', 'rural',\n",
- " 'rural', 'rural', 'rural', 'rural', 'rural'], dtype=object) associated_networks
(station)
object
...
standard_name : other associated networks long_name : other associated networks units : description : String pair of associated network name and station reference. Format: network1:station_reference1;network2:station_reference2 array(['WDCA:GAWAAR__MBI;GAW:MBI', 'GAW:USH', 'GAW:ILL', 'GAW:VOH', 'nan',\n",
- " 'nan', 'WDCA:GAWAAT__SNB;GAW:SNB;ZAMG:SNB', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'GAW:ZOE', 'nan', 'nan', 'GAW:OFF', 'GAW:EUP',\n",
- " 'GAW:VEZ', 'GAW:RJP', 'WDCA:GAWAGB__BMW;GAW:BMW', 'GAW:EUK', 'GAW:JFJ',\n",
- " 'GAW:PAY', 'GAW:TAE', 'GAW:CMT', 'GAW:RIG', 'GAW:BRM',\n",
- " 'WDCA:GAWACL__TLL;GAW:TLL', 'GAW:CVO', 'WDCA:GAWACY__CYP;GAW:CYP',\n",
- " 'GAW:SVT', 'WDCA:GAWACZ__KOS;GAW:KOS', 'nan',\n",
- " 'GAW:WES;UBA:DEUB001;WMO:10020-0', 'GAW:WAL', 'GAW:SSL', 'GAW:NGL',\n",
- " 'nan', 'GAW:ZGT;UBA:DEUB028', 'WDCA:GAWADE__HPB;GAW:HPB',\n",
- " 'WDCA:GAWADE__ZSF;GAW:ZSF', 'GAW:NMY', 'GAW:KLD', 'GAW:SNO', 'nan',\n",
- " 'WDCA:GAWADK__SUM;GAW:SUM', 'GAW:UBG', 'GAW:LAH', 'GAW:VSD', 'GAW:SPM',\n",
- " 'GAW:NIA', 'GAW:MHN', 'GAW:VIZ', 'GAW:NIE', 'GAW:CPM', 'GAW:CCR', 'nan',\n",
- " 'GAW:ZRR', 'GAW:PNS', 'GAW:ELT', 'GAW:OSV', 'GAW:DON', 'GAW:IZO',\n",
- " 'GAW:UTO', 'nan', 'GAW:OUX', 'WDCA:GAWAFI__PAL;GAW:PAL', 'nan', 'nan',\n",
- " 'nan', 'GAW:PYE', 'nan', 'GAW:TAD', 'nan', 'nan', 'nan', 'GAW:PDM',\n",
- " 'nan', 'nan', 'GAW:PUY', 'GAW:EDM', 'GAW:LNR', 'GAW:YRW', 'GAW:HMS',\n",
- " 'nan', 'GAW:ASH', 'GAW:BUS', 'GAW:LBR', 'GAW:LNH', 'GAW:SBN', 'GAW:NBH',\n",
- " 'GAW:WKF', 'GAW:AUC', 'GAW:WAO', 'nan', 'GAW:SIS', 'nan', 'GAW:ATS',\n",
- " 'GAW:FIK', 'WDCA:GAWAHU__KPS;GAW:KPS', 'nan',\n",
- " 'WDCA:GAWAID__BKT;GAW:BKT', 'GAW:VTO', 'WDCA:GAWAIE__MHD;GAW:MHD',\n",
- " 'WDCA:GAWAIT__IPR;GAW:IPR', 'GAW:CMN', 'WDCA:GAWAIT__CGR;GAW:CGR',\n",
- " 'nan', 'GAW:SYO', 'WDCA:GAWAJP__RYO;GAW:RYO',\n",
- " 'WDCA:GAWAJP__MNM;GAW:MNM', 'WDCA:GAWAJP__YON;GAW:YON',\n",
- " 'WDCA:GAWAKR__AMY;GAW:AMY', 'GAW:JGS', 'GAW:PLA', 'GAW:RCV', 'GAW:ZSN',\n",
- " 'WDCA:GAWAMT__GLH;GAW:GLH', 'GAW:EIB', 'GAW:KMW', 'nan', 'GAW:DZI',\n",
- " 'GAW:CES', 'WDCA:GAWANO__BIR;GAW:BIR', 'GAW:TUV', 'GAW:KRV',\n",
- " 'WDCA:GAWANO__ZEP;GAW:ZEP', 'GAW:PRB', 'GAW:SVV', 'GAW:SND', 'nan',\n",
- " 'nan', 'nan', 'GAW:BHD', 'WDCA:GAWANZ__ARH;GAW:ARH',\n",
- " 'WDCA:GAWANZ__LAU;GAW:LAU', 'GAW:JCZ', 'GAW:SNZ', 'GAW:LEB', 'GAW:DIG',\n",
- " 'GAW:KAM', 'WDCA:GAWARU__TIK;GAW:TIK', 'GAW:BRE', 'GAW:ESR', 'GAW:RAO',\n",
- " 'nan', 'nan', 'Airbase:SE0116A', 'nan', 'GAW:NKV', 'GAW:VDL', 'nan',\n",
- " 'GAW:IRB', 'nan', 'GAW:KVV', 'GAW:CHO', 'GAW:STL', 'GAW:STA', 'GAW:TOP',\n",
- " 'WDCA:GAWAUSAKBRW;GAW:BRW', 'GAW:BOS;BSRN:34;AERONET:Boulder',\n",
- " 'WDCA:GAWAUSHIMLO;GAW:MLO', 'WDCA:GAWAUS__SPO;GAW:SPO',\n",
- " 'WDCA:GAWAUSCATHD;GAW:THD', 'WDCA:GAWAID__PDI;GAW:PDI', 'GAW:CPT'],\n",
- " dtype=object) city
(station)
object
...
standard_name : city long_name : city units : description : Name of the city the station is located in. array(['nan', 'Ushuaia', 'Illmitz', 'Oberdrauburg', 'Retz', 'Sulzberg',\n",
- " 'Grosskirchheim', 'Poellauberg', 'Nussdorf Am Haunsberg', 'Litschau',\n",
- " 'Altlengbach', 'Durnstein', 'Ganserndorf',\n",
- " 'Trautmannsdorf An Der Leitha', 'Reichraming', 'Sankt Lambrecht',\n",
- " 'Hart Bei Graz', 'Bertrix', 'Baelen', 'Noville-Les-Bois', 'Chepelare',\n",
- " 'Hamilton', 'nan', 'Lauterbrunnen', 'Payerne', 'Aadorf',\n",
- " 'Grand-Savagnier', 'Kussnacht', 'Gunzwil', 'nan', 'nan', 'Peristerona',\n",
- " 'Svratka', 'Cechtice', 'Stachy', 'Westerland', 'Soltendieck',\n",
- " 'Oberried', 'nan', 'Gehlberg', 'Zingst', 'Hohenpeissenberg', 'Ehrwald',\n",
- " 'nan', 'nan', 'nan', 'Roskilde', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Retuerta De Bullaque', 'Boiro', 'Es Castell', 'Viznar', 'Llanes',\n",
- " 'Campisabalos', 'Cadaques', 'Higuera De Vargas', 'Zarra', 'Santiz',\n",
- " 'Juncosa', 'Chantada', 'nan', 'Guimar', 'nan', 'Virolahti', 'nan',\n",
- " 'nan', 'Schirmeck', 'Revin', 'nan', 'Marciac', 'Saint-Hippolyte',\n",
- " 'La Chataigneraie', 'Le Monetier-Les-Bains', 'Banize',\n",
- " 'La Ferriere-Aux-Etangs', 'Campan', 'nan', 'Dun-Sur-Auron', 'Orcines',\n",
- " 'nan', 'nan', 'Bovey Tracey', 'Pickering', 'nan', "Bishop'S Castle",\n",
- " 'Penicuik', 'Hope Valley', 'Seaford', 'Cookley', 'Templeton', 'Soham',\n",
- " 'Penicuik', 'Sheringham', 'Saint Osyth', 'Lerwick', 'Andover',\n",
- " 'Aliartos', 'Elounda', 'Lajosmizse', 'Szentgotthard', 'Matur',\n",
- " 'Cahersiveen', 'nan', 'Cadrezzate', 'Abetone', 'Tre Fontane',\n",
- " "Giano Dell'Umbria", 'nan', 'nan', 'nan', 'Yonakuni', 'nan', 'nan',\n",
- " 'Neringa', 'Rucava', 'Vecpiebalga', 'Gharb', 'Borculo', 'Ulrum',\n",
- " 'Sint Anthonis', 'Noordwijkerhout', 'Lopik', 'Birkeland', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'Akrehamn', 'Hurdal', 'nan', 'Skien', 'nan',\n",
- " 'Mcmurdo Station', 'nan', 'Zelechow', 'Karpacz', 'Leba',\n",
- " 'Banie Mazurskie', 'Nis', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Kagerod', 'Bjorklinge', 'nan', 'Vindeln', 'nan', 'Kostel', 'Topolsica',\n",
- " 'Cerklje Na Gorenjskem', 'nan', 'Nova Lesna', 'nan', 'nan', 'nan',\n",
- " 'Niwot', 'nan', 'nan', 'Westhaven-Moonstone', 'Thi Tran Tuan Giao',\n",
- " 'nan'], dtype=object) climatology
(station)
object
...
standard_name : climatology long_name : climatology units : description : Name of the climatology of which the observations pertain to. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) contact_email_address
(station)
object
...
standard_name : contact email address long_name : contact email address units : description : Email address of the principal data contact for the specific reported data. array(['barlasina@smn.gov.ar', 'mcupeiro@smn.gov.ar', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'e.adriaenssens@vmm.be', 'e.adriaenssens@vmm.be',\n",
- " 'e.adriaenssens@vmm.be', 'nan', 'Audra.mcclure@noaa.gov',\n",
- " 'sara.crepinsek@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'martin.steinbacher@empa.ch', 'katie.read@ncas.ac.uk', 'nan', 'nan',\n",
- " 'dvorska.a@czechglobe.cz', 'nan', 'anke.penzlin@uba.de',\n",
- " 'anke.penzlin@uba.de', 'anke.penzlin@uba.de', 'anke.penzlin@uba.de',\n",
- " 'anke.penzlin@uba.de', 'anke.penzlin@uba.de', 'Robert.Holla@dwd.de',\n",
- " 'cedric.couret@uba.de', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Audra.mcclure@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'ctorresg@aemet.es', 'kaisa.korpi@fmi.fi', 'mika.vestenius@fmi.fi',\n",
- " 'kaisa.korpi@fmi.fi', 'mika.vestenius@fmi.fi', 'nan',\n",
- " 'therese.salameh@mines-douai.fr', 'nan',\n",
- " 'therese.salameh@mines-douai.fr', 'nan',\n",
- " 'therese.salameh@mines-douai.fr', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'labancz.k@met.hu', 'labancz.k@met.hu',\n",
- " 'martin.steinbacher@empa.ch', 'nan', 'nan', 'jean.putaud@ec.europa.eu',\n",
- " 'p.cristofanelli@isac.cnr.it', 'p.cristofanelli@isac.cnr.it',\n",
- " 'm.angelucci@arpa.umbria.it', 'antarctic@met.kishou.go.jp',\n",
- " 'ghg_obs@met.kishou.go.jp', 'ghg_obs@met.kishou.go.jp',\n",
- " 'ghg_obs@met.kishou.go.jp', 'sulla@korea.kr', 'sulla@korea.kr', 'nan',\n",
- " 'nan', 'nan', 'marvic.grima@um.edu.mt', 'rob.zwartjes@rivm.nl',\n",
- " 'rob.zwartjes@rivm.nl', 'rob.zwartjes@rivm.nl', 'rob.zwartjes@rivm.nl',\n",
- " 'rob.zwartjes@rivm.nl', 'agh@nilu.no', 'waa@nilu.no', 'agh@nilu.no',\n",
- " 'agh@nilu.no', 'agh@nilu.no', 'kt@nilu.no', 'agh@nilu.no',\n",
- " 'waa@nilu.no', 'agh@nilu.no', 'agh@nilu.no', 'sylvia.nichol@niwa.co.nz',\n",
- " 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'sara.crepinsek@noaa.gov', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'roman.kocuvan@eimv.si', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'martin.steinbacher@empa.ch',\n",
- " 'thumeka.mkololo@weathersa.co.za'], dtype=object) contact_institution
(station)
object
...
standard_name : contact institution long_name : contact institution units : description : Institution of the principal data contact for the specific reported data. array(['Servicio Meteorológico Nacional', 'Servicio Meteorologico Nacional',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'Flanders Environment Agency',\n",
- " 'Flanders Environment Agency', 'Flanders Environment Agency', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Physical Sciences Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Swiss Federal Laboratories for Materials Science and Technology',\n",
- " 'National centre for Atmospheric Science', 'nan', 'nan',\n",
- " 'Global Change Research Centre AS CR v. v. i.', 'nan',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Meteorological Observatory Hohenpeissenberg',\n",
- " 'German Environment Agency', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Agencia Estatal de Meteorologia', 'nan',\n",
- " 'Finnish Meteorological Institute', 'nan',\n",
- " 'Finnish Meteorological Institute', 'nan', 'Ecole des Mines de Douai',\n",
- " 'nan', 'Ecole des Mines de Douai', 'nan', 'Ecole des Mines de Douai',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Hungarian Meteorological Service', 'Hungarian Meteorological Service',\n",
- " 'Swiss Federal Laboratories for Materials Science and Technology',\n",
- " 'nan', 'nan', 'EC Joint Research Centre',\n",
- " 'National Research Council of Italy',\n",
- " 'National Research Council of Italy',\n",
- " 'Arpa Umbria - Umbria Regional Agency for Environmental Protection',\n",
- " 'Japan Meteorological Agency', 'Japan Meteorological Agency',\n",
- " 'Japan Meteorological Agency', 'Japan Meteorological Agency',\n",
- " 'National Institute of Meteorological Sciences',\n",
- " 'National Institute of Meteorological Sciences', 'nan', 'nan', 'nan',\n",
- " 'University of Malta', 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Rijks instituut voor volgezondsheid en milieu',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'National Institute of Water and Atmospheric Research',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Physical Sciences Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Milan Vidmar Electric Power Research Institute', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'Swiss Federal Laboratories for Materials Science and Technology',\n",
- " 'South African Weather Service'], dtype=object) contact_name
(station)
object
...
standard_name : contact name long_name : contact name units : description : Full name of the principal data contact for the specific reported data. array(['Maria Elena Barlasina', 'Manuel Cupeiro', 'Marina Froelich',\n",
- " 'Marina Froelich', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Marina Froelich',\n",
- " 'Wolfgang Spangl', 'Marina Froelich', 'Elke Adriaenssens',\n",
- " 'Elke Adriaenssens', 'Elke Adriaenssens', 'Emiliya Stoyneva',\n",
- " 'Audra Mcclure-Begley', 'Sara Crepinsek', 'Robert Gehrig',\n",
- " 'Robert Gehrig', 'Robert Gehrig', 'Robert Gehrig', 'Robert Gehrig',\n",
- " 'Thomas Seitz', 'Martin Steinbacher', 'Katie Read',\n",
- " 'Christos Papadopoulos', 'Jaroslav Pekarek', 'Jaroslav Pekarek',\n",
- " 'Jaroslav Pekarek', 'Anke Penzlin', 'Anke Penzlin', 'Anke Penzlin',\n",
- " 'Anke Penzlin', 'Anke Penzlin', 'Anke Penzlin', 'Robert Holla',\n",
- " 'Cedric Couret', 'Rolf Weller', 'Rune Keller', 'Rune Keller',\n",
- " 'Rune Keller', 'Audra Mcclure-Begley', 'Rune Keller', 'Truuts Toivo',\n",
- " 'Truuts Toivo', 'Juan Martinez', 'Juan Martinez', 'Alberto Gonzalez',\n",
- " 'Alberto Gonzalez', 'Alberto Gonzalez', 'Alberto Gonzalez',\n",
- " 'Alberto Gonzalez', 'Alberto Gonzalez', 'Abellan Maj Britt Larka',\n",
- " 'Alberto Gonzalez', 'Alberto Gonzalez', 'Alberto Gonzalez',\n",
- " 'Alberto Gonzalez', 'Carlos Torres', 'Timo Salmi', 'Mika Vestenius',\n",
- " 'Timo Salmi', 'Mika Vestenius', 'Patrice Coddeville',\n",
- " 'Patrice Coddeville', 'Patrice Coddeville', 'Patrice Coddeville',\n",
- " 'Patrice Coddeville', 'Patrice Coddeville', 'Patrice Coddeville',\n",
- " 'Patrice Coddeville', 'Patrice Coddeville', 'Patrice Coddeville',\n",
- " 'Stephane Sauvage', 'Stephane Sauvage', 'Patrice Coddeville',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Geoff Broughton', 'Geoff Broughton',\n",
- " 'Geoff Broughton', 'Keith Vincent', 'A Adamopoulos',\n",
- " 'Nikos Mihalopoulos', 'Krisztina Labancz', 'Krisztina Labancz',\n",
- " 'Joerg Klausen', 'Barbara Oleary', 'Geoff Broughton', 'Jp Putaud',\n",
- " 'Paolo Cristofanelli', 'Paolo Cristofanelli', 'Monica Angelucci',\n",
- " 'Yoki Mori', 'Susumu Hashimoto', 'Susumu Hashimoto', 'Susumu Hashimoto',\n",
- " 'Sumin Kim', 'Sumin Kim', 'Dalia Sopauskiene', 'Frolova Marina',\n",
- " 'Frolova Marina', 'Marvic Grima', 'Jacobus P.J. Berkhout',\n",
- " 'Jacobus P.J. Berkhout', 'Jacobus P.J. Berkhout',\n",
- " 'Jacobus P.J. Berkhout', 'Ronald Spoor', 'Anne Hjellbrekke',\n",
- " 'Wenche Aas', 'Anne Hjellbrekke', 'Anne Hjellbrekke',\n",
- " 'Anne Hjellbrekke', 'Kjetil Tørseth', 'Anne Hjellbrekke', 'Wenche Aas',\n",
- " 'Anne Hjellbrekke', 'Anne Hjellbrekke', 'Sylvia Nichol',\n",
- " 'Audra Mcclure-Begley', 'Audra Mcclure-Begley', 'Magdalena Bogucka',\n",
- " 'Magdalena Bogucka', 'Magdalena Bogucka', 'Anna Degorska',\n",
- " 'Jasmina Knezevic', 'Sara Crepinsek', 'Karin Sjoberg', 'Karin Sjoberg',\n",
- " 'Karin Sjoberg', 'Karin Sjøberg', 'Karin Sjøberg', 'Karin Sjöberg',\n",
- " 'Karin Sjöberg', 'Karin Sjoberg', 'Karin Sjoberg', 'Karin Sjoberg',\n",
- " 'Murovec Marijana', 'Marijana Murovec', 'Marijana Murovec',\n",
- " 'Marta Mitosinkova', 'Marta Mitosinkova', 'Marta Mitosinkova',\n",
- " 'Marta Mitosinkova', 'Audra Mcclure-Begley', 'Audra Mcclure-Begley',\n",
- " 'Audra Mcclure-Begley', 'Audra Mcclure-Begley', 'Audra Mcclure-Begley',\n",
- " 'Martin Steinbacher', 'Thumeka Mkololo'], dtype=object) country
(station)
object
...
standard_name : country long_name : country units : description : Name of the country the station is located in. array(['Argentina', 'Argentina', 'Austria', 'Austria', 'Austria', 'Austria',\n",
- " 'Austria', 'Austria', 'Austria', 'Austria', 'Austria', 'Austria',\n",
- " 'Austria', 'Austria', 'Austria', 'Austria', 'Austria', 'Belgium',\n",
- " 'Belgium', 'Belgium', 'Bulgaria', 'Bermuda', 'Canada', 'Switzerland',\n",
- " 'Switzerland', 'Switzerland', 'Switzerland', 'Switzerland',\n",
- " 'Switzerland', 'Chile', 'Cape Verde', 'Cyprus', 'Czech Republic',\n",
- " 'Czech Republic', 'Czech Republic', 'Germany', 'Germany', 'Germany',\n",
- " 'Germany', 'Germany', 'Germany', 'Germany', 'Germany', 'Germany',\n",
- " 'Denmark', 'Denmark', 'Denmark', 'Denmark', 'Denmark', 'Estonia',\n",
- " 'Estonia', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain',\n",
- " 'Spain', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain', 'Spain',\n",
- " 'Finland', 'Finland', 'Finland', 'Finland', 'France', 'France',\n",
- " 'France', 'France', 'France', 'France', 'France', 'France', 'France',\n",
- " 'France', 'France', 'France', 'France', 'United Kingdom',\n",
- " 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United Kingdom',\n",
- " 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United Kingdom',\n",
- " 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United Kingdom',\n",
- " 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United Kingdom',\n",
- " 'Greece', 'Greece', 'Hungary', 'Hungary', 'Indonesia', 'Ireland',\n",
- " 'Ireland', 'Italy', 'Italy', 'Italy', 'Italy', 'Japan', 'Japan',\n",
- " 'Japan', 'Japan', 'South Korea', 'South Korea', 'Lithuania', 'Latvia',\n",
- " 'Latvia', 'Malta', 'Netherlands', 'Netherlands', 'Netherlands',\n",
- " 'Netherlands', 'Netherlands', 'Norway', 'Norway', 'Norway', 'Norway',\n",
- " 'Norway', 'Norway', 'Norway', 'Norway', 'Norway', 'Norway',\n",
- " 'New Zealand', 'New Zealand', 'New Zealand', 'Poland', 'Poland',\n",
- " 'Poland', 'Poland', 'Serbia', 'Russia', 'Sweden', 'Sweden', 'Sweden',\n",
- " 'Sweden', 'Sweden', 'Sweden', 'Sweden', 'Sweden', 'Sweden', 'Sweden',\n",
- " 'Slovenia', 'Slovenia', 'Slovenia', 'Slovakia', 'Slovakia', 'Slovakia',\n",
- " 'Slovakia', 'United States', 'United States', 'United States',\n",
- " 'United States', 'United States', 'Vietnam', 'South Africa'],\n",
- " dtype=object) daily_native_max_gap_percent
(station, time)
uint8
...
standard_name : daily native max gap percent long_name : daily native max gap percent units : % description : Percentage of the maximum data gap in the daily averaged measurement UTC window filled by native resolution data, relative to the total window length. [120960 values with dtype=uint8] daily_native_representativity_percent
(station, time)
uint8
...
standard_name : daily native representativity percent long_name : daily native representativity percent units : % description : Percentage of the daily averaged measurement UTC window represented by native resolution data. [120960 values with dtype=uint8] daily_passing_vehicles
(station)
float32
...
standard_name : daily passing vehicles long_name : average daily number of passing vehicles units : description : Average number of vehicles passing daily. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) data_level
(station)
object
...
standard_name : data level long_name : data level units : description : Data level of data reported. This varies per network. If data level is variable per measurement, and not static per reported file, then this is set as "variable". If there is no reported data level this is set as "none" array(['2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
- " '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0'], dtype=object) data_licence
(station)
object
...
standard_name : data licence long_name : data licence units : description : Information pertaining to the data licence governing the redistribution/publication of the ingested network data. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) day_night_code
(station, time)
uint8
...
standard_name : day/night code long_name : day/night code per measurement units : description : Binary indication if measurement was made during the day or night. Day=0, Night=1. The classification is made by calculating the solar elevation angle for a latitude/longitude/measurement height at a mid-measurement window timestamp. If the solar elevation angle is > 0, it is classed as daytime, otherwise it is nightime. Classification is 255 if cannot be made. [120960 values with dtype=uint8] daytime_traffic_speed
(station)
float32
...
standard_name : daytime traffic speed long_name : average daytime speed of passing traffic units : km hr-1 description : Average daytime speed of the passing traffic where measurements are being made (if applicable), in kilometres per hour. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) derived_uncertainty_per_measurement
(station, time)
float32
...
standard_name : derived measurement uncertainty per measurement long_name : derived measurement uncertainty per measurement units : nmol mol-1 description : Derived measurement uncertainty (±) of methodology, for a specific measurement. This is calculated through the quadratic addition of reported (or if not available, documented) accuracy and precision metrics. This is given in absolute terms in nmol mol-1. [120960 values with dtype=float32] distance_to_building
(station)
float32
...
standard_name : distance to building long_name : distance to the nearest building units : m description : Distance to the nearest building of the inlet/instrument/sampler, in metres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) distance_to_junction
(station)
float32
...
standard_name : distance to junction long_name : distance to the nearest road junction units : m description : Distance to the nearest road junction of the inlet/instrument/sampler, in metres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) distance_to_kerb
(station)
float32
...
standard_name : distance to kerb long_name : distance to the street kerb units : m description : Distance to the street kerb of the inlet/instrument/sampler, in metres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) distance_to_source
(station)
float32
...
standard_name : distance to source long_name : distance to the main emission source units : km description : Distance to the main emission source (see variable: "main_emission_source") of the inlet/instrument/sampler, in kilometres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) ellipsoid
(station)
object
...
standard_name : ellipsoid long_name : ellipsoid units : description : The ellipsoidal model of the earth used as a basis for 2D and 3D geographic coordinate systems. array(['WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84',\n",
- " 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84', 'WGS 84'],\n",
- " dtype=object) horizontal_datum
(station)
object
...
standard_name : horizontal datum long_name : horizontal datum units : description : Name of the horizontal datum used in defining geodetic latitudes and longitudes on the Earth's surface. The datum is set when positioning an ellipsoid model of the Earth to an anchor point. If not explicitely stated then this is assumed to be 'World Geodetic System 1984'. array(['WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984'],\n",
- " dtype=object) hourly_native_max_gap_percent
(station, time)
uint8
...
standard_name : hourly native max gap percent long_name : hourly native max gap percent units : % description : Percentage of the maximum data gap in the hourly averaged measurement UTC window filled by native resolution data, relative to the total window length. [120960 values with dtype=uint8] hourly_native_representativity_percent
(station, time)
uint8
...
standard_name : hourly native representativity percent long_name : hourly native representativity percent units : % description : Percentage of the hourly averaged measurement UTC window represented by native resolution data. [120960 values with dtype=uint8] land_use
(station)
object
...
standard_name : land use long_name : standardised network provided land use type units : description : Standardised network provided classification, describing the dominant land use in the area of the reporting station. array(['barren-rock', 'urban-transportation', 'open', 'open',\n",
- " 'urban-agricultural', 'open', 'barren-rock', 'forest',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open',\n",
- " 'urban-agricultural', 'urban-agricultural', 'urban-agricultural',\n",
- " 'forest', 'open', 'urban-residential', 'urban-residential',\n",
- " 'urban-agricultural', 'urban-agricultural', 'forest',\n",
- " 'urban-residential', 'barren-rock', 'barren-rock', 'urban-agricultural',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open',\n",
- " 'urban-agricultural', 'barren-desert', 'barren-rock',\n",
- " 'urban-agricultural', 'urban-agricultural', 'urban-agricultural',\n",
- " 'forest', 'open', 'forest', 'open', 'forest', 'forest', 'open', 'open',\n",
- " 'barren-rock', 'snow', 'urban-agricultural', 'snow', 'urban-industrial',\n",
- " 'snow', 'forest', 'open', 'forest', 'open', 'open', 'open', 'forest',\n",
- " 'open', 'open', 'barren-rock', 'forest', 'open', 'open',\n",
- " 'urban-agricultural', 'open', 'open', 'barren-rock', 'open', 'forest',\n",
- " 'forest', 'forest', 'forest', 'forest', 'urban-agricultural',\n",
- " 'urban-agricultural', 'open', 'open', 'open', 'open', 'open',\n",
- " 'barren-rock', 'open', 'open', 'forest', 'urban-agricultural', 'forest',\n",
- " 'urban-agricultural', 'urban-agricultural', 'wetland', 'open', 'open',\n",
- " 'open', 'open', 'urban-agricultural', 'urban-agricultural',\n",
- " 'urban-agricultural', 'open', 'open', 'urban-agricultural', 'open',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open', 'forest', 'forest',\n",
- " 'forest', 'urban-agricultural', 'open', 'urban-park', 'open',\n",
- " 'urban-agricultural', 'open', 'snow', 'forest', 'barren-rock',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open', 'open',\n",
- " 'urban-agricultural', 'open', 'barren-rock', 'urban-agricultural',\n",
- " 'urban-agricultural', 'urban-agricultural', 'open',\n",
- " 'urban-agricultural', 'forest', 'forest', 'forest', 'barren-rock',\n",
- " 'forest', 'forest', 'urban-agricultural', 'forest', 'snow', 'forest',\n",
- " 'open', 'snow', 'urban-agricultural', 'urban-agricultural',\n",
- " 'barren-rock', 'open', 'forest', 'forest', 'open', 'open', 'forest',\n",
- " 'forest', 'urban-agricultural', 'urban-agricultural', 'forest',\n",
- " 'forest', 'forest', 'forest', 'open', 'forest', 'forest', 'open',\n",
- " 'barren-rock', 'open', 'open', 'urban-agricultural', 'wetland',\n",
- " 'urban-agricultural', 'barren-rock', 'snow', 'urban-residential',\n",
- " 'forest', 'open'], dtype=object) local_time
(station, time)
datetime64[ns]
...
standard_name : local time long_name : local time description : Time in hours since 0001-01-01 00:00 local time. Time given refers to the start of the time window the measurement is representative of (temporal resolution). axis : T tz : local [120960 values with dtype=datetime64[ns]] main_emission_source
(station)
object
...
standard_name : main emission source long_name : standardised network provided main emission source units : description : Standardised network provided classification, describing the main emission source influencing air measured at a station. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) mean_solar_time
(station, time)
datetime64[ns]
...
standard_name : mean solar time long_name : mean solar time description : Time in hours since 0001-01-01 00:00 mean solar time. Time given refers to the start of the time window the measurement is representative of (temporal resolution). axis : T tz : mean solar [120960 values with dtype=datetime64[ns]] measurement_altitude
(station)
float32
...
standard_name : measurement altitude long_name : measurement altitude relative to mean sea level units : m description : Altitude of the inlet/instrument/sampler, relative to the stated vertical datum, in metres. array([2.0450e+02, 2.6000e+01, 1.1700e+02, 1.0200e+03, 3.1500e+02, 1.0200e+03,\n",
- " 3.1160e+03, 1.1700e+03, 7.3000e+02, 5.7000e+02, 5.8100e+02, 3.2000e+02,\n",
- " 1.6100e+02, 2.4000e+02, 8.9900e+02, 1.6480e+03, 4.8100e+02, 4.3000e+02,\n",
- " 2.9500e+02, 1.6000e+02, 1.7500e+03, 2.1400e+02, 6.1000e+02, 3.5780e+03,\n",
- " 4.8900e+02, 5.3900e+02, 1.1370e+03, 1.0310e+03, 7.9700e+02, 2.2300e+03,\n",
- " 2.5000e+01, 5.3500e+02, 7.3800e+02, 5.8500e+02, 1.1210e+03, 2.2500e+01,\n",
- " 8.1000e+01, 1.2170e+03, 6.8200e+01, 9.5300e+02, 9.0000e+00, 1.0000e+03,\n",
- " 2.6710e+03, 5.6000e+01, 1.0000e+01, 2.0000e+01, 3.0000e+00, 3.2480e+03,\n",
- " 1.0000e+01, 3.2000e+01, 6.0000e+00, 9.1700e+02, 6.8300e+02, 7.8000e+01,\n",
- " 1.2300e+03, 1.3400e+02, 1.3600e+03, 7.6000e+01, 3.9300e+02, 8.8500e+02,\n",
- " 9.8500e+02, 4.7000e+02, 5.0600e+02, 3.5000e+01, 2.4050e+03, 1.2000e+01,\n",
- " 9.0000e+00, 3.1500e+02, 5.6900e+02, 7.7500e+02, 3.9400e+02, 6.2000e+02,\n",
- " 2.0400e+02, 8.3600e+02, 1.3700e+02, 1.7500e+03, 8.1000e+02, 3.0900e+02,\n",
- " 2.8770e+03, 6.0500e+02, 1.8200e+02, 1.4770e+03, 2.4300e+02, 1.2600e+02,\n",
- " 1.1900e+02, 2.6700e+02, 2.7000e+02, 3.7000e+02, 1.8000e+02, 4.2000e+02,\n",
- " 1.2000e+02, 4.6000e+01, 1.6000e+02, 5.0000e+00, 2.6000e+02, 1.6000e+01,\n",
- " 8.0000e+00, 8.5000e+01, 7.8000e+01, 1.1000e+02, 2.5400e+02, 1.2800e+02,\n",
- " 3.1600e+02, 8.6400e+02, 1.1000e+01, 5.0000e+00, 2.1100e+02, 2.1720e+03,\n",
- " 9.0000e+00, 1.0920e+03, 2.3500e+01, 2.7200e+02, 1.8000e+01, 4.4000e+01,\n",
- " 1.2600e+02, 8.9500e+01, 5.0000e+00, 1.8000e+01, 1.8800e+02, 1.9000e+02,\n",
- " 2.2000e+01, 3.0000e+00, 3.0000e+01, 6.0000e+00, 6.0000e+00, 2.1900e+02,\n",
- " 4.3900e+02, 2.1000e+02, 4.7400e+02, 1.6000e+02, 3.0000e+01, 1.5000e+01,\n",
- " 3.0000e+02, 1.5530e+03, 2.0000e+01, 9.6000e+01, 1.9400e+02, 3.8000e+02,\n",
- " 1.8224e+02, 1.6075e+03, 5.0000e+00, 1.6135e+02, 8.1300e+02, 1.8000e+01,\n",
- " 4.0400e+02, 4.7500e+02, 5.0000e+00, 1.8000e+02, 6.5000e+01, 1.9000e+02,\n",
- " 4.5000e+01, 2.6100e+02, 2.2500e+02, 1.3200e+02, 5.2500e+02, 7.7300e+02,\n",
- " 1.7640e+03, 2.0080e+03, 8.0800e+02, 3.4500e+02, 1.1300e+02, 2.1000e+01,\n",
- " 1.6950e+03, 3.4070e+03, 2.8510e+03, 1.1700e+02, 1.4900e+03, 2.3800e+02],\n",
- " dtype=float32) measurement_methodology
(station)
object
...
standard_name : measurement methodology long_name : standardised measurement methodology units : description : Standardised name of the measurement methodology. array(['ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry',\n",
- " 'ultraviolet photometry', 'ultraviolet photometry'], dtype=object) measurement_scale
(station)
object
...
standard_name : measurement scale long_name : standardised network provided measurement scale units : description : Standardised network provided classification, a denotation of the geographic scope of the air quality measurements made. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_calibration_scale
(station)
object
...
standard_name : measuring instrument calibration scale long_name : measuring instrument calibration scale name units : description : Name of calibration scale used for the calibration of the measuring instrument. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_absorption_cross_section
(station)
object
...
standard_name : measuring instrument documented absorption cross section long_name : measuring instrument documented absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in instrumental manual/documentation. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['1.1476e-17', '1.1476e-17', 'nan', 'nan', 'nan', 'nan', '1.1476e-17',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17', 'nan', '1.1476e-17', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', 'nan', 'nan', 'nan', '1.1476e-17', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', '1.1476e-17', 'nan', '1.1476e-17', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17'], dtype=object) measuring_instrument_documented_accuracy
(station)
object
...
standard_name : measuring instrument documented measurement accuracy long_name : measuring instrument documented measurement accuracy units : nmol mol-1 description : Measurement accuracy (±), as given in the instrumental manual/documentation. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['1.0%', '1.0%', 'nan', 'nan', 'nan', 'nan', '1.0%', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', 'nan', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0%', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.0%', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0%', '1.0%', '1.0%', '1.0%', 'nan', 'nan',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '5.0', '1.0%', '1.0%', '1.0%', '1.0%',\n",
- " '1.0%', 'nan', 'nan', 'nan', '1.0%', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', '1.0%', 'nan', '1.0%',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%', '1.0%', '1.0%', 'nan', 'nan', 'nan', 'nan', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '1.0%'], dtype=object) measuring_instrument_documented_flow_rate
(station)
object
...
standard_name : measuring instrument documented flow rate long_name : measuring instrument documented flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['1.0-3.0', '1.0-3.0', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0',\n",
- " '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '0.5',\n",
- " '0.8', '1.0-3.0', '0.8', '0.7', '0.7', '0.7', '0.7', '0.7', '0.7',\n",
- " '1.0-3.0', '1.0-3.0', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', '1.0-3.0', '1.0-3.0',\n",
- " '1.0-3.0', '1.0-3.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0-3.0', '1.0-3.0', '0.7',\n",
- " '1.0-3.0', 'nan', 'nan', '1.0-3.0', '1.0-3.0', '1.0-3.0', '0.8', '1.5',\n",
- " '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', 'nan', 'nan',\n",
- " 'nan', '1.0-3.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0-3.0',\n",
- " '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', 'nan',\n",
- " '1.0-3.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', '1.0-3.0', '0.8', '1.0-3.0', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0', '1.0-3.0',\n",
- " '1.0-3.0'], dtype=object) measuring_instrument_documented_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented lower limit of detection long_name : measuring instrument documented lower limit of detection units : nmol mol-1 description : Lower limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([1. , 1. , nan, nan, nan, nan, 0.5, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
- " 0.5, 1. , 0.5, 0.5, 0.4, 0.5, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1. ,\n",
- " 0.5, nan, nan, nan, nan, 1. , nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, 1. , 0.5, 0.5, 0.5, 0.5, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, 0.5, 0.5, 0.5, 1. , nan, nan, 0.5, 0.5, 0.5, 0.6, nan, 0.5,\n",
- " 0.5, 0.5, 0.5, 0.5, nan, nan, nan, 0.5, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5, 1. , 1. , 0.5, 0.5,\n",
- " 0.5, 0.5, nan, 0.5, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " 1. , 0.4, 1. , nan, nan, nan, nan, 1. , 0.5, 1. , 1. , 1. , 0.5, 0.5],\n",
- " dtype=float32) measuring_instrument_documented_measurement_resolution
(station)
float32
...
standard_name : measuring instrument documented measurement resolution long_name : measuring instrument documented measurement resolution units : nmol mol-1 description : Measurement resolution, as given in instrumental manual/documentation. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, 0.001, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan], dtype=float32) measuring_instrument_documented_precision
(station)
object
...
standard_name : measuring instrument documented measurement precision long_name : measuring instrument documented measurement precision units : nmol mol-1 description : Measurement precision (±), as given in instrumental manual/documentation. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['1.0', '1.0', 'nan', 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0',\n",
- " '1.0', '1.0/1.0%', '0.5%>=100.0', '1.0', '0.5%>=100.0', '1.0%', '1.0%',\n",
- " '1.0%', '1.0%', '1.0%', '1.0%', '1.0', '1.0', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0', '1.0',\n",
- " '1.0', '1.0', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.0', '1.0', '1.0%', '1.0', 'nan', 'nan',\n",
- " '1.0', '1.0', '1.0', '0.5%', '5.0', '1.0', '1.0', '1.0', '1.0', '1.0',\n",
- " 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0',\n",
- " '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', 'nan', '1.0', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0',\n",
- " '0.5%>=100.0', '1.0', 'nan', 'nan', 'nan', 'nan', '1.0', '1.0', '1.0',\n",
- " '1.0', '1.0', '1.0', '1.0'], dtype=object) measuring_instrument_documented_span_drift
(station)
object
...
standard_name : measuring instrument documented span drift long_name : measuring instrument documented span drift units : nmol mol-1 description : Span drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['1.0%/month', '1.0%/month', 'nan', 'nan', 'nan', 'nan', '1.0%/month',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', '0.5%/day', '1.0%/day', '1.0%/month',\n",
- " '1.0%/day', '0.5/day', '0.5/day', '0.5/day', '0.5/day', '0.5/day',\n",
- " '0.5/day', '1.0%/month', '1.0%/month', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%/month', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%/month', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%/month', '1.0%/month', '0.5/day', '1.0%/month', 'nan', 'nan',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/day', '5.0/month',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " 'nan', 'nan', 'nan', '1.0%/month', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', 'nan', '1.0%/month', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0%/month',\n",
- " '1.0%/day', '1.0%/month', 'nan', 'nan', 'nan', 'nan', '1.0%/month',\n",
- " '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month', '1.0%/month',\n",
- " '1.0%/month'], dtype=object) measuring_instrument_documented_uncertainty
(station)
object
...
standard_name : measuring instrument documented measurement uncertainty long_name : measuring instrument documented measurement uncertainty units : nmol mol-1 description : Measurement uncertainty (±), as given in the instrumental manual/documentation. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented upper limit of detection long_name : measuring instrument documented upper limit of detection units : nmol mol-1 description : Upper limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([200000., 200000., nan, nan, nan, nan, 200000., nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, 200000., 200000., 200000.,\n",
- " 200000., 200000., 200000., 200000., 200000., 200000., 200000., 20000.,\n",
- " 10000., 200000., 10000., 100000., 100000., 100000., 100000., 100000.,\n",
- " 100000., 200000., 200000., nan, nan, nan, nan, 200000.,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " 200000., 200000., 200000., 200000., 200000., nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, 200000., nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, 200000., 200000., 100000., 200000.,\n",
- " nan, nan, 200000., 200000., 200000., 10000., 1000., 200000.,\n",
- " 200000., 200000., 200000., 200000., nan, nan, nan, 200000.,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, 200000.,\n",
- " 200000., 200000., 200000., 200000., 200000., 200000., nan, 200000.,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, 200000., 10000., 200000., nan, nan, nan,\n",
- " nan, 200000., 200000., 200000., 200000., 200000., 200000., 200000.],\n",
- " dtype=float32) measuring_instrument_documented_zero_drift
(station)
object
...
standard_name : measuring instrument documented zero drift long_name : measuring instrument documented zero drift units : nmol mol-1 description : Zero drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['1.0/day', '1.0/day', 'nan', 'nan', 'nan', 'nan', '1.0/day', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0/day', '1.0/day', '1.0/day', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '0.5/day', '0.5/day',\n",
- " '0.5/day', '0.5/day', '0.5/day', '0.5/day', '1.0/day', '1.0/day', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0/day', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0/day', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0/day', '1.0/day', '0.5/day', '1.0/day', 'nan', 'nan',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '5.0/month', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', 'nan', 'nan', 'nan',\n",
- " '1.0/day', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0/day', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day', 'nan', '1.0/day',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.0/day', '1.0/day', '1.0/day', 'nan', 'nan', 'nan', 'nan', '1.0/day',\n",
- " '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day', '1.0/day'],\n",
- " dtype=object) measuring_instrument_documented_zonal_drift
(station)
object
...
standard_name : measuring instrument documented zonal drift long_name : measuring instrument documented zonal drift units : nmol mol-1 description : Zonal drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_further_details
(station)
object
...
standard_name : measuring instrument further details long_name : measuring instrument further details units : description : Further associated details regarding the specifics of the measurement methodology/instrument. array(['no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:https://books.google.es/books?id=suisdwaaqbaj&pg=pa48&lpg=pa48&dq=dasibi+1108&source=bl&ots=tnxwozqtbt&sig=acfu3u0ba3efwduqenfd2g2zk-rqp7cqfg&hl=en&sa=x&ved=2ahukewjrk8bx2f7fahud4oakhsr1a5wq6aewdhoecaaqaq#v=onepage&q=dasibi%201108&f=false',\n",
- " 'nan', 'nan',\n",
- " 'specifications online:https://translate.google.com/translate?hl=en&sl=ja&u=http://www.dylec.co.jp/01ozone/m1100.html&prev=search',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here: http://www.data.jma.go.jp/gmd/env/ghg_obs/en/station/station_minamitorishima.html',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here: http://www.data.jma.go.jp/gmd/env/ghg_obs/en/station/station_minamitorishima.html',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here: http://www.data.jma.go.jp/gmd/env/ghg_obs/en/station/station_minamitorishima.html',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:https://www.rivm.nl/en/documents_and_publications/scientific/reports/1993/juni/acceptance_procedure_and_testresults_of_the_ozone_analyzers_model_49w_made_by_thermo_environmental_instruments_on_behalf_of_the_dutch_national_air_quality_monitoring_network',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'no manual or specifications available but confirmation it is uses ultraviolet photometry here:http://cdiac.ess-dive.lbl.gov/programs/narsto/compendium_combined.pdf',\n",
- " 'nan', 'nan'], dtype=object) measuring_instrument_inlet_information
(station)
object
...
standard_name : measuring instrument inlet information long_name : measuring instrument measurement inlet information units : description : Description of sampling inlet of the measuring instrument. array(['inlet type: downward-facing tube;description: 5 m above ground on top of the shelter,2 m above the roof * 6.35 mm ptfe tubing - length=4 m up to the instrument;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 6.0 mm;length: 4.0 m',\n",
- " 'inlet type: downward-facing tube;description: 7 m above ground on top of the station building,2 m above the roof * 19 mm pfa tubing - length=6 m up to the instrument - aditional pump to increase flow rate - residence time < 5 s',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;tube material: stainless steel;inner diameter: 150.0 mm;length: 4.0 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: inlet approx. 2 m above the building and 5 m above the ground;coarse mesh at the top of the inlet to protect against spiders etc.;47 mm teflon filter (5 micron) just upstream of the instrument inlet.;tube material: teflon;outer diameter: 6.35 mm;length: 10.0 m',\n",
- " 'inlet type: hat or hood;description: 13m heated glass sampling inlet (40mm diameter) (residence time 4 secs),then 2m of 1/4inch teflon tubing at a flow rate of 1.5 l/min,then through a particulate filter (1-2micron) before it enters the instrument.',\n",
- " 'nan', 'description: downward facing with hood',\n",
- " 'inlet type: downward-facing tube',\n",
- " 'inlet type: hat or hood;description: downward facing with hood',\n",
- " 'inlet type: hat or hood', 'inlet type: hat or hood',\n",
- " 'inlet type: hat or hood', 'inlet type: hat or hood',\n",
- " 'inlet type: hat or hood', 'inlet type: hat or hood',\n",
- " 'inlet type: hat or hood;description: upward;tube material: teflon;outer diameter: 12.8 mm;inner diameter: 8.7 mm;length: 6.0 m',\n",
- " 'nan',\n",
- " 'inlet type: hat or hood;description: monthly leak test,inlet filter checked every 6 months,ozone scrubber checked and replaced if needed once/year',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'inlet type: stainless steel',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: upstream from manifold,tube length=6.5m,flow rate=17l/min;tube material: teflon;outer diameter: 41.5 mm;inner diameter: 28.0 mm;length: 1.5 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;tube material: teflon;outer diameter: 6.35 mm;length: 4.0 m',\n",
- " 'inlet type: hat or hood;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 3.95 mm;length: 4.5 m',\n",
- " 'inlet type: hat or hood;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 3.95 mm;length: 3.5 m',\n",
- " 'inlet type: hat or hood;description: inlet approx. 2.5 m above the station and 10 m above the ground;tube material: teflon',\n",
- " 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: pvc_home_made;tube material: teflon;outer diameter: 60.0 mm;inner diameter: 27.0 mm;length: 4.0 m',\n",
- " 'inlet type: hat or hood;description: the air intake is composed by an external (outside building) steel pipe (internally covered by teflon) and the internal (inside building) pyrex pipe;tube material: teflon;outer diameter: 6.35 mm;length: 1.5 m',\n",
- " 'inlet type: hat or hood;description: the air intake is composed by an external (outside building) steel pipe (internally covered by teflon) and the internal (inside building) pyrex pipe;tube material: teflon;outer diameter: 6.35 mm;length: 1.5 m',\n",
- " 'nan',\n",
- " 'inlet type: stainless steel;description: covered with hat;tube material: teflon;outer diameter: 8.0 mm;inner diameter: 6.0 mm;length: 6.0 m',\n",
- " 'inlet type: hat or hood;description: inlet filter checked every 2 months,leak test every 3 months,ozone scrubber checked and replaced if needed once/year',\n",
- " 'inlet type: hat or hood;description: inlet filter checked every 2 months,leak test every 3 months,ozone scrubber checked and replaced if needed once/year',\n",
- " 'inlet type: hat or hood;description: inlet filter checked every 2 months,leak test every 3 months,ozone scrubber checked and replaced if needed once/year',\n",
- " 'inlet type: hat or hood;description: hat is made of pfa teflon. there is still mesh filter under the hat bottom to prevente bugs. inlet is connected to a glass manifold,from where o3 analyzer is connected by 1/4inch tubing.;outer diameter: 13.0 mm;inner diameter: 10.0 mm;length: 9.0 m',\n",
- " 'inlet type: hat or hood;description: hat is made of pfa teflon. there is still mesh filter under the hat bottom to prevente bugs. inlet is connected to a glass manifold,from where o3 analyzer is connected by 1/4inch tubing.;outer diameter: 13.0 mm;inner diameter: 10.0 mm;length: 9.5 m',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'inlet type: downward-facing tube;description: downward-facing tube;tube material: teflon;outer diameter: 38.0 mm;length: 4.5 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'description: downward facing', 'description: downward facing',\n",
- " 'description: downward facing', 'inlet type: hat or hood', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: open conductive tubing;description: air inlet to instrument through teflone tube,connected to major glassinlet tube,mounted on the roof of object;tube material: teflon;outer diameter: 6.35 mm;length: 4.0 m',\n",
- " 'inlet type: hat or hood;description: air inlet to instrument through teflone tube,connected to major glassinlet tube,mounted on the roof of object;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.5 mm;length: 1.0 m',\n",
- " 'inlet type: open conductive tubing;description: air inlet to instrument through teflone tube,connected to glassinlet tube,mounted on the adge of balcony roof.',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: downward facing,teflon inlet bell;tube material: teflon;outer diameter: 6.35 mm;inner diameter: 4.76 mm;length: 7.0 m',\n",
- " 'inlet type: hat or hood;description: inlet approx. 6 m above the building and 12 m above the ground;coarse mesh at the top of the inlet to protect against spiders etc.;47 mm teflon filter (5 micron) just upstream of the instrument inlet.;tube material: teflon;outer diameter: 6.35 mm;length: 15.0 m',\n",
- " 'inlet type: downward-facing tube;description: teflon tubing,covered with rain hat;tube material: teflon;outer diameter: 8.0 mm;inner diameter: 6.0 mm;length: 30.0 m'],\n",
- " dtype=object) measuring_instrument_manual_name
(station)
object
...
standard_name : measuring instrument manual name long_name : measuring instrument manual name units : description : Path to the location in the esarchive of the manual for the specific measuring instrument. array(['Thermo_49C_Manual.pdf', 'Thermo_49C_Manual.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Ecotech_EC9810_Manual.pdf', 'Teledyne_T400_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Teledyne_T400_Manual.pdf', 'Horiba_APOA370_Specs.pdf',\n",
- " 'Horiba_APOA370_Specs.pdf', 'Horiba_APOA370_Specs.pdf',\n",
- " 'Horiba_APOA370_Specs.pdf', 'Horiba_APOA370_Specs.pdf',\n",
- " 'Horiba_APOA370_Specs.pdf', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Thermo_49C_Manual.pdf', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Horiba_APOA370_Specs.pdf', 'Thermo_49C_Manual.pdf', 'nan', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Teledyne_400A_Manual.pdf', 'Ebara_EG-3000F_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49C_Manual.pdf', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49C_Manual.pdf', 'Teledyne_T400_Manual.pdf',\n",
- " 'Thermo_49C_Manual.pdf', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo_49C_Manual.pdf', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49C_Manual.pdf', 'Thermo_49C_Manual.pdf',\n",
- " 'Thermo_49C_Manual.pdf', 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf',\n",
- " 'Thermo_49i_Manual.pdf,Thermo_49i_Specs.pdf'], dtype=object) measuring_instrument_name
(station)
object
...
standard_name : measuring instrument name long_name : standardised measuring instrument name units : description : Standardised name of the measuring instrument. array(['Thermo 49C', 'Thermo 49C', 'nan', 'nan', 'nan', 'nan', 'Thermo 49i',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Thermo 49i', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Thermo 49C', 'Thermo 49i', 'Ecotech EC9810', 'Teledyne T400',\n",
- " 'Thermo 49i', 'Teledyne T400', 'Horiba APOA370', 'Horiba APOA370',\n",
- " 'Horiba APOA370', 'Horiba APOA370', 'Horiba APOA370', 'Horiba APOA370',\n",
- " 'Thermo 49C', 'Thermo 49i', 'Environnement SA O341M', 'nan', 'nan',\n",
- " 'nan', 'Thermo 49C', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Thermo 49C', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'Thermo 49i', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'Thermo 49i', 'Thermo 49i', 'Horiba APOA370',\n",
- " 'Thermo 49C', 'nan', 'nan', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Teledyne 400A', 'Ebara EG-3000F', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Thermo 49i', 'Thermo 49i', 'Thermo 49i', 'nan', 'nan', 'nan',\n",
- " 'Thermo 49i', 'Thermo 49w', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'Thermo 49i',\n",
- " 'Thermo 49C', 'Thermo 49C', 'Thermo 49i', 'Thermo 49i', 'Thermo 49i',\n",
- " 'Thermo 49i', 'nan', 'Thermo 49i', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'Thermo 49C', 'Teledyne T400',\n",
- " 'Thermo 49C', 'nan', 'nan', 'nan', 'nan', 'Thermo 49C', 'Thermo 49i',\n",
- " 'Thermo 49C', 'Thermo 49C', 'Thermo 49C', 'Thermo 49i', 'Thermo 49i'],\n",
- " dtype=object) measuring_instrument_process_details
(station)
object
...
standard_name : measuring instrument process details long_name : measuring instrument process details units : description : Miscellaneous details regarding assumptions made in the standardisation of the measurement methodology/instrument. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'make assumption measuring instrument is dasibi 1108 w/gen', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
- " dtype=object) measuring_instrument_reported_absorption_cross_section
(station)
object
...
standard_name : measuring instrument reported absorption cross section long_name : measuring instrument reported absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in metadata. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['nan', '1.1476e-17', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', 'nan', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.1476e-17', 'nan', '1.1476e-17', 'nan', 'nan', 'nan', '1.1476e-17',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17', 'nan', 'nan', '1.147e-17', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.1476e-17', '1.1476e-17', 'nan', 'nan', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '1.1476e-17', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.1476e-17',\n",
- " '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17', '1.1476e-17',\n",
- " '1.1476e-17'], dtype=object) measuring_instrument_reported_accuracy
(station)
object
...
standard_name : measuring instrument reported measurement accuracy long_name : measuring instrument reported measurement accuracy units : nmol mol-1 description : Measurement accuracy (±), as given in metadata. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_flow_rate
(station)
object
...
standard_name : measuring instrument reported flow rate long_name : measuring instrument reported flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['1.5', '12.0', 'nan', 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.2', '1.1', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0',\n",
- " '1.4', 'nan', 'nan', '1.3', 'nan', '1.2', '1.2', '1.2', '1.2', '1.2',\n",
- " '1.2', '1.0', 'nan', '1.5', 'nan', 'nan', 'nan', '0.9', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '1.5', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '0.5', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " '0.5', '0.6', '0.6', '1.0', 'nan', 'nan', '1.0', '1.5', '1.5', 'nan',\n",
- " '1.5', '1.5', '1.5', '1.5', '20.0', '20.0', 'nan', 'nan', 'nan', '1.5',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.4', '1.3', 'nan', 'nan',\n",
- " 'nan', '0.7', 'nan', '1.3', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', '0.6', '0.8', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.1', '1.3', '1.2', '1.0', '1.3', '0.5', '1.0'], dtype=object) measuring_instrument_reported_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported lower limit of detection long_name : measuring instrument reported lower limit of detection units : nmol mol-1 description : Lower limit of detection of measurement methodology, as given in metadata. array([1. , 1. , nan, nan, nan, nan, 1. ,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " 1. , 1. , nan, nan, nan, nan, nan,\n",
- " nan, 1. , 0.1 , 0.3007 , 2. , 1. , 2. ,\n",
- " 1. , 1. , 1. , 1. , 1. , 1. , 0.6 ,\n",
- " 0.58 , 1. , nan, nan, nan, 1. , nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, 1. , nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, 1. , nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, 0.5 , 0.5 , 0.5 , 1. , nan,\n",
- " nan, 0.7 , 1. , 1. , 0.601 , 1. , 1. ,\n",
- " 1. , 1. , 0.5 , 0.5 , nan, nan, nan,\n",
- " 4. , nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, 1. , 1. , nan, nan,\n",
- " nan, 0.5 , 1.703967, 1. , nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan,\n",
- " 0.5 , 0.6 , 0.5 , nan, nan, nan, nan,\n",
- " 1. , 1. , 1. , 1. , 1. , 1. , 1. ],\n",
- " dtype=float32) measuring_instrument_reported_measurement_resolution
(station)
float32
...
standard_name : measuring instrument reported measurement resolution long_name : measuring instrument reported measurement resolution units : nmol mol-1 description : Measurement resolution, as given in metadata. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) measuring_instrument_reported_precision
(station)
object
...
standard_name : measuring instrument reported measurement precision long_name : measuring instrument reported measurement precision units : nmol mol-1 description : Measurement precision (±), as given in metadata. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_span_drift
(station)
object
...
standard_name : measuring instrument reported span drift long_name : measuring instrument reported span drift units : nmol mol-1 description : Span drift of measuring instrument per unit of time, as given in metadata. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_uncertainty
(station)
object
...
standard_name : measuring instrument reported measurement uncertainty long_name : measuring instrument reported measurement uncertainty units : nmol mol-1 description : Measurement uncertainty (±), as given in metadata. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). It can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', '10.0', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', '1.0',\n",
- " '3.0', 'nan', '1.7', 'nan', '1.7', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '10.0', 'nan', 'nan', 'nan', '1.0', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '3.1', 'nan', '1.0', 'nan', 'nan', '0.5', 'nan', 'nan', 'nan',\n",
- " '1.0', '1.0', '1.0', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', '1.0', '1.0', '1.0', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', '1.0', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', 'nan'], dtype=object) measuring_instrument_reported_units
(station)
object
...
standard_name : measuring instrument reported units long_name : measuring instrument reported measurement units units : description : Units that the measured parameter are natively reported in. array(['nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'ug m-3', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'ug m-3', 'nmol mol-1',\n",
- " 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'ug m-3',\n",
- " 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'nmol mol-1', 'nmol mol-1', 'ug m-3', 'ug m-3', 'ug m-3', 'ug m-3',\n",
- " 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1', 'nmol mol-1',\n",
- " 'nmol mol-1', 'nmol mol-1'], dtype=object) measuring_instrument_reported_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported upper limit of detection long_name : measuring instrument reported upper limit of detection units : nmol mol-1 description : Upper limit of detection of measurement methodology, as given in metadata. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) measuring_instrument_reported_zero_drift
(station)
object
...
standard_name : measuring instrument reported zero drift long_name : measuring instrument reported zero drift units : nmol mol-1 description : Zero drift of measuring instrument per unit of time, as given in metadata. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_zonal_drift
(station)
object
...
standard_name : measuring instrument reported zonal drift long_name : measuring instrument reported zonal drift units : nmol mol-1 description : Zonal drift of measuring instrument per unit of time, as given in metadata. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_sampling_type
(station)
object
...
standard_name : measuring instrument sampling type long_name : standardised sampling type of the measuring instrument units : description : Standardised name of the measuring instrument sampling type. array(['low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous',\n",
- " 'low volume continuous', 'low volume continuous'], dtype=object) monthly_native_max_gap_percent
(station, time)
uint8
...
standard_name : monthly native max gap percent long_name : monthly native max gap percent units : % description : Percentage of the maximum data gap in the monthly averaged measurement UTC window filled by native resolution data, relative to the total window length. [120960 values with dtype=uint8] monthly_native_representativity_percent
(station, time)
uint8
...
standard_name : monthly native representativity percent long_name : monthly native representativity percent units : % description : Percentage of the monthly averaged measurement UTC window represented by native resolution data. [120960 values with dtype=uint8] network
(station)
object
...
standard_name : network long_name : data network units : description : The name of the network which reports data for the specific station in question. array(['EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS',\n",
- " 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS', 'EBAS'], dtype=object) network_maintenance_details
(station)
object
...
standard_name : network maintenance details long_name : network maintenance details units : description : Extra details provided by the reporting network about the operational maintenance done at the station. array(['maintenance description: inlet filter checked every 1 month.;humidity/temp. control: none',\n",
- " 'maintenance description: monthly leak test and uv lamp test,inlet filter checked every 1 months,manual zero and spam check every 3 months;zero/span check type: automatic;zero/span check interval: 1d',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'maintenance description: biweekly leak test,inlet filter checked every 2 months,ozone scrubber checked and replaced if needed once/year;zero/span check type: automatic;zero/span check interval: 23h;humidity/temp. control: none',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'maintenance description: regular replacements of the inlet filters and check of all instrument parameters.;zero/span check type: manual;humidity/temp. control: none',\n",
- " 'humidity/temp. control: none', 'nan',\n",
- " 'humidity/temp. control description: unknown',\n",
- " 'humidity/temp. control: other (see metadata)',\n",
- " 'humidity/temp. control: none;humidity/temp. control description: unknown',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'maintenance description: inlet filter changed every 2 weeks,ozone scrubber checked and replaced if needed once/year,5 calibrations in 2018;zero/span check type: manual;zero/span check interval: 3mo;humidity/temp. control: temperature controlled;humidity/temp. control description: temperature controlled at 4°c above ambient air',\n",
- " 'nan', 'humidity/temp. control: none', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control description: automatic', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'humidity/temp. control: none', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'maintenance description: monthly leak test,inlet filter checked every 2 months,ozone scrubber checked and replaced if needed once/year;zero/span check type: manual;zero/span check interval: 3mo;humidity/temp. control: temperature conditioning at 20 deg. c;humidity/temp. control description: none',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control: none',\n",
- " 'maintenance description: monthly leak test,inlet filter and ozone scrubber checked and replaced if needed;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none',\n",
- " 'maintenance description: monthly leak test,inlet filter and ozone scrubber checked and replaced if needed;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none',\n",
- " 'maintenance description: inlet filter (47 mm teflon filter (5 micron)) changed every year;humidity/temp. control: other (see metadata);humidity/temp. control description: a cold trap (5 deg c) is used to reduce the humidity in the sample and to avoid downstream condensation. lab temp is set to 25 degc.',\n",
- " 'nan', 'nan',\n",
- " 'maintenance description: full cleaning twice a year;zero/span check type: automatic;zero/span check interval: 23h;humidity/temp. control: nafion dryer;humidity/temp. control description: 180cm',\n",
- " 'zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none',\n",
- " 'zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none',\n",
- " 'nan',\n",
- " 'zero/span check type: manual;zero/span check interval: 1y;humidity/temp. control: none',\n",
- " 'humidity/temp. control: other (see metadata);humidity/temp. control description: the air is dried with an electronic cooler unit at a dew-point temperature about 5 degrees centigrade.',\n",
- " 'humidity/temp. control: other (see metadata);humidity/temp. control description: the air is dried with an electronic cooler unit at a dew-point temperature about 5 degrees centigrade.',\n",
- " 'humidity/temp. control: other (see metadata);humidity/temp. control description: the air is dried with an electronic cooler unit at a dew-point temperature about 5 degrees centigrade.',\n",
- " 'maintenance description: inlet filter checked every 1 month,ozone scrubber checked and replaced if needed once/year,calibration every 6 months;zero/span check type: manual;zero/span check interval: 1mo;humidity/temp. control description: humidity control using silica gel',\n",
- " 'maintenance description: inlet filter checked every 1 month,ozone scrubber checked and replaced if needed once/year,calibration every 6 months;zero/span check type: manual;zero/span check interval: 1mo;humidity/temp. control description: humidity control using silica gel',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'maintenance description: zero and three points span check calibration with a primary standard every six months,leak test every six months,inlet filter replaced every 2 weeks,ozone scrubber replaced every two years;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: nafion dryer',\n",
- " 'blank correction: not blank corrected',\n",
- " 'blank correction: not blank corrected',\n",
- " 'blank correction: not blank corrected',\n",
- " 'blank correction: not blank corrected',\n",
- " 'blank correction: not blank corrected', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control: none', 'nan',\n",
- " 'humidity/temp. control description: automatic', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'maintenance description: flow check every 3 month,inlet filter replaced every 2 months,ozone scrubber checked and replaced if needed once/year;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: temperature conditioning at 20 deg. c;humidity/temp. control description: <airconditioning of room>',\n",
- " 'zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none;humidity/temp. control description: implemented air conditioner is removing heat from a station,thus cooling the air,and removing humidity',\n",
- " 'humidity/temp. control: temperature conditioning at 20 deg. c;humidity/temp. control description: airconditioning of room with instrument.',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'humidity/temp. control description: automatic',\n",
- " 'maintenance description: inlet filter (47 mm teflon filter (5 micron)) changed every 2 months. coarse filter is visually inspected twice a year. zero and span checks are irregularly performed with an external zero air unit and the internal ozonator.;zero/span check type: manual;humidity/temp. control: none',\n",
- " 'maintenance description: monthly leak test,inlet filter checked every 2 months,ozone scrubber checked and replaced if needed once/year;zero/span check type: automatic;zero/span check interval: 1d;humidity/temp. control: none;humidity/temp. control description: instrument house in air conditioned room'],\n",
- " dtype=object) network_miscellaneous_details
(station)
object
...
standard_name : network miscellaneous details long_name : network miscellaneous details units : description : Extra miscellanous details provided by the reporting network. array(["data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data from september 24,2001 until november 17,2003,were flagged as erroneous due to problems with ozone loss in the air inlet. -data/det.limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50117. measurement uncertainty converted from '19.9534 ug/m3' at 293.15 k,1013.25 hpa to '10.0 nmol/mol',conversion factor 0.50117. detection limit converted from '1.9953 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50117.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " 'nan',\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'ppb' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on export from ebas from 'ug/m3' at 265.15 k,653.0 hpa to 'nmol/mol',conversion factor 0.703.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan',\n",
- " "prior to shipment to and deployment in chile,the instrument was calibrated at wcc-empa against a primary ozone standard (standard reference photometer (srp),no 15,in 2010. -- data converted from 'nmol/mol' to 'ug/m3',conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '0.1 ug/m3' at 293.15 k,1013.25 hpa to '0.1 nmol/mol',conversion factor 0.501.",\n",
- " 'nan',\n",
- " "no water vapor,temperature and ozone correction -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '3.392 ug/m3' at 293.15 k,1013.25 hpa to '1.7 nmol/mol',conversion factor 0.5012. detection limit converted from '3.981 ug/m3' at 293.15 k,1013.25 hpa to '2.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "no water vapor,temperature and ozone correction -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '3.392 ug/m3' at 293.15 k,1013.25 hpa to '1.7 nmol/mol',conversion factor 0.5012. detection limit converted from '3.981 ug/m3' at 293.15 k,1013.25 hpa to '2.0 nmol/mol',conversion factor 0.5012.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50.",\n",
- " "hourly avarages of half-hourly values -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.50.",\n",
- " "detection limit is 3 sigma scattering during zero air measurements referring to 1 min integration -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995342748. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50116703. detection limit converted from '1.1972056 ug/m3' at 293.15 k,1013.25 hpa to '0.6 nmol/mol',conversion factor 0.50116703.",\n",
- " "detection limit is 3 standard deviation during zero air measurements. -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50117. detection limit converted from '1.1573 ug/m3' at 293.15 k,1013.25 hpa to '0.58 nmol/mol',conversion factor 0.50117.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (demore et al.,jpl,pasadena,calif.,1994) -- data converted on import into ebas from nmol/mol to ug/m3 at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '19.95 ug/m3' at 293.15 k,1013.25 hpa to '10.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "data processed according to standard method -- data and detection limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan',\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501167.",\n",
- " 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. detection limit converted from '0.5 nmol/mol' to '0.9977 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50117. detection limit converted from '0.9977 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.50117.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. measurement uncertainty converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '0.998 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.5012. detection limit converted from '1.397 ug/m3' at 293.15 k,1013.25 hpa to '0.7 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "nothing -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. detection limit converted from '0.601 nmol/mol' to '1.1992 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.995343. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50117. detection limit converted from '1.1992 ug/m3' at 293.15 k,1013.25 hpa to '0.601 nmol/mol',conversion factor 0.50117.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (hearn,1961). -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (hearn,1961). -- data,measurement uncertainty,detection limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (hearn,1961). -- data,measurement uncertainty,detection limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "these data assumes an absorption cross section of 1.1476 x 10-17 cm2 molecule-1 (hearn,1961). -- data,measurement uncertainty,detection limit converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'ppb' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '0.5 ppb' to '1.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'ppb' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '0.5 ppb' to '1.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. detection limit converted from '4.0 nmol/mol' to '7.981 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. detection limit converted from '7.981 ug/m3' at 293.15 k,1013.25 hpa to '4.0 nmol/mol',conversion factor 0.5012.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,no conversion factor (no nonzero,valid values). -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,no conversion factor (no nonzero,valid values). -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at standard conditions (293.15 k,1013.25 hpa),conversion factor 1.9953. variable metadata uncertainty converted. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. measurement uncertainty converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 2.00. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 2.00. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 2.00. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " "data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.50.",\n",
- " 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " "on site calibration performet every four months,in acredited laboratory once a year. -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '0.6 nmol/mol' to '1.2 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.2 ug/m3' at 293.15 k,1013.25 hpa to '0.6 nmol/mol',conversion factor 0.501.",\n",
- " "on site callibration of instrument 3 times a year and once a year calibration in acredited laboratory. -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '1.0 ug/m3' at 293.15 k,1013.25 hpa to '0.5 nmol/mol',conversion factor 0.501.",\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.99534. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "prior to shipment to and deployment in vietnam,the instrument was calibrated at wcc-empa against a primary ozone standard (standard reference photometer (srp),no 15,in april 2012. -- data converted from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.5012. measurement uncertainty converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012. detection limit converted from '1.995 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.5012.",\n",
- " "filtered data valid data -- data converted on import into ebas from 'nmol/mol' to 'ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. detection limit converted from '1.0 nmol/mol' to '2.0 ug/m3' at 293.15 k,1013.25 hpa,conversion factor 1.9953. -- data converted on export from ebas from 'ug/m3' at 293.15 k,1013.25 hpa to 'nmol/mol',conversion factor 0.501. detection limit converted from '2.0 ug/m3' at 293.15 k,1013.25 hpa to '1.0 nmol/mol',conversion factor 0.501."],\n",
- " dtype=object) network_provided_volume_standard_pressure
(station)
float64
...
standard_name : network provided volume standard pressure long_name : network provided volume standard pressure units : hPa description : The pressure (in hPa) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typially normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported pressure should be reported as the internal pressure of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas pressure is assumed to be the known network standard pressure for the measured component. array([1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25,\n",
- " 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25, 1013.25]) network_provided_volume_standard_temperature
(station)
float64
...
standard_name : network provided volume standard temperature long_name : network provided volume standard temperature units : K description : The temperature (in Kelvin) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typially normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported temperature should be reported as the internal temperature of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas temperature is assumed to be the known network standard temperature for the measured component. array([293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15, 293.15,\n",
- " 293.15, 293.15, 293.15, 293.15, 293.15, 293.15]) network_qa_details
(station)
object
...
standard_name : network qa details long_name : network qa details units : description : Extra details provided by the reporting network about the in-network quality assurance of measurements. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) network_sampling_details
(station)
object
...
standard_name : network sampling details long_name : network sampling details units : description : Extra details provided by the reporting network about the sampling methods employed. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) network_uncertainty_details
(station)
object
...
standard_name : network uncertainty details long_name : network uncertainty details units : description : Extra details provided by the reporting network about the uncertainties involved with the measurement methods employed. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan',\n",
- " 'zero/negative values: zero and neg. values higher than -lod are measured at low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;detection limit explained: determined by instrument counting statistics,no detection limit flag used;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;detection limit explained: determined by instrument counting statistics,no detection limit flag used;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'detection limit explained: taken from instrument manual;zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes only calibration uncertainty',\n",
- " 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan',\n",
- " 'measurement uncertainty explained: actually max(0.5;7%) to include statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan',\n",
- " 'zero/negative values: zero and neg. values may not appear',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'nan',\n",
- " 'zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'zero/negative values: zero/negative values reported as measured if within detection limit,flagged with 780',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan',\n",
- " 'detection limit explained: determined by instrument counting statistics;zero/negative values: zero and neg. values reported as measured if they are inside 0 ± detection limit,flagged with 781;other negative values flaged 456',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty;zero/negative values: zero and neg. values may appear due to statistical variations at very low concentrations',\n",
- " 'measurement uncertainty explained: includes statistical uncertainty of individual sample and calibration uncertainty',\n",
- " 'nan'], dtype=object) population
(station)
float32
...
standard_name : population long_name : population units : description : Population size of the nearest urban settlement. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) primary_sampling_further_details
(station)
object
...
standard_name : primary sampling further details long_name : primary sampling further details units : description : Further associated details regarding the specifics of the primary sampling instrument/type. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_documented_flow_rate
(station)
object
...
standard_name : primary sampling instrument documented flow rate long_name : primary sampling instrument documented sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_manual_name
(station)
object
...
standard_name : primary sampling instrument manual name long_name : primary sampling instrument manual name units : description : Path to the location in the esarchive of the manual for the specific primary sampling instrument. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_name
(station)
object
...
standard_name : primary sampling instrument name long_name : standardised primary sampling instrument name units : description : Standardised name of the primary sampling instrument (if no specific instrument is used, or known, this is the standardised primary sampling type). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_reported_flow_rate
(station)
object
...
standard_name : primary sampling instrument reported flow rate long_name : primary sampling instrument reported sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_process_details
(station)
object
...
standard_name : primary sampling process details long_name : primary sampling process details units : description : Miscellaneous details regarding assumptions made in the standardisation of the primary sampling type/instrument. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) primary_sampling_type
(station)
object
...
standard_name : primary sampling type long_name : standardised primary sampling type units : description : Standardised primary sampling type. array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) principal_investigator_email_address
(station)
object
...
standard_name : principal investigator email address long_name : principal investigator email address units : description : Email address of the principal scientific investigator for the specific reported data. array(['barlasina@smn.gov.ar', 'mcupeiro@smn.gov.ar', 'nan', 'nan', 'nan',\n",
- " 'nan', 'iris.buxbaum@umweltbundesamt.at', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'e.adriaenssens@vmm.be',\n",
- " 'e.adriaenssens@vmm.be', 'e.adriaenssens@vmm.be', 'nan',\n",
- " 'Audra.mcclure@noaa.gov', 'sara.crepinsek@noaa.gov', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'katie.read@ncas.ac.uk', 'nan',\n",
- " 'jan.komarek@chmi.cz', 'kominkova.k@czechglobe.cz', 'hadinger@chmi.cz',\n",
- " 'bryan.hellack@uba.de', 'bryan.hellack@uba.de', 'bryan.hellack@uba.de',\n",
- " 'bryan.hellack@uba.de', 'bryan.hellack@uba.de', 'bryan.hellack@uba.de',\n",
- " 'Dagmar.Kubistin@dwd.de', 'cedric.couret@uba.de', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Audra.mcclure@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'ctorresg@aemet.es', 'heidi.hellen@fmi.fi',\n",
- " 'heidi.hellen@fmi.fi', 'heidi.hellen@fmi.fi', 'mika.vestenius@fmi.fi',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'ferenczi.z@met.hu', 'ferenczi.z@met.hu',\n",
- " 'reza_elka@yahoo.com', 'nan', 'nan', 'nan',\n",
- " 'p.cristofanelli@isac.cnr.it', 'p.cristofanelli@isac.cnr.it',\n",
- " 'm.angelucci@arpa.umbria.it', 'antarctic@met.kishou.go.jp',\n",
- " 'ghg_obs@met.kishou.go.jp', 'ghg_obs@met.kishou.go.jp',\n",
- " 'ghg_obs@met.kishou.go.jp', 'sulla@korea.kr', 'sulla@korea.kr', 'nan',\n",
- " 'nan', 'nan', 'ray.ellul@um.edu.mt', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'waa@nilu.no', 'nan', 'waa@nilu.no', 'waa@nilu.no', 'waa@nilu.no',\n",
- " 'kt@nilu.no', 'waa@nilu.no', 'nan', 'waa@nilu.no', 'waa@nilu.no',\n",
- " 'sylvia.nichol@niwa.co.nz', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'sara.crepinsek@noaa.gov', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'rudi.voncina@eimv.si', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'Audra.mcclure@noaa.gov',\n",
- " 'Audra.mcclure@noaa.gov', 'anhnnhn@gmail.com',\n",
- " 'thumeka.mkololo@weathersa.co.za'], dtype=object) principal_investigator_institution
(station)
object
...
standard_name : principal investigator institution long_name : principal investigator institution units : description : Institution of the principal scientific investigator for the specific reported data. array(['Servicio Meteorológico Nacional', 'Servicio Meteorologico Nacional',\n",
- " 'nan', 'nan', 'nan', 'nan', 'Umweltbundesamt Wien', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Flanders Environment Agency', 'Flanders Environment Agency',\n",
- " 'Flanders Environment Agency', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Physical Sciences Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National centre for Atmospheric Science', 'nan',\n",
- " 'Czech Hydrometeorological Instute',\n",
- " 'Global Change Research Centre AS CR v. v. i.',\n",
- " 'Czech Hydrometeorological Instute', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Umweltbundesamt Langen',\n",
- " 'Umweltbundesamt Langen', 'Meteorological Observatory Hohenpeissenberg',\n",
- " 'German Environment Agency', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Agencia Estatal de Meteorologia', 'nan', 'nan', 'nan',\n",
- " 'Finnish Meteorological Institute', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Hungarian Meteorological Service', 'Hungarian Meteorological Service',\n",
- " 'Badan Meteorologi Klimatologi dan Geofisika', 'nan', 'nan', 'nan',\n",
- " 'National Research Council of Italy',\n",
- " 'National Research Council of Italy',\n",
- " 'Arpa Umbria - Umbria Regional Agency for Environmental Protection',\n",
- " 'Japan Meteorological Agency', 'Japan Meteorological Agency',\n",
- " 'Japan Meteorological Agency', 'Japan Meteorological Agency',\n",
- " 'National Institute of Meteorological Sciences',\n",
- " 'National Institute of Meteorological Sciences', 'nan', 'nan', 'nan',\n",
- " 'University of Malta', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'Norwegian Institute for Air Research',\n",
- " 'National Institute of Water and Atmospheric Research',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Physical Sciences Division',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'Milan Vidmar Electric Power Research Institute', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Monitoring Division',\n",
- " 'Hydro-Meteorological and Environmental Station Network Center',\n",
- " 'South African Weather Service'], dtype=object) principal_investigator_name
(station)
object
...
standard_name : principal investigator name long_name : principal investigator name units : description : Full name of the principal scientific investigator for the specific reported data. array(['Maria Elena Barlasina', 'Manuel Cupeiro', 'Marina Froelich',\n",
- " 'Marina Froelich', 'Wolfgang Spangl', 'Wolfgang Spangl', 'Iris Buxbaum',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Wolfgang Spangl', 'Wolfgang Spangl',\n",
- " 'Wolfgang Spangl', 'Marina Froelich', 'Wolfgang Spangl',\n",
- " 'Marina Froelich', 'Elke Adriaenssens', 'Elke Adriaenssens',\n",
- " 'Elke Adriaenssens', 'Emiliya Stoyneva', 'Audra Mcclure-Begley',\n",
- " 'Sara Crepinsek', 'Robert Gehrig', 'Robert Gehrig', 'Robert Gehrig',\n",
- " 'Robert Gehrig', 'Robert Gehrig', 'Christoph Hueglin', 'Gaston Torres',\n",
- " 'Katie Read', 'Chrysanthos Savvides', 'Milan Vana', 'Jaroslav Pekarek',\n",
- " 'Milan Vana', 'Markus Wallasch', 'Markus Wallasch', 'Markus Wallasch',\n",
- " 'Markus Wallasch', 'Markus Wallasch', 'Markus Wallasch',\n",
- " 'Dagmar Kubistin', 'Cedric Couret', 'Rolf Weller', 'Rune Keller',\n",
- " 'Rune Keller', 'Rune Keller', 'Audra Mcclure-Begley', 'Rune Keller',\n",
- " 'Truuts Toivo', 'Usin Eve', 'Juan Martinez', 'Juan Martinez',\n",
- " 'Fermin Elizaga', 'Fermin Elizaga', 'Maj-Britt Larka', 'Fermin Elizaga',\n",
- " 'Fermin Elizaga', 'Fermin Elizaga', 'Bartolome Maria Lopez',\n",
- " 'Fermin Elizaga', 'Fermin Elizaga', 'Fermin Elizaga', 'Fermin Elizaga',\n",
- " 'Carlos Torres', 'Hannele Hakola', 'Hannele Hakola', 'Hannele Hakola',\n",
- " 'Mika Vestenius', 'Patrice Coddeville', 'Stephane Sauvage',\n",
- " 'Stephane Sauvage', 'Stephane Sauvage', 'Stephane Sauvage',\n",
- " 'Stephane Sauvage', 'Stephane Sauvage', 'Stephane Sauvage',\n",
- " 'Patrice Coddeville', 'Stephane Sauvage', 'Stephane Sauvage',\n",
- " 'Stephane Sauvage', 'Stephane Sauvage', 'Keith Vincent',\n",
- " 'Keith Vincent', 'Keith Vincent', 'Keith Vincent', 'Keith Vincent',\n",
- " 'Keith Vincent', 'Keith Vincent', 'Keith Vincent', 'Keith Vincent',\n",
- " 'Keith Vincent', 'Keith Vincent', 'Keith Vincent', 'Keith Vincent',\n",
- " 'Keith Vincent', 'Keith Vincent', 'Keith Vincent', 'Willis Paul',\n",
- " 'A Adamopoulos', 'Nikos Mihalopoulos', 'Zita Ferenczi', 'Zita Ferenczi',\n",
- " 'Joerg Klausen', 'Barbara Oleary', 'Keith Vincent', 'F Lagler',\n",
- " 'Paolo Cristofanelli', 'Paolo Cristofanelli', 'Monica Angelucci',\n",
- " 'Yoshinobu Tanaka', 'Atsushi Takizawa', 'Atsushi Takizawa',\n",
- " 'Atsushi Takizawa', 'Sumin Kim', 'Sumin Kim', 'Rasa Girgzdiene',\n",
- " 'Dubakova Iveta', 'Dubakova Iveta', 'Raymond Ellul',\n",
- " 'Jacobus P.J. Berkhout', 'Housz Floris C Ingen',\n",
- " 'Jacobus P.J. Berkhout', 'Jacobus P.J. Berkhout', 'Hans Berkhout',\n",
- " 'Wenche Aas', 'Erik Andresen', 'Wenche Aas', 'Wenche Aas', 'Wenche Aas',\n",
- " 'Kjetil Tørseth', 'Wenche Aas', 'Erik Andresen', 'Wenche Aas',\n",
- " 'Wenche Aas', 'Sylvia Nichol', 'Audra Mcclure-Begley',\n",
- " 'Audra Mcclure-Begley', 'Magdalena Bogucka', 'Magdalena Bogucka',\n",
- " 'Magdalena Bogucka', 'Zdzislaw Przadka', 'Jasmina Knezevic',\n",
- " 'Sara Crepinsek', 'Karin Sjoberg', 'Karin Sjoberg', 'Karin Sjoberg',\n",
- " 'Karin Sjøberg', 'Karin Sjøberg', 'Karin Sjöberg', 'Karin Sjöberg',\n",
- " 'Karin Sjoberg', 'Karin Sjoberg', 'Karin Sjoberg', 'Mateja Gjerek',\n",
- " 'Rudi Voncina', 'Mateja Gjerek', 'Marta Mitosinkova',\n",
- " 'Marta Mitosinkova', 'Marta Mitosinkova', 'Marta Mitosinkova',\n",
- " 'Audra Mcclure-Begley', 'Audra Mcclure-Begley', 'Audra Mcclure-Begley',\n",
- " 'Audra Mcclure-Begley', 'Audra Mcclure-Begley', 'Nhat Anh Nguyen',\n",
- " 'Thumeka Mkololo'], dtype=object) process_warnings
(station)
object
...
standard_name : process warnings long_name : process warnings units : description : Warnings accumulated through GHOST processing regarding the data that should be considered. array(['Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ', '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ', '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " '', 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Area Classification Metadata - Took Most Applicable Next Value. No Station Classification Metadata - Took Most Applicable Next Value. No Land Use Metadata - Took Most Applicable Next Value. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " '', 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " '',\n",
- " 'No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " '',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. No Measurement Altitude Metadata - Took Most Applicable Recent Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " '',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. No Measurement Altitude Metadata - Took Most Applicable Next Value. Sampling Height assumed to be Measurement Altitude - Altitude. No Measuring Instrument Name Metadata - Took Most Applicable Next Value. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Original altitude value within a certain tolerance (11.0) of other value/s. Set altitude to be the modal value in the group. Original altitude value is sub-string of other value/s. Set altitude to be the most detailed value in the group. Original sampling_height value within a certain tolerance (11.0) of other value/s. Set sampling_height to be the modal value in the group. Original sampling_height value is sub-string of other value/s. Set sampling_height to be the most detailed value in the group. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. ',\n",
- " 'No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'Measurement Altitude assumed to be Altitude + Sampling Height. ',\n",
- " 'No Area Classification Metadata - Used Manually Compiled Metadata. No Station Classification Metadata - Used Manually Compiled Metadata. No Land Use Metadata - Used Manually Compiled Metadata. No Terrain Metadata - Used Manually Compiled Metadata. Measurement Altitude assumed to be Altitude + Sampling Height. '],\n",
- " dtype=object) projection
(station)
object
...
standard_name : projection long_name : projection units : description : Name of the projected coordinate system of the original provided station position x, y coordinates. If the original coordinates are not projected, then this is set as 'geographic'. array(['GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC'], dtype=object) reported_uncertainty_per_measurement
(station, time)
float32
...
standard_name : reported measurement uncertainty per measurement long_name : reported measurement uncertainty per measurement units : nmol mol-1 description : Reported measurement uncertainty (±) of methodology, for a specific measurement. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This is given in absolute terms in nmol mol-1. [120960 values with dtype=float32] representative_radius
(station)
float32
...
standard_name : representative_radius long_name : representative_radius units : km description : Radius of representativity of the measurements made (i.e. for what distance scale around the sampling point would the measurements be very similar?), given in kilometres. A quantitative version of the "measurement_scale" classification. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) sample_preparation_further_details
(station)
object
...
standard_name : sample preparation further details long_name : sample preparation further details units : description : Further associated details regarding the specifics of the sample preparation types/instruments. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) sample_preparation_process_details
(station)
object
...
standard_name : sample preparation process details long_name : sample preparation process details units : description : Miscellaneous details regarding assumptions made in the standardisation of the sample preparation types/techniques. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) sample_preparation_techniques
(station)
object
...
standard_name : sample preparation techniques long_name : standardised specific preparation techniques units : description : Standardised sample preparation techniques utilised in the measurement process. Mutiple names are separated by ";". array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) sample_preparation_types
(station)
object
...
standard_name : sample preparation types long_name : standardised sample preparation types units : description : Standardised sample preparation types utilised in the measurement process. Mutiple types are separated by ";". array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) sampling_height
(station)
float32
...
standard_name : sampling height long_name : sampling height relative to ground units : m description : Height above the ground of the inlet/instrument/sampler, in metres. array([ 5. , 7. , 0. , 0. , 0. , 0. ,\n",
- " 10. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 30. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 5. ,\n",
- " 7.5 , 3. , 3. , 50. , 3. , 10.5 ,\n",
- " 7. , 12. , 6.2 , 16. , 8. , 10. ,\n",
- " 0. , 7. , 0. , 0. , 0. , 10. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 5. , 5. ,\n",
- " 5. , 5. , 4. , 0. , 4. , 0. ,\n",
- " 4. , 0. , 4. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 12. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 4. , 3. ,\n",
- " 2. , 0. , 0. , 0. , 2. , 7. ,\n",
- " 4. , 0. , 5. , 8. , 8. , 10. ,\n",
- " 40. , 9.5 , 0. , 0. , 0. , 7. ,\n",
- " 2. , 2. , 2. , 2. , 2. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 10. , 10. , 10. ,\n",
- " 2.240005, 4.5 , 3. , 4.35 , 0. , 10. ,\n",
- " 0. , 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 5. , 3. ,\n",
- " 10. , 0. , 0. , 0. , 0. , 10. ,\n",
- " 6. , 10. , 10. , 10. , 12. , 4. ],\n",
- " dtype=float32) sconco3
(station, time)
float32
...
standard_name : ozone long_name : ozone units : nmol mol-1 description : Measured value of surface ozone for the stated temporal resolution. [120960 values with dtype=float32] season_code
(station, time)
uint8
...
standard_name : season code long_name : season code per measurement units : description : Code decreeing if measurement was made during the Spring, Summer, Autumn or Winter Seasons. Spring=0, Summer=1, Autumn=2, Winter=3. The classification is made by evaluating which season the local-time of a mid-measurement window timestamp falls in. [120960 values with dtype=uint8] station_classification
(station)
object
...
standard_name : station classification long_name : standardised network provided station classification units : description : Standardised network provided classification, categorising the type of air measured by a station. array(['background', 'point_source-industrial', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'nan', 'nan',\n",
- " 'background', 'nan', 'nan', 'nan', 'background', 'background', 'nan',\n",
- " 'nan', 'nan', 'nan', 'background', 'background', 'background',\n",
- " 'background', 'nan', 'nan', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'nan', 'background',\n",
- " 'background', 'background', 'background', 'nan', 'nan', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'nan', 'background', 'background', 'background',\n",
- " 'background', 'nan', 'background', 'nan', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'nan',\n",
- " 'background', 'nan', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'nan', 'nan',\n",
- " 'background', 'nan', 'nan', 'background', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'nan', 'background', 'background', 'background', 'nan', 'nan',\n",
- " 'background', 'background', 'nan', 'background', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'nan', 'background', 'background', 'nan',\n",
- " 'background', 'nan', 'background', 'nan', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'nan',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'nan', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'nan', 'nan', 'background', 'background',\n",
- " 'nan', 'background', 'background', 'background', 'background',\n",
- " 'background', 'background', 'background', 'background', 'background'],\n",
- " dtype=object) station_name
(station)
object
...
standard_name : station name long_name : station name units : description : Name of station where the measurement was conducted. array(['Marambio', 'Ushuaia', 'Illmitz', 'Vorhegg', 'Pillersdorf Bei Retz',\n",
- " 'Sulzberg', 'Sonnblick', 'Masenberg', 'Haunsberg', 'Heidenreichstein',\n",
- " 'Forsthof', 'Dunkelsteinerwald', 'Gänserndorf', 'Stixneusiedl',\n",
- " 'Zoebelboden', 'Grebenzen Bei St. Lamprecht', 'Graz Lustbuehel',\n",
- " 'Offagne', 'Eupen', 'Vezin', 'Rojen Peak', 'Tudor Hill (Bermuda)',\n",
- " 'Eureka', 'Jungfraujoch', 'Payerne', 'Tänikon', 'Chaumont', 'Rigi',\n",
- " 'Beromünster', 'El Tololo', 'Cape Verde Atmospheric Observatory',\n",
- " 'Agia Marina Xyliatou / Cyprus Atmospheric Observatory', 'Svratouch',\n",
- " 'Kosetice (Noak)', 'Churanov', 'Westerland', 'Waldhof', 'Schauinsland',\n",
- " 'Neuglobsow', 'Schmücke', 'Zingst', 'Hohenpeissenberg',\n",
- " 'Zugspitze-Schneefernerhaus', 'Neumayer', 'Keldsnor',\n",
- " 'Villum Research Station,Station Nord', 'Risoe', 'Summit', 'Ulborg',\n",
- " 'Lahemaa', 'Vilsandi', 'San Pablo De Los Montes', 'Noia', 'Mahón',\n",
- " 'Víznar', 'Niembro', 'Campisabalos', 'Cabo De Creus', 'Barcarrota',\n",
- " 'Zarra', 'Penausende', 'Els Torms', 'O Saviñao', 'Doñana', 'Izana',\n",
- " 'Utö', 'Virolahti Iii', 'Oulanka', 'Pallas (Sammaltunturi)', 'Donon',\n",
- " 'Revin', 'Morvan', 'Peyrusse Vieille', 'Montandon', 'La Tardière',\n",
- " 'Le Casset', 'Montfranc', 'La Coulonche', 'Pic Du Midi',\n",
- " 'Saint-Nazaire-Le-Désert', 'Verneuil', 'Puy De Dôme', 'Eskdalemuir',\n",
- " 'Lough Navar', 'Yarner Wood', 'High Muffles', 'Strath Vaich Dam',\n",
- " 'Aston Hill', 'Bush', 'Ladybower Res.', 'Lullington Heath', 'Sibton',\n",
- " 'Narberth', 'Wicken Fen', 'Auchencorth Moss', 'Weybourne', 'St. Osyth',\n",
- " 'Lerwick', 'Chilbolton Observatory', 'Aliartos', 'Finokalia',\n",
- " 'K-Puszta', 'Farkasfa', 'Bukit Kototabang', 'Valentia Observatory',\n",
- " 'Mace Head', 'Ispra', 'Mt Cimone', 'Capo Granitola', 'Monte Martano',\n",
- " 'Syowa', 'Ryori', 'Minamitorishima', 'Yonaguni', 'Anmyeon-Do', 'Gosan',\n",
- " 'Preila', 'Rucava', 'Zoseni', 'Giordan Lighthouse', 'Eibergen',\n",
- " 'Kollumerwaard', 'Vredepeel', 'De Zilk', 'Cabauw Wielsekade',\n",
- " 'Birkenes Ii', 'Tustervatn', 'Kårvatn',\n",
- " 'Zeppelin Mountain (Ny-Ålesund)', 'Prestebakke', 'Svanvik', 'Sandve',\n",
- " 'Hurdal', 'Trollhaugen', 'Haukenes', 'Baring Head', 'Arrival Heights',\n",
- " 'Lauder', 'Jarczew', 'Sniezka', 'Leba', 'Diabla Gora', 'Kamenicki Vis',\n",
- " 'Tiksi', 'Bredkälen', 'Esrange', 'Råö', 'Asa', 'Östad', 'Hallahus',\n",
- " 'Norunda Stenen', 'Norra-Kvill', 'Vindeln', 'Grimsö', 'Iskrba',\n",
- " 'Zarodnje', 'Krvavec', 'Chopok', 'Stará Lesná', 'Starina', 'Topolniky',\n",
- " 'Barrow', 'Boulder Table Mountain', 'Mauna Loa', 'South Pole',\n",
- " 'Trinidad Head', 'Pha Din', 'Cape Point'], dtype=object) station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : description : reference ID for station. array(['AR0001R_UVP', 'AR0002G_UVP', 'AT0002R_UVP', 'AT0005R_UVP',\n",
- " 'AT0030R_UVP', 'AT0032R_UVP', 'AT0034G_UVP', 'AT0040R_UVP',\n",
- " 'AT0041R_UVP', 'AT0042R_UVP', 'AT0043R_UVP', 'AT0045R_UVP',\n",
- " 'AT0046R_UVP', 'AT0047R_UVP', 'AT0048R_UVP', 'AT0049R_UVP',\n",
- " 'AT0050R_UVP', 'BE0001R_UVP', 'BE0032R_UVP', 'BE0035R_UVP',\n",
- " 'BG0053R_UVP', 'BM0001R_UVP', 'CA0103R_UVP', 'CH0001G_UVP',\n",
- " 'CH0002R_UVP', 'CH0003R_UVP', 'CH0004R_UVP', 'CH0005R_UVP',\n",
- " 'CH0053R_UVP', 'CL0001R_UVP', 'CV0001G_UVP', 'CY0002R_UVP',\n",
- " 'CZ0001R_UVP', 'CZ0003R_UVP', 'CZ0005R_UVP', 'DE0001R_UVP',\n",
- " 'DE0002R_UVP', 'DE0003R_UVP', 'DE0007R_UVP', 'DE0008R_UVP',\n",
- " 'DE0009R_UVP', 'DE0043G_UVP', 'DE0054R_UVP', 'DE0060G_UVP',\n",
- " 'DK0005R_UVP', 'DK0010G_UVP', 'DK0012R_UVP', 'DK0025G_UVP',\n",
- " 'DK0031R_UVP', 'EE0009R_UVP', 'EE0011R_UVP', 'ES0001R_UVP',\n",
- " 'ES0005R_UVP', 'ES0006R_UVP', 'ES0007R_UVP', 'ES0008R_UVP',\n",
- " 'ES0009R_UVP', 'ES0010R_UVP', 'ES0011R_UVP', 'ES0012R_UVP',\n",
- " 'ES0013R_UVP', 'ES0014R_UVP', 'ES0016R_UVP', 'ES0017R_UVP',\n",
- " 'ES0018G_UVP', 'FI0009R_UVP', 'FI0018R_UVP', 'FI0022R_UVP',\n",
- " 'FI0096G_UVP', 'FR0008R_UVP', 'FR0009R_UVP', 'FR0010R_UVP',\n",
- " 'FR0013R_UVP', 'FR0014R_UVP', 'FR0015R_UVP', 'FR0016R_UVP',\n",
- " 'FR0017R_UVP', 'FR0018R_UVP', 'FR0019R_UVP', 'FR0023R_UVP',\n",
- " 'FR0025R_UVP', 'FR0030R_UVP', 'GB0002R_UVP', 'GB0006R_UVP',\n",
- " 'GB0013R_UVP', 'GB0014R_UVP', 'GB0015R_UVP', 'GB0031R_UVP',\n",
- " 'GB0033R_UVP', 'GB0037R_UVP', 'GB0038R_UVP', 'GB0039R_UVP',\n",
- " 'GB0043R_UVP', 'GB0045R_UVP', 'GB0048R_UVP', 'GB0049R_UVP',\n",
- " 'GB0050R_UVP', 'GB0052R_UVP', 'GB1055R_UVP', 'GR0001R_UVP',\n",
- " 'GR0002R_UVP', 'HU0002R_UVP', 'HU0003R_UVP', 'ID1013R_UVP',\n",
- " 'IE0001R_UVP', 'IE0031R_UVP', 'IT0004R_UVP', 'IT0009R_UVP',\n",
- " 'IT0014R_UVP', 'IT0019R_UVP', 'JP0002G_UVP', 'JP1020R_UVP--S1',\n",
- " 'JP1028G_UVP', 'JP1029R_UVP', 'KR0100R_UVP', 'KR0101R_UVP',\n",
- " 'LT0015R_UVP', 'LV0010R_UVP', 'LV0016R_UVP', 'MT0001R_UVP',\n",
- " 'NL0007R_UVP', 'NL0009R_UVP', 'NL0010R_UVP', 'NL0091R_UVP',\n",
- " 'NL0644R_UVP', 'NO0002R_UVP', 'NO0015R_UVP', 'NO0039R_UVP',\n",
- " 'NO0042G_UVP', 'NO0043R_UVP', 'NO0047R_UVP', 'NO0052R_UVP',\n",
- " 'NO0056R_UVP', 'NO0059G_UVP', 'NO0489R_UVP', 'NZ0001R_UVP',\n",
- " 'NZ0002R_UVP', 'NZ0003G_UVP', 'PL0002R_UVP', 'PL0003R_UVP',\n",
- " 'PL0004R_UVP', 'PL0005R_UVP', 'RS0005R_UVP', 'RU0100R_UVP',\n",
- " 'SE0005R_UVP', 'SE0013R_UVP', 'SE0014R_UVP', 'SE0018R_UVP',\n",
- " 'SE0019R_UVP', 'SE0020R_UVP', 'SE0022R_UVP', 'SE0032R_UVP',\n",
- " 'SE0035R_UVP', 'SE0039R_UVP', 'SI0008R_UVP', 'SI0031R_UVP',\n",
- " 'SI0032R_UVP', 'SK0002R_UVP', 'SK0004R_UVP', 'SK0006R_UVP',\n",
- " 'SK0007R_UVP', 'US0008R_UVP', 'US0901R_UVP', 'US1200R_UVP',\n",
- " 'US6004G_UVP', 'US6005G_UVP', 'VN0001R_UVP', 'ZA0001G_UVP'],\n",
- " dtype=object) station_timezone
(station)
object
...
standard_name : station timezone long_name : station timezone units : description : Name of the local timezone that the measuring station is located in. This is automatically generated using Timezone Finder Python package (taking longitude and latitude as inputs). array(['America/Argentina/Ushuaia', 'America/Argentina/Ushuaia',\n",
- " 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna',\n",
- " 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna',\n",
- " 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna',\n",
- " 'Europe/Vienna', 'Europe/Vienna', 'Europe/Vienna', 'Europe/Brussels',\n",
- " 'Europe/Brussels', 'Europe/Brussels', 'Europe/Sofia',\n",
- " 'Atlantic/Bermuda', 'America/Rankin_Inlet', 'Europe/Zurich',\n",
- " 'Europe/Zurich', 'Europe/Zurich', 'Europe/Zurich', 'Europe/Zurich',\n",
- " 'Europe/Zurich', 'America/Santiago', 'Atlantic/Cape_Verde',\n",
- " 'Asia/Nicosia', 'Europe/Prague', 'Europe/Prague', 'Europe/Prague',\n",
- " 'Europe/Berlin', 'Europe/Berlin', 'Europe/Berlin', 'Europe/Berlin',\n",
- " 'Europe/Berlin', 'Europe/Berlin', 'Europe/Berlin', 'Europe/Berlin',\n",
- " 'Etc/UTC', 'Europe/Copenhagen', 'America/Godthab', 'Europe/Copenhagen',\n",
- " 'America/Godthab', 'Europe/Copenhagen', 'Europe/Tallinn',\n",
- " 'Europe/Tallinn', 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid',\n",
- " 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid',\n",
- " 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid', 'Europe/Madrid',\n",
- " 'Europe/Madrid', 'Europe/Madrid', 'Atlantic/Canary', 'Europe/Helsinki',\n",
- " 'Europe/Helsinki', 'Europe/Helsinki', 'Europe/Helsinki', 'Europe/Paris',\n",
- " 'Europe/Paris', 'Europe/Paris', 'Europe/Paris', 'Europe/Paris',\n",
- " 'Europe/Paris', 'Europe/Paris', 'Europe/Paris', 'Europe/Paris',\n",
- " 'Europe/Paris', 'Europe/Paris', 'Europe/Paris', 'Europe/Paris',\n",
- " 'Europe/London', 'Europe/London', 'Europe/London', 'Europe/London',\n",
- " 'Europe/London', 'Europe/London', 'Europe/London', 'Europe/London',\n",
- " 'Europe/London', 'Europe/London', 'Europe/London', 'Europe/London',\n",
- " 'Europe/London', 'Europe/London', 'Europe/London', 'Europe/London',\n",
- " 'Europe/London', 'Europe/Athens', 'Europe/Athens', 'Europe/Budapest',\n",
- " 'Europe/Budapest', 'Asia/Jakarta', 'Europe/Dublin', 'Europe/Dublin',\n",
- " 'Europe/Rome', 'Europe/Rome', 'Europe/Rome', 'Europe/Rome',\n",
- " 'Antarctica/Syowa', 'Asia/Tokyo', 'Asia/Tokyo', 'Asia/Tokyo',\n",
- " 'Asia/Seoul', 'Asia/Seoul', 'Europe/Vilnius', 'Europe/Riga',\n",
- " 'Europe/Riga', 'Europe/Malta', 'Europe/Amsterdam', 'Europe/Amsterdam',\n",
- " 'Europe/Amsterdam', 'Europe/Amsterdam', 'Europe/Amsterdam',\n",
- " 'Europe/Oslo', 'Europe/Oslo', 'Europe/Oslo', 'Arctic/Longyearbyen',\n",
- " 'Europe/Oslo', 'Europe/Oslo', 'Europe/Oslo', 'Europe/Oslo',\n",
- " 'Antarctica/Troll', 'Europe/Oslo', 'Pacific/Auckland',\n",
- " 'Antarctica/McMurdo', 'Pacific/Auckland', 'Europe/Warsaw',\n",
- " 'Europe/Warsaw', 'Europe/Warsaw', 'Europe/Warsaw', 'Europe/Belgrade',\n",
- " 'Asia/Yakutsk', 'Europe/Stockholm', 'Europe/Stockholm',\n",
- " 'Europe/Stockholm', 'Europe/Stockholm', 'Europe/Stockholm',\n",
- " 'Europe/Stockholm', 'Europe/Stockholm', 'Europe/Stockholm',\n",
- " 'Europe/Stockholm', 'Europe/Stockholm', 'Europe/Ljubljana',\n",
- " 'Europe/Ljubljana', 'Europe/Ljubljana', 'Europe/Bratislava',\n",
- " 'Europe/Bratislava', 'Europe/Bratislava', 'Europe/Bratislava',\n",
- " 'America/Anchorage', 'America/Denver', 'Pacific/Honolulu',\n",
- " 'Antarctica/McMurdo', 'America/Los_Angeles', 'Asia/Bangkok',\n",
- " 'Africa/Johannesburg'], dtype=object) street_type
(station)
object
...
standard_name : street type long_name : type of street units : description : Type of street where measurements are being made (if applicable). array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object) street_width
(station)
float32
...
standard_name : street width long_name : width of the street units : m description : Width of the street where measurements are being made (if applicable), in metres. array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
- " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
- " dtype=float32) terrain
(station)
object
...
standard_name : terrain long_name : standardised network provided terrain type units : description : Standardised network provided classification, describing the dominant terrain in the area of the reporting station. array(['nan', 'coastal', 'nan', 'nan', 'nan', 'nan', 'mountain', 'nan',\n",
- " 'complex', 'complex', 'nan', 'complex', 'complex', 'complex', 'nan',\n",
- " 'mountain', 'complex', 'complex', 'complex', 'complex', 'mountain',\n",
- " 'coastal', 'nan', 'mountain', 'complex', 'complex', 'nan', 'nan', 'nan',\n",
- " 'mountain', 'coastal', 'complex', 'complex', 'nan', 'nan', 'coastal',\n",
- " 'nan', 'complex', 'complex', 'nan', 'coastal', 'mountain', 'mountain',\n",
- " 'nan', 'coastal', 'nan', 'complex', 'mountain', 'nan', 'nan', 'coastal',\n",
- " 'complex', 'nan', 'complex', 'nan', 'complex', 'nan', 'coastal', 'nan',\n",
- " 'nan', 'complex', 'nan', 'complex', 'nan', 'mountain', 'coastal',\n",
- " 'coastal', 'nan', 'nan', 'nan', 'complex', 'complex', 'nan', 'complex',\n",
- " 'complex', 'mountain', 'nan', 'complex', 'mountain', 'nan', 'nan',\n",
- " 'mountain', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'complex', 'nan',\n",
- " 'nan', 'nan', 'complex', 'complex', 'nan', 'coastal', 'complex',\n",
- " 'coastal', 'nan', 'complex', 'coastal', 'complex', 'nan', 'mountain',\n",
- " 'complex', 'coastal', 'complex', 'mountain', 'coastal', 'mountain',\n",
- " 'nan', 'coastal', 'coastal', 'coastal', 'coastal', 'coastal', 'coastal',\n",
- " 'complex', 'nan', 'coastal', 'complex', 'nan', 'complex', 'coastal',\n",
- " 'complex', 'nan', 'nan', 'nan', 'mountain', 'nan', 'nan', 'complex',\n",
- " 'nan', 'nan', 'nan', 'coastal', 'nan', 'nan', 'complex', 'mountain',\n",
- " 'coastal', 'nan', 'nan', 'coastal', 'complex', 'nan', 'coastal', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'complex', 'complex',\n",
- " 'mountain', 'mountain', 'complex', 'nan', 'nan', 'coastal', 'nan',\n",
- " 'mountain', 'nan', 'coastal', 'mountain', 'coastal'], dtype=object) vertical_datum
(station)
object
...
standard_name : vertical datum long_name : vertical datum units : description : Name of the vertical datum used to define vertical elevation on the Earth. The datum is a surface of zero elevation to which other heights can be reference against. If not explicitely stated then this is assumed to be 'tidal - mean sea level'. array(['TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL'], dtype=object) weekday_weekend_code
(station, time)
uint8
...
standard_name : weekday/weekend code long_name : weekday/weekend code per measurement units : description : Binary indication if measurement was made during the weekday or weekend. Weekday=0, Weekend=1. The classification is made by evaluating if the local-time of a mid-measurement window timestamp falls on a weekday or on the weekend. Classification is 255 if cannot be made. [120960 values with dtype=uint8] flag
(station, time, N_flag_codes)
int64
...
units : axis : long_name : standard_name : flag [22498560 values with dtype=int64] qa
(station, time, N_qa_codes)
int64
...
units : axis : long_name : standard_name : N_qa_codes [9313920 values with dtype=int64] Attributes: (11)
title : Surface ozone data in the EBAS network in 2018-04. institution : Barcelona Supercomputing Center source : Surface observations creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es conventions : CF-1.7 data_version : 1.3.3 history : Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dmn station /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc\n",
- "Tue Mar 30 12:37:03 2021: ncrcat -L 1 /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AR0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AR0002G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0030R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0032R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0034G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0040R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0041R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0042R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0043R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0045R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0046R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0047R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0048R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0049R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/AT0050R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BE0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BE0032R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BE0035R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BG0053R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/BM0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CA0103R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0001G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0004R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CH0053R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CL0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CV0001G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CY0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CZ0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CZ0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/CZ0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0007R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0043G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0054R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DE0060G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0010G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0012R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0025G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/DK0031R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/EE0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/EE0011R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0006R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0007R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0010R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0011R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0012R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0016R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0017R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ES0018G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FI0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FI0018R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FI0022R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FI0096G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0010R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0015R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0016R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0017R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0018R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0019R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0023R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0025R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/FR0030R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0006R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0015R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0031R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0033R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0037R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0038R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0039R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0043R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0045R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0048R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0049R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0050R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB0052R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GB1055R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GR0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/GR0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/HU0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/HU0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ID1013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IE0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IE0031R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IT0004R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IT0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IT0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/IT0019R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/JP0002G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/JP1020R_UVP--S1.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/JP1028G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/JP1029R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/KR0100R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/KR0101R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/LT0015R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/LV0010R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/LV0016R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/MT0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0007R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0009R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0010R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0091R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NL0644R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0015R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0039R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0042G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0043R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0047R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0052R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0056R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0059G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NO0489R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NZ0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NZ0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/NZ0003G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/PL0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/PL0003R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/PL0004R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/PL0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/RS0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/RU0100R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0005R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0013R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0014R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0018R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0019R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0020R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0022R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0032R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0035R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SE0039R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SI0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SI0031R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SI0032R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SK0002R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SK0004R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SK0006R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/SK0007R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US0008R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US0901R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US1200R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US6004G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/US6005G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/VN0001R_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/temporary/2018/04/ZA0001G_UVP.nc /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc NCO : 4.7.2 nco_openmp_thread_number : 1 Conventions : CF-1.7 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 720, station: 168, N_flag_codes: 186, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- " * station (station) float64 ...\n",
- "Dimensions without coordinates: N_flag_codes, N_qa_codes\n",
- "Data variables: (12/179)\n",
- " latitude (station) float64 ...\n",
- " longitude (station) float64 ...\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " ... ...\n",
- " street_width (station) float32 ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- " flag (station, time, N_flag_codes) int64 ...\n",
- " qa (station, time, N_qa_codes) int64 ...\n",
- "Attributes:\n",
- " title: Surface ozone data in the EBAS network in 2018...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.3.3\n",
- " history: Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dm...\n",
- " NCO: 4.7.2\n",
- " nco_openmp_thread_number: 1\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('prv_obs_file.nc')"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -14665,7 +2093,9 @@
"metadata": {},
"outputs": [],
"source": [
- "exp_path = '/gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc'"
+ "# Original path: /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc\n",
+ "# Interpolated experiment to EBAS network from CAMS\n",
+ "exp_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_interp_sconco3_201804.nc'"
]
},
{
@@ -14677,577 +2107,6 @@
"### 2.1. Read dataset"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Open with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (grid_edge: 1125, station: 175, model_latitude: 211, model_longitude: 351, time: 720)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n",
- "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
- "Data variables:\n",
- " grid_edge_latitude (grid_edge) float64 29.9 30.1 30.3 ... 29.9 29.9\n",
- " grid_edge_longitude (grid_edge) float64 -25.1 -25.1 ... -24.9 -25.1\n",
- " latitude (station) float64 -64.24 -54.85 ... 21.57 -34.35\n",
- " longitude (station) float64 -56.62 -68.31 ... 103.5 18.49\n",
- " model_centre_latitude (model_latitude, model_longitude) float64 30.0 .....\n",
- " model_centre_longitude (model_latitude, model_longitude) float64 -25.0 ....\n",
- " sconco3 (station, time) float32 ...\n",
- " station_reference (station) object 'AR0001R_UVP' ... 'ZA0001G_UVP'\n",
- "Attributes:\n",
- " title: Inverse distance weighting (4 neighbours) interpolated ca...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Experiment cams61_chimere_ph2\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.0\n",
- " history: Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
- " NCO: 4.7.2 Dimensions: grid_edge : 1125station : 175model_latitude : 211model_longitude : 351time : 720
Coordinates: (1)
Data variables: (8)
grid_edge_latitude
(grid_edge)
float64
...
standard_name : grid edge latitude long_name : grid edge latitude units : decimal degrees North description : Latitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner). axis : Y array([29.9 , 30.1 , 30.299999, ..., 29.9 , 29.9 , 29.9 ]) grid_edge_longitude
(grid_edge)
float64
...
standard_name : grid edge longitude long_name : grid edge longitude units : decimal degrees East description : Longitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner). axis : X array([-25.1 , -25.1 , -25.1 , ..., -24.700001, -24.9 ,\n",
- " -25.1 ]) latitude
(station)
float64
...
standard_name : latitude units : decimal degrees North long_name : latitude description : Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum. axis : Y array([-64.24006 , -54.848465, -22.103333, -31.668611, 47.766666, 46.677778,\n",
- " 48.721111, 47.529167, 47.05407 , 46.693611, 47.348056, 47.973056,\n",
- " 48.878611, 48.106111, 48.371111, 48.334722, 48.050833, 47.838611,\n",
- " 47.040277, 47.066944, 49.877778, 50.629421, 50.503333, 41.695833,\n",
- " 32.27 , 80.050003, 46.5475 , 46.813056, 47.479722, 47.049722,\n",
- " 47.0675 , 47.189614, -30.17254 , 16.86403 , 35.0381 , 49.735084,\n",
- " 49.573394, 49.066667, 54.925556, 52.802222, 47.914722, 53.166667,\n",
- " 50.65 , 54.4368 , 47.801498, 47.4165 , -70.666 , 54.746495,\n",
- " 81.6 , 55.693588, 72.580002, 56.290424, 59.5 , 58.383333,\n",
- " 39.54694 , 42.72056 , 39.87528 , 37.23722 , 43.43917 , 41.27417 ,\n",
- " 42.31917 , 38.47278 , 39.08278 , 41.23889 , 41.39389 , 42.63472 ,\n",
- " 37.05194 , 28.309 , 59.779167, 60.53002 , 66.320278, 67.973333,\n",
- " 48.5 , 49.9 , 47.266667, 43.616667, 47.3 , 46.65 ,\n",
- " 45. , 45.8 , 48.633333, 42.936667, 48.708611, 44.569444,\n",
- " 46.814728, 45.772223, 55.313056, 54.443056, 50.596389, 54.334444,\n",
- " 57.734444, 52.503889, 55.858611, 53.398889, 50.792778, 52.293889,\n",
- " 51.781784, 52.298333, 55.79216 , 52.950556, 51.778056, 60.13922 ,\n",
- " -75.62 , 51.149617, 38.366667, 35.316667, 46.966667, 46.91 ,\n",
- " -0.20194 , 51.939722, 53.32583 , 45.8 , 44.183333, 37.571111,\n",
- " 35.5182 , 42.805462, -69.005 , 39.0319 , 24.2883 , 24.466941,\n",
- " 36.538334, 33.293917, 55.376111, 56.161944, 57.135278, 41.536111,\n",
- " 36.0722 , 52.083333, 53.333889, 51.541111, 52.3 , 51.974444,\n",
- " 58.38853 , 65.833333, 62.783333, 78.90715 , 59. , 69.45 ,\n",
- " 59.2 , 60.372386, -72.0117 , 59.2 , -41.408192, -77.832001,\n",
- " -45.037998, 51.814408, 50.736444, 54.753894, 54.15 , 43.4 ,\n",
- " 71.586166, 63.85 , 67.883333, 57.394 , 57.1645 , 57.9525 ,\n",
- " 56.0429 , 60.0858 , 57.816667, 64.25 , 59.728 , 45.566667,\n",
- " 46.428611, 46.299444, 48.933333, 49.15 , 49.05 , 47.96 ,\n",
- " 71.323013, 40.12498 , 19.53623 , -89.996948, 41.0541 , 21.5731 ,\n",
- " -34.35348 ]) longitude
(station)
float64
...
standard_name : longitude units : decimal degrees East long_name : longitude description : Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum. axis : X array([-5.662478e+01, -6.831069e+01, -6.560083e+01, -6.388194e+01,\n",
- " 1.676667e+01, 1.297222e+01, 1.594222e+01, 9.926667e+00,\n",
- " 1.295794e+01, 1.391500e+01, 1.588222e+01, 1.301611e+01,\n",
- " 1.504667e+01, 1.591944e+01, 1.554667e+01, 1.673056e+01,\n",
- " 1.667667e+01, 1.444139e+01, 1.433000e+01, 1.549361e+01,\n",
- " 5.203611e+00, 6.001019e+00, 4.989444e+00, 2.473861e+01,\n",
- " -6.488000e+01, -8.641666e+01, 7.985000e+00, 6.944722e+00,\n",
- " 8.904722e+00, 6.979444e+00, 8.463889e+00, 8.175434e+00,\n",
- " -7.079923e+01, -2.486752e+01, 3.305780e+01, 1.603420e+01,\n",
- " 1.508028e+01, 1.360000e+01, 8.309722e+00, 1.075944e+01,\n",
- " 7.908611e+00, 1.303333e+01, 1.076667e+01, 1.272490e+01,\n",
- " 1.100962e+01, 1.097964e+01, -8.266000e+00, 1.073616e+01,\n",
- " -1.667000e+01, 1.208580e+01, -3.848000e+01, 8.427486e+00,\n",
- " 2.590000e+01, 2.181667e+01, -4.350560e+00, -8.923610e+00,\n",
- " 4.316390e+00, -3.534170e+00, -4.850000e+00, -3.142500e+00,\n",
- " 3.315830e+00, -6.923610e+00, -1.101110e+00, -5.897500e+00,\n",
- " 7.347200e-01, -7.704720e+00, -6.555280e+00, -1.649940e+01,\n",
- " 2.137722e+01, 2.766754e+01, 2.940167e+01, 2.411611e+01,\n",
- " 7.133333e+00, 4.633333e+00, 4.083333e+00, 1.833330e-01,\n",
- " 6.833333e+00, -7.500000e-01, 6.466667e+00, 2.066667e+00,\n",
- " -4.500000e-01, 1.419440e-01, 2.158889e+00, 5.278972e+00,\n",
- " 2.610008e+00, 2.964886e+00, -3.204167e+00, -7.870000e+00,\n",
- " -3.713056e+00, -8.075000e-01, -4.774444e+00, -3.033056e+00,\n",
- " -3.205000e+00, -1.753333e+00, 1.794440e-01, 1.463056e+00,\n",
- " -4.691462e+00, 2.927780e-01, -3.242900e+00, 1.121944e+00,\n",
- " 1.082230e+00, -1.185319e+00, -2.618000e+01, -1.438228e+00,\n",
- " 2.308333e+01, 2.566667e+01, 1.958333e+01, 1.632000e+01,\n",
- " 1.003181e+02, -1.024444e+01, -9.899440e+00, 8.633333e+00,\n",
- " 1.070000e+01, 1.265972e+01, 1.263050e+01, 1.256564e+01,\n",
- " 3.959056e+01, 1.418222e+02, 1.539833e+02, 1.230109e+02,\n",
- " 1.263300e+02, 1.261631e+02, 2.103056e+01, 2.117306e+01,\n",
- " 2.590556e+01, 2.069389e+01, 1.421840e+01, 6.566667e+00,\n",
- " 6.277222e+00, 5.853611e+00, 4.500000e+00, 4.923611e+00,\n",
- " 8.252000e+00, 1.391667e+01, 8.883333e+00, 1.188668e+01,\n",
- " 1.153333e+01, 3.003333e+01, 5.200000e+00, 1.107814e+01,\n",
- " 2.535100e+00, 9.516667e+00, 1.748708e+02, 1.666600e+02,\n",
- " 1.696840e+02, 2.197242e+01, 1.573950e+01, 1.753426e+01,\n",
- " 2.206667e+01, 2.195000e+01, 1.289188e+02, 1.533333e+01,\n",
- " 2.106667e+01, 1.191400e+01, 1.478250e+01, 1.240300e+01,\n",
- " 1.314800e+01, 1.750528e+01, 1.556667e+01, 1.976667e+01,\n",
- " 1.547200e+01, 1.486667e+01, 1.500333e+01, 1.453861e+01,\n",
- " 1.958333e+01, 2.028333e+01, 2.226667e+01, 1.786056e+01,\n",
- " -1.566115e+02, -1.052368e+02, -1.555762e+02, -2.480000e+01,\n",
- " -1.241510e+02, 1.035157e+02, 1.848968e+01]) model_centre_latitude
(model_latitude, model_longitude)
float64
...
standard_name : model centre latitude long_name : model centre latitude units : decimal degrees North description : 2D meshed grid centre latitudes with 211 latitudes in 351 bands of longitude axis : Y array([[30. , 30. , 30. , ..., 30. , 30. , 30. ],\n",
- " [30.200001, 30.200001, 30.200001, ..., 30.200001, 30.200001, 30.200001],\n",
- " [30.4 , 30.4 , 30.4 , ..., 30.4 , 30.4 , 30.4 ],\n",
- " ...,\n",
- " [71.599998, 71.599998, 71.599998, ..., 71.599998, 71.599998, 71.599998],\n",
- " [71.800003, 71.800003, 71.800003, ..., 71.800003, 71.800003, 71.800003],\n",
- " [72. , 72. , 72. , ..., 72. , 72. , 72. ]]) model_centre_longitude
(model_latitude, model_longitude)
float64
...
standard_name : model centre longitude long_name : model centre longitude units : decimal degrees East description : 2D meshed grid centre longitudes with 351 longitudes in 211 bands of latitude axis : X array([[-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " ...,\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ]]) sconco3
(station, time)
float32
...
long_name : ozone units : nmol mol-1 standard_name : ozone descrption : Interpolated value of ozone from the experiment cams61_chimere_ph2 with reference to the measurement stations in the EBAS network [126000 values with dtype=float32] station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : unitless description : reference ID for station. array(['AR0001R_UVP', 'AR0002G_UVP', 'AR0004R_UVP', 'AR0005R_UVP',\n",
- " 'AT0002R_UVP', 'AT0005R_UVP', 'AT0030R_UVP', 'AT0032R_UVP',\n",
- " 'AT0034G_UVP', 'AT0038R_UVP', 'AT0040R_UVP', 'AT0041R_UVP',\n",
- " 'AT0042R_UVP', 'AT0043R_UVP', 'AT0045R_UVP', 'AT0046R_UVP',\n",
- " 'AT0047R_UVP', 'AT0048R_UVP', 'AT0049R_UVP', 'AT0050R_UVP',\n",
- " 'BE0001R_UVP', 'BE0032R_UVP', 'BE0035R_UVP', 'BG0053R_UVP',\n",
- " 'BM0001R_UVP', 'CA0103R_UVP', 'CH0001G_UVP', 'CH0002R_UVP',\n",
- " 'CH0003R_UVP', 'CH0004R_UVP', 'CH0005R_UVP', 'CH0053R_UVP',\n",
- " 'CL0001R_UVP', 'CV0001G_UVP', 'CY0002R_UVP', 'CZ0001R_UVP',\n",
- " 'CZ0003R_UVP', 'CZ0005R_UVP', 'DE0001R_UVP', 'DE0002R_UVP',\n",
- " 'DE0003R_UVP', 'DE0007R_UVP', 'DE0008R_UVP', 'DE0009R_UVP',\n",
- " 'DE0043G_UVP', 'DE0054R_UVP', 'DE0060G_UVP', 'DK0005R_UVP',\n",
- " 'DK0010G_UVP', 'DK0012R_UVP', 'DK0025G_UVP', 'DK0031R_UVP',\n",
- " 'EE0009R_UVP', 'EE0011R_UVP', 'ES0001R_UVP', 'ES0005R_UVP',\n",
- " 'ES0006R_UVP', 'ES0007R_UVP', 'ES0008R_UVP', 'ES0009R_UVP',\n",
- " 'ES0010R_UVP', 'ES0011R_UVP', 'ES0012R_UVP', 'ES0013R_UVP',\n",
- " 'ES0014R_UVP', 'ES0016R_UVP', 'ES0017R_UVP', 'ES0018G_UVP',\n",
- " 'FI0009R_UVP', 'FI0018R_UVP', 'FI0022R_UVP', 'FI0096G_UVP',\n",
- " 'FR0008R_UVP', 'FR0009R_UVP', 'FR0010R_UVP', 'FR0013R_UVP',\n",
- " 'FR0014R_UVP', 'FR0015R_UVP', 'FR0016R_UVP', 'FR0017R_UVP',\n",
- " 'FR0018R_UVP', 'FR0019R_UVP', 'FR0020R_UVP', 'FR0023R_UVP',\n",
- " 'FR0025R_UVP', 'FR0030R_UVP', 'GB0002R_UVP', 'GB0006R_UVP',\n",
- " 'GB0013R_UVP', 'GB0014R_UVP', 'GB0015R_UVP', 'GB0031R_UVP',\n",
- " 'GB0033R_UVP', 'GB0037R_UVP', 'GB0038R_UVP', 'GB0039R_UVP',\n",
- " 'GB0043R_UVP', 'GB0045R_UVP', 'GB0048R_UVP', 'GB0049R_UVP',\n",
- " 'GB0050R_UVP', 'GB0052R_UVP', 'GB0059G_UVP', 'GB1055R_UVP',\n",
- " 'GR0001R_UVP', 'GR0002R_UVP', 'HU0002R_UVP', 'HU0003R_UVP',\n",
- " 'ID1013R_UVP', 'IE0001R_UVP', 'IE0031R_UVP', 'IT0004R_UVP',\n",
- " 'IT0009R_UVP', 'IT0014R_UVP', 'IT0018R_UVP', 'IT0019R_UVP',\n",
- " 'JP0002G_UVP', 'JP1020R_UVP--S1', 'JP1028G_UVP', 'JP1029R_UVP',\n",
- " 'KR0100R_UVP', 'KR0101R_UVP', 'LT0015R_UVP', 'LV0010R_UVP',\n",
- " 'LV0016R_UVP', 'MK0007R_UVP', 'MT0001R_UVP', 'NL0007R_UVP',\n",
- " 'NL0009R_UVP', 'NL0010R_UVP', 'NL0091R_UVP', 'NL0644R_UVP',\n",
- " 'NO0002R_UVP', 'NO0015R_UVP', 'NO0039R_UVP', 'NO0042G_UVP',\n",
- " 'NO0043R_UVP', 'NO0047R_UVP', 'NO0052R_UVP', 'NO0056R_UVP',\n",
- " 'NO0059G_UVP', 'NO0489R_UVP', 'NZ0001R_UVP', 'NZ0002R_UVP',\n",
- " 'NZ0003G_UVP', 'PL0002R_UVP', 'PL0003R_UVP', 'PL0004R_UVP',\n",
- " 'PL0005R_UVP', 'RS0005R_UVP', 'RU0100R_UVP', 'SE0005R_UVP',\n",
- " 'SE0013R_UVP', 'SE0014R_UVP', 'SE0018R_UVP', 'SE0019R_UVP',\n",
- " 'SE0020R_UVP', 'SE0022R_UVP', 'SE0032R_UVP', 'SE0035R_UVP',\n",
- " 'SE0039R_UVP', 'SI0008R_UVP', 'SI0031R_UVP', 'SI0032R_UVP',\n",
- " 'SK0002R_UVP', 'SK0004R_UVP', 'SK0006R_UVP', 'SK0007R_UVP',\n",
- " 'US0008R_UVP', 'US0901R_UVP', 'US1200R_UVP', 'US6004G_UVP',\n",
- " 'US6005G_UVP', 'VN0001R_UVP', 'ZA0001G_UVP'], dtype=object) Attributes: (9)
title : Inverse distance weighting (4 neighbours) interpolated cams61_chimere_ph2 experiment data for the component sconco3 with reference to the measurement stations in the EBAS network in 2018-04. institution : Barcelona Supercomputing Center source : Experiment cams61_chimere_ph2 creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es conventions : CF-1.7 data_version : 1.0 history : Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc NCO : 4.7.2 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (grid_edge: 1125, station: 175, model_latitude: 211, model_longitude: 351, time: 720)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n",
- "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
- "Data variables:\n",
- " grid_edge_latitude (grid_edge) float64 ...\n",
- " grid_edge_longitude (grid_edge) float64 ...\n",
- " latitude (station) float64 ...\n",
- " longitude (station) float64 ...\n",
- " model_centre_latitude (model_latitude, model_longitude) float64 ...\n",
- " model_centre_longitude (model_latitude, model_longitude) float64 ...\n",
- " sconco3 (station, time) float32 ...\n",
- " station_reference (station) object ...\n",
- "Attributes:\n",
- " title: Inverse distance weighting (4 neighbours) interpolated ca...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Experiment cams61_chimere_ph2\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.0\n",
- " history: Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
- " NCO: 4.7.2"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset(exp_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Open with NES"
- ]
- },
{
"cell_type": "code",
"execution_count": 14,
@@ -15256,7 +2115,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 14,
@@ -16212,13 +3071,6 @@
"### 2.2. Write dataset"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Write with NES"
- ]
- },
{
"cell_type": "code",
"execution_count": 20,
@@ -16245,7 +3097,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:594: UserWarning: WARNING!!! Providentia datasets cannot be written in parallel yet. Changing to serial mode.\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:595: UserWarning: WARNING!!! Providentia datasets cannot be written in parallel yet. Changing to serial mode.\n",
" warnings.warn(msg)\n"
]
}
@@ -16253,588 +3105,6 @@
"source": [
"exp_interp_nes.to_netcdf('prv_exp_file.nc', info=True)"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Reopen with xarray"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 720, station: 175, model_latitude: 211, model_longitude: 351, grid_edge: 1125)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n",
- " * station (station) float64 0.0 1.0 2.0 ... 172.0 173.0 174.0\n",
- "Dimensions without coordinates: model_latitude, model_longitude, grid_edge\n",
- "Data variables:\n",
- " lat (station) float64 -64.24 -54.85 ... 21.57 -34.35\n",
- " lon (station) float64 -56.62 -68.31 ... 103.5 18.49\n",
- " model_centre_longitude (model_latitude, model_longitude) float64 -25.0 ....\n",
- " model_centre_latitude (model_latitude, model_longitude) float64 30.0 .....\n",
- " grid_edge_longitude (grid_edge) float64 -25.1 -25.1 ... -24.9 -25.1\n",
- " grid_edge_latitude (grid_edge) float64 29.9 30.1 30.3 ... 29.9 29.9\n",
- " sconco3 (station, time) float32 ...\n",
- " station_reference (station) object 'AR0001R_UVP' ... 'ZA0001G_UVP'\n",
- "Attributes:\n",
- " title: Inverse distance weighting (4 neighbours) interpolated ca...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Experiment cams61_chimere_ph2\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.0\n",
- " history: Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
- " NCO: 4.7.2\n",
- " Conventions: CF-1.7 Dimensions: time : 720station : 175model_latitude : 211model_longitude : 351grid_edge : 1125
Coordinates: (2)
Data variables: (8)
lat
(station)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([-64.24006 , -54.848465, -22.103333, -31.668611, 47.766666, 46.677778,\n",
- " 48.721111, 47.529167, 47.05407 , 46.693611, 47.348056, 47.973056,\n",
- " 48.878611, 48.106111, 48.371111, 48.334722, 48.050833, 47.838611,\n",
- " 47.040277, 47.066944, 49.877778, 50.629421, 50.503333, 41.695833,\n",
- " 32.27 , 80.050003, 46.5475 , 46.813056, 47.479722, 47.049722,\n",
- " 47.0675 , 47.189614, -30.17254 , 16.86403 , 35.0381 , 49.735084,\n",
- " 49.573394, 49.066667, 54.925556, 52.802222, 47.914722, 53.166667,\n",
- " 50.65 , 54.4368 , 47.801498, 47.4165 , -70.666 , 54.746495,\n",
- " 81.6 , 55.693588, 72.580002, 56.290424, 59.5 , 58.383333,\n",
- " 39.54694 , 42.72056 , 39.87528 , 37.23722 , 43.43917 , 41.27417 ,\n",
- " 42.31917 , 38.47278 , 39.08278 , 41.23889 , 41.39389 , 42.63472 ,\n",
- " 37.05194 , 28.309 , 59.779167, 60.53002 , 66.320278, 67.973333,\n",
- " 48.5 , 49.9 , 47.266667, 43.616667, 47.3 , 46.65 ,\n",
- " 45. , 45.8 , 48.633333, 42.936667, 48.708611, 44.569444,\n",
- " 46.814728, 45.772223, 55.313056, 54.443056, 50.596389, 54.334444,\n",
- " 57.734444, 52.503889, 55.858611, 53.398889, 50.792778, 52.293889,\n",
- " 51.781784, 52.298333, 55.79216 , 52.950556, 51.778056, 60.13922 ,\n",
- " -75.62 , 51.149617, 38.366667, 35.316667, 46.966667, 46.91 ,\n",
- " -0.20194 , 51.939722, 53.32583 , 45.8 , 44.183333, 37.571111,\n",
- " 35.5182 , 42.805462, -69.005 , 39.0319 , 24.2883 , 24.466941,\n",
- " 36.538334, 33.293917, 55.376111, 56.161944, 57.135278, 41.536111,\n",
- " 36.0722 , 52.083333, 53.333889, 51.541111, 52.3 , 51.974444,\n",
- " 58.38853 , 65.833333, 62.783333, 78.90715 , 59. , 69.45 ,\n",
- " 59.2 , 60.372386, -72.0117 , 59.2 , -41.408192, -77.832001,\n",
- " -45.037998, 51.814408, 50.736444, 54.753894, 54.15 , 43.4 ,\n",
- " 71.586166, 63.85 , 67.883333, 57.394 , 57.1645 , 57.9525 ,\n",
- " 56.0429 , 60.0858 , 57.816667, 64.25 , 59.728 , 45.566667,\n",
- " 46.428611, 46.299444, 48.933333, 49.15 , 49.05 , 47.96 ,\n",
- " 71.323013, 40.12498 , 19.53623 , -89.996948, 41.0541 , 21.5731 ,\n",
- " -34.35348 ]) lon
(station)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([-5.662478e+01, -6.831069e+01, -6.560083e+01, -6.388194e+01,\n",
- " 1.676667e+01, 1.297222e+01, 1.594222e+01, 9.926667e+00,\n",
- " 1.295794e+01, 1.391500e+01, 1.588222e+01, 1.301611e+01,\n",
- " 1.504667e+01, 1.591944e+01, 1.554667e+01, 1.673056e+01,\n",
- " 1.667667e+01, 1.444139e+01, 1.433000e+01, 1.549361e+01,\n",
- " 5.203611e+00, 6.001019e+00, 4.989444e+00, 2.473861e+01,\n",
- " -6.488000e+01, -8.641666e+01, 7.985000e+00, 6.944722e+00,\n",
- " 8.904722e+00, 6.979444e+00, 8.463889e+00, 8.175434e+00,\n",
- " -7.079923e+01, -2.486752e+01, 3.305780e+01, 1.603420e+01,\n",
- " 1.508028e+01, 1.360000e+01, 8.309722e+00, 1.075944e+01,\n",
- " 7.908611e+00, 1.303333e+01, 1.076667e+01, 1.272490e+01,\n",
- " 1.100962e+01, 1.097964e+01, -8.266000e+00, 1.073616e+01,\n",
- " -1.667000e+01, 1.208580e+01, -3.848000e+01, 8.427486e+00,\n",
- " 2.590000e+01, 2.181667e+01, -4.350560e+00, -8.923610e+00,\n",
- " 4.316390e+00, -3.534170e+00, -4.850000e+00, -3.142500e+00,\n",
- " 3.315830e+00, -6.923610e+00, -1.101110e+00, -5.897500e+00,\n",
- " 7.347200e-01, -7.704720e+00, -6.555280e+00, -1.649940e+01,\n",
- " 2.137722e+01, 2.766754e+01, 2.940167e+01, 2.411611e+01,\n",
- " 7.133333e+00, 4.633333e+00, 4.083333e+00, 1.833330e-01,\n",
- " 6.833333e+00, -7.500000e-01, 6.466667e+00, 2.066667e+00,\n",
- " -4.500000e-01, 1.419440e-01, 2.158889e+00, 5.278972e+00,\n",
- " 2.610008e+00, 2.964886e+00, -3.204167e+00, -7.870000e+00,\n",
- " -3.713056e+00, -8.075000e-01, -4.774444e+00, -3.033056e+00,\n",
- " -3.205000e+00, -1.753333e+00, 1.794440e-01, 1.463056e+00,\n",
- " -4.691462e+00, 2.927780e-01, -3.242900e+00, 1.121944e+00,\n",
- " 1.082230e+00, -1.185319e+00, -2.618000e+01, -1.438228e+00,\n",
- " 2.308333e+01, 2.566667e+01, 1.958333e+01, 1.632000e+01,\n",
- " 1.003181e+02, -1.024444e+01, -9.899440e+00, 8.633333e+00,\n",
- " 1.070000e+01, 1.265972e+01, 1.263050e+01, 1.256564e+01,\n",
- " 3.959056e+01, 1.418222e+02, 1.539833e+02, 1.230109e+02,\n",
- " 1.263300e+02, 1.261631e+02, 2.103056e+01, 2.117306e+01,\n",
- " 2.590556e+01, 2.069389e+01, 1.421840e+01, 6.566667e+00,\n",
- " 6.277222e+00, 5.853611e+00, 4.500000e+00, 4.923611e+00,\n",
- " 8.252000e+00, 1.391667e+01, 8.883333e+00, 1.188668e+01,\n",
- " 1.153333e+01, 3.003333e+01, 5.200000e+00, 1.107814e+01,\n",
- " 2.535100e+00, 9.516667e+00, 1.748708e+02, 1.666600e+02,\n",
- " 1.696840e+02, 2.197242e+01, 1.573950e+01, 1.753426e+01,\n",
- " 2.206667e+01, 2.195000e+01, 1.289188e+02, 1.533333e+01,\n",
- " 2.106667e+01, 1.191400e+01, 1.478250e+01, 1.240300e+01,\n",
- " 1.314800e+01, 1.750528e+01, 1.556667e+01, 1.976667e+01,\n",
- " 1.547200e+01, 1.486667e+01, 1.500333e+01, 1.453861e+01,\n",
- " 1.958333e+01, 2.028333e+01, 2.226667e+01, 1.786056e+01,\n",
- " -1.566115e+02, -1.052368e+02, -1.555762e+02, -2.480000e+01,\n",
- " -1.241510e+02, 1.035157e+02, 1.848968e+01]) model_centre_longitude
(model_latitude, model_longitude)
float64
...
units : degrees_east axis : X long_name : model centre longitude standard_name : model centre longitude description : 2D meshed grid centre longitudes with 351 longitudes in 211 bands of latitude array([[-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " ...,\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ],\n",
- " [-25. , -24.799999, -24.6 , ..., 44.599998, 44.799999,\n",
- " 45. ]]) model_centre_latitude
(model_latitude, model_longitude)
float64
...
units : degrees_north axis : Y long_name : model centre latitude standard_name : model centre latitude array([[30. , 30. , 30. , ..., 30. , 30. , 30. ],\n",
- " [30.200001, 30.200001, 30.200001, ..., 30.200001, 30.200001, 30.200001],\n",
- " [30.4 , 30.4 , 30.4 , ..., 30.4 , 30.4 , 30.4 ],\n",
- " ...,\n",
- " [71.599998, 71.599998, 71.599998, ..., 71.599998, 71.599998, 71.599998],\n",
- " [71.800003, 71.800003, 71.800003, ..., 71.800003, 71.800003, 71.800003],\n",
- " [72. , 72. , 72. , ..., 72. , 72. , 72. ]]) grid_edge_longitude
(grid_edge)
float64
...
units : degrees_east axis : X long_name : grid edge longitude standard_name : grid edge longitude description : Longitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner). array([-25.1 , -25.1 , -25.1 , ..., -24.700001, -24.9 ,\n",
- " -25.1 ]) grid_edge_latitude
(grid_edge)
float64
...
units : degrees_north axis : Y long_name : grid edge latitude standard_name : grid edge latitude description : Latitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner). array([29.9 , 30.1 , 30.299999, ..., 29.9 , 29.9 , 29.9 ]) sconco3
(station, time)
float32
...
long_name : ozone units : nmol mol-1 standard_name : ozone descrption : Interpolated value of ozone from the experiment cams61_chimere_ph2 with reference to the measurement stations in the EBAS network [126000 values with dtype=float32] station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : description : reference ID for station. array(['AR0001R_UVP', 'AR0002G_UVP', 'AR0004R_UVP', 'AR0005R_UVP',\n",
- " 'AT0002R_UVP', 'AT0005R_UVP', 'AT0030R_UVP', 'AT0032R_UVP',\n",
- " 'AT0034G_UVP', 'AT0038R_UVP', 'AT0040R_UVP', 'AT0041R_UVP',\n",
- " 'AT0042R_UVP', 'AT0043R_UVP', 'AT0045R_UVP', 'AT0046R_UVP',\n",
- " 'AT0047R_UVP', 'AT0048R_UVP', 'AT0049R_UVP', 'AT0050R_UVP',\n",
- " 'BE0001R_UVP', 'BE0032R_UVP', 'BE0035R_UVP', 'BG0053R_UVP',\n",
- " 'BM0001R_UVP', 'CA0103R_UVP', 'CH0001G_UVP', 'CH0002R_UVP',\n",
- " 'CH0003R_UVP', 'CH0004R_UVP', 'CH0005R_UVP', 'CH0053R_UVP',\n",
- " 'CL0001R_UVP', 'CV0001G_UVP', 'CY0002R_UVP', 'CZ0001R_UVP',\n",
- " 'CZ0003R_UVP', 'CZ0005R_UVP', 'DE0001R_UVP', 'DE0002R_UVP',\n",
- " 'DE0003R_UVP', 'DE0007R_UVP', 'DE0008R_UVP', 'DE0009R_UVP',\n",
- " 'DE0043G_UVP', 'DE0054R_UVP', 'DE0060G_UVP', 'DK0005R_UVP',\n",
- " 'DK0010G_UVP', 'DK0012R_UVP', 'DK0025G_UVP', 'DK0031R_UVP',\n",
- " 'EE0009R_UVP', 'EE0011R_UVP', 'ES0001R_UVP', 'ES0005R_UVP',\n",
- " 'ES0006R_UVP', 'ES0007R_UVP', 'ES0008R_UVP', 'ES0009R_UVP',\n",
- " 'ES0010R_UVP', 'ES0011R_UVP', 'ES0012R_UVP', 'ES0013R_UVP',\n",
- " 'ES0014R_UVP', 'ES0016R_UVP', 'ES0017R_UVP', 'ES0018G_UVP',\n",
- " 'FI0009R_UVP', 'FI0018R_UVP', 'FI0022R_UVP', 'FI0096G_UVP',\n",
- " 'FR0008R_UVP', 'FR0009R_UVP', 'FR0010R_UVP', 'FR0013R_UVP',\n",
- " 'FR0014R_UVP', 'FR0015R_UVP', 'FR0016R_UVP', 'FR0017R_UVP',\n",
- " 'FR0018R_UVP', 'FR0019R_UVP', 'FR0020R_UVP', 'FR0023R_UVP',\n",
- " 'FR0025R_UVP', 'FR0030R_UVP', 'GB0002R_UVP', 'GB0006R_UVP',\n",
- " 'GB0013R_UVP', 'GB0014R_UVP', 'GB0015R_UVP', 'GB0031R_UVP',\n",
- " 'GB0033R_UVP', 'GB0037R_UVP', 'GB0038R_UVP', 'GB0039R_UVP',\n",
- " 'GB0043R_UVP', 'GB0045R_UVP', 'GB0048R_UVP', 'GB0049R_UVP',\n",
- " 'GB0050R_UVP', 'GB0052R_UVP', 'GB0059G_UVP', 'GB1055R_UVP',\n",
- " 'GR0001R_UVP', 'GR0002R_UVP', 'HU0002R_UVP', 'HU0003R_UVP',\n",
- " 'ID1013R_UVP', 'IE0001R_UVP', 'IE0031R_UVP', 'IT0004R_UVP',\n",
- " 'IT0009R_UVP', 'IT0014R_UVP', 'IT0018R_UVP', 'IT0019R_UVP',\n",
- " 'JP0002G_UVP', 'JP1020R_UVP--S1', 'JP1028G_UVP', 'JP1029R_UVP',\n",
- " 'KR0100R_UVP', 'KR0101R_UVP', 'LT0015R_UVP', 'LV0010R_UVP',\n",
- " 'LV0016R_UVP', 'MK0007R_UVP', 'MT0001R_UVP', 'NL0007R_UVP',\n",
- " 'NL0009R_UVP', 'NL0010R_UVP', 'NL0091R_UVP', 'NL0644R_UVP',\n",
- " 'NO0002R_UVP', 'NO0015R_UVP', 'NO0039R_UVP', 'NO0042G_UVP',\n",
- " 'NO0043R_UVP', 'NO0047R_UVP', 'NO0052R_UVP', 'NO0056R_UVP',\n",
- " 'NO0059G_UVP', 'NO0489R_UVP', 'NZ0001R_UVP', 'NZ0002R_UVP',\n",
- " 'NZ0003G_UVP', 'PL0002R_UVP', 'PL0003R_UVP', 'PL0004R_UVP',\n",
- " 'PL0005R_UVP', 'RS0005R_UVP', 'RU0100R_UVP', 'SE0005R_UVP',\n",
- " 'SE0013R_UVP', 'SE0014R_UVP', 'SE0018R_UVP', 'SE0019R_UVP',\n",
- " 'SE0020R_UVP', 'SE0022R_UVP', 'SE0032R_UVP', 'SE0035R_UVP',\n",
- " 'SE0039R_UVP', 'SI0008R_UVP', 'SI0031R_UVP', 'SI0032R_UVP',\n",
- " 'SK0002R_UVP', 'SK0004R_UVP', 'SK0006R_UVP', 'SK0007R_UVP',\n",
- " 'US0008R_UVP', 'US0901R_UVP', 'US1200R_UVP', 'US6004G_UVP',\n",
- " 'US6005G_UVP', 'VN0001R_UVP', 'ZA0001G_UVP'], dtype=object) Attributes: (10)
title : Inverse distance weighting (4 neighbours) interpolated cams61_chimere_ph2 experiment data for the component sconco3 with reference to the measurement stations in the EBAS network in 2018-04. institution : Barcelona Supercomputing Center source : Experiment cams61_chimere_ph2 creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es conventions : CF-1.7 data_version : 1.0 history : Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc NCO : 4.7.2 Conventions : CF-1.7 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 720, station: 175, model_latitude: 211, model_longitude: 351, grid_edge: 1125)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n",
- " * station (station) float64 0.0 1.0 2.0 ... 172.0 173.0 174.0\n",
- "Dimensions without coordinates: model_latitude, model_longitude, grid_edge\n",
- "Data variables:\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- " model_centre_longitude (model_latitude, model_longitude) float64 ...\n",
- " model_centre_latitude (model_latitude, model_longitude) float64 ...\n",
- " grid_edge_longitude (grid_edge) float64 ...\n",
- " grid_edge_latitude (grid_edge) float64 ...\n",
- " sconco3 (station, time) float32 ...\n",
- " station_reference (station) object ...\n",
- "Attributes:\n",
- " title: Inverse distance weighting (4 neighbours) interpolated ca...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Experiment cams61_chimere_ph2\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.0\n",
- " history: Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
- " NCO: 4.7.2\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('prv_exp_file.nc')"
- ]
}
],
"metadata": {
diff --git a/tutorials/2.Creation/2.1.Create_Regular.ipynb b/tutorials/2.Creation/2.1.Create_Regular.ipynb
index 59d2d5d982578b38d67c15d21c396e8fc83590dd..8c0c6e0ae13f3b276e00e5c923300ec65792f0b4 100644
--- a/tutorials/2.Creation/2.1.Create_Regular.ipynb
+++ b/tutorials/2.Creation/2.1.Create_Regular.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"from nes import *"
]
},
@@ -45,7 +44,8 @@
"text": [
"Rank 000: Creating regular_grid.nc\n",
"Rank 000: NetCDF ready to write\n",
- "Rank 000: Dimensions done\n"
+ "Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n"
]
}
],
@@ -60,382 +60,9 @@
"outputs": [
{
"data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, lev: 1, lat: 10, lon: 10)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * lev (lev) float64 0.0\n",
- " * lat (lat) float64 41.15 41.25 41.35 41.45 ... 41.75 41.85 41.95 42.05\n",
- " * lon (lon) float64 1.85 1.95 2.05 2.15 2.25 2.35 2.45 2.55 2.65 2.75\n",
- "Data variables:\n",
- " crs |S1 b''\n",
- "Attributes:\n",
- " Conventions: CF-1.7 "
- ],
"text/plain": [
- "\n",
- "Dimensions: (time: 1, lev: 1, lat: 10, lon: 10)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * lev (lev) float64 0.0\n",
- " * lat (lat) float64 41.15 41.25 41.35 41.45 ... 41.75 41.85 41.95 42.05\n",
- " * lon (lon) float64 1.85 1.95 2.05 2.15 2.25 2.35 2.45 2.55 2.65 2.75\n",
- "Data variables:\n",
- " crs |S1 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
+ "{'data': array([41.15, 41.25, 41.35, 41.45, 41.55, 41.65, 41.75, 41.85, 41.95,\n",
+ " 42.05])}"
]
},
"execution_count": 4,
@@ -444,7 +71,27 @@
}
],
"source": [
- "xr.open_dataset('regular_grid.nc')"
+ "regular_grid.lat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([1.85, 1.95, 2.05, 2.15, 2.25, 2.35, 2.45, 2.55, 2.65, 2.75])}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "regular_grid.lon"
]
}
],
diff --git a/tutorials/2.Creation/2.2.Create_Rotated.ipynb b/tutorials/2.Creation/2.2.Create_Rotated.ipynb
index aefd2ceda87dd6e845779ea695467cf7bb0cd12f..63a85b645eb3831a5b1dc523116d8797a76d7968 100644
--- a/tutorials/2.Creation/2.2.Create_Rotated.ipynb
+++ b/tutorials/2.Creation/2.2.Create_Rotated.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"from nes import *"
]
},
@@ -46,7 +45,8 @@
"text": [
"Rank 000: Creating rotated_grid.nc\n",
"Rank 000: NetCDF ready to write\n",
- "Rank 000: Dimensions done\n"
+ "Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n"
]
}
],
@@ -61,404 +61,38 @@
"outputs": [
{
"data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, lev: 1, rlat: 271, rlon: 351)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * lev (lev) float64 0.0\n",
- " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
- " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
- "Data variables:\n",
- " lat (rlat, rlon) float64 16.35 16.43 16.52 ... 58.83 58.68 58.53\n",
- " lon (rlat, rlon) float64 -22.18 -22.02 -21.85 ... 88.05 88.23\n",
- " rotated_pole |S1 b''\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions: time : 1lev : 1rlat : 271rlon : 351
Coordinates: (4)
Data variables: (3)
lat
(rlat, rlon)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([[16.350341, 16.432934, 16.515147, ..., 16.515147, 16.432934, 16.350341],\n",
- " [16.527429, 16.610244, 16.692679, ..., 16.692679, 16.610244, 16.527429],\n",
- " [16.704473, 16.78751 , 16.870167, ..., 16.870167, 16.78751 , 16.704473],\n",
- " ...,\n",
- " [58.320945, 58.472682, 58.624312, ..., 58.624312, 58.472682, 58.320945],\n",
- " [58.426281, 58.578207, 58.730029, ..., 58.730029, 58.578207, 58.426281],\n",
- " [58.530794, 58.682904, 58.834914, ..., 58.834914, 58.682904, 58.530794]]) lon
(rlat, rlon)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([[-22.181266, -22.01667 , -21.851799, ..., 41.851799, 42.01667 ,\n",
- " 42.181266],\n",
- " [-22.278178, -22.113181, -21.947906, ..., 41.947906, 42.113181,\n",
- " 42.278178],\n",
- " [-22.375267, -22.20987 , -22.044192, ..., 42.044192, 42.20987 ,\n",
- " 42.375267],\n",
- " ...,\n",
- " [-67.577665, -67.397071, -67.215366, ..., 87.215366, 87.397071,\n",
- " 87.577665],\n",
- " [-67.901883, -67.722467, -67.541948, ..., 87.541948, 87.722467,\n",
- " 87.901883],\n",
- " [-68.22804 , -68.049824, -67.870513, ..., 87.870513, 88.049824,\n",
- " 88.22804 ]]) rotated_pole
()
|S1
...
grid_mapping_name : rotated_latitude_longitude grid_north_pole_latitude : 39 grid_north_pole_longitude : -170 Attributes: (1)
"
- ],
"text/plain": [
- "\n",
- "Dimensions: (time: 1, lev: 1, rlat: 271, rlon: 351)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * lev (lev) float64 0.0\n",
- " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
- " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
- "Data variables:\n",
- " lat (rlat, rlon) float64 ...\n",
- " lon (rlat, rlon) float64 ...\n",
- " rotated_pole |S1 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
+ "{'data': array([-27. , -26.8, -26.6, -26.4, -26.2, -26. , -25.8, -25.6, -25.4,\n",
+ " -25.2, -25. , -24.8, -24.6, -24.4, -24.2, -24. , -23.8, -23.6,\n",
+ " -23.4, -23.2, -23. , -22.8, -22.6, -22.4, -22.2, -22. , -21.8,\n",
+ " -21.6, -21.4, -21.2, -21. , -20.8, -20.6, -20.4, -20.2, -20. ,\n",
+ " -19.8, -19.6, -19.4, -19.2, -19. , -18.8, -18.6, -18.4, -18.2,\n",
+ " -18. , -17.8, -17.6, -17.4, -17.2, -17. , -16.8, -16.6, -16.4,\n",
+ " -16.2, -16. , -15.8, -15.6, -15.4, -15.2, -15. , -14.8, -14.6,\n",
+ " -14.4, -14.2, -14. , -13.8, -13.6, -13.4, -13.2, -13. , -12.8,\n",
+ " -12.6, -12.4, -12.2, -12. , -11.8, -11.6, -11.4, -11.2, -11. ,\n",
+ " -10.8, -10.6, -10.4, -10.2, -10. , -9.8, -9.6, -9.4, -9.2,\n",
+ " -9. , -8.8, -8.6, -8.4, -8.2, -8. , -7.8, -7.6, -7.4,\n",
+ " -7.2, -7. , -6.8, -6.6, -6.4, -6.2, -6. , -5.8, -5.6,\n",
+ " -5.4, -5.2, -5. , -4.8, -4.6, -4.4, -4.2, -4. , -3.8,\n",
+ " -3.6, -3.4, -3.2, -3. , -2.8, -2.6, -2.4, -2.2, -2. ,\n",
+ " -1.8, -1.6, -1.4, -1.2, -1. , -0.8, -0.6, -0.4, -0.2,\n",
+ " 0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6,\n",
+ " 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4,\n",
+ " 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. , 5.2,\n",
+ " 5.4, 5.6, 5.8, 6. , 6.2, 6.4, 6.6, 6.8, 7. ,\n",
+ " 7.2, 7.4, 7.6, 7.8, 8. , 8.2, 8.4, 8.6, 8.8,\n",
+ " 9. , 9.2, 9.4, 9.6, 9.8, 10. , 10.2, 10.4, 10.6,\n",
+ " 10.8, 11. , 11.2, 11.4, 11.6, 11.8, 12. , 12.2, 12.4,\n",
+ " 12.6, 12.8, 13. , 13.2, 13.4, 13.6, 13.8, 14. , 14.2,\n",
+ " 14.4, 14.6, 14.8, 15. , 15.2, 15.4, 15.6, 15.8, 16. ,\n",
+ " 16.2, 16.4, 16.6, 16.8, 17. , 17.2, 17.4, 17.6, 17.8,\n",
+ " 18. , 18.2, 18.4, 18.6, 18.8, 19. , 19.2, 19.4, 19.6,\n",
+ " 19.8, 20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4,\n",
+ " 21.6, 21.8, 22. , 22.2, 22.4, 22.6, 22.8, 23. , 23.2,\n",
+ " 23.4, 23.6, 23.8, 24. , 24.2, 24.4, 24.6, 24.8, 25. ,\n",
+ " 25.2, 25.4, 25.6, 25.8, 26. , 26.2, 26.4, 26.6, 26.8,\n",
+ " 27. ])}"
]
},
"execution_count": 4,
@@ -467,7 +101,65 @@
}
],
"source": [
- "xr.open_dataset('rotated_grid.nc')"
+ "rotated_grid.rlat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([-35. , -34.8, -34.6, -34.4, -34.2, -34. , -33.8, -33.6, -33.4,\n",
+ " -33.2, -33. , -32.8, -32.6, -32.4, -32.2, -32. , -31.8, -31.6,\n",
+ " -31.4, -31.2, -31. , -30.8, -30.6, -30.4, -30.2, -30. , -29.8,\n",
+ " -29.6, -29.4, -29.2, -29. , -28.8, -28.6, -28.4, -28.2, -28. ,\n",
+ " -27.8, -27.6, -27.4, -27.2, -27. , -26.8, -26.6, -26.4, -26.2,\n",
+ " -26. , -25.8, -25.6, -25.4, -25.2, -25. , -24.8, -24.6, -24.4,\n",
+ " -24.2, -24. , -23.8, -23.6, -23.4, -23.2, -23. , -22.8, -22.6,\n",
+ " -22.4, -22.2, -22. , -21.8, -21.6, -21.4, -21.2, -21. , -20.8,\n",
+ " -20.6, -20.4, -20.2, -20. , -19.8, -19.6, -19.4, -19.2, -19. ,\n",
+ " -18.8, -18.6, -18.4, -18.2, -18. , -17.8, -17.6, -17.4, -17.2,\n",
+ " -17. , -16.8, -16.6, -16.4, -16.2, -16. , -15.8, -15.6, -15.4,\n",
+ " -15.2, -15. , -14.8, -14.6, -14.4, -14.2, -14. , -13.8, -13.6,\n",
+ " -13.4, -13.2, -13. , -12.8, -12.6, -12.4, -12.2, -12. , -11.8,\n",
+ " -11.6, -11.4, -11.2, -11. , -10.8, -10.6, -10.4, -10.2, -10. ,\n",
+ " -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4, -8.2,\n",
+ " -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6, -6.4,\n",
+ " -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8, -4.6,\n",
+ " -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. , -2.8,\n",
+ " -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2, -1. ,\n",
+ " -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6, 0.8,\n",
+ " 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6,\n",
+ " 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4,\n",
+ " 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. , 6.2,\n",
+ " 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8, 8. ,\n",
+ " 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6, 9.8,\n",
+ " 10. , 10.2, 10.4, 10.6, 10.8, 11. , 11.2, 11.4, 11.6,\n",
+ " 11.8, 12. , 12.2, 12.4, 12.6, 12.8, 13. , 13.2, 13.4,\n",
+ " 13.6, 13.8, 14. , 14.2, 14.4, 14.6, 14.8, 15. , 15.2,\n",
+ " 15.4, 15.6, 15.8, 16. , 16.2, 16.4, 16.6, 16.8, 17. ,\n",
+ " 17.2, 17.4, 17.6, 17.8, 18. , 18.2, 18.4, 18.6, 18.8,\n",
+ " 19. , 19.2, 19.4, 19.6, 19.8, 20. , 20.2, 20.4, 20.6,\n",
+ " 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. , 22.2, 22.4,\n",
+ " 22.6, 22.8, 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2,\n",
+ " 24.4, 24.6, 24.8, 25. , 25.2, 25.4, 25.6, 25.8, 26. ,\n",
+ " 26.2, 26.4, 26.6, 26.8, 27. , 27.2, 27.4, 27.6, 27.8,\n",
+ " 28. , 28.2, 28.4, 28.6, 28.8, 29. , 29.2, 29.4, 29.6,\n",
+ " 29.8, 30. , 30.2, 30.4, 30.6, 30.8, 31. , 31.2, 31.4,\n",
+ " 31.6, 31.8, 32. , 32.2, 32.4, 32.6, 32.8, 33. , 33.2,\n",
+ " 33.4, 33.6, 33.8, 34. , 34.2, 34.4, 34.6, 34.8, 35. ])}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rotated_grid.rlon"
]
}
],
diff --git a/tutorials/2.Creation/2.3.Create-Points.ipynb b/tutorials/2.Creation/2.3.Create-Points.ipynb
index 6ff8534623dbe88b2af5044cf7aee263d06b00a3..b06aed5d9053be4ca57293a7f2bcd437e4d20dd0 100644
--- a/tutorials/2.Creation/2.3.Create-Points.ipynb
+++ b/tutorials/2.Creation/2.3.Create-Points.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"import pandas as pd\n",
"import numpy as np\n",
"from nes import *"
@@ -237,6 +236,7 @@
"Rank 000: Creating points_grid_1.nc\n",
"Rank 000: NetCDF ready to write\n",
"Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n",
"Rank 000: Writing station_code var (1/2)\n",
"Rank 000: Var station_code created (1/2)\n",
"Rank 000: Var station_code data (1/2)\n",
@@ -262,508 +262,6 @@
"points_grid.to_netcdf('points_grid_1.nc', info=True)"
]
},
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, station: 134)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * station (station) float64 0.0 1.0 2.0 3.0 ... 131.0 132.0 133.0\n",
- " lat (station) float64 41.38 41.73 41.57 ... 41.24 42.36\n",
- " lon (station) float64 2.086 1.839 2.015 ... 1.857 1.459\n",
- "Data variables:\n",
- " station_code (station) object 'ES0266A' 'ES0392A' ... 'ES9994A'\n",
- " area_classification (station) object 'urban-centre' ... 'nan'\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions:
Coordinates: (4)
Data variables: (2)
station_code
(station)
object
...
array(['ES0266A', 'ES0392A', 'ES0395A', 'ES0559A', 'ES0567A', 'ES0584A',\n",
- " 'ES0586A', 'ES0691A', 'ES0692A', 'ES0694A', 'ES0700A', 'ES0704A',\n",
- " 'ES0963A', 'ES0971A', 'ES0991A', 'ES1018A', 'ES1117A', 'ES1120A',\n",
- " 'ES1122A', 'ES1123A', 'ES1124A', 'ES1125A', 'ES1126A', 'ES1135A',\n",
- " 'ES1148A', 'ES1201A', 'ES1208A', 'ES1215A', 'ES1220A', 'ES1222A',\n",
- " 'ES1225A', 'ES1231A', 'ES1248A', 'ES1262A', 'ES1275A', 'ES1310A',\n",
- " 'ES1311A', 'ES1312A', 'ES1339A', 'ES1347A', 'ES1348A', 'ES1362A',\n",
- " 'ES1379A', 'ES1390A', 'ES1396A', 'ES1397A', 'ES1408A', 'ES1413A',\n",
- " 'ES1438A', 'ES1447A', 'ES1453A', 'ES1480A', 'ES1506A', 'ES1551A',\n",
- " 'ES1555A', 'ES1559A', 'ES1588A', 'ES1642A', 'ES1663A', 'ES1665A',\n",
- " 'ES1666A', 'ES1679A', 'ES1680A', 'ES1684A', 'ES1754A', 'ES1773A',\n",
- " 'ES1775A', 'ES1776A', 'ES1778A', 'ES1812A', 'ES1813A', 'ES1814A',\n",
- " 'ES1815A', 'ES1816A', 'ES1817A', 'ES1839A', 'ES1841A', 'ES1842A',\n",
- " 'ES1843A', 'ES1851A', 'ES1852A', 'ES1853A', 'ES1854A', 'ES1855A',\n",
- " 'ES1856A', 'ES1861A', 'ES1870A', 'ES1871A', 'ES1872A', 'ES1874A',\n",
- " 'ES1887A', 'ES1891A', 'ES1892A', 'ES1895A', 'ES1896A', 'ES1899A',\n",
- " 'ES1900A', 'ES1903A', 'ES1909A', 'ES1910A', 'ES1923A', 'ES1928A',\n",
- " 'ES1929A', 'ES1930A', 'ES1931A', 'ES1936A', 'ES1948A', 'ES1964A',\n",
- " 'ES1965A', 'ES1982A', 'ES1983A', 'ES1992A', 'ES1999A', 'ES2009A',\n",
- " 'ES2011A', 'ES2012A', 'ES2017A', 'ES2027A', 'ES2033A', 'ES2034A',\n",
- " 'ES2035A', 'ES2071A', 'ES2079A', 'ES2043A', 'ES2090A', 'ES0554A',\n",
- " 'ES0977A', 'ES1398A', 'ES1200A', 'ES2087A', 'ES2091A', 'ES2088A',\n",
- " 'ES1908A', 'ES9994A'], dtype=object) area_classification
(station)
object
...
array(['urban-centre', 'urban-suburban', 'urban-centre', 'urban-centre',\n",
- " 'urban-centre', 'urban-suburban', 'urban-centre', 'urban-centre',\n",
- " 'urban-centre', 'urban-suburban', 'urban-centre', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-suburban', 'urban-suburban', 'urban-centre',\n",
- " 'urban-suburban', 'urban-suburban', 'rural-near_city', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-centre', 'urban-suburban', 'urban-suburban',\n",
- " 'urban-centre', 'rural-regional', 'urban-suburban', 'urban-suburban',\n",
- " 'rural', 'rural-regional', 'urban-centre', 'urban-centre',\n",
- " 'rural-regional', 'urban-centre', 'urban-suburban', 'rural-regional',\n",
- " 'rural', 'urban-suburban', 'urban-suburban', 'rural-regional',\n",
- " 'rural-regional', 'urban-centre', 'rural-regional', 'urban-suburban',\n",
- " 'urban-centre', 'urban-suburban', 'urban-suburban', 'urban-suburban',\n",
- " 'urban-centre', 'urban-suburban', 'urban-centre', 'urban-centre',\n",
- " 'urban-centre', 'urban-centre', 'urban-centre', 'urban-suburban',\n",
- " 'rural', 'urban-suburban', 'urban-suburban', 'urban-suburban',\n",
- " 'urban-centre', 'urban-centre', 'urban-suburban', 'urban-centre',\n",
- " 'rural-near_city', 'urban-suburban', 'urban-suburban', 'urban-centre',\n",
- " 'rural-remote', 'rural-near_city', 'rural-regional', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-centre', 'urban-suburban', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-suburban', 'urban-suburban', 'urban-suburban',\n",
- " 'urban-suburban', 'rural-near_city', 'rural-regional',\n",
- " 'rural-near_city', 'urban-centre', 'urban-suburban', 'urban-suburban',\n",
- " 'urban-suburban', 'urban-centre', 'urban-suburban', 'rural-near_city',\n",
- " 'urban-centre', 'urban-centre', 'urban-suburban', 'urban-suburban',\n",
- " 'rural-near_city', 'urban-centre', 'urban-suburban', 'rural-near_city',\n",
- " 'urban-suburban', 'rural-near_city', 'urban-centre', 'urban-suburban',\n",
- " 'rural-near_city', 'urban-suburban', 'rural-near_city',\n",
- " 'rural-near_city', 'urban-suburban', 'urban-suburban', 'rural-remote',\n",
- " 'urban-suburban', 'urban-centre', 'urban-centre', 'rural-near_city',\n",
- " 'urban-suburban', 'urban-suburban', 'rural', 'urban-centre', 'rural',\n",
- " 'rural', 'urban-suburban', 'urban-centre', 'urban-suburban', 'nan',\n",
- " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
- " dtype=object) Attributes: (1)
"
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 1, station: 134)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * station (station) float64 0.0 1.0 2.0 3.0 ... 131.0 132.0 133.0\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- "Data variables:\n",
- " station_code (station) object ...\n",
- " area_classification (station) object ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('points_grid_1.nc')"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -773,7 +271,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -1237,7 +735,7 @@
"[367 rows x 84 columns]"
]
},
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -1250,7 +748,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -1261,7 +759,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -1271,7 +769,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -1288,7 +786,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -1297,7 +795,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -1307,6 +805,7 @@
"Rank 000: Creating points_grid_2.nc\n",
"Rank 000: NetCDF ready to write\n",
"Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n",
"Rank 000: Writing station_name var (1/3)\n",
"Rank 000: Var station_name created (1/3)\n",
"Rank 000: Var station_name data (1/3)\n",
@@ -1337,442 +836,6 @@
"source": [
"points_grid.to_netcdf('points_grid_2.nc', info=True)"
]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 365, station: 83, strlen: 75)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2017-01-01 2017-01-02 ... 2017-12-31\n",
- " * station (station) float64 0.0 1.0 2.0 3.0 4.0 ... 79.0 80.0 81.0 82.0\n",
- " lat (station) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
- " lon (station) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
- "Dimensions without coordinates: strlen\n",
- "Data variables:\n",
- " station_name (station, strlen) object 'B' 'a' 'r' 'c' 'e' ... '' '' '' ''\n",
- " station_code (station, strlen) object 'E' 'S' '1' '4' '3' ... '' '' '' ''\n",
- " pm10 (station, time) float64 19.6 27.2 35.7 30.9 ... nan nan nan\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions: time : 365station : 83strlen : 75
Coordinates: (4)
Data variables: (3)
station_name
(station, strlen)
object
...
array([['B', 'a', 'r', ..., '', '', ''],\n",
- " ['B', 'a', 'd', ..., '', '', ''],\n",
- " ['B', 'a', 'd', ..., '', '', ''],\n",
- " ...,\n",
- " ['S', 'a', 'n', ..., '', '', ''],\n",
- " ['L', 'a', ' ', ..., '', '', ''],\n",
- " ['V', 'i', 'c', ..., '', '', '']], dtype=object) station_code
(station, strlen)
object
...
array([['E', 'S', '1', ..., '', '', ''],\n",
- " ['E', 'S', '1', ..., '', '', ''],\n",
- " ['E', 'S', '2', ..., '', '', ''],\n",
- " ...,\n",
- " ['E', 'S', '1', ..., '', '', ''],\n",
- " ['E', 'S', '9', ..., '', '', ''],\n",
- " ['E', 'S', '1', ..., '', '', '']], dtype=object) pm10
(station, time)
float64
...
array([[19.6 , 27.2 , 35.7 , ..., 24.6 , 27.4 , 17.3 ],\n",
- " [ nan, 20.86, nan, ..., 21. , nan, 12.5 ],\n",
- " [20. , 23. , 32. , ..., 24. , 15. , 13. ],\n",
- " ...,\n",
- " [ nan, 22.06, 35.84, ..., 14.09, nan, nan],\n",
- " [ nan, nan, nan, ..., nan, nan, nan],\n",
- " [ nan, nan, nan, ..., nan, nan, nan]]) Attributes: (1)
"
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 365, station: 83, strlen: 75)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2017-01-01 2017-01-02 ... 2017-12-31\n",
- " * station (station) float64 0.0 1.0 2.0 3.0 4.0 ... 79.0 80.0 81.0 82.0\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- "Dimensions without coordinates: strlen\n",
- "Data variables:\n",
- " station_name (station, strlen) object ...\n",
- " station_code (station, strlen) object ...\n",
- " pm10 (station, time) float64 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('points_grid_2.nc')"
- ]
}
],
"metadata": {
diff --git a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb
index c67543b633a75dece8ac5208223f4392003f7220..b27f816904df62598613c2fc2e2b26508364a6a9 100644
--- a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb
+++ b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"import pandas as pd\n",
"import numpy as np\n",
"from datetime import datetime, timedelta\n",
@@ -405,6 +404,7 @@
"Rank 000: Creating points_grid_no2.nc\n",
"Rank 000: NetCDF ready to write\n",
"Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n",
"Rank 000: Writing station_name var (1/2)\n",
"Rank 000: Var station_name created (1/2)\n",
"Rank 000: Var station_name data (1/2)\n",
@@ -417,7 +417,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_nameInput dtype=, data dtype=object\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n",
" warnings.warn(msg)\n"
]
},
@@ -435,422 +435,6 @@
"del points_grid"
]
},
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 43818, station: 2, strlen: 75)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2017-01-01 ... 2021-12-31T17:00:00\n",
- " * station (station) float64 0.0 1.0\n",
- " lat (station) float64 41.37 41.32\n",
- " lon (station) float64 2.185 2.135\n",
- "Dimensions without coordinates: strlen\n",
- "Data variables:\n",
- " station_name (station, strlen) object 'N' 'O' '2' '-' 'U' ... '' '' '' ''\n",
- " sconcno2 (time, station) float64 64.64 49.08 68.16 ... 12.76 28.66\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions: time : 43818station : 2strlen : 75
Coordinates: (4)
Data variables: (2)
station_name
(station, strlen)
object
...
array([['N', 'O', '2', '-', 'U', 'M', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', ''],\n",
- " ['N', 'O', '2', '-', 'Z', 'A', 'L', ' ', 'P', 'r', 'a', 't', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '']], dtype=object) sconcno2
(time, station)
float64
...
array([[64.64, 49.08],\n",
- " [68.16, 53. ],\n",
- " [68.29, 46.75],\n",
- " ...,\n",
- " [29.1 , 25.79],\n",
- " [ 9.24, 29.82],\n",
- " [12.76, 28.66]]) Attributes: (1)
"
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 43818, station: 2, strlen: 75)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2017-01-01 ... 2021-12-31T17:00:00\n",
- " * station (station) float64 0.0 1.0\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- "Dimensions without coordinates: strlen\n",
- "Data variables:\n",
- " station_name (station, strlen) object ...\n",
- " sconcno2 (time, station) float64 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('points_grid_no2.nc')"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -860,7 +444,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -870,7 +454,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -1024,7 +608,7 @@
"[43818 rows x 6 columns]"
]
},
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -1035,9 +619,76 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done sconcno2_201701.nc\n",
+ "Done sconcno2_201702.nc\n",
+ "Done sconcno2_201703.nc\n",
+ "Done sconcno2_201704.nc\n",
+ "Done sconcno2_201705.nc\n",
+ "Done sconcno2_201706.nc\n",
+ "Done sconcno2_201707.nc\n",
+ "Done sconcno2_201708.nc\n",
+ "Done sconcno2_201709.nc\n",
+ "Done sconcno2_201710.nc\n",
+ "Done sconcno2_201711.nc\n",
+ "Done sconcno2_201712.nc\n",
+ "Done sconcno2_201801.nc\n",
+ "Done sconcno2_201802.nc\n",
+ "Done sconcno2_201803.nc\n",
+ "Done sconcno2_201804.nc\n",
+ "Done sconcno2_201805.nc\n",
+ "Done sconcno2_201806.nc\n",
+ "Done sconcno2_201807.nc\n",
+ "Done sconcno2_201808.nc\n",
+ "Done sconcno2_201809.nc\n",
+ "Done sconcno2_201810.nc\n",
+ "Done sconcno2_201811.nc\n",
+ "Done sconcno2_201812.nc\n",
+ "Done sconcno2_201901.nc\n",
+ "Done sconcno2_201902.nc\n",
+ "Done sconcno2_201903.nc\n",
+ "Done sconcno2_201904.nc\n",
+ "Done sconcno2_201905.nc\n",
+ "Done sconcno2_201906.nc\n",
+ "Done sconcno2_201907.nc\n",
+ "Done sconcno2_201908.nc\n",
+ "Done sconcno2_201909.nc\n",
+ "Done sconcno2_201910.nc\n",
+ "Done sconcno2_201911.nc\n",
+ "Done sconcno2_201912.nc\n",
+ "Done sconcno2_202001.nc\n",
+ "Done sconcno2_202002.nc\n",
+ "Done sconcno2_202003.nc\n",
+ "Done sconcno2_202004.nc\n",
+ "Done sconcno2_202005.nc\n",
+ "Done sconcno2_202006.nc\n",
+ "Done sconcno2_202007.nc\n",
+ "Done sconcno2_202008.nc\n",
+ "Done sconcno2_202009.nc\n",
+ "Done sconcno2_202010.nc\n",
+ "Done sconcno2_202011.nc\n",
+ "Done sconcno2_202012.nc\n",
+ "Done sconcno2_202101.nc\n",
+ "Done sconcno2_202102.nc\n",
+ "Done sconcno2_202103.nc\n",
+ "Done sconcno2_202104.nc\n",
+ "Done sconcno2_202105.nc\n",
+ "Done sconcno2_202106.nc\n",
+ "Done sconcno2_202107.nc\n",
+ "Done sconcno2_202108.nc\n",
+ "Done sconcno2_202109.nc\n",
+ "Done sconcno2_202110.nc\n",
+ "Done sconcno2_202111.nc\n",
+ "Done sconcno2_202112.nc\n"
+ ]
+ }
+ ],
"source": [
"for (year, month), current in df_data.groupby(['year', 'month']):\n",
"\n",
@@ -1068,832 +719,20 @@
" # Assign metadata\n",
" points_grid.variables = metadata\n",
" \n",
- "\n",
" # Making directory\n",
- " netcdf_path = '/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2'\n",
+ " netcdf_path = 'port_barcelona/port-barcelona/hourly/sconcno2/'\n",
" if not os.path.exists(os.path.dirname(netcdf_path)):\n",
" os.makedirs(os.path.dirname(netcdf_path))\n",
" \n",
+ " # To run Providentia, this folder should be moved to:\n",
+ " # '/esarchive/obs/' as in '/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2'\n",
+ " \n",
" # Save files\n",
- " points_grid.to_netcdf(netcdf_path + '/sconcno2_{0}{1}.nc'.format(year, str(month).zfill(2)))\n",
+ " points_grid.to_netcdf(netcdf_path + 'sconcno2_{0}{1}.nc'.format(year, str(month).zfill(2)))\n",
" \n",
" del points_grid\n",
" print('Done sconcno2_{0}{1}.nc'.format(year, str(month).zfill(2)))"
]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 744, station: 2)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2017-05-01 ... 2017-05-31T23:00:00\n",
- " * station (station) float64 0.0 1.0\n",
- "Data variables:\n",
- " station_name (station) |S75 b'NO2-UM' b'NO2-ZAL Prat'\n",
- " altitude (station) float64 nan nan\n",
- " sconcno2 (time, station) float64 8.77 23.49 1.76 ... 27.11 13.87 30.07\n",
- " lat (station) float64 41.37 41.32\n",
- " lon (station) float64 2.185 2.135\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions:
Coordinates: (2)
Data variables: (5)
station_name
(station)
|S75
...
array([b'NO2-UM', b'NO2-ZAL Prat'], dtype='|S75') altitude
(station)
float64
...
units : meters standard_name : altitude sconcno2
(time, station)
float64
...
array([[ 8.77, 23.49],\n",
- " [ 1.76, 21.73],\n",
- " [ 0.57, 11.44],\n",
- " ...,\n",
- " [ 0. , 26.42],\n",
- " [ 0.07, 27.11],\n",
- " [13.87, 30.07]]) lat
(station)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([41.373777, 41.317277]) lon
(station)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([2.184514, 2.134501]) Attributes: (1)
"
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 744, station: 2)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2017-05-01 ... 2017-05-31T23:00:00\n",
- " * station (station) float64 0.0 1.0\n",
- "Data variables:\n",
- " station_name (station) |S75 ...\n",
- " altitude (station) float64 ...\n",
- " sconcno2 (time, station) float64 ...\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2/sconcno2_201705.nc')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 738, station: 2)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-12-01 ... 2021-12-31T17:00:00\n",
- " * station (station) float64 0.0 1.0\n",
- "Data variables:\n",
- " station_name (station) |S75 b'NO2-UM' b'NO2-ZAL Prat'\n",
- " altitude (station) float64 nan nan\n",
- " sconcno2 (time, station) float64 13.33 nan 13.42 ... 29.82 12.76 28.66\n",
- " lat (station) float64 41.37 41.32\n",
- " lon (station) float64 2.185 2.135\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions:
Coordinates: (2)
Data variables: (5)
station_name
(station)
|S75
...
array([b'NO2-UM', b'NO2-ZAL Prat'], dtype='|S75') altitude
(station)
float64
...
units : meters standard_name : altitude sconcno2
(time, station)
float64
...
array([[13.33, nan],\n",
- " [13.42, nan],\n",
- " [ 0.74, nan],\n",
- " ...,\n",
- " [29.1 , 25.79],\n",
- " [ 9.24, 29.82],\n",
- " [12.76, 28.66]]) lat
(station)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([41.373777, 41.317277]) lon
(station)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([2.184514, 2.134501]) Attributes: (1)
"
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 738, station: 2)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2021-12-01 ... 2021-12-31T17:00:00\n",
- " * station (station) float64 0.0 1.0\n",
- "Data variables:\n",
- " station_name (station) |S75 ...\n",
- " altitude (station) float64 ...\n",
- " sconcno2 (time, station) float64 ...\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2/sconcno2_202112.nc')"
- ]
}
],
"metadata": {
diff --git a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb
index bc5eb4e1f71f9de1cd934fe677f93b39fb99c8c3..4484f438b7f11ec2c5f219c52347047ef2593807 100644
--- a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb
+++ b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"import pandas as pd\n",
"import numpy as np\n",
"from datetime import datetime, timedelta\n",
@@ -371,6 +370,7 @@
"Rank 000: Creating points_grid_nh3.nc\n",
"Rank 000: NetCDF ready to write\n",
"Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n",
"Rank 000: Writing station_name var (1/2)\n",
"Rank 000: Var station_name created (1/2)\n",
"Rank 000: Var station_name data (1/2)\n",
@@ -385,7 +385,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:336: UserWarning: WARNING!!! Different data types for variable station_nameInput dtype=, data dtype=object\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n",
" warnings.warn(msg)\n"
]
}
@@ -395,430 +395,6 @@
"del points_grid"
]
},
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 12, station: 2, strlen: 75)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-01-01 2019-02-01 ... 2019-12-01\n",
- " * station (station) float64 0.0 1.0\n",
- "Dimensions without coordinates: strlen\n",
- "Data variables:\n",
- " lat (station) float64 41.39 41.4\n",
- " lon (station) float64 2.115 2.153\n",
- " station_name (station, strlen) object 't' 'r' 'a' 'f' 'f' ... '' '' '' ''\n",
- " sconcnh3 (time, station) float64 4.989 2.553 3.423 ... 2.511 0.0 0.0\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions: time : 12station : 2strlen : 75
Coordinates: (2)
Data variables: (4)
lat
(station)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([41.3875, 41.3987]) lon
(station)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude station_name
(station, strlen)
object
...
array([['t', 'r', 'a', 'f', 'f', 'i', 'c', '_', 's', 'i', 't', 'e', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', ''],\n",
- " ['u', 'r', 'b', 'a', 'n', '_', 's', 'i', 't', 'e', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
- " '', '', '', '', '', '', '', '', '']], dtype=object) sconcnh3
(time, station)
float64
...
array([[4.988988, 2.553235],\n",
- " [3.422535, 1.556226],\n",
- " [2.675065, 1.686355],\n",
- " [3.425522, 1.975486],\n",
- " [5.314809, 1.119245],\n",
- " [3.139495, 1.626567],\n",
- " [0. , 2.226856],\n",
- " [0. , 2.469638],\n",
- " [0. , 3.727355],\n",
- " [0. , 1.535056],\n",
- " [0. , 2.511152],\n",
- " [0. , 0. ]]) Attributes: (1)
"
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 12, station: 2, strlen: 75)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-01-01 2019-02-01 ... 2019-12-01\n",
- " * station (station) float64 0.0 1.0\n",
- "Dimensions without coordinates: strlen\n",
- "Data variables:\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- " station_name (station, strlen) object ...\n",
- " sconcnh3 (time, station) float64 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('points_grid_nh3.nc')"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -828,7 +404,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -838,7 +414,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -981,7 +557,7 @@
"2019-12-01 0.000000 0.000000 12 2019"
]
},
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -992,7 +568,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -1045,812 +621,19 @@
" points_grid.variables = metadata\n",
" \n",
" # Making directory\n",
- " netcdf_path = '/esarchive/obs/csic/csic/monthly/sconcnh3/'\n",
+ " netcdf_path = 'csic/csic/monthly/sconcnh3/'\n",
" if not os.path.exists(os.path.dirname(netcdf_path)):\n",
" os.makedirs(os.path.dirname(netcdf_path))\n",
" \n",
+ " # To run Providentia, this folder should be moved to:\n",
+ " # '/esarchive/obs/' as in '/esarchive/obs/csic/csic/monthly/sconcnh3/'\n",
+ " \n",
" # Save files\n",
" points_grid.to_netcdf(netcdf_path + '/sconcnh3_{0}{1}.nc'.format(year, str(month).zfill(2)))\n",
" \n",
" del points_grid\n",
" print('Done sconcnh3_{0}{1}.nc'.format(year, str(month).zfill(2)))"
]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, station: 2)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-05-01\n",
- " * station (station) float64 0.0 1.0\n",
- "Data variables:\n",
- " lat (station) float64 41.39 41.4\n",
- " lon (station) float64 2.115 2.153\n",
- " station_name (station) object 'traffic_site' 'urban_site'\n",
- " altitude (station) float64 nan nan\n",
- " sconcnh3 (time, station) float64 5.315 1.119\n",
- "Attributes:\n",
- " Conventions: CF-1.7 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 1, station: 2)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-05-01\n",
- " * station (station) float64 0.0 1.0\n",
- "Data variables:\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- " station_name (station) object ...\n",
- " altitude (station) float64 ...\n",
- " sconcnh3 (time, station) float64 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('/esarchive/obs/csic/csic/monthly/sconcnh3/sconcnh3_201905.nc')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, station: 2)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-01-01\n",
- " * station (station) float64 0.0 1.0\n",
- "Data variables:\n",
- " lat (station) float64 41.39 41.4\n",
- " lon (station) float64 2.115 2.153\n",
- " station_name (station) object 'traffic_site' 'urban_site'\n",
- " altitude (station) float64 nan nan\n",
- " sconcnh3 (time, station) float64 4.989 2.553\n",
- "Attributes:\n",
- " Conventions: CF-1.7 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (time: 1, station: 2)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2019-01-01\n",
- " * station (station) float64 0.0 1.0\n",
- "Data variables:\n",
- " lat (station) float64 ...\n",
- " lon (station) float64 ...\n",
- " station_name (station) object ...\n",
- " altitude (station) float64 ...\n",
- " sconcnh3 (time, station) float64 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset('/esarchive/obs/csic/csic/monthly/sconcnh3/sconcnh3_201901.nc')"
- ]
}
],
"metadata": {
diff --git a/tutorials/2.Creation/2.6.Create-LCC.ipynb b/tutorials/2.Creation/2.6.Create-LCC.ipynb
index 93f811db060d1e4e004e882e44dc8958e37fb783..3c1e675bf69b08f75994321b11a11682f9648199 100644
--- a/tutorials/2.Creation/2.6.Create-LCC.ipynb
+++ b/tutorials/2.Creation/2.6.Create-LCC.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"from nes import *"
]
},
@@ -49,7 +48,8 @@
"text": [
"Rank 000: Creating lcc_grid.nc\n",
"Rank 000: NetCDF ready to write\n",
- "Rank 000: Dimensions done\n"
+ "Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n"
]
}
],
@@ -64,388 +64,87 @@
"outputs": [
{
"data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, lev: 1, y: 397, x: 397)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * lev (lev) float64 0.0\n",
- " * y (y) float64 -7.951e+05 -7.911e+05 ... 7.849e+05 7.889e+05\n",
- " * x (x) float64 -8.058e+05 -8.018e+05 ... 7.742e+05 7.782e+05\n",
- "Data variables:\n",
- " lat (y, x) float64 ...\n",
- " lon (y, x) float64 ...\n",
- " Lambert_conformal |S1 b''\n",
- "Attributes:\n",
- " Conventions: CF-1.7 "
- ],
"text/plain": [
- "\n",
- "Dimensions: (time: 1, lev: 1, y: 397, x: 397)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * lev (lev) float64 0.0\n",
- " * y (y) float64 -7.951e+05 -7.911e+05 ... 7.849e+05 7.889e+05\n",
- " * x (x) float64 -8.058e+05 -8.018e+05 ... 7.742e+05 7.782e+05\n",
- "Data variables:\n",
- " lat (y, x) float64 ...\n",
- " lon (y, x) float64 ...\n",
- " Lambert_conformal |S1 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
+ "{'data': array([-795137.125, -791137.125, -787137.125, -783137.125, -779137.125,\n",
+ " -775137.125, -771137.125, -767137.125, -763137.125, -759137.125,\n",
+ " -755137.125, -751137.125, -747137.125, -743137.125, -739137.125,\n",
+ " -735137.125, -731137.125, -727137.125, -723137.125, -719137.125,\n",
+ " -715137.125, -711137.125, -707137.125, -703137.125, -699137.125,\n",
+ " -695137.125, -691137.125, -687137.125, -683137.125, -679137.125,\n",
+ " -675137.125, -671137.125, -667137.125, -663137.125, -659137.125,\n",
+ " -655137.125, -651137.125, -647137.125, -643137.125, -639137.125,\n",
+ " -635137.125, -631137.125, -627137.125, -623137.125, -619137.125,\n",
+ " -615137.125, -611137.125, -607137.125, -603137.125, -599137.125,\n",
+ " -595137.125, -591137.125, -587137.125, -583137.125, -579137.125,\n",
+ " -575137.125, -571137.125, -567137.125, -563137.125, -559137.125,\n",
+ " -555137.125, -551137.125, -547137.125, -543137.125, -539137.125,\n",
+ " -535137.125, -531137.125, -527137.125, -523137.125, -519137.125,\n",
+ " -515137.125, -511137.125, -507137.125, -503137.125, -499137.125,\n",
+ " -495137.125, -491137.125, -487137.125, -483137.125, -479137.125,\n",
+ " -475137.125, -471137.125, -467137.125, -463137.125, -459137.125,\n",
+ " -455137.125, -451137.125, -447137.125, -443137.125, -439137.125,\n",
+ " -435137.125, -431137.125, -427137.125, -423137.125, -419137.125,\n",
+ " -415137.125, -411137.125, -407137.125, -403137.125, -399137.125,\n",
+ " -395137.125, -391137.125, -387137.125, -383137.125, -379137.125,\n",
+ " -375137.125, -371137.125, -367137.125, -363137.125, -359137.125,\n",
+ " -355137.125, -351137.125, -347137.125, -343137.125, -339137.125,\n",
+ " -335137.125, -331137.125, -327137.125, -323137.125, -319137.125,\n",
+ " -315137.125, -311137.125, -307137.125, -303137.125, -299137.125,\n",
+ " -295137.125, -291137.125, -287137.125, -283137.125, -279137.125,\n",
+ " -275137.125, -271137.125, -267137.125, -263137.125, -259137.125,\n",
+ " -255137.125, -251137.125, -247137.125, -243137.125, -239137.125,\n",
+ " -235137.125, -231137.125, -227137.125, -223137.125, -219137.125,\n",
+ " -215137.125, -211137.125, -207137.125, -203137.125, -199137.125,\n",
+ " -195137.125, -191137.125, -187137.125, -183137.125, -179137.125,\n",
+ " -175137.125, -171137.125, -167137.125, -163137.125, -159137.125,\n",
+ " -155137.125, -151137.125, -147137.125, -143137.125, -139137.125,\n",
+ " -135137.125, -131137.125, -127137.125, -123137.125, -119137.125,\n",
+ " -115137.125, -111137.125, -107137.125, -103137.125, -99137.125,\n",
+ " -95137.125, -91137.125, -87137.125, -83137.125, -79137.125,\n",
+ " -75137.125, -71137.125, -67137.125, -63137.125, -59137.125,\n",
+ " -55137.125, -51137.125, -47137.125, -43137.125, -39137.125,\n",
+ " -35137.125, -31137.125, -27137.125, -23137.125, -19137.125,\n",
+ " -15137.125, -11137.125, -7137.125, -3137.125, 862.875,\n",
+ " 4862.875, 8862.875, 12862.875, 16862.875, 20862.875,\n",
+ " 24862.875, 28862.875, 32862.875, 36862.875, 40862.875,\n",
+ " 44862.875, 48862.875, 52862.875, 56862.875, 60862.875,\n",
+ " 64862.875, 68862.875, 72862.875, 76862.875, 80862.875,\n",
+ " 84862.875, 88862.875, 92862.875, 96862.875, 100862.875,\n",
+ " 104862.875, 108862.875, 112862.875, 116862.875, 120862.875,\n",
+ " 124862.875, 128862.875, 132862.875, 136862.875, 140862.875,\n",
+ " 144862.875, 148862.875, 152862.875, 156862.875, 160862.875,\n",
+ " 164862.875, 168862.875, 172862.875, 176862.875, 180862.875,\n",
+ " 184862.875, 188862.875, 192862.875, 196862.875, 200862.875,\n",
+ " 204862.875, 208862.875, 212862.875, 216862.875, 220862.875,\n",
+ " 224862.875, 228862.875, 232862.875, 236862.875, 240862.875,\n",
+ " 244862.875, 248862.875, 252862.875, 256862.875, 260862.875,\n",
+ " 264862.875, 268862.875, 272862.875, 276862.875, 280862.875,\n",
+ " 284862.875, 288862.875, 292862.875, 296862.875, 300862.875,\n",
+ " 304862.875, 308862.875, 312862.875, 316862.875, 320862.875,\n",
+ " 324862.875, 328862.875, 332862.875, 336862.875, 340862.875,\n",
+ " 344862.875, 348862.875, 352862.875, 356862.875, 360862.875,\n",
+ " 364862.875, 368862.875, 372862.875, 376862.875, 380862.875,\n",
+ " 384862.875, 388862.875, 392862.875, 396862.875, 400862.875,\n",
+ " 404862.875, 408862.875, 412862.875, 416862.875, 420862.875,\n",
+ " 424862.875, 428862.875, 432862.875, 436862.875, 440862.875,\n",
+ " 444862.875, 448862.875, 452862.875, 456862.875, 460862.875,\n",
+ " 464862.875, 468862.875, 472862.875, 476862.875, 480862.875,\n",
+ " 484862.875, 488862.875, 492862.875, 496862.875, 500862.875,\n",
+ " 504862.875, 508862.875, 512862.875, 516862.875, 520862.875,\n",
+ " 524862.875, 528862.875, 532862.875, 536862.875, 540862.875,\n",
+ " 544862.875, 548862.875, 552862.875, 556862.875, 560862.875,\n",
+ " 564862.875, 568862.875, 572862.875, 576862.875, 580862.875,\n",
+ " 584862.875, 588862.875, 592862.875, 596862.875, 600862.875,\n",
+ " 604862.875, 608862.875, 612862.875, 616862.875, 620862.875,\n",
+ " 624862.875, 628862.875, 632862.875, 636862.875, 640862.875,\n",
+ " 644862.875, 648862.875, 652862.875, 656862.875, 660862.875,\n",
+ " 664862.875, 668862.875, 672862.875, 676862.875, 680862.875,\n",
+ " 684862.875, 688862.875, 692862.875, 696862.875, 700862.875,\n",
+ " 704862.875, 708862.875, 712862.875, 716862.875, 720862.875,\n",
+ " 724862.875, 728862.875, 732862.875, 736862.875, 740862.875,\n",
+ " 744862.875, 748862.875, 752862.875, 756862.875, 760862.875,\n",
+ " 764862.875, 768862.875, 772862.875, 776862.875, 780862.875,\n",
+ " 784862.875, 788862.875])}"
]
},
"execution_count": 4,
@@ -454,7 +153,106 @@
}
],
"source": [
- "xr.open_dataset('lcc_grid.nc')"
+ "lcc_grid.y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([-805847.688, -801847.688, -797847.688, -793847.688, -789847.688,\n",
+ " -785847.688, -781847.688, -777847.688, -773847.688, -769847.688,\n",
+ " -765847.688, -761847.688, -757847.688, -753847.688, -749847.688,\n",
+ " -745847.688, -741847.688, -737847.688, -733847.688, -729847.688,\n",
+ " -725847.688, -721847.688, -717847.688, -713847.688, -709847.688,\n",
+ " -705847.688, -701847.688, -697847.688, -693847.688, -689847.688,\n",
+ " -685847.688, -681847.688, -677847.688, -673847.688, -669847.688,\n",
+ " -665847.688, -661847.688, -657847.688, -653847.688, -649847.688,\n",
+ " -645847.688, -641847.688, -637847.688, -633847.688, -629847.688,\n",
+ " -625847.688, -621847.688, -617847.688, -613847.688, -609847.688,\n",
+ " -605847.688, -601847.688, -597847.688, -593847.688, -589847.688,\n",
+ " -585847.688, -581847.688, -577847.688, -573847.688, -569847.688,\n",
+ " -565847.688, -561847.688, -557847.688, -553847.688, -549847.688,\n",
+ " -545847.688, -541847.688, -537847.688, -533847.688, -529847.688,\n",
+ " -525847.688, -521847.688, -517847.688, -513847.688, -509847.688,\n",
+ " -505847.688, -501847.688, -497847.688, -493847.688, -489847.688,\n",
+ " -485847.688, -481847.688, -477847.688, -473847.688, -469847.688,\n",
+ " -465847.688, -461847.688, -457847.688, -453847.688, -449847.688,\n",
+ " -445847.688, -441847.688, -437847.688, -433847.688, -429847.688,\n",
+ " -425847.688, -421847.688, -417847.688, -413847.688, -409847.688,\n",
+ " -405847.688, -401847.688, -397847.688, -393847.688, -389847.688,\n",
+ " -385847.688, -381847.688, -377847.688, -373847.688, -369847.688,\n",
+ " -365847.688, -361847.688, -357847.688, -353847.688, -349847.688,\n",
+ " -345847.688, -341847.688, -337847.688, -333847.688, -329847.688,\n",
+ " -325847.688, -321847.688, -317847.688, -313847.688, -309847.688,\n",
+ " -305847.688, -301847.688, -297847.688, -293847.688, -289847.688,\n",
+ " -285847.688, -281847.688, -277847.688, -273847.688, -269847.688,\n",
+ " -265847.688, -261847.688, -257847.688, -253847.688, -249847.688,\n",
+ " -245847.688, -241847.688, -237847.688, -233847.688, -229847.688,\n",
+ " -225847.688, -221847.688, -217847.688, -213847.688, -209847.688,\n",
+ " -205847.688, -201847.688, -197847.688, -193847.688, -189847.688,\n",
+ " -185847.688, -181847.688, -177847.688, -173847.688, -169847.688,\n",
+ " -165847.688, -161847.688, -157847.688, -153847.688, -149847.688,\n",
+ " -145847.688, -141847.688, -137847.688, -133847.688, -129847.688,\n",
+ " -125847.688, -121847.688, -117847.688, -113847.688, -109847.688,\n",
+ " -105847.688, -101847.688, -97847.688, -93847.688, -89847.688,\n",
+ " -85847.688, -81847.688, -77847.688, -73847.688, -69847.688,\n",
+ " -65847.688, -61847.688, -57847.688, -53847.688, -49847.688,\n",
+ " -45847.688, -41847.688, -37847.688, -33847.688, -29847.688,\n",
+ " -25847.688, -21847.688, -17847.688, -13847.688, -9847.688,\n",
+ " -5847.688, -1847.688, 2152.312, 6152.312, 10152.312,\n",
+ " 14152.312, 18152.312, 22152.312, 26152.312, 30152.312,\n",
+ " 34152.312, 38152.312, 42152.312, 46152.312, 50152.312,\n",
+ " 54152.312, 58152.312, 62152.312, 66152.312, 70152.312,\n",
+ " 74152.312, 78152.312, 82152.312, 86152.312, 90152.312,\n",
+ " 94152.312, 98152.312, 102152.312, 106152.312, 110152.312,\n",
+ " 114152.312, 118152.312, 122152.312, 126152.312, 130152.312,\n",
+ " 134152.312, 138152.312, 142152.312, 146152.312, 150152.312,\n",
+ " 154152.312, 158152.312, 162152.312, 166152.312, 170152.312,\n",
+ " 174152.312, 178152.312, 182152.312, 186152.312, 190152.312,\n",
+ " 194152.312, 198152.312, 202152.312, 206152.312, 210152.312,\n",
+ " 214152.312, 218152.312, 222152.312, 226152.312, 230152.312,\n",
+ " 234152.312, 238152.312, 242152.312, 246152.312, 250152.312,\n",
+ " 254152.312, 258152.312, 262152.312, 266152.312, 270152.312,\n",
+ " 274152.312, 278152.312, 282152.312, 286152.312, 290152.312,\n",
+ " 294152.312, 298152.312, 302152.312, 306152.312, 310152.312,\n",
+ " 314152.312, 318152.312, 322152.312, 326152.312, 330152.312,\n",
+ " 334152.312, 338152.312, 342152.312, 346152.312, 350152.312,\n",
+ " 354152.312, 358152.312, 362152.312, 366152.312, 370152.312,\n",
+ " 374152.312, 378152.312, 382152.312, 386152.312, 390152.312,\n",
+ " 394152.312, 398152.312, 402152.312, 406152.312, 410152.312,\n",
+ " 414152.312, 418152.312, 422152.312, 426152.312, 430152.312,\n",
+ " 434152.312, 438152.312, 442152.312, 446152.312, 450152.312,\n",
+ " 454152.312, 458152.312, 462152.312, 466152.312, 470152.312,\n",
+ " 474152.312, 478152.312, 482152.312, 486152.312, 490152.312,\n",
+ " 494152.312, 498152.312, 502152.312, 506152.312, 510152.312,\n",
+ " 514152.312, 518152.312, 522152.312, 526152.312, 530152.312,\n",
+ " 534152.312, 538152.312, 542152.312, 546152.312, 550152.312,\n",
+ " 554152.312, 558152.312, 562152.312, 566152.312, 570152.312,\n",
+ " 574152.312, 578152.312, 582152.312, 586152.312, 590152.312,\n",
+ " 594152.312, 598152.312, 602152.312, 606152.312, 610152.312,\n",
+ " 614152.312, 618152.312, 622152.312, 626152.312, 630152.312,\n",
+ " 634152.312, 638152.312, 642152.312, 646152.312, 650152.312,\n",
+ " 654152.312, 658152.312, 662152.312, 666152.312, 670152.312,\n",
+ " 674152.312, 678152.312, 682152.312, 686152.312, 690152.312,\n",
+ " 694152.312, 698152.312, 702152.312, 706152.312, 710152.312,\n",
+ " 714152.312, 718152.312, 722152.312, 726152.312, 730152.312,\n",
+ " 734152.312, 738152.312, 742152.312, 746152.312, 750152.312,\n",
+ " 754152.312, 758152.312, 762152.312, 766152.312, 770152.312,\n",
+ " 774152.312, 778152.312])}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lcc_grid.x"
]
}
],
diff --git a/tutorials/2.Creation/2.7.Create_Mercator.ipynb b/tutorials/2.Creation/2.7.Create_Mercator.ipynb
index 344aa4c440ce9ebe5054523f667566e4b95ca3d6..a69ee6a405c0e9a2e993a5ab12e1a7d477b4d631 100644
--- a/tutorials/2.Creation/2.7.Create_Mercator.ipynb
+++ b/tutorials/2.Creation/2.7.Create_Mercator.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"from nes import *"
]
},
@@ -47,7 +46,8 @@
"text": [
"Rank 000: Creating mercator_grid.nc\n",
"Rank 000: NetCDF ready to write\n",
- "Rank 000: Dimensions done\n"
+ "Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n"
]
}
],
@@ -62,411 +62,47 @@
"outputs": [
{
"data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 1, lev: 1, y: 236, x: 210)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * lev (lev) float64 0.0\n",
- " * y (y) float64 -5.382e+06 -5.332e+06 ... 6.318e+06 6.368e+06\n",
- " * x (x) float64 -1.01e+05 -5.102e+04 -1.018e+03 ... 1.03e+07 1.035e+07\n",
- "Data variables:\n",
- " lat (y, x) float64 -43.67 -43.67 -43.67 -43.67 ... 49.75 49.75 49.75\n",
- " lon (y, x) float64 -18.91 -18.46 -18.01 -17.56 ... 74.1 74.55 75.0\n",
- " mercator |S1 b''\n",
- "Attributes:\n",
- " Conventions: CF-1.7 Dimensions: time : 1lev : 1y : 236x : 210
Coordinates: (4)
Data variables: (3)
lat
(y, x)
float64
...
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([[-43.665383, -43.665383, -43.665383, ..., -43.665383, -43.665383,\n",
- " -43.665383],\n",
- " [-43.33832 , -43.33832 , -43.33832 , ..., -43.33832 , -43.33832 ,\n",
- " -43.33832 ],\n",
- " [-43.009473, -43.009473, -43.009473, ..., -43.009473, -43.009473,\n",
- " -43.009473],\n",
- " ...,\n",
- " [ 49.159788, 49.159788, 49.159788, ..., 49.159788, 49.159788,\n",
- " 49.159788],\n",
- " [ 49.453582, 49.453582, 49.453582, ..., 49.453582, 49.453582,\n",
- " 49.453582],\n",
- " [ 49.745614, 49.745614, 49.745614, ..., 49.745614, 49.745614,\n",
- " 49.745614]]) lon
(y, x)
float64
...
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([[-18.907765, -18.458454, -18.009143, ..., 74.099525, 74.548836,\n",
- " 74.998146],\n",
- " [-18.907765, -18.458454, -18.009143, ..., 74.099525, 74.548836,\n",
- " 74.998146],\n",
- " [-18.907765, -18.458454, -18.009143, ..., 74.099525, 74.548836,\n",
- " 74.998146],\n",
- " ...,\n",
- " [-18.907765, -18.458454, -18.009143, ..., 74.099525, 74.548836,\n",
- " 74.998146],\n",
- " [-18.907765, -18.458454, -18.009143, ..., 74.099525, 74.548836,\n",
- " 74.998146],\n",
- " [-18.907765, -18.458454, -18.009143, ..., 74.099525, 74.548836,\n",
- " 74.998146]]) mercator
()
|S1
...
grid_mapping_name : mercator standard_parallel : -1.5 longitude_of_projection_origin : -18.0 Attributes: (1)
"
- ],
"text/plain": [
- "\n",
- "Dimensions: (time: 1, lev: 1, y: 236, x: 210)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 1996-12-31\n",
- " * lev (lev) float64 0.0\n",
- " * y (y) float64 -5.382e+06 -5.332e+06 ... 6.318e+06 6.368e+06\n",
- " * x (x) float64 -1.01e+05 -5.102e+04 -1.018e+03 ... 1.03e+07 1.035e+07\n",
- "Data variables:\n",
- " lat (y, x) float64 ...\n",
- " lon (y, x) float64 ...\n",
- " mercator |S1 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7"
+ "{'data': array([-5382460., -5332460., -5282460., -5232460., -5182460., -5132460.,\n",
+ " -5082460., -5032460., -4982460., -4932460., -4882460., -4832460.,\n",
+ " -4782460., -4732460., -4682460., -4632460., -4582460., -4532460.,\n",
+ " -4482460., -4432460., -4382460., -4332460., -4282460., -4232460.,\n",
+ " -4182460., -4132460., -4082460., -4032460., -3982460., -3932460.,\n",
+ " -3882460., -3832460., -3782460., -3732460., -3682460., -3632460.,\n",
+ " -3582460., -3532460., -3482460., -3432460., -3382460., -3332460.,\n",
+ " -3282460., -3232460., -3182460., -3132460., -3082460., -3032460.,\n",
+ " -2982460., -2932460., -2882460., -2832460., -2782460., -2732460.,\n",
+ " -2682460., -2632460., -2582460., -2532460., -2482460., -2432460.,\n",
+ " -2382460., -2332460., -2282460., -2232460., -2182460., -2132460.,\n",
+ " -2082460., -2032460., -1982460., -1932460., -1882460., -1832460.,\n",
+ " -1782460., -1732460., -1682460., -1632460., -1582460., -1532460.,\n",
+ " -1482460., -1432460., -1382460., -1332460., -1282460., -1232460.,\n",
+ " -1182460., -1132460., -1082460., -1032460., -982460., -932460.,\n",
+ " -882460., -832460., -782460., -732460., -682460., -632460.,\n",
+ " -582460., -532460., -482460., -432460., -382460., -332460.,\n",
+ " -282460., -232460., -182460., -132460., -82460., -32460.,\n",
+ " 17540., 67540., 117540., 167540., 217540., 267540.,\n",
+ " 317540., 367540., 417540., 467540., 517540., 567540.,\n",
+ " 617540., 667540., 717540., 767540., 817540., 867540.,\n",
+ " 917540., 967540., 1017540., 1067540., 1117540., 1167540.,\n",
+ " 1217540., 1267540., 1317540., 1367540., 1417540., 1467540.,\n",
+ " 1517540., 1567540., 1617540., 1667540., 1717540., 1767540.,\n",
+ " 1817540., 1867540., 1917540., 1967540., 2017540., 2067540.,\n",
+ " 2117540., 2167540., 2217540., 2267540., 2317540., 2367540.,\n",
+ " 2417540., 2467540., 2517540., 2567540., 2617540., 2667540.,\n",
+ " 2717540., 2767540., 2817540., 2867540., 2917540., 2967540.,\n",
+ " 3017540., 3067540., 3117540., 3167540., 3217540., 3267540.,\n",
+ " 3317540., 3367540., 3417540., 3467540., 3517540., 3567540.,\n",
+ " 3617540., 3667540., 3717540., 3767540., 3817540., 3867540.,\n",
+ " 3917540., 3967540., 4017540., 4067540., 4117540., 4167540.,\n",
+ " 4217540., 4267540., 4317540., 4367540., 4417540., 4467540.,\n",
+ " 4517540., 4567540., 4617540., 4667540., 4717540., 4767540.,\n",
+ " 4817540., 4867540., 4917540., 4967540., 5017540., 5067540.,\n",
+ " 5117540., 5167540., 5217540., 5267540., 5317540., 5367540.,\n",
+ " 5417540., 5467540., 5517540., 5567540., 5617540., 5667540.,\n",
+ " 5717540., 5767540., 5817540., 5867540., 5917540., 5967540.,\n",
+ " 6017540., 6067540., 6117540., 6167540., 6217540., 6267540.,\n",
+ " 6317540., 6367540.])}"
]
},
"execution_count": 4,
@@ -475,7 +111,79 @@
}
],
"source": [
- "xr.open_dataset('mercator_grid.nc')"
+ "mercator_grid.y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([-1.01017500e+05, -5.10175000e+04, -1.01750000e+03, 4.89825000e+04,\n",
+ " 9.89825000e+04, 1.48982500e+05, 1.98982500e+05, 2.48982500e+05,\n",
+ " 2.98982500e+05, 3.48982500e+05, 3.98982500e+05, 4.48982500e+05,\n",
+ " 4.98982500e+05, 5.48982500e+05, 5.98982500e+05, 6.48982500e+05,\n",
+ " 6.98982500e+05, 7.48982500e+05, 7.98982500e+05, 8.48982500e+05,\n",
+ " 8.98982500e+05, 9.48982500e+05, 9.98982500e+05, 1.04898250e+06,\n",
+ " 1.09898250e+06, 1.14898250e+06, 1.19898250e+06, 1.24898250e+06,\n",
+ " 1.29898250e+06, 1.34898250e+06, 1.39898250e+06, 1.44898250e+06,\n",
+ " 1.49898250e+06, 1.54898250e+06, 1.59898250e+06, 1.64898250e+06,\n",
+ " 1.69898250e+06, 1.74898250e+06, 1.79898250e+06, 1.84898250e+06,\n",
+ " 1.89898250e+06, 1.94898250e+06, 1.99898250e+06, 2.04898250e+06,\n",
+ " 2.09898250e+06, 2.14898250e+06, 2.19898250e+06, 2.24898250e+06,\n",
+ " 2.29898250e+06, 2.34898250e+06, 2.39898250e+06, 2.44898250e+06,\n",
+ " 2.49898250e+06, 2.54898250e+06, 2.59898250e+06, 2.64898250e+06,\n",
+ " 2.69898250e+06, 2.74898250e+06, 2.79898250e+06, 2.84898250e+06,\n",
+ " 2.89898250e+06, 2.94898250e+06, 2.99898250e+06, 3.04898250e+06,\n",
+ " 3.09898250e+06, 3.14898250e+06, 3.19898250e+06, 3.24898250e+06,\n",
+ " 3.29898250e+06, 3.34898250e+06, 3.39898250e+06, 3.44898250e+06,\n",
+ " 3.49898250e+06, 3.54898250e+06, 3.59898250e+06, 3.64898250e+06,\n",
+ " 3.69898250e+06, 3.74898250e+06, 3.79898250e+06, 3.84898250e+06,\n",
+ " 3.89898250e+06, 3.94898250e+06, 3.99898250e+06, 4.04898250e+06,\n",
+ " 4.09898250e+06, 4.14898250e+06, 4.19898250e+06, 4.24898250e+06,\n",
+ " 4.29898250e+06, 4.34898250e+06, 4.39898250e+06, 4.44898250e+06,\n",
+ " 4.49898250e+06, 4.54898250e+06, 4.59898250e+06, 4.64898250e+06,\n",
+ " 4.69898250e+06, 4.74898250e+06, 4.79898250e+06, 4.84898250e+06,\n",
+ " 4.89898250e+06, 4.94898250e+06, 4.99898250e+06, 5.04898250e+06,\n",
+ " 5.09898250e+06, 5.14898250e+06, 5.19898250e+06, 5.24898250e+06,\n",
+ " 5.29898250e+06, 5.34898250e+06, 5.39898250e+06, 5.44898250e+06,\n",
+ " 5.49898250e+06, 5.54898250e+06, 5.59898250e+06, 5.64898250e+06,\n",
+ " 5.69898250e+06, 5.74898250e+06, 5.79898250e+06, 5.84898250e+06,\n",
+ " 5.89898250e+06, 5.94898250e+06, 5.99898250e+06, 6.04898250e+06,\n",
+ " 6.09898250e+06, 6.14898250e+06, 6.19898250e+06, 6.24898250e+06,\n",
+ " 6.29898250e+06, 6.34898250e+06, 6.39898250e+06, 6.44898250e+06,\n",
+ " 6.49898250e+06, 6.54898250e+06, 6.59898250e+06, 6.64898250e+06,\n",
+ " 6.69898250e+06, 6.74898250e+06, 6.79898250e+06, 6.84898250e+06,\n",
+ " 6.89898250e+06, 6.94898250e+06, 6.99898250e+06, 7.04898250e+06,\n",
+ " 7.09898250e+06, 7.14898250e+06, 7.19898250e+06, 7.24898250e+06,\n",
+ " 7.29898250e+06, 7.34898250e+06, 7.39898250e+06, 7.44898250e+06,\n",
+ " 7.49898250e+06, 7.54898250e+06, 7.59898250e+06, 7.64898250e+06,\n",
+ " 7.69898250e+06, 7.74898250e+06, 7.79898250e+06, 7.84898250e+06,\n",
+ " 7.89898250e+06, 7.94898250e+06, 7.99898250e+06, 8.04898250e+06,\n",
+ " 8.09898250e+06, 8.14898250e+06, 8.19898250e+06, 8.24898250e+06,\n",
+ " 8.29898250e+06, 8.34898250e+06, 8.39898250e+06, 8.44898250e+06,\n",
+ " 8.49898250e+06, 8.54898250e+06, 8.59898250e+06, 8.64898250e+06,\n",
+ " 8.69898250e+06, 8.74898250e+06, 8.79898250e+06, 8.84898250e+06,\n",
+ " 8.89898250e+06, 8.94898250e+06, 8.99898250e+06, 9.04898250e+06,\n",
+ " 9.09898250e+06, 9.14898250e+06, 9.19898250e+06, 9.24898250e+06,\n",
+ " 9.29898250e+06, 9.34898250e+06, 9.39898250e+06, 9.44898250e+06,\n",
+ " 9.49898250e+06, 9.54898250e+06, 9.59898250e+06, 9.64898250e+06,\n",
+ " 9.69898250e+06, 9.74898250e+06, 9.79898250e+06, 9.84898250e+06,\n",
+ " 9.89898250e+06, 9.94898250e+06, 9.99898250e+06, 1.00489825e+07,\n",
+ " 1.00989825e+07, 1.01489825e+07, 1.01989825e+07, 1.02489825e+07,\n",
+ " 1.02989825e+07, 1.03489825e+07])}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mercator_grid.x"
]
}
],
diff --git a/tutorials/2.Creation/2.8.Create_Global.ipynb b/tutorials/2.Creation/2.8.Create_Global.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..107f0ddac3984805be9cf0e1b1c8642c9c5ead16
--- /dev/null
+++ b/tutorials/2.Creation/2.8.Create_Global.ipynb
@@ -0,0 +1,113 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to create global grids"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nes import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "inc_lat = 0.1\n",
+ "inc_lon = 0.1\n",
+ "global_grid = create_nes(projection='global', \n",
+ " inc_lat=inc_lat, inc_lon=inc_lon)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Creating global_grid.nc\n",
+ "Rank 000: NetCDF ready to write\n",
+ "Rank 000: Dimensions done\n",
+ "Rank 000: Cell measures done\n"
+ ]
+ }
+ ],
+ "source": [
+ "global_grid.to_netcdf('global_grid.nc', info=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([-89.95, -89.85, -89.75, ..., 89.65, 89.75, 89.85])}"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "global_grid.lat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([-179.95, -179.85, -179.75, ..., 179.65, 179.75, 179.85])}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "global_grid.lon"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/3.Statistics/3.1.Statistics.ipynb b/tutorials/3.Statistics/3.1.Statistics.ipynb
index de61875f45e85ef701042edc9a6c8e00b238d673..f3566e4c8d598a6fd1a33734a7b0107cac546534 100644
--- a/tutorials/3.Statistics/3.1.Statistics.ipynb
+++ b/tutorials/3.Statistics/3.1.Statistics.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Calculate simple statistics"
+ "# Calculate daily statistics"
]
},
{
@@ -20,7 +20,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Open NetCDF"
+ "## 1. Read dataset"
]
},
{
@@ -29,21 +29,18 @@
"metadata": {},
"outputs": [
{
- "ename": "FileNotFoundError",
- "evalue": "/gpfs/scratch/bsc32/bsc32538/a4mg/nmmb-monarch/ARCHIVE/000/2022050312/MONARCH_d01_2022050312.nc",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/load_nes.py\u001b[0m in \u001b[0;36mopen_netcdf\u001b[0;34m(path, comm, xarray, info, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, balanced)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mxarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mFileNotFoundError\u001b[0m: /gpfs/scratch/bsc32/bsc32538/a4mg/nmmb-monarch/ARCHIVE/000/2022050312/MONARCH_d01_2022050312.nc"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 571 ms, sys: 93.9 ms, total: 665 ms\n",
+ "Wall time: 10.5 s\n"
]
}
],
"source": [
- "# TODO: Change path\n",
- "cams_file = \"/gpfs/scratch/bsc32/bsc32538/a4mg/nmmb-monarch/ARCHIVE/000/2022050312/MONARCH_d01_2022050312.nc\"\n",
+ "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n",
+ "# Rotated grid from MONARCH\n",
+ "cams_file = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'\n",
"%time nessy = open_netcdf(path=cams_file, info=True)"
]
},
@@ -53,15 +50,14 @@
"metadata": {},
"outputs": [
{
- "ename": "NameError",
- "evalue": "name 'nessy' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnessy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m: name 'nessy' is not defined"
- ]
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
@@ -72,15 +68,61 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Time\n",
- "NES.time : list of time steps (datetime)"
+ "### 1.1. Time"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[datetime.datetime(2022, 11, 15, 12, 0),\n",
+ " datetime.datetime(2022, 11, 15, 13, 0),\n",
+ " datetime.datetime(2022, 11, 15, 14, 0),\n",
+ " datetime.datetime(2022, 11, 15, 15, 0),\n",
+ " datetime.datetime(2022, 11, 15, 16, 0),\n",
+ " datetime.datetime(2022, 11, 15, 17, 0),\n",
+ " datetime.datetime(2022, 11, 15, 18, 0),\n",
+ " datetime.datetime(2022, 11, 15, 19, 0),\n",
+ " datetime.datetime(2022, 11, 15, 20, 0),\n",
+ " datetime.datetime(2022, 11, 15, 21, 0),\n",
+ " datetime.datetime(2022, 11, 15, 22, 0),\n",
+ " datetime.datetime(2022, 11, 15, 23, 0),\n",
+ " datetime.datetime(2022, 11, 16, 0, 0),\n",
+ " datetime.datetime(2022, 11, 16, 1, 0),\n",
+ " datetime.datetime(2022, 11, 16, 2, 0),\n",
+ " datetime.datetime(2022, 11, 16, 3, 0),\n",
+ " datetime.datetime(2022, 11, 16, 4, 0),\n",
+ " datetime.datetime(2022, 11, 16, 5, 0),\n",
+ " datetime.datetime(2022, 11, 16, 6, 0),\n",
+ " datetime.datetime(2022, 11, 16, 7, 0),\n",
+ " datetime.datetime(2022, 11, 16, 8, 0),\n",
+ " datetime.datetime(2022, 11, 16, 9, 0),\n",
+ " datetime.datetime(2022, 11, 16, 10, 0),\n",
+ " datetime.datetime(2022, 11, 16, 11, 0),\n",
+ " datetime.datetime(2022, 11, 16, 12, 0),\n",
+ " datetime.datetime(2022, 11, 16, 13, 0),\n",
+ " datetime.datetime(2022, 11, 16, 14, 0),\n",
+ " datetime.datetime(2022, 11, 16, 15, 0),\n",
+ " datetime.datetime(2022, 11, 16, 16, 0),\n",
+ " datetime.datetime(2022, 11, 16, 17, 0),\n",
+ " datetime.datetime(2022, 11, 16, 18, 0),\n",
+ " datetime.datetime(2022, 11, 16, 19, 0),\n",
+ " datetime.datetime(2022, 11, 16, 20, 0),\n",
+ " datetime.datetime(2022, 11, 16, 21, 0),\n",
+ " datetime.datetime(2022, 11, 16, 22, 0),\n",
+ " datetime.datetime(2022, 11, 16, 23, 0),\n",
+ " datetime.datetime(2022, 11, 17, 0, 0)]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy.time"
]
@@ -89,23 +131,73 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Level, Latitude, Longitude"
+ "### 1.2. Level, Latitude, Longitude"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(data=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
+ " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
+ " mask=False,\n",
+ " fill_value=999999,\n",
+ " dtype=int32),\n",
+ " 'dimensions': ('lm',),\n",
+ " 'units': '',\n",
+ " 'long_name': 'layer id'}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy.lev"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[16.350338, 16.43293 , 16.515146, ..., 16.515146, 16.43293 ,\n",
+ " 16.350338],\n",
+ " [16.527426, 16.610239, 16.692677, ..., 16.692677, 16.610243,\n",
+ " 16.527426],\n",
+ " [16.704472, 16.787508, 16.870167, ..., 16.870167, 16.78751 ,\n",
+ " 16.704472],\n",
+ " ...,\n",
+ " [58.32095 , 58.472683, 58.62431 , ..., 58.62431 , 58.472683,\n",
+ " 58.32095 ],\n",
+ " [58.426285, 58.5782 , 58.730026, ..., 58.730026, 58.5782 ,\n",
+ " 58.426285],\n",
+ " [58.530792, 58.6829 , 58.83492 , ..., 58.83492 , 58.682903,\n",
+ " 58.530792]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20,\n",
+ " dtype=float32),\n",
+ " 'dimensions': ('rlat', 'rlon'),\n",
+ " 'long_name': 'latitude',\n",
+ " 'units': 'degrees_north',\n",
+ " 'standard_name': 'latitude',\n",
+ " 'coordinates': 'lon lat'}"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy.lat"
]
@@ -114,36 +206,87 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Variables\n",
- "\n",
- "- List of variables in lazy mode: No data"
+ "### 1.3. Variables"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### In lazy mode"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['lmp', 'IM', 'JM', 'LM', 'IHRST', 'I_PAR_STA', 'J_PAR_STA', 'NPHS', 'NCLOD', 'NHEAT', 'NPREC', 'NRDLW', 'NRDSW', 'NSRFC', 'AVGMAXLEN', 'MDRMINout', 'MDRMAXout', 'MDIMINout', 'MDIMAXout', 'IDAT', 'DXH', 'SG1', 'SG2', 'DSG1', 'DSG2', 'SGML1', 'SGML2', 'SLDPTH', 'ISLTYP', 'IVGTYP', 'NCFRCV', 'NCFRST', 'FIS', 'GLAT', 'GLON', 'PD', 'VLAT', 'VLON', 'ACPREC', 'CUPREC', 'MIXHT', 'PBLH', 'RLWTOA', 'RSWIN', 'U10', 'USTAR', 'V10', 'RMOL', 'T2', 'relative_humidity_2m', 'T', 'U', 'V', 'SH2O', 'SMC', 'STC', 'AERO_ACPREC', 'AERO_CUPREC', 'AERO_DEPDRY', 'AERO_OPT_R', 'DRE_SW_TOA', 'DRE_SW_SFC', 'DRE_LW_TOA', 'DRE_LW_SFC', 'ENG_SW_SFC', 'ADRYDEP', 'WETDEP', 'PH_NO2', 'HSUM', 'POLR', 'aerosol_optical_depth_dim', 'aerosol_optical_depth', 'satellite_AOD_dim', 'satellite_AOD', 'aerosol_loading_dim', 'aerosol_loading', 'clear_sky_AOD_dim', 'clear_sky_AOD', 'layer_thickness', 'mid_layer_pressure', 'interface_pressure', 'relative_humidity', 'mid_layer_height', 'mid_layer_height_agl', 'air_density', 'dry_pm10_mass', 'dry_pm2p5_mass', 'QC', 'QR', 'QS', 'QG', 'aero_dust_001', 'aero_dust_002', 'aero_dust_003', 'aero_dust_004', 'aero_dust_005', 'aero_dust_006', 'aero_dust_007', 'aero_dust_008', 'aero_ssa_001', 'aero_ssa_002', 'aero_ssa_003', 'aero_ssa_004', 'aero_ssa_005', 'aero_ssa_006', 'aero_ssa_007', 'aero_ssa_008', 'aero_om_001', 'aero_om_002', 'aero_om_003', 'aero_om_004', 'aero_om_005', 'aero_om_006', 'aero_bc_001', 'aero_bc_002', 'aero_so4_001', 'aero_no3_001', 'aero_no3_002', 'aero_no3_003', 'aero_nh4_001', 'aero_unsp_001', 'aero_unsp_002', 'aero_unsp_003', 'aero_unsp_004', 'aero_unsp_005', 'NO2', 'NO', 'O3', 'NO3', 'N2O5', 'HNO3', 'HONO', 'PNA', 'H2O2', 'NTR', 'ROOH', 'FORM', 'ALD2', 'ALDX', 'PAR', 'CO', 'MEPX', 'MEOH', 'FACD', 'PAN', 'PACD', 'AACD', 'PANX', 'OLE', 'ETH', 'IOLE', 'TOL', 'CRES', 'OPEN', 'MGLY', 'XYL', 'ISOP', 'ISPD', 'TERP', 'SO2', 'SULF', 'ETOH', 'ETHA', 'CL2', 'HOCL', 'FMCL', 'HCL', 'BENZENE', 'SESQ', 'NH3', 'DMS', 'SOAP_I', 'SOAP_T', 'SOAP_F', 'SOAP_A', 'O', 'O1D', 'OH', 'HO2', 'XO2', 'XO2N', 'MEO2', 'HCO3', 'C2O3', 'CXO3', 'ROR', 'TO2', 'TOLRO2', 'CRO', 'XYLRO2', 'ISOPRXN', 'TRPRXN', 'SULRXN', 'CL', 'CLO', 'TOLNRXN', 'TOLHRXN', 'XYLNRXN', 'XYLHRXN', 'BENZRO2', 'BNZNRXN', 'BNZHRXN', 'SESQRXN', 'aerosol_extinction_dim', 'aerosol_extinction_DUST_1', 'aerosol_extinction_DUST_2', 'aerosol_extinction_DUST_3', 'aerosol_extinction_DUST_4', 'aerosol_extinction_DUST_5', 'aerosol_extinction_DUST_6', 'aerosol_extinction_DUST_7', 'aerosol_extinction_DUST_8', 'aerosol_extinction_SALT_total', 'aerosol_extinction_OM_total', 'aerosol_extinction_BC_total', 'aerosol_extinction_SO4_total', 'aerosol_extinction_NO3_total', 'aerosol_extinction_NH4_total', 'aerosol_extinction_UNSPC_1', 'aerosol_extinction_UNSPC_2', 'aerosol_extinction_UNSPC_3', 'aerosol_extinction_UNSPC_4', 'aerosol_extinction_UNSPC_5'])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"nessy.variables.keys()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'O3': {'data': None,\n",
+ " 'dimensions': ('time', 'lm', 'rlat', 'rlon'),\n",
+ " 'long_name': 'TRACERS_044',\n",
+ " 'units': 'unknown',\n",
+ " 'standard_name': 'TRACERS_044',\n",
+ " 'coordinates': 'lon lat',\n",
+ " 'grid_mapping': 'rotated_pole'}}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Selecting only one variable and descarting the rest.\n",
"nessy.keep_vars('O3')\n",
"nessy.variables"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### After load"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Loading O3 var (1/1)\n",
+ "Rank 000: Loaded O3 var ((37, 24, 271, 351))\n",
+ "CPU times: user 311 ms, sys: 1.5 s, total: 1.81 s\n",
+ "Wall time: 11.3 s\n"
+ ]
+ }
+ ],
"source": [
"# Loading variable data from NetCDF file\n",
"%time nessy.load()"
@@ -151,19 +294,58 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(37, 24, 271, 351)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "print(nessy.variables['O3']['data'].shape)\n",
- "print(nessy.variables['O3']['dimensions'])"
+ "nessy.variables['O3']['data'].shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('time', 'lm', 'rlat', 'rlon')"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy.variables['O3']['dimensions']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 106 ms, sys: 104 ms, total: 210 ms\n",
+ "Wall time: 3.03 s\n"
+ ]
+ }
+ ],
"source": [
"# Writing NetCDF\n",
"%time nessy.to_netcdf('o3_test.nc')"
@@ -173,49 +355,149 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Calculate daily statistics"
+ "## 2. Calculate daily statistics"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 74.8 ms, sys: 21 ms, total: 95.8 ms\n",
+ "Wall time: 260 ms\n"
+ ]
+ }
+ ],
"source": [
"%time nessy.daily_statistic(op=\"mean\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2, 24, 271, 351)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "print(nessy.variables['O3']['data'].shape)\n",
- "print(nessy.variables['O3']['dimensions'])"
+ "nessy.variables['O3']['data'].shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('time', 'lm', 'rlat', 'rlon')"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy.variables['O3']['dimensions']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 18.2 ms, sys: 8.24 ms, total: 26.4 ms\n",
+ "Wall time: 402 ms\n"
+ ]
+ }
+ ],
"source": [
"%time nessy.to_netcdf('o3_daily_mean_test.nc')"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'time: mean (interval: 1hr)'"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# See metadata\n",
+ "nessy.variables['O3']['cell_methods']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[datetime.datetime(2022, 11, 15, 0, 0), datetime.datetime(2022, 11, 16, 0, 0)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# See time\n",
+ "nessy.time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[[datetime.datetime(2022, 11, 15, 12, 0),\n",
+ " datetime.datetime(2022, 11, 15, 23, 0)],\n",
+ " [datetime.datetime(2022, 11, 16, 0, 0),\n",
+ " datetime.datetime(2022, 11, 16, 23, 0)]]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "print(\"Metadata 'cell_methods':\", nessy.variables['O3']['cell_methods'])\n",
- "\n",
- "print(\"Time:\", nessy.time)\n",
- "print(\"Time bounds:\", len(nessy.time_bnds))\n",
- "\n",
- "print(nessy.time_bnds[0])"
+ "# See bounds\n",
+ "nessy.time_bnds"
]
}
],
diff --git a/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb b/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb
index c8fc331d3062aba6cc52874ff61c60f64c957794..0fce0381e7f94c5a876685d748a252831a2aad37 100644
--- a/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb
+++ b/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# How to interpolate vertically"
+ "# Vertical interpolation"
]
},
{
@@ -13,7 +13,8 @@
"metadata": {},
"outputs": [],
"source": [
- "from nes import *"
+ "from nes import *\n",
+ "from datetime import datetime"
]
},
{
@@ -36,8 +37,9 @@
"metadata": {},
"outputs": [],
"source": [
- "# TODO: Change path\n",
- "source_path = '/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc'"
+ "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n",
+ "# Rotated grid from MONARCH\n",
+ "source_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'"
]
},
{
@@ -46,35 +48,61 @@
"metadata": {},
"outputs": [
{
- "ename": "FileNotFoundError",
- "evalue": "/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msource_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msource_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m73\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msource_grid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/load_nes.py\u001b[0m in \u001b[0;36mopen_netcdf\u001b[0;34m(path, comm, xarray, info, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, balanced)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mxarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mFileNotFoundError\u001b[0m: /gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc"
- ]
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "source_grid = open_netcdf(path=source_path, info=True, avoid_first_hours=12, avoid_last_hours=73)\n",
+ "source_grid = open_netcdf(path=source_path, info=True)\n",
"source_grid"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
+ "source": [
+ "source_grid.sel(time_min=datetime(year=2022, month=11, day=16, hour=0), \n",
+ " time_max=datetime(year=2022, month=11, day=16, hour=0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(data=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
+ " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
+ " mask=False,\n",
+ " fill_value=999999,\n",
+ " dtype=int32),\n",
+ " 'dimensions': ('lm',),\n",
+ " 'units': '',\n",
+ " 'long_name': 'layer id'}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"source_grid.lev"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -83,9 +111,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Loading mid_layer_height_agl var (1/2)\n",
+ "Rank 000: Loaded mid_layer_height_agl var ((1, 24, 271, 351))\n",
+ "Rank 000: Loading O3 var (2/2)\n",
+ "Rank 000: Loaded O3 var ((1, 24, 271, 351))\n"
+ ]
+ }
+ ],
"source": [
"source_grid.load()"
]
@@ -99,7 +138,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -108,7 +147,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -117,27 +156,72 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\tO3 vertical methods\n",
+ "\t\tO3 time step 2022-11-16 00:00:00 (1/1))\n"
+ ]
+ }
+ ],
"source": [
- "interpolated_source_grid = source_grid.interpolate_vertical(level_list, info=True, kind='linear', extrapolate=None)"
+ "interpolated_source_grid = source_grid.interpolate_vertical(level_list, info=True, \n",
+ " kind='linear', extrapolate=None)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': [0.0,\n",
+ " 50.0,\n",
+ " 100.0,\n",
+ " 250.0,\n",
+ " 500.0,\n",
+ " 750.0,\n",
+ " 1000.0,\n",
+ " 2000.0,\n",
+ " 3000.0,\n",
+ " 5000.0],\n",
+ " 'long_name': 'Mid-layer height above ground level',\n",
+ " 'standard_name': 'height_agl',\n",
+ " 'units': 'm',\n",
+ " 'coordinates': 'lon lat'}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid.lev"
]
@@ -158,28 +242,58 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "source_data = open_netcdf(path=source_path, info=True, avoid_first_hours=12, avoid_last_hours=73)\n",
+ "source_data = open_netcdf(path=source_path, info=True)\n",
"source_data"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
- "source_data.keep_vars(['O3'])"
+ "source_data.sel(time_min=datetime(year=2022, month=11, day=16, hour=0), \n",
+ " time_max=datetime(year=2022, month=11, day=16, hour=0))"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
+ "source": [
+ "source_data.keep_vars('O3')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Loading O3 var (1/1)\n",
+ "Rank 000: Loaded O3 var ((1, 24, 271, 351))\n"
+ ]
+ }
+ ],
"source": [
"source_data.load()"
]
@@ -193,28 +307,58 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "source_levels = open_netcdf(path=source_path, info=True, avoid_first_hours=12, avoid_last_hours=73)\n",
+ "source_levels = open_netcdf(path=source_path, info=True)\n",
"source_levels"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
- "source_levels.keep_vars(['mid_layer_height_agl'])"
+ "source_levels.sel(time_min=datetime(year=2022, month=11, day=16, hour=0), \n",
+ " time_max=datetime(year=2022, month=11, day=16, hour=0))"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
+ "source": [
+ "source_levels.keep_vars(['mid_layer_height_agl'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Loading mid_layer_height_agl var (1/1)\n",
+ "Rank 000: Loaded mid_layer_height_agl var ((1, 24, 271, 351))\n"
+ ]
+ }
+ ],
"source": [
"source_levels.load()"
]
@@ -228,9 +372,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\tO3 vertical methods\n",
+ "\t\tO3 time step 2022-11-16 00:00:00 (1/1))\n"
+ ]
+ }
+ ],
"source": [
"interpolated_source_grid = source_data.interpolate_vertical(level_list, new_src_vertical=source_levels,\n",
" info=True, kind='linear', extrapolate=None)"
@@ -238,18 +391,53 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': [0.0,\n",
+ " 50.0,\n",
+ " 100.0,\n",
+ " 250.0,\n",
+ " 500.0,\n",
+ " 750.0,\n",
+ " 1000.0,\n",
+ " 2000.0,\n",
+ " 3000.0,\n",
+ " 5000.0],\n",
+ " 'long_name': 'Mid-layer height above ground level',\n",
+ " 'standard_name': 'height_agl',\n",
+ " 'units': 'm',\n",
+ " 'coordinates': 'lon lat'}"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid.lev"
]
@@ -272,6 +460,11 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
}
},
"nbformat": 4,
diff --git a/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb b/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb
index bebf7989d5a4ea3514d10f63d8519b92c69c091a..59189fa9bbdda796a72bcfac9d8f874b74faab20 100644
--- a/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb
+++ b/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# How to interpolate horizontally"
+ "# Horizontal interpolation"
]
},
{
@@ -29,7 +29,9 @@
"metadata": {},
"outputs": [],
"source": [
- "source_path = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc'"
+ "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n",
+ "# Rotated grid from MONARCH\n",
+ "source_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'"
]
},
{
@@ -40,7 +42,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 3,
@@ -62,26 +64,27 @@
"data": {
"text/plain": [
"{'data': masked_array(\n",
- " data=[[16.35033798, 16.43292999, 16.51514626, ..., 16.51514626,\n",
- " 16.43292999, 16.35033798],\n",
- " [16.52742577, 16.61023903, 16.69267654, ..., 16.69267654,\n",
- " 16.61024284, 16.52742577],\n",
- " [16.70447159, 16.78750801, 16.87016678, ..., 16.87016678,\n",
- " 16.78750992, 16.70447159],\n",
+ " data=[[16.350338, 16.43293 , 16.515146, ..., 16.515146, 16.43293 ,\n",
+ " 16.350338],\n",
+ " [16.527426, 16.610239, 16.692677, ..., 16.692677, 16.610243,\n",
+ " 16.527426],\n",
+ " [16.704472, 16.787508, 16.870167, ..., 16.870167, 16.78751 ,\n",
+ " 16.704472],\n",
" ...,\n",
- " [58.32094955, 58.47268295, 58.62430954, ..., 58.62430954,\n",
- " 58.47268295, 58.32094955],\n",
- " [58.42628479, 58.57820129, 58.73002625, ..., 58.73002625,\n",
- " 58.57820129, 58.42628479],\n",
- " [58.53079224, 58.68289948, 58.83491898, ..., 58.83491898,\n",
- " 58.68290329, 58.53079224]],\n",
+ " [58.32095 , 58.472683, 58.62431 , ..., 58.62431 , 58.472683,\n",
+ " 58.32095 ],\n",
+ " [58.426285, 58.5782 , 58.730026, ..., 58.730026, 58.5782 ,\n",
+ " 58.426285],\n",
+ " [58.530792, 58.6829 , 58.83492 , ..., 58.83492 , 58.682903,\n",
+ " 58.530792]],\n",
" mask=False,\n",
- " fill_value=1e+20),\n",
+ " fill_value=1e+20,\n",
+ " dtype=float32),\n",
" 'dimensions': ('rlat', 'rlon'),\n",
+ " 'long_name': 'latitude',\n",
" 'units': 'degrees_north',\n",
- " 'axis': 'Y',\n",
- " 'long_name': 'latitude coordinate',\n",
- " 'standard_name': 'latitude'}"
+ " 'standard_name': 'latitude',\n",
+ " 'coordinates': 'lon lat'}"
]
},
"execution_count": 4,
@@ -102,26 +105,27 @@
"data": {
"text/plain": [
"{'data': masked_array(\n",
- " data=[[-22.18126488, -22.01667213, -21.85179901, ..., 41.8517952 ,\n",
- " 42.01666641, 42.18125916],\n",
- " [-22.27817917, -22.11318588, -21.94790459, ..., 41.94789886,\n",
- " 42.11317444, 42.27817154],\n",
- " [-22.37526703, -22.2098732 , -22.04418945, ..., 42.04418564,\n",
- " 42.2098732 , 42.37526321],\n",
+ " data=[[-22.181265, -22.016672, -21.851799, ..., 41.851795, 42.016666,\n",
+ " 42.18126 ],\n",
+ " [-22.27818 , -22.113186, -21.947905, ..., 41.9479 , 42.113174,\n",
+ " 42.27817 ],\n",
+ " [-22.375267, -22.209873, -22.04419 , ..., 42.044186, 42.209873,\n",
+ " 42.375263],\n",
" ...,\n",
- " [-67.57766724, -67.39706421, -67.21534729, ..., 87.21533966,\n",
- " 87.39705658, 87.57765961],\n",
- " [-67.90187836, -67.72247314, -67.54193878, ..., 87.54193878,\n",
- " 87.72245789, 87.90187073],\n",
- " [-68.22803497, -68.04981995, -67.87051392, ..., 87.87050629,\n",
- " 88.04981995, 88.22803497]],\n",
+ " [-67.57767 , -67.397064, -67.21535 , ..., 87.21534 , 87.39706 ,\n",
+ " 87.57766 ],\n",
+ " [-67.90188 , -67.72247 , -67.54194 , ..., 87.54194 , 87.72246 ,\n",
+ " 87.90187 ],\n",
+ " [-68.228035, -68.04982 , -67.870514, ..., 87.87051 , 88.04982 ,\n",
+ " 88.228035]],\n",
" mask=False,\n",
- " fill_value=1e+20),\n",
+ " fill_value=1e+20,\n",
+ " dtype=float32),\n",
" 'dimensions': ('rlat', 'rlon'),\n",
+ " 'long_name': 'longitude',\n",
" 'units': 'degrees_east',\n",
- " 'axis': 'X',\n",
- " 'long_name': 'longitude coordinate',\n",
- " 'standard_name': 'longitude'}"
+ " 'standard_name': 'longitude',\n",
+ " 'coordinates': 'lon lat'}"
]
},
"execution_count": 5,
@@ -137,13 +141,22 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {},
+ "outputs": [],
+ "source": [
+ "source_grid.keep_vars('O3')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Rank 000: Loading O3_all var (1/1)\n",
- "Rank 000: Loaded O3_all var ((1, 24, 271, 351))\n"
+ "Rank 000: Loading O3 var (1/1)\n",
+ "Rank 000: Loaded O3 var ((37, 24, 271, 351))\n"
]
}
],
@@ -174,43 +187,104 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
- "ename": "FileNotFoundError",
- "evalue": "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# TODO: Change path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdst_grid_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdst_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdst_grid_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mdst_grid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/load_nes.py\u001b[0m in \u001b[0;36mopen_netcdf\u001b[0;34m(path, comm, xarray, info, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, balanced)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mxarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mFileNotFoundError\u001b[0m: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc"
- ]
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "# TODO: Change path\n",
- "dst_grid_path = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc'\n",
+ "# Original path: /esarchive/exp/snes/a5g1/ip/daily_max/sconco3/sconco3_2022111500.nc\n",
+ "# LCC grid with a coverage over the Iberian Peninsula (4x4km)\n",
+ "dst_grid_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/sconco3_2022111500.nc'\n",
"dst_grid = open_netcdf(path=dst_grid_path, info=True)\n",
"dst_grid"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[32.51078415, 32.51420212, 32.51760101, ..., 32.54065323,\n",
+ " 32.53738403, 32.53408432],\n",
+ " [32.54635239, 32.54976654, 32.55315399, ..., 32.57624817,\n",
+ " 32.57295609, 32.56965637],\n",
+ " [32.58191681, 32.58533096, 32.58874512, ..., 32.61181641,\n",
+ " 32.60853195, 32.60523224],\n",
+ " ...,\n",
+ " [46.58963013, 46.59383011, 46.59801102, ..., 46.62640381,\n",
+ " 46.62236404, 46.61830521],\n",
+ " [46.62516785, 46.62936783, 46.63354874, ..., 46.66195679,\n",
+ " 46.65791702, 46.65385818],\n",
+ " [46.66069794, 46.66490173, 46.66908264, ..., 46.69750214,\n",
+ " 46.69345856, 46.6893959 ]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('y', 'x'),\n",
+ " 'units': 'degrees_north',\n",
+ " 'axis': 'Y',\n",
+ " 'long_name': 'latitude coordinate',\n",
+ " 'standard_name': 'latitude'}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"dst_grid.lat"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[-11.54882812, -11.50665283, -11.46447754, ..., 5.17233276,\n",
+ " 5.21453857, 5.25671387],\n",
+ " [-11.55288696, -11.51068115, -11.46847534, ..., 5.1762085 ,\n",
+ " 5.21844482, 5.26065063],\n",
+ " [-11.5569458 , -11.51473999, -11.47250366, ..., 5.18008423,\n",
+ " 5.22235107, 5.2645874 ],\n",
+ " ...,\n",
+ " [-13.51443481, -13.46273804, -13.41101074, ..., 7.05273438,\n",
+ " 7.10449219, 7.15625 ],\n",
+ " [-13.52056885, -13.46884155, -13.41708374, ..., 7.05859375,\n",
+ " 7.1104126 , 7.16220093],\n",
+ " [-13.5267334 , -13.47494507, -13.42315674, ..., 7.06448364,\n",
+ " 7.11630249, 7.16812134]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('y', 'x'),\n",
+ " 'units': 'degrees_east',\n",
+ " 'axis': 'X',\n",
+ " 'long_name': 'longitude coordinate',\n",
+ " 'standard_name': 'longitude'}"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"dst_grid.lon"
]
@@ -224,9 +298,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating Weight Matrix\n",
+ "Weight Matrix done!\n",
+ "Applying weights\n",
+ "\tO3 horizontal methods\n",
+ "Formatting\n"
+ ]
+ }
+ ],
"source": [
"interpolated_source_grid = source_grid.interpolate_horizontal(dst_grid, weight_matrix_path=None, \n",
" kind='NearestNeighbour', n_neighbours=4,\n",
@@ -235,27 +321,100 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[32.51078415, 32.51420212, 32.51760101, ..., 32.54065323,\n",
+ " 32.53738403, 32.53408432],\n",
+ " [32.54635239, 32.54976654, 32.55315399, ..., 32.57624817,\n",
+ " 32.57295609, 32.56965637],\n",
+ " [32.58191681, 32.58533096, 32.58874512, ..., 32.61181641,\n",
+ " 32.60853195, 32.60523224],\n",
+ " ...,\n",
+ " [46.58963013, 46.59383011, 46.59801102, ..., 46.62640381,\n",
+ " 46.62236404, 46.61830521],\n",
+ " [46.62516785, 46.62936783, 46.63354874, ..., 46.66195679,\n",
+ " 46.65791702, 46.65385818],\n",
+ " [46.66069794, 46.66490173, 46.66908264, ..., 46.69750214,\n",
+ " 46.69345856, 46.6893959 ]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('y', 'x'),\n",
+ " 'units': 'degrees_north',\n",
+ " 'axis': 'Y',\n",
+ " 'long_name': 'latitude coordinate',\n",
+ " 'standard_name': 'latitude'}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid.lat"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[-11.54882812, -11.50665283, -11.46447754, ..., 5.17233276,\n",
+ " 5.21453857, 5.25671387],\n",
+ " [-11.55288696, -11.51068115, -11.46847534, ..., 5.1762085 ,\n",
+ " 5.21844482, 5.26065063],\n",
+ " [-11.5569458 , -11.51473999, -11.47250366, ..., 5.18008423,\n",
+ " 5.22235107, 5.2645874 ],\n",
+ " ...,\n",
+ " [-13.51443481, -13.46273804, -13.41101074, ..., 7.05273438,\n",
+ " 7.10449219, 7.15625 ],\n",
+ " [-13.52056885, -13.46884155, -13.41708374, ..., 7.05859375,\n",
+ " 7.1104126 , 7.16220093],\n",
+ " [-13.5267334 , -13.47494507, -13.42315674, ..., 7.06448364,\n",
+ " 7.11630249, 7.16812134]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20),\n",
+ " 'dimensions': ('y', 'x'),\n",
+ " 'units': 'degrees_east',\n",
+ " 'axis': 'X',\n",
+ " 'long_name': 'longitude coordinate',\n",
+ " 'standard_name': 'longitude'}"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid.lon"
]
@@ -276,7 +435,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -293,18 +452,42 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([40.1, 40.3, 40.5, 40.7, 40.9, 41.1, 41.3, 41.5, 41.7, 41.9, 42.1,\n",
+ " 42.3, 42.5, 42.7, 42.9, 43.1, 43.3, 43.5, 43.7, 43.9])}"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"dst_grid.lat"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5, 1.7, 1.9, 2.1, 2.3, 2.5,\n",
+ " 2.7, 2.9, 3.1, 3.3, 3.5, 3.7, 3.9])}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"dst_grid.lon"
]
@@ -318,9 +501,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating Weight Matrix\n",
+ "Weight Matrix done!\n",
+ "Applying weights\n",
+ "\tO3 horizontal methods\n",
+ "Formatting\n"
+ ]
+ }
+ ],
"source": [
"interpolated_source_grid = source_grid.interpolate_horizontal(dst_grid, weight_matrix_path=None, \n",
" kind='NearestNeighbour', n_neighbours=4,\n",
@@ -329,27 +524,62 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([40.1, 40.3, 40.5, 40.7, 40.9, 41.1, 41.3, 41.5, 41.7, 41.9, 42.1,\n",
+ " 42.3, 42.5, 42.7, 42.9, 43.1, 43.3, 43.5, 43.7, 43.9])}"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid.lat"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5, 1.7, 1.9, 2.1, 2.3, 2.5,\n",
+ " 2.7, 2.9, 3.1, 3.3, 3.5, 3.7, 3.9])}"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"interpolated_source_grid.lon"
]
@@ -372,6 +602,11 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
}
},
"nbformat": 4,
diff --git a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..48b02162d5e4e8167290f02a4975d774d920f514
--- /dev/null
+++ b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Horizontal conservative interpolation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nes import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Read data to interpolate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n",
+ "# Rotated grid from MONARCH (NAMEE)\n",
+ "source_path = \"/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "source_grid = open_netcdf(path=source_path, info=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[16.350338, 16.43293 , 16.515146, ..., 16.515146, 16.43293 ,\n",
+ " 16.350338],\n",
+ " [16.527426, 16.610239, 16.692677, ..., 16.692677, 16.610243,\n",
+ " 16.527426],\n",
+ " [16.704472, 16.787508, 16.870167, ..., 16.870167, 16.78751 ,\n",
+ " 16.704472],\n",
+ " ...,\n",
+ " [58.32095 , 58.472683, 58.62431 , ..., 58.62431 , 58.472683,\n",
+ " 58.32095 ],\n",
+ " [58.426285, 58.5782 , 58.730026, ..., 58.730026, 58.5782 ,\n",
+ " 58.426285],\n",
+ " [58.530792, 58.6829 , 58.83492 , ..., 58.83492 , 58.682903,\n",
+ " 58.530792]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20,\n",
+ " dtype=float32),\n",
+ " 'dimensions': ('rlat', 'rlon'),\n",
+ " 'long_name': 'latitude',\n",
+ " 'units': 'degrees_north',\n",
+ " 'standard_name': 'latitude',\n",
+ " 'coordinates': 'lon lat'}"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "source_grid.lat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[-22.181265, -22.016672, -21.851799, ..., 41.851795, 42.016666,\n",
+ " 42.18126 ],\n",
+ " [-22.27818 , -22.113186, -21.947905, ..., 41.9479 , 42.113174,\n",
+ " 42.27817 ],\n",
+ " [-22.375267, -22.209873, -22.04419 , ..., 42.044186, 42.209873,\n",
+ " 42.375263],\n",
+ " ...,\n",
+ " [-67.57767 , -67.397064, -67.21535 , ..., 87.21534 , 87.39706 ,\n",
+ " 87.57766 ],\n",
+ " [-67.90188 , -67.72247 , -67.54194 , ..., 87.54194 , 87.72246 ,\n",
+ " 87.90187 ],\n",
+ " [-68.228035, -68.04982 , -67.870514, ..., 87.87051 , 88.04982 ,\n",
+ " 88.228035]],\n",
+ " mask=False,\n",
+ " fill_value=1e+20,\n",
+ " dtype=float32),\n",
+ " 'dimensions': ('rlat', 'rlon'),\n",
+ " 'long_name': 'longitude',\n",
+ " 'units': 'degrees_east',\n",
+ " 'standard_name': 'longitude',\n",
+ " 'coordinates': 'lon lat'}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "source_grid.lon"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Loading O3 var (1/1)\n",
+ "Rank 000: Loaded O3 var ((37, 24, 271, 351))\n"
+ ]
+ }
+ ],
+ "source": [
+ "source_grid.keep_vars('O3')\n",
+ "source_grid.load()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Create destination grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lat_1 = 37\n",
+ "lat_2 = 43\n",
+ "lon_0 = -3\n",
+ "lat_0 = 40\n",
+ "nx = 397\n",
+ "ny = 397\n",
+ "inc_x = 4000\n",
+ "inc_y = 4000\n",
+ "x_0 = -807847.688\n",
+ "y_0 = -797137.125\n",
+ "dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,\n",
+ " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, times=source_grid.get_full_times())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Interpolation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.1. Without creating weight matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([[32.49460816, 32.49803615, 32.50144726, ..., 32.52460207,\n",
+ " 32.52130788, 32.51799681],\n",
+ " [32.53024662, 32.5336765 , 32.5370895 , ..., 32.5602571 ,\n",
+ " 32.55696109, 32.55364819],\n",
+ " [32.5658877 , 32.56931947, 32.57273435, ..., 32.59591474,\n",
+ " 32.59261692, 32.58930218],\n",
+ " ...,\n",
+ " [46.60231473, 46.60653579, 46.6107361 , ..., 46.63924881,\n",
+ " 46.63519229, 46.63111499],\n",
+ " [46.63791957, 46.64214277, 46.6463452 , ..., 46.67487234,\n",
+ " 46.67081377, 46.66673441],\n",
+ " [46.67352141, 46.67774674, 46.68195131, ..., 46.71049289,\n",
+ " 46.70643226, 46.70235083]])}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interp_nes.lat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([[-11.56484687, -11.52259289, -11.48033507, ..., 5.18764453,\n",
+ " 5.22992847, 5.27220869],\n",
+ " [-11.56892309, -11.52664925, -11.48437156, ..., 5.1915433 ,\n",
+ " 5.23384715, 5.27614727],\n",
+ " [-11.57300318, -11.53070946, -11.48841188, ..., 5.19544578,\n",
+ " 5.23776955, 5.2800896 ],\n",
+ " ...,\n",
+ " [-13.53865445, -13.48682654, -13.4349915 , ..., 7.07590089,\n",
+ " 7.12778439, 7.17966098],\n",
+ " [-13.54481681, -13.49295916, -13.44109437, ..., 7.08179741,\n",
+ " 7.13371074, 7.18561716],\n",
+ " [-13.55098635, -13.49909893, -13.44720434, ..., 7.0877008 ,\n",
+ " 7.139644 , 7.19158028]])}"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interp_nes.lon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2. Creating weight matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating Weight Matrix\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2041: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
+ " time_var.units, time_var.calendar)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Weight Matrix done!\n"
+ ]
+ }
+ ],
+ "source": [
+ "wm_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True,\n",
+ " weight_matrix_path=\"CONS_WM_NAMEE_to_IP.nc\", only_create_wm=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', wm=wm_nes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([[32.49460816, 32.49803615, 32.50144726, ..., 32.52460207,\n",
+ " 32.52130788, 32.51799681],\n",
+ " [32.53024662, 32.5336765 , 32.5370895 , ..., 32.5602571 ,\n",
+ " 32.55696109, 32.55364819],\n",
+ " [32.5658877 , 32.56931947, 32.57273435, ..., 32.59591474,\n",
+ " 32.59261692, 32.58930218],\n",
+ " ...,\n",
+ " [46.60231473, 46.60653579, 46.6107361 , ..., 46.63924881,\n",
+ " 46.63519229, 46.63111499],\n",
+ " [46.63791957, 46.64214277, 46.6463452 , ..., 46.67487234,\n",
+ " 46.67081377, 46.66673441],\n",
+ " [46.67352141, 46.67774674, 46.68195131, ..., 46.71049289,\n",
+ " 46.70643226, 46.70235083]])}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interp_nes.lat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([[-11.56484687, -11.52259289, -11.48033507, ..., 5.18764453,\n",
+ " 5.22992847, 5.27220869],\n",
+ " [-11.56892309, -11.52664925, -11.48437156, ..., 5.1915433 ,\n",
+ " 5.23384715, 5.27614727],\n",
+ " [-11.57300318, -11.53070946, -11.48841188, ..., 5.19544578,\n",
+ " 5.23776955, 5.2800896 ],\n",
+ " ...,\n",
+ " [-13.53865445, -13.48682654, -13.4349915 , ..., 7.07590089,\n",
+ " 7.12778439, 7.17966098],\n",
+ " [-13.54481681, -13.49295916, -13.44109437, ..., 7.08179741,\n",
+ " 7.13371074, 7.18561716],\n",
+ " [-13.55098635, -13.49909893, -13.44720434, ..., 7.0877008 ,\n",
+ " 7.139644 , 7.19158028]])}"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interp_nes.lon"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/4.Interpolation/4.3.Providentia_Interpolation.ipynb b/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb
similarity index 95%
rename from tutorials/4.Interpolation/4.3.Providentia_Interpolation.ipynb
rename to tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb
index 307b81ea599a825ae25b1ac09449b917ad57a2e3..3b60ef0036d9502820be7c26e1f4deba4c9f5571 100644
--- a/tutorials/4.Interpolation/4.3.Providentia_Interpolation.ipynb
+++ b/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb
@@ -13,8 +13,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from nes import *\n",
- "import xarray as xr"
+ "from nes import *"
]
},
{
@@ -30,7 +29,9 @@
"metadata": {},
"outputs": [],
"source": [
- "source_path = '/esarchive/recon/ecmwf/cams61/cams61_chimere_ph2/eu/hourly/sconco3/sconco3_201804.nc'"
+ "# Original path: /esarchive/recon/ecmwf/cams61/cams61_chimere_ph2/eu/hourly/sconco3/sconco3_201804.nc\n",
+ "# Regular lat-lon grid from CAMS \n",
+ "source_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_sconco3_201804.nc'"
]
},
{
@@ -41,7 +42,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 3,
@@ -225,21 +226,15 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2500: UserWarning: No time has been specified. The first one will be selected.\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2602: UserWarning: No vertical level has been specified. The first one will be selected.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2612: UserWarning: No time has been specified. The first one will be selected.\n",
" warnings.warn(msg)\n"
]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rank 000: Loading sconco3 var (1/1)\n",
- "Rank 000: Loaded sconco3 var ((1, 1, 211, 351))\n"
- ]
}
],
"source": [
- "source_grid.to_shapefile('model', var_list=['sconco3'])"
+ "source_grid.to_shapefile(path='model_shp', var_list=['sconco3'])"
]
},
{
@@ -381,7 +376,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 10,
@@ -390,7 +385,9 @@
}
],
"source": [
- "dst_grid_path = '/gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.4/hourly/sconco3/sconco3_201804.nc'\n",
+ "# Original path: /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc\n",
+ "# Points grid from EBAS network\n",
+ "dst_grid_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/obs_sconco3_201804.nc'\n",
"dst_grid = open_netcdf(path=dst_grid_path, info=True)\n",
"dst_grid"
]
@@ -681,7 +678,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\tsconco3 horizontal interpolation\n"
+ "Creating Weight Matrix\n",
+ "Weight Matrix done!\n",
+ "Applying weights\n",
+ "\tsconco3 horizontal methods\n",
+ "Formatting\n"
]
}
],
@@ -836,25 +837,19 @@
"execution_count": 17,
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rank 000: Loading sconco3 var (1/1)\n",
- "Rank 000: Loaded sconco3 var ((168, 1))\n"
- ]
- },
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2500: UserWarning: No time has been specified. The first one will be selected.\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2602: UserWarning: No vertical level has been specified. The first one will be selected.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2612: UserWarning: No time has been specified. The first one will be selected.\n",
" warnings.warn(msg)\n"
]
}
],
"source": [
- "interpolated_source_grid.to_shapefile('interpolated_points', var_list=['sconco3'])"
+ "interpolated_source_grid.to_shapefile(path='interpolated_points_shp', var_list=['sconco3'])"
]
},
{
@@ -906,17 +901,17 @@
" \n",
" 2 \n",
" POINT (16.76667 47.76667) \n",
- " 39.719296 \n",
+ " 39.719934 \n",
" \n",
" \n",
" 3 \n",
" POINT (12.97222 46.67778) \n",
- " 33.808729 \n",
+ " 33.805122 \n",
" \n",
" \n",
" 4 \n",
" POINT (15.94222 48.72111) \n",
- " 35.400927 \n",
+ " 35.401011 \n",
" \n",
" \n",
" ... \n",
@@ -926,22 +921,22 @@
" \n",
" 163 \n",
" POINT (-155.57616 19.53623) \n",
- " 50.547115 \n",
+ " 50.547116 \n",
" \n",
" \n",
" 164 \n",
" POINT (-24.80000 -89.99695) \n",
- " 53.328123 \n",
+ " 53.660154 \n",
" \n",
" \n",
" 165 \n",
" POINT (-124.15100 41.05410) \n",
- " 50.316593 \n",
+ " 50.316594 \n",
" \n",
" \n",
" 166 \n",
" POINT (103.51570 21.57310) \n",
- " 38.617185 \n",
+ " 36.253923 \n",
" \n",
" \n",
" 167 \n",
@@ -958,14 +953,14 @@
"FID \n",
"0 POINT (-56.62478 -64.24006) 53.660180\n",
"1 POINT (-68.31069 -54.84846) 53.726555\n",
- "2 POINT (16.76667 47.76667) 39.719296\n",
- "3 POINT (12.97222 46.67778) 33.808729\n",
- "4 POINT (15.94222 48.72111) 35.400927\n",
+ "2 POINT (16.76667 47.76667) 39.719934\n",
+ "3 POINT (12.97222 46.67778) 33.805122\n",
+ "4 POINT (15.94222 48.72111) 35.401011\n",
".. ... ...\n",
- "163 POINT (-155.57616 19.53623) 50.547115\n",
- "164 POINT (-24.80000 -89.99695) 53.328123\n",
- "165 POINT (-124.15100 41.05410) 50.316593\n",
- "166 POINT (103.51570 21.57310) 38.617185\n",
+ "163 POINT (-155.57616 19.53623) 50.547116\n",
+ "164 POINT (-24.80000 -89.99695) 53.660154\n",
+ "165 POINT (-124.15100 41.05410) 50.316594\n",
+ "166 POINT (103.51570 21.57310) 36.253923\n",
"167 POINT (18.48968 -34.35348) 44.863281\n",
"\n",
"[168 rows x 2 columns]"
diff --git a/tutorials/5.Others/5.6.Interpolation_Test.ipynb b/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb
similarity index 51%
rename from tutorials/5.Others/5.6.Interpolation_Test.ipynb
rename to tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb
index eafb9960c2130bb38ffaaec25e2e56f096d9a7b6..e3fdd56f56adb63ef546294476cb086bf15f8260 100644
--- a/tutorials/5.Others/5.6.Interpolation_Test.ipynb
+++ b/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb
@@ -15,7 +15,6 @@
"source": [
"from nes import *\n",
"import geopandas as gpd\n",
- "import xarray as xr\n",
"import matplotlib.pyplot as plt\n",
"import copy"
]
@@ -49,7 +48,9 @@
"metadata": {},
"outputs": [],
"source": [
- "path_0 = '/esarchive/exp/monarch/a4s2/global/hourly/od550du/od550du-000_2020060100.nc'"
+ "# Original path: /esarchive/exp/monarch/a4s2/global/hourly/od550du/od550du-000_2020060100.nc\n",
+ "# Regular lat-lon grid from MONARCH\n",
+ "path_0 = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_od550du-000_2020060100.nc'"
]
},
{
@@ -59,414 +60,8 @@
"outputs": [
{
"data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (time: 25, lev: 1, lat: 181, lon: 257)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2020-06-01 2020-06-01T01:00:00 ... 2020-06-02\n",
- " * lev (lev) float64 0.0\n",
- " * lat (lat) float64 -90.0 -89.0 -88.0 -87.0 -86.0 ... 87.0 88.0 89.0 90.0\n",
- " * lon (lon) float64 -180.0 -178.6 -177.2 -175.8 ... 177.2 178.6 180.0\n",
- "Data variables:\n",
- " od550du (time, lev, lat, lon) float32 ...\n",
- " crs int32 -2147483647\n",
- "Attributes:\n",
- " Conventions: CF-1.7\n",
- " comment: Generated on marenostrum4 Dimensions: time : 25lev : 1lat : 181lon : 257
Coordinates: (4)
time
(time)
datetime64[ns]
2020-06-01 ... 2020-06-02
standard_name : time long_name : time array(['2020-06-01T00:00:00.000000000', '2020-06-01T01:00:00.000000000',\n",
- " '2020-06-01T02:00:00.000012999', '2020-06-01T03:00:00.000000000',\n",
- " '2020-06-01T04:00:00.000000000', '2020-06-01T05:00:00.000013000',\n",
- " '2020-06-01T06:00:00.000000000', '2020-06-01T07:00:00.000000000',\n",
- " '2020-06-01T08:00:00.000013000', '2020-06-01T09:00:00.000000000',\n",
- " '2020-06-01T10:00:00.000000000', '2020-06-01T11:00:00.000012999',\n",
- " '2020-06-01T12:00:00.000000000', '2020-06-01T13:00:00.000000000',\n",
- " '2020-06-01T14:00:00.000012999', '2020-06-01T15:00:00.000000000',\n",
- " '2020-06-01T16:00:00.000000000', '2020-06-01T17:00:00.000012999',\n",
- " '2020-06-01T18:00:00.000000000', '2020-06-01T19:00:00.000000000',\n",
- " '2020-06-01T20:00:00.000013000', '2020-06-01T21:00:00.000000000',\n",
- " '2020-06-01T22:00:00.000000000', '2020-06-01T23:00:00.000013000',\n",
- " '2020-06-02T00:00:00.000000000'], dtype='datetime64[ns]') lev
(lev)
float64
0.0
lat
(lat)
float64
-90.0 -89.0 -88.0 ... 89.0 90.0
units : degrees_north axis : Y long_name : latitude coordinate standard_name : latitude array([-90., -89., -88., -87., -86., -85., -84., -83., -82., -81., -80., -79.,\n",
- " -78., -77., -76., -75., -74., -73., -72., -71., -70., -69., -68., -67.,\n",
- " -66., -65., -64., -63., -62., -61., -60., -59., -58., -57., -56., -55.,\n",
- " -54., -53., -52., -51., -50., -49., -48., -47., -46., -45., -44., -43.,\n",
- " -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31.,\n",
- " -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19.,\n",
- " -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7.,\n",
- " -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5.,\n",
- " 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17.,\n",
- " 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
- " 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,\n",
- " 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53.,\n",
- " 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65.,\n",
- " 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77.,\n",
- " 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,\n",
- " 90.]) lon
(lon)
float64
-180.0 -178.6 ... 178.6 180.0
units : degrees_east axis : X long_name : longitude coordinate standard_name : longitude array([-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ]) Data variables: (2)
Attributes: (2)
Conventions : CF-1.7 comment : Generated on marenostrum4 "
- ],
"text/plain": [
- "\n",
- "Dimensions: (time: 25, lev: 1, lat: 181, lon: 257)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2020-06-01 2020-06-01T01:00:00 ... 2020-06-02\n",
- " * lev (lev) float64 0.0\n",
- " * lat (lat) float64 -90.0 -89.0 -88.0 -87.0 -86.0 ... 87.0 88.0 89.0 90.0\n",
- " * lon (lon) float64 -180.0 -178.6 -177.2 -175.8 ... 177.2 178.6 180.0\n",
- "Data variables:\n",
- " od550du (time, lev, lat, lon) float32 ...\n",
- " crs int32 ...\n",
- "Attributes:\n",
- " Conventions: CF-1.7\n",
- " comment: Generated on marenostrum4"
+ ""
]
},
"execution_count": 4,
@@ -474,26 +69,6 @@
"output_type": "execute_result"
}
],
- "source": [
- "xr.open_dataset(path_0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
"data_model = open_netcdf(path=path_0, info=True)\n",
"data_model"
@@ -501,7 +76,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -527,7 +102,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -639,7 +214,7 @@
"[46517 rows x 2 columns]"
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -659,12 +234,12 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -703,555 +278,18 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
- "path_obs = '/esarchive/obs/ghost/AERONET_v3_lev1.5/1.4/hourly/od550aero/od550aero_202006.nc'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (station: 366, time: 720, N_flag_codes: 190, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
- "Data variables: (12/180)\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NMVOC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NOx_emissions (station) float32 ...\n",
- " ... ...\n",
- " station_timezone (station) object ...\n",
- " street_type (station) object ...\n",
- " street_width (station) float32 ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- "Attributes:\n",
- " title: Surface aerosol optical depth at 550nm data in the AERONE...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " version: 1.4 Dimensions: station : 366time : 720N_flag_codes : 190N_qa_codes : 77
Coordinates: (1)
Data variables: (180)
ASTER_v3_altitude
(station)
float32
...
standard_name : ASTER v3 altitude long_name : ASTER v3 altitude, relative to EGM96 geoid datum units : m description : Altitude from ASTER v3 digital elevation model, relative to EGM96 geoid vertical datum, in metres. The dataset was generated using 1,880,306 Level-1A scenes (taken from the NASA TERRA spacecraft) acquired between March 1, 2000 and November 30, 2013. The ASTER GDEM was created by stacking all individual cloud-masked scene DEMs and non-cloud-masked scene DEMs, then applying various algorithms to remove abnormal data. A statistical approach is not always effective for anomaly removal in areas with a limited number of images. Several existing reference DEMs were used to replace residual anomalies caused by the insufficient number of stacked scenes. In addition to ASTER GDEM, the ASTER Global Water Body Database (ASTWBD) was generated as a by-product to correct elevation values of water body surfaces like sea, rivers, and lakes. The ASTWBD was applied to GDEM to provide proper elevation values for water body surfaces. The sea and lake have a flattened elevation value. The river has a stepped-down elevation value from the upper stream to the lower stream. Native resolution of 1 arc second ~= 30m at the equator. array([1313., 18., 15., ..., 234., 454., 179.], dtype=float32) EDGAR_v4.3.2_annual_average_BC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average BC emissions long_name : EDGAR v4.3.2 annual average black carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average BC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([2.489332e-13, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 2.421291e-13], dtype=float32) EDGAR_v4.3.2_annual_average_CO_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average CO emissions long_name : EDGAR v4.3.2 annual average carbon monoxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average CO emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([3.161767e-11, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 6.931332e-13], dtype=float32) EDGAR_v4.3.2_annual_average_NH3_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NH3 emissions long_name : EDGAR v4.3.2 annual average ammonia emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NH3 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([9.423107e-16, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 1.087701e-15], dtype=float32) EDGAR_v4.3.2_annual_average_NMVOC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NMVOC emissions long_name : EDGAR v4.3.2 annual average non-methane volatile organic compound emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NMVOC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([6.275388e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 4.111606e-12], dtype=float32) EDGAR_v4.3.2_annual_average_NOx_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average NOx emissions long_name : EDGAR v4.3.2 annual average emissions of nitrogen oxides units : kg m-2 s-1 description : EDGAR v4.3.2 annual average NOx emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([6.169598e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 1.253201e-11], dtype=float32) EDGAR_v4.3.2_annual_average_OC_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average OC emissions long_name : EDGAR v4.3.2 annual average organic carbon emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average OC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([1.244637e-13, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 1.210442e-13], dtype=float32) EDGAR_v4.3.2_annual_average_PM10_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average PM10 emissions long_name : EDGAR v4.3.2 annual average PM10 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average PM10 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([1.244739e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 1.211099e-12], dtype=float32) EDGAR_v4.3.2_annual_average_SO2_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average SO2 emissions long_name : EDGAR v4.3.2 annual average sulphur dioxide emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average SO2 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([4.236494e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 7.338124e-12], dtype=float32) EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions long_name : EDGAR v4.3.2 annual average biogenic PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average biogenic PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([3.510429e-17, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 3.058946e-16], dtype=float32) EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions
(station)
float32
...
standard_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions long_name : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions units : kg m-2 s-1 description : EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees. array([1.244664e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
- " 0.000000e+00, 1.210459e-12], dtype=float32) ESDAC_Iwahashi_landform_classification
(station)
object
...
standard_name : ESDAC Iwahashi landform classification long_name : ESDAC Iwahashi landform classification units : unitless description : European Soil Data Centre (ESDAC) Iwahashi landform classification. The classification presents relief classes which are classified using an unsupervised nested-means algorithms and a three part geometric signature. Slope gradient, surface texture and local convexity are calculated based on the SRTM30 digital elevation model, within a given window size and classified according to the inherent data set properties. This is a dynamic landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['medium steep - coarse texture - low convexity',\n",
- " 'medium steep - fine texture - low convexity',\n",
- " 'gentle - coarse texture - low convexity', ...,\n",
- " 'medium steep - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium gentle - fine texture - high convexity'], dtype=object) ESDAC_Meybeck_landform_classification
(station)
object
...
standard_name : ESDAC Meybeck landform classification long_name : ESDAC Meybeck landform classification units : unitless description : European Soil Data Centre (ESDAC) Meybeck landform classification. The classification presents relief classes which are calculated based on the relief roughness. Roughness and elevation are classified based on a digital elevation model according to static thresholds, with a given window size. This is a static landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['high altitude plains', 'water', 'nan', ..., 'very low plateaus',\n",
- " 'mid altitude plains', 'lowlands'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 25km long_name : ESDAC modal Iwahashi landform classification in 25km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 25km around station location. array(['medium steep - coarse texture - low convexity', 'water', 'water', ...,\n",
- " 'medium steep - fine texture - high convexity',\n",
- " 'medium gentle - fine texture - high convexity',\n",
- " 'medium steep - fine texture - high convexity'], dtype=object) ESDAC_modal_Iwahashi_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Iwahashi landform classification 5km long_name : ESDAC modal Iwahashi landform classification in 5km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 5km around station location. array(['medium steep - coarse texture - low convexity', 'water', 'water', ...,\n",
- " 'medium steep - coarse texture - low convexity',\n",
- " 'medium gentle - fine texture - low convexity',\n",
- " 'medium steep - fine texture - high convexity'], dtype=object) ESDAC_modal_Meybeck_landform_classification_25km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 25km long_name : ESDAC modal Meybeck landform classification in 25km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 25km around station location. array(['mid altitude plateaus', 'water', 'nan', ..., 'very low plateaus',\n",
- " 'mid altitude plains', 'plains'], dtype=object) ESDAC_modal_Meybeck_landform_classification_5km
(station)
object
...
standard_name : ESDAC modal Meybeck landform classification 5km long_name : ESDAC modal Meybeck landform classification in 5km radius units : unitless description : Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 5km around station location. array(['high altitude plains', 'water', 'nan', ..., 'very low plateaus',\n",
- " 'mid altitude plains', 'plains'], dtype=object) ETOPO1_altitude
(station)
float32
...
standard_name : ETOPO1 altitude long_name : ETOPO1 altitude, relative to sea level datum units : m description : Altitude from ETOPO1 digital elevation model, relative to sea level vertical datum, in metres. Over Antarctica and Greenland the elevation given is on top of the ice sheets. Native resolution of 1 arc minute. A correction for coastal sites is made: if the derived altitude is <= -5 m, the maximum altitude of the neighbouring grid boxes will be used instead. If all neighbouring grid boxes have altitudes <= -5 m, the original value will be retained. array([1299., 26., 2., ..., 216., 456., 176.], dtype=float32) ETOPO1_max_altitude_difference_5km
(station)
float32
...
standard_name : ETOPO1 max altitude difference 5km long_name : ETOPO1 maximum altitude difference between the ETOPO1_altitude and all ETOPO1 altitudes in 5km radius units : m description : Altitude difference between the ETOPO1_altitude, and the minimum ETOP1 altitude in a radius of 5 km around the station location, in metres. array([ 24., 145., 5., ..., 31., 9., 26.], dtype=float32) GHOST_version
(station)
object
...
standard_name : GHOST version long_name : Globally Harmonised Observational Surface Treatment (GHOST) version units : unitless description : Version of the Globally Harmonised Observational Surface Treatment (GHOST). array(['1.4', '1.4', '1.4', ..., '1.4', '1.4', '1.4'], dtype=object) GHSL_average_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL average built up area density 25km long_name : GHSL average built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 25km around the station location. array([ 0.973138, 0.069935, 0.118563, ..., 4.052589, 0.882098, 13.686182],\n",
- " dtype=float32) GHSL_average_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL average built up area density 5km long_name : GHSL average built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) average built up area density in a radius of 5km around the station location. array([ 0.779646, 1.149631, 0.344782, ..., 41.303318, 15.780167, 12.955944],\n",
- " dtype=float32) GHSL_average_population_density_25km
(station)
float32
...
standard_name : GHSL average population density 25km long_name : GHSL average population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 25km around the station location. array([1.933994e+01, 2.114581e+00, 3.526324e-01, ..., 3.771514e+02,\n",
- " 3.493256e+02, 1.995498e+02], dtype=float32) GHSL_average_population_density_5km
(station)
float32
...
standard_name : GHSL average population density 5km long_name : GHSL average population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) average population density in a radius of 5km around the station location. array([8.326788e+01, 3.396778e+01, 4.800151e-01, ..., 4.657755e+03,\n",
- " 6.104640e+03, 2.213394e+02], dtype=float32) GHSL_built_up_area_density
(station)
float32
...
standard_name : GHSL built up area density long_name : GHSL built up area density units : % description : Global Human Settlement Layer (GHSL) built up area density (technical label: GHS_BUILT_LDSMT_GLOBE_R2018A), in units of built-up area percent per gridcell (0-100). The product is a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). Native resolution of 0.25 x 0.25 kilometres. array([ 0. , 0. , 0. , ..., 83.7568, 0. , 33.7712],\n",
- " dtype=float32) GHSL_max_built_up_area_density_25km
(station)
float32
...
standard_name : GHSL max built up area density 25km long_name : GHSL max built up area density in 25km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 25km around the station location. array([ 98., 95., 65., ..., 100., 100., 100.], dtype=float32) GHSL_max_built_up_area_density_5km
(station)
float32
...
standard_name : GHSL max built up area density 5km long_name : GHSL max built up area density in 5km radius units : % description : Global Human Settlement Layer (GHSL) max built up area density in a radius of 5km around the station location. array([ 87., 95., 26., ..., 100., 100., 99.], dtype=float32) GHSL_max_population_density_25km
(station)
float32
...
standard_name : GHSL max population density 25km long_name : GHSL max population density in 25km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 25km around the station location. array([ 8958., 2350., 549., ..., 11219., 45549., 9135.], dtype=float32) GHSL_max_population_density_5km
(station)
float32
...
standard_name : GHSL max population density 5km long_name : GHSL max population density in 5km radius units : xx km–2 description : Global Human Settlement Layer (GHSL) max population density in a radius of 5km around the station location. array([8.9580e+03, 2.3500e+03, 4.3000e+01, ..., 1.1219e+04, 4.5549e+04,\n",
- " 1.7360e+03], dtype=float32) GHSL_modal_settlement_model_classification_25km
(station)
object
...
standard_name : GHSL modal settlement model classification 25km long_name : GHSL modal settlement model classification in 25km radius units : unitless description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 25km around station location. array(['very low density rural', 'water', 'water', ...,\n",
- " 'very low density rural', 'very low density rural',\n",
- " 'very low density rural'], dtype=object) GHSL_modal_settlement_model_classification_5km
(station)
object
...
standard_name : GHSL modal settlement model classification 5km long_name : GHSL modal settlement model classification in 5km radius units : unitless description : Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 5km around station location. array(['very low density rural', 'water', 'water', ..., 'urban centre',\n",
- " 'very low density rural', 'low density rural'], dtype=object) GHSL_population_density
(station)
float32
...
standard_name : GHSL population density long_name : GHSL population density units : xx km–2 description : Global Human Settlement Layer (GHSL) population density (technical label: GHS_POP_MT_GLOBE_R2019A), in populus per squared kilometre. It depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the GHSL global layer per corresponding epoch. Native resolution of 0.25 x 0.25 kilometres. array([ 0. , 0. , 0. , ..., 9396.993 , 0. , 556.0051],\n",
- " dtype=float32) GHSL_settlement_model_classification
(station)
object
...
standard_name : GHSL settlement model classification long_name : GHSL settlement model classification units : unitless description : Global Human Settlement Layer (GHSL) settlement model classification (technical label: GHS_SMOD_POPMT_GLOBE_R2019A). The classification delineates and classify settlement typologies via a logic of population size, population and built-up area densities as a refinement of the ‘degree of urbanization’ method described by EUROSTAT. The classification is derived by using the GHS_POP_MT_GLOBE_R2019A and GHS_BUILT_LDSMT_GLOBE_R2018A products. The GHS Settlement Model grid is an improvement of the GHS Settlement Grid (R2016A) introducing a more detailed classification of settlements in two levels, also called ‘refined degree of urbanization’. The Settlement Model is provided at detailed level (Second Level - L2). The First Level, as a porting of the Degree of Urbanization adopted by EUROSTAT can be obtained aggregating L2. Native resolution of 1.0 x 1.0 kilometres. array(['very low density rural', 'very low density rural',\n",
- " 'very low density rural', ..., 'urban centre', 'rural cluster',\n",
- " 'low density rural'], dtype=object) GPW_average_population_density_25km
(station)
float32
...
standard_name : GPW average population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 25 km around the station location. array([124.32709 , 2.79323 , 0.764419, ..., 357.12894 , 273.1742 ,\n",
- " 212.34114 ], dtype=float32) GPW_average_population_density_5km
(station)
float32
...
standard_name : GPW average population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), average population density in a radius of 5 km around the station location. array([1.226605e+01, 4.290382e+01, 5.904162e-02, ..., 7.327646e+02,\n",
- " 4.929010e+02, 2.843317e+02], dtype=float32) GPW_max_population_density_25km
(station)
float32
...
standard_name : GPW max population density 25km long_name : GPW average population density in 25km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 25 km around the station location. array([75960.96 , 832.58167, 280.91034, ..., 1197.667 , 2789.2234 ,\n",
- " 5616.408 ], dtype=float32) GPW_max_population_density_5km
(station)
float32
...
standard_name : GPW max population density 5km long_name : GPW average population density in 5km radius units : xx km–2 description : Gridded Population of the World (GPW), maximum population density in a radius of 5 km around the station location. array([1.226605e+01, 8.325817e+02, 9.681910e-01, ..., 1.085922e+03,\n",
- " 2.789223e+03, 3.627036e+02], dtype=float32) GPW_population_density
(station)
float32
...
standard_name : GPW population density long_name : GPW population density units : xx km–2 description : Gridded Population of the World (GPW), population density, in populus per squared kilometre, from either version 3 and 4 of the provided gridded datasets, dependent on the data year: v3 (1990-2000), v4 (2000-2015). Native resolution of 0.04166 x 0.04166 for v3 data; native resolution of 0.0083 x 0.0083 degrees for v4 data. array([1.226605e+01, 1.010118e+02, 8.580016e-02, ..., 7.309171e+02,\n",
- " 1.188315e+02, 2.668087e+02], dtype=float32) GSFC_coastline_proximity
(station)
float32
...
standard_name : GSFC coastline proximity long_name : GSFC proximity to the coastline units : km description : Proximity to the coastline provided by the NASA Goddard Space Flight Center (GSFC) Ocean Color Group, in kilometres, produced using the Generic Mapping Tools package. Native resolution of 0.01 x 0.01 degrees. Negative distances represent locations over land (including land-locked bodies of water), while positive distances represent locations over the ocean. There is an uncertainty of up to 1 km in the computed distance at any given point. array([-718., 0., -1., ..., -162., -950., -606.], dtype=float32) Joly-Peuch_classification_code
(station)
float32
...
standard_name : Joly-Peuch classification code long_name : Joly-Peuch classification code units : unitless description : Joly-Peuch European classification code (range of 1-10) designed to objectively stratify stations between those diplaying rural and urban signatures (most rural == 1, most urban == 10). This classification is objectively made per species. The species that this is done for are: O3, NO2, SO2, CO, PM10, PM2.5. See reference here: https://www.sciencedirect.com/science/article/abs/pii/S1352231011012088 array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) Koppen-Geiger_classification
(station)
object
...
standard_name : Koppen-Geiger classification long_name : Koppen-Geiger classification units : unitless description : Koppen-Geiger classification, classifying the global climates into 5 main groups (30 total groups with subcategories). Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". See citation: Beck, H.E., N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Nature Scientific Data, 2018. array(['arid - steppe - cold', 'temperate - dry summer - hot summer',\n",
- " 'polar - tundra', ..., 'arid - steppe - cold', 'arid - desert - hot',\n",
- " 'cold - no dry season - warm summer'], dtype=object) Koppen-Geiger_modal_classification_25km
(station)
object
...
standard_name : Koppen-Geiger modal classification 25km long_name : Koppen-Geiger classification units : unitless description : Modal Koppen-Geiger classification in radius of 25km around station location. array(['arid - steppe - cold', 'water', 'water', ..., 'arid - steppe - cold',\n",
- " 'arid - desert - hot', 'cold - no dry season - warm summer'],\n",
- " dtype=object) Koppen-Geiger_modal_classification_5km
(station)
object
...
standard_name : Koppen-Geiger modal classification 5km long_name : Koppen-Geiger classification units : unitless description : Modal Koppen-Geiger classification in radius of 5km around station location. array(['arid - steppe - cold', 'water', 'water', ..., 'arid - steppe - cold',\n",
- " 'arid - desert - hot', 'cold - no dry season - warm summer'],\n",
- " dtype=object) MODIS_MCD12C1_v6_IGBP_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP land use long_name : MODIS MCD12C1 v6 IGBP land use units : unitless description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['grasslands', 'grasslands', 'permanent wetlands', ...,\n",
- " 'urban and built-up lands', 'grasslands', 'woody savannas'],\n",
- " dtype=object) MODIS_MCD12C1_v6_LAI
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI long_name : MODIS MCD12C1 v6 Leaf Area Index units : unitless description : Majority Leaf Area Index class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['grasslands', 'grasslands', 'grasslands', ...,\n",
- " 'urban and built-up lands', 'grasslands', 'savannas'], dtype=object) MODIS_MCD12C1_v6_UMD_land_use
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD land use long_name : MODIS MCD12C1 v6 UMD land use units : unitless description : Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is "water bodies", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['grasslands', 'grasslands', 'permanent wetlands', ...,\n",
- " 'urban and built-up lands', 'grasslands', 'woody savannas'],\n",
- " dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 25km long_name : MODIS MCD12C1 v6 IGBP modal land use in 25km radius units : unitless description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['grasslands', 'water bodies', 'water bodies', ..., 'croplands',\n",
- " 'grasslands', 'mixed forests'], dtype=object) MODIS_MCD12C1_v6_modal_IGBP_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 IGBP modal land use 5km long_name : MODIS MCD12C1 v6 IGBP modal land use in 5km radius units : unitless description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. array(['grasslands', 'water bodies', 'water bodies', ...,\n",
- " 'urban and built-up lands', 'grasslands', 'mixed forests'], dtype=object) MODIS_MCD12C1_v6_modal_LAI_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 25km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 25km radius units : unitless description : Modal Leaf Area Index in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['grasslands', 'water bodies', 'water bodies', ...,\n",
- " 'broadleaf croplands', 'grasslands', 'savannas'], dtype=object) MODIS_MCD12C1_v6_modal_LAI_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 LAI modal 5km long_name : MODIS MCD12C1 v6 modal Leaf Area Index in 5km radius units : unitless description : Modal Leaf Area Index in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. array(['grasslands', 'water bodies', 'water bodies', ...,\n",
- " 'urban and built-up lands', 'grasslands', 'savannas'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_25km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 25km long_name : MODIS MCD12C1 v6 UMD modal land use in 25km radius units : unitless description : Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['grasslands', 'water bodies', 'water bodies', ..., 'croplands',\n",
- " 'grasslands', 'mixed forests'], dtype=object) MODIS_MCD12C1_v6_modal_UMD_land_use_5km
(station)
object
...
standard_name : MODIS MCD12C1 v6 UMD modal land use 5km long_name : MODIS MCD12C1 v6 UMD modal land use in 5km radius units : unitless description : Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. array(['grasslands', 'water bodies', 'water bodies', ...,\n",
- " 'urban and built-up lands', 'grasslands', 'mixed forests'], dtype=object) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 25km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 6., 0., 17., ..., 24., 1., 33.], dtype=float32) NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 average nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 average nighttime stable lights in 5km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 6., 6., 37., ..., 61., 13., 34.], dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 25km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 25km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([63., 16., 63., ..., 63., 56., 61.], dtype=float32) NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 max nighttime stable lights 5km long_name : NOAA DMSP-OLS version 4 maximum nighttime stable lights in 5km radius units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([26., 16., 60., ..., 63., 56., 56.], dtype=float32) NOAA-DMSP-OLS_v4_nighttime_stable_lights
(station)
float32
...
standard_name : NOAA-DMSP-OLS v4 nighttime stable lights long_name : NOAA DMSP-OLS version 4 nighttime stable lights units : unitless description : National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 nighttime stable lights. Native resolution of 0.0083 x 0.0083 degrees. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63. array([ 5., 11., 54., ..., 63., 9., 28.], dtype=float32) OMI_level3_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column annual average NO2 long_name : OMI level3 column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([6.301160e+15, 3.021980e+15, 3.681956e+15, ..., 4.460874e+15,\n",
- " 2.814616e+15, 9.600188e+15], dtype=float32) OMI_level3_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 column cloud screened annual average NO2 long_name : OMI level3 column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([6.354764e+15, 3.141651e+15, 3.933728e+15, ..., 4.377470e+15,\n",
- " 2.837691e+15, 7.143090e+15], dtype=float32) OMI_level3_tropospheric_column_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column annual average NO2 long_name : OMI level3 tropospheric column annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([3.723849e+15, 4.691550e+14, 3.824027e+14, ..., 1.833370e+15,\n",
- " 6.993819e+14, 7.325085e+15], dtype=float32) OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2
(station)
float32
...
standard_name : OMI level3 tropospheric column cloud screened annual average NO2 long_name : OMI level3 tropospheric column cloud screened annual average nitrogen dioxide units : xx cm-2 description : AURA Ozone monitoring instrument (OMI) level3 tropospheric column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees. array([3.713093e+15, 5.222752e+14, 5.292174e+14, ..., 1.688689e+15,\n",
- " 7.069353e+14, 4.294799e+15], dtype=float32) UMBC_anthrome_classification
(station)
object
...
standard_name : UMBC anthrome classification long_name : UMBC anthrome classification units : unitless description : University of Maryland Baltimore County (UMBC) anthrome classification, describing the anthropogenic land use (for the year 2000). There are 20 distinct classifications. Native resolution of 0.0833 x 0.0833 degrees. A correction for costal sites is made: if the native anthrome class is "water", then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an "ocean" site, all the surrounding gridcells will be water also, and therefore the class will be maintained as "water". array(['residential rangelands', 'water', 'wild treeless and barren lands',\n",
- " ..., 'urban', 'urban', 'pastoral villages'], dtype=object) UMBC_modal_anthrome_classification_25km
(station)
object
...
standard_name : UMBC modal anthrome classification 25km long_name : UMBC modal anthrome classification in 25km radius units : unitless description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 25km around station location. array(['residential rangelands', 'water', 'water', ..., 'populated croplands',\n",
- " 'residential rainfed croplands', 'residential rangelands'], dtype=object) UMBC_modal_anthrome_classification_5km
(station)
object
...
standard_name : UMBC modal anthrome classification 5km long_name : UMBC modal anthrome classification in 5km radius units : unitless description : University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 5km around station location. array(['residential rangelands', 'water', 'wild treeless and barren lands',\n",
- " ..., 'urban', 'urban', 'pastoral villages'], dtype=object) WMO_region
(station)
object
...
standard_name : WMO region long_name : WMO region code units : unitless description : World Meteorological Organization (WMO) region of station. The available regions are: Africa, Asia, South America, "Northern America, Central America and the Caribbean", South-West Pacific, Europe and Antarctica. array(['Asia', 'Europe', 'North America, Central America And The Caribbean',\n",
- " ..., 'Europe', 'Africa', 'Europe'], dtype=object) WWF_TEOW_biogeographical_realm
(station)
object
...
standard_name : WWF TEOW biogeographical realm long_name : WWF TEOW biogeographical realm units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 8 biogeographical realms. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['palearctic', 'palearctic', 'nearctic', ..., 'palearctic',\n",
- " 'afrotropics', 'palearctic'], dtype=object) WWF_TEOW_biome
(station)
object
...
standard_name : WWF TEOW biome long_name : WWF TEOW biome units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 14 biomes. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['temperate grasslands, savannas and shrublands',\n",
- " 'temperate broadleaf and mixed forests', 'tundra', ...,\n",
- " 'mediterranean forests, woodlands and scrub',\n",
- " 'tropical and subtropical grasslands, savannas and shrublands',\n",
- " 'temperate broadleaf and mixed forests'], dtype=object) WWF_TEOW_terrestrial_ecoregion
(station)
object
...
standard_name : WWF TEOW terrestrial ecoregion long_name : WWF TEOW terrestrial ecoregion units : unitless description : Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 825 terrestrial ecoregions. Ecoregions are relatively large units of land containing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. array(['mongolian-manchurian grassland', 'azores temperate mixed forests',\n",
- " 'arctic coastal tundra', ...,\n",
- " 'iberian sclerophyllous and semi-deciduous forests',\n",
- " 'west sudanian savanna', 'sarmatic mixed forests'], dtype=object) administrative_country_division_1
(station)
object
...
standard_name : administrative country division 1 long_name : administrative country division 1 units : unitless description : Name of the first (i.e. largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['Inner Mongolia', 'Azores', 'Alaska', ..., 'Aragon', 'Zinder',\n",
- " 'Moskovskaya'], dtype=object) administrative_country_division_2
(station)
object
...
standard_name : administrative country division 2 long_name : administrative country division 2 units : unitless description : Name of the second (i.e. second largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs). array(['nan', 'Santa Cruz Da Graciosa', 'North Slope Borough', ...,\n",
- " 'Provincia De Zaragoza', 'nan', 'nan'], dtype=object) altitude
(station)
float32
...
standard_name : altitude long_name : altitude relative to mean sea level units : m description : Altitude of the ground level at the station, relative to the stated vertical datum, in metres. array([1314. , 15.2, 2. , ..., 250. , 456. , 200. ], dtype=float32) annual_native_max_gap_percent
(station, time)
uint8
...
standard_name : annual native max gap percent long_name : annual native max gap percent units : % description : Percentage of the maximum data gap in the annual averaged measurement UTC window filled by native resolution data, relative to the total window length. [263520 values with dtype=uint8] annual_native_representativity_percent
(station, time)
uint8
...
standard_name : annual native representativity percent long_name : annual native representativity percent units : % description : Percentage of the annual averaged measurement UTC window represented by native resolution data. [263520 values with dtype=uint8] area_classification
(station)
object
...
standard_name : area classification long_name : standardised network provided area classification units : unitless description : Standardised network provided classification, describing type of area a measurement station is situated in. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) associated_networks
(station)
object
...
standard_name : other associated networks long_name : other associated networks units : unitless description : String pair of associated network name and station reference. Format: network1:station_reference1;network2:station_reference2 array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) city
(station)
object
...
standard_name : city long_name : city units : unitless description : Name of the city the station is located in. array(['Baliang', 'Lagoa', 'Nuiqsut', ..., 'Zaragoza', 'Zinder', 'Karinskoye'],\n",
- " dtype=object) climatology
(station)
object
...
standard_name : climatology long_name : climatology units : unitless description : Name of the climatology of which the observations pertain to. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) contact_email_address
(station)
object
...
standard_name : contact email address long_name : contact email address units : unitless description : Email address of the principal data contact for the specific reported data. array(['Brent.N.Holben@nasa.gov', 'Brent.N.Holben@nasa.gov',\n",
- " 'Brent.N.Holben@nasa.gov', ..., 'Brent.N.Holben@nasa.gov',\n",
- " 'Brent.N.Holben@nasa.gov', 'Brent.N.Holben@nasa.gov'], dtype=object) contact_institution
(station)
object
...
standard_name : contact institution long_name : contact institution units : unitless description : Institution of the principal data contact for the specific reported data. array(['NASA', 'NASA', 'NASA', ..., 'NASA', 'NASA', 'NASA'], dtype=object) contact_name
(station)
object
...
standard_name : contact name long_name : contact name units : unitless description : Full name of the principal data contact for the specific reported data. array(['Brent Holben', 'Brent Holben', 'Brent Holben', ..., 'Brent Holben',\n",
- " 'Brent Holben', 'Brent Holben'], dtype=object) country
(station)
object
...
standard_name : country long_name : country units : unitless description : Name of the country the station is located in. array(['China', 'Portugal', 'United States', ..., 'Spain', 'Niger', 'Russia'],\n",
- " dtype=object) daily_native_max_gap_percent
(station, time)
uint8
...
standard_name : daily native max gap percent long_name : daily native max gap percent units : % description : Percentage of the maximum data gap in the daily averaged measurement UTC window filled by native resolution data, relative to the total window length. [263520 values with dtype=uint8] daily_native_representativity_percent
(station, time)
uint8
...
standard_name : daily native representativity percent long_name : daily native representativity percent units : % description : Percentage of the daily averaged measurement UTC window represented by native resolution data. [263520 values with dtype=uint8] daily_passing_vehicles
(station)
float32
...
standard_name : daily passing vehicles long_name : average daily number of passing vehicles units : unitless description : Average number of vehicles passing daily. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) data_level
(station)
object
...
standard_name : data level long_name : data level units : unitless description : Data level of data reported. This varies per network. If data level is variable per measurement, and not static per reported file, then this is set as "variable". If there is no reported data level this is set as "none" array(['1.5', '1.5', '1.5', ..., '1.5', '1.5', '1.5'], dtype=object) data_licence
(station)
object
...
standard_name : data licence long_name : data licence units : unitless description : Information pertaining to the data licence governing the redistribution/publication of the ingested network data. array(['Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html',\n",
- " 'Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html',\n",
- " 'Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html',\n",
- " ...,\n",
- " 'Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html',\n",
- " 'Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html',\n",
- " 'Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html'],\n",
- " dtype=object) day_night_code
(station, time)
uint8
...
standard_name : day/night code long_name : day/night code per measurement units : unitless description : Binary indication if measurement was made during the day or night. Day=0, Night=1. The classification is made by calculating the solar elevation angle for a latitude/longitude/measurement height at a mid-measurement window timestamp. If the solar elevation angle is > 0, it is classed as daytime, otherwise it is nightime. Classification is 255 if cannot be made. [263520 values with dtype=uint8] daytime_traffic_speed
(station)
float32
...
standard_name : daytime traffic speed long_name : average daytime speed of passing traffic units : km hr-1 description : Average daytime speed of the passing traffic where measurements are being made (if applicable), in kilometres per hour. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) derived_uncertainty_per_measurement
(station, time)
float32
...
standard_name : derived measurement uncertainty per measurement long_name : derived measurement uncertainty per measurement units : unitless description : Derived measurement uncertainty (±) of methodology, for a specific measurement. This is calculated through the quadratic addition of reported (or if not available, documented) accuracy and precision metrics. This is given in absolute terms in unitless. [263520 values with dtype=float32] distance_to_building
(station)
float32
...
standard_name : distance to building long_name : distance to the nearest building units : m description : Distance to the nearest building of the inlet/instrument/sampler, in metres. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) distance_to_junction
(station)
float32
...
standard_name : distance to junction long_name : distance to the nearest road junction units : m description : Distance to the nearest road junction of the inlet/instrument/sampler, in metres. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) distance_to_kerb
(station)
float32
...
standard_name : distance to kerb long_name : distance to the street kerb units : m description : Distance to the street kerb of the inlet/instrument/sampler, in metres. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) distance_to_source
(station)
float32
...
standard_name : distance to source long_name : distance to the main emission source units : km description : Distance to the main emission source (see variable: "main_emission_source") of the inlet/instrument/sampler, in kilometres. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) ellipsoid
(station)
object
...
standard_name : ellipsoid long_name : ellipsoid units : unitless description : The ellipsoidal model of the earth used as a basis for 2D and 3D geographic coordinate systems. array(['WGS 84', 'WGS 84', 'WGS 84', ..., 'WGS 84', 'WGS 84', 'WGS 84'],\n",
- " dtype=object) flag
(station, time, N_flag_codes)
float32
...
standard_name : flags long_name : data reporter provided standardised flags units : unitless description : List of associated data flag codes per measurement, indicating the data quality of a specific measurement, provided by the data reporter. Fill value code of 255. [50068800 values with dtype=float32] horizontal_datum
(station)
object
...
standard_name : horizontal datum long_name : horizontal datum units : unitless description : Name of the horizontal datum used in defining geodetic latitudes and longitudes on the Earth's surface. The datum is set when positioning an ellipsoid model of the Earth to an anchor point. If not explicitely stated then this is assumed to be 'World Geodetic System 1984'. array(['WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', ..., 'WORLD GEODETIC SYSTEM 1984',\n",
- " 'WORLD GEODETIC SYSTEM 1984', 'WORLD GEODETIC SYSTEM 1984'],\n",
- " dtype=object) hourly_native_max_gap_percent
(station, time)
uint8
...
standard_name : hourly native max gap percent long_name : hourly native max gap percent units : % description : Percentage of the maximum data gap in the hourly averaged measurement UTC window filled by native resolution data, relative to the total window length. [263520 values with dtype=uint8] hourly_native_representativity_percent
(station, time)
uint8
...
standard_name : hourly native representativity percent long_name : hourly native representativity percent units : % description : Percentage of the hourly averaged measurement UTC window represented by native resolution data. [263520 values with dtype=uint8] land_use
(station)
object
...
standard_name : land use long_name : standardised network provided land use type units : unitless description : Standardised network provided classification, describing the dominant land use in the area of the reporting station. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) latitude
(station)
float64
...
standard_name : latitude long_name : latitude units : decimal degrees North description : Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum. axis : Y array([40.8517 , 39.09109 , 70.4995 , ..., 41.6334 , 13.776683, 55.695 ]) local_time
(station, time)
datetime64[ns]
...
standard_name : local time long_name : local time description : Time in minutes since 0001-01-01 00:00 local time. Time given refers to the start of the time window the measurement is representative of (temporal resolution). axis : T tz : local [263520 values with dtype=datetime64[ns]] longitude
(station)
float64
...
standard_name : longitude long_name : longitude units : decimal degrees East description : Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum. axis : X array([ 109.6288 , -28.02917 , -149.88 , ..., -0.88235 , 8.990233,\n",
- " 36.775 ]) main_emission_source
(station)
object
...
standard_name : main emission source long_name : standardised network provided main emission source units : unitless description : Standardised network provided classification, describing the main emission source influencing air measured at a station. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) mean_solar_time
(station, time)
datetime64[ns]
...
standard_name : mean solar time long_name : mean solar time description : Time in minutes since 0001-01-01 00:00 mean solar time. Time given refers to the start of the time window the measurement is representative of (temporal resolution). axis : T tz : mean solar [263520 values with dtype=datetime64[ns]] measurement_altitude
(station)
float32
...
standard_name : measurement altitude long_name : measurement altitude relative to mean sea level units : m description : Altitude of the inlet/instrument/sampler, relative to the stated vertical datum, in metres. array([1314. , 15.2, 2. , ..., 250. , 456. , 200. ], dtype=float32) measurement_methodology
(station)
object
...
standard_name : measurement methodology long_name : standardised measurement methodology units : unitless description : Standardised name of the measurement methodology. array(['photometry - direct', 'photometry - direct', 'photometry - direct',\n",
- " ..., 'photometry - direct', 'photometry - direct',\n",
- " 'photometry - direct'], dtype=object) measurement_scale
(station)
object
...
standard_name : measurement scale long_name : standardised network provided measurement scale units : unitless description : Standardised network provided classification, a denotation of the geographic scope of the air quality measurements made. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_calibration_scale
(station)
object
...
standard_name : measuring instrument calibration scale long_name : measuring instrument calibration scale name units : unitless description : Name of calibration scale used for the calibration of the measuring instrument. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_absorption_cross_section
(station)
object
...
standard_name : measuring instrument documented absorption cross section long_name : measuring instrument documented absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in instrumental manual/documentation. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_accuracy
(station)
object
...
standard_name : measuring instrument documented measurement accuracy long_name : measuring instrument documented measurement accuracy units : unitless description : Measurement accuracy (±), as given in the instrumental manual/documentation. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_flow_rate
(station)
object
...
standard_name : measuring instrument documented flow rate long_name : measuring instrument documented flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented lower limit of detection long_name : measuring instrument documented lower limit of detection units : unitless description : Lower limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) measuring_instrument_documented_measurement_resolution
(station)
float32
...
standard_name : measuring instrument documented measurement resolution long_name : measuring instrument documented measurement resolution units : unitless description : Measurement resolution, as given in instrumental manual/documentation. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) measuring_instrument_documented_precision
(station)
object
...
standard_name : measuring instrument documented measurement precision long_name : measuring instrument documented measurement precision units : unitless description : Measurement precision (±), as given in instrumental manual/documentation. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['0.1%', '0.1%', '0.1%', ..., '0.1%', '0.1%', '0.1%'], dtype=object) measuring_instrument_documented_span_drift
(station)
object
...
standard_name : measuring instrument documented span drift long_name : measuring instrument documented span drift units : unitless description : Span drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_uncertainty
(station)
object
...
standard_name : measuring instrument documented measurement uncertainty long_name : measuring instrument documented measurement uncertainty units : unitless description : Measurement uncertainty (±), as given in the instrumental manual/documentation. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument documented upper limit of detection long_name : measuring instrument documented upper limit of detection units : unitless description : Upper limit of detection of measurement methodology, as given in the instrumental manual/documentation. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) measuring_instrument_documented_zero_drift
(station)
object
...
standard_name : measuring instrument documented zero drift long_name : measuring instrument documented zero drift units : unitless description : Zero drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_documented_zonal_drift
(station)
object
...
standard_name : measuring instrument documented zonal drift long_name : measuring instrument documented zonal drift units : unitless description : Zonal drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_further_details
(station)
object
...
standard_name : measuring instrument further details long_name : measuring instrument further details units : unitless description : Further associated details regarding the specifics of the measurement methodology/instrument. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_inlet_information
(station)
object
...
standard_name : measuring instrument inlet information long_name : measuring instrument measurement inlet information units : unitless description : Description of sampling inlet of the measuring instrument. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_manual_name
(station)
object
...
standard_name : measuring instrument manual name long_name : measuring instrument manual name units : unitless description : Path to the location in the esarchive of the manual for the specific measuring instrument. array(['Cimel_CE318T_Manual.pdf', 'Cimel_CE318T_Manual.pdf',\n",
- " 'Cimel_CE318T_Manual.pdf', ..., 'Cimel_CE318T_Manual.pdf',\n",
- " 'Cimel_CE318T_Manual.pdf', 'Cimel_CE318T_Manual.pdf'], dtype=object) measuring_instrument_name
(station)
object
...
standard_name : measuring instrument name long_name : standardised measuring instrument name units : unitless description : Standardised name of the measuring instrument. array(['Cimel CE318-T', 'Cimel CE318-T', 'Cimel CE318-T', ..., 'Cimel CE318-T',\n",
- " 'Cimel CE318-T', 'Cimel CE318-T'], dtype=object) measuring_instrument_process_details
(station)
object
...
standard_name : measuring instrument process details long_name : measuring instrument process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the measurement methodology/instrument. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_absorption_cross_section
(station)
object
...
standard_name : measuring instrument reported absorption cross section long_name : measuring instrument reported absorption cross section units : cm2 description : Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in metadata. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_accuracy
(station)
object
...
standard_name : measuring instrument reported measurement accuracy long_name : measuring instrument reported measurement accuracy units : unitless description : Measurement accuracy (±), as given in metadata. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_flow_rate
(station)
object
...
standard_name : measuring instrument reported flow rate long_name : measuring instrument reported flow rate units : l min-1 description : Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_lower_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported lower limit of detection long_name : measuring instrument reported lower limit of detection units : unitless description : Lower limit of detection of measurement methodology, as given in metadata. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) measuring_instrument_reported_measurement_resolution
(station)
float32
...
standard_name : measuring instrument reported measurement resolution long_name : measuring instrument reported measurement resolution units : unitless description : Measurement resolution, as given in metadata. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) measuring_instrument_reported_precision
(station)
object
...
standard_name : measuring instrument reported measurement precision long_name : measuring instrument reported measurement precision units : unitless description : Measurement precision (±), as given in metadata. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_span_drift
(station)
object
...
standard_name : measuring instrument reported span drift long_name : measuring instrument reported span drift units : unitless description : Span drift of measuring instrument per unit of time, as given in metadata. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_uncertainty
(station)
object
...
standard_name : measuring instrument reported measurement uncertainty long_name : measuring instrument reported measurement uncertainty units : unitless description : Measurement uncertainty (±), as given in metadata. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). It can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%>=50). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_units
(station)
object
...
standard_name : measuring instrument reported units long_name : measuring instrument reported measurement units units : unitless description : Units that the measured parameter are natively reported in. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_upper_limit_of_detection
(station)
float32
...
standard_name : measuring instrument reported upper limit of detection long_name : measuring instrument reported upper limit of detection units : unitless description : Upper limit of detection of measurement methodology, as given in metadata. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) measuring_instrument_reported_zero_drift
(station)
object
...
standard_name : measuring instrument reported zero drift long_name : measuring instrument reported zero drift units : unitless description : Zero drift of measuring instrument per unit of time, as given in metadata. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_reported_zonal_drift
(station)
object
...
standard_name : measuring instrument reported zonal drift long_name : measuring instrument reported zonal drift units : unitless description : Zonal drift of measuring instrument per unit of time, as given in metadata. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%>=50/day). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) measuring_instrument_sampling_type
(station)
object
...
standard_name : measuring instrument sampling type long_name : standardised sampling type of the measuring instrument units : unitless description : Standardised name of the measuring instrument sampling type. array(['remote', 'remote', 'remote', ..., 'remote', 'remote', 'remote'],\n",
- " dtype=object) monthly_native_max_gap_percent
(station, time)
uint8
...
standard_name : monthly native max gap percent long_name : monthly native max gap percent units : % description : Percentage of the maximum data gap in the monthly averaged measurement UTC window filled by native resolution data, relative to the total window length. [263520 values with dtype=uint8] monthly_native_representativity_percent
(station, time)
uint8
...
standard_name : monthly native representativity percent long_name : monthly native representativity percent units : % description : Percentage of the monthly averaged measurement UTC window represented by native resolution data. [263520 values with dtype=uint8] network
(station)
object
...
standard_name : network long_name : data network units : unitless description : The name of the network which reports data for the specific station in question. array(['AERONET_v3_lev1.5', 'AERONET_v3_lev1.5', 'AERONET_v3_lev1.5', ...,\n",
- " 'AERONET_v3_lev1.5', 'AERONET_v3_lev1.5', 'AERONET_v3_lev1.5'],\n",
- " dtype=object) network_maintenance_details
(station)
object
...
standard_name : network maintenance details long_name : network maintenance details units : unitless description : Extra details provided by the reporting network about the operational maintenance done at the station. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) network_miscellaneous_details
(station)
object
...
standard_name : network miscellaneous details long_name : network miscellaneous details units : unitless description : Extra miscellanous details provided by the reporting network. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) network_provided_volume_standard_pressure
(station)
float64
...
standard_name : network provided volume standard pressure long_name : network provided volume standard pressure units : hPa description : The pressure (in hPa) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typically normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported pressure should be reported as the internal pressure of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas pressure is assumed to be the known network standard pressure for the measured component. array([nan, nan, nan, ..., nan, nan, nan]) network_provided_volume_standard_temperature
(station)
float64
...
standard_name : network provided volume standard temperature long_name : network provided volume standard temperature units : K description : The temperature (in Kelvin) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typically normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported temperature should be reported as the internal temperature of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas temperature is assumed to be the known network standard temperature for the measured component. array([nan, nan, nan, ..., nan, nan, nan]) network_qa_details
(station)
object
...
standard_name : network qa details long_name : network qa details units : unitless description : Extra details provided by the reporting network about the in-network quality assurance of measurements. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) network_sampling_details
(station)
object
...
standard_name : network sampling details long_name : network sampling details units : unitless description : Extra details provided by the reporting network about the sampling methods employed. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) network_uncertainty_details
(station)
object
...
standard_name : network uncertainty details long_name : network uncertainty details units : unitless description : Extra details provided by the reporting network about the uncertainties involved with the measurement methods employed. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) od550aero
(station, time)
float32
...
standard_name : aerosol optical depth at 550nm long_name : aerosol optical depth at 550nm units : unitless description : Measured value of surface aerosol optical depth at 550nm for the stated temporal resolution. [263520 values with dtype=float32] population
(station)
float32
...
standard_name : population long_name : population units : unitless description : Population size of the nearest urban settlement. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) primary_sampling_further_details
(station)
object
...
standard_name : primary sampling further details long_name : primary sampling further details units : unitless description : Further associated details regarding the specifics of the primary sampling instrument/type. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_documented_flow_rate
(station)
object
...
standard_name : primary sampling instrument documented flow rate long_name : primary sampling instrument documented sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_manual_name
(station)
object
...
standard_name : primary sampling instrument manual name long_name : primary sampling instrument manual name units : unitless description : Path to the location in the esarchive of the manual for the specific primary sampling instrument. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_name
(station)
object
...
standard_name : primary sampling instrument name long_name : standardised primary sampling instrument name units : unitless description : Standardised name of the primary sampling instrument (if no specific instrument is used, or known, this is the standardised primary sampling type). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) primary_sampling_instrument_reported_flow_rate
(station)
object
...
standard_name : primary sampling instrument reported flow rate long_name : primary sampling instrument reported sampling flow rate units : l min-1 description : Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) primary_sampling_process_details
(station)
object
...
standard_name : primary sampling process details long_name : primary sampling process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the primary sampling type/instrument. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) primary_sampling_type
(station)
object
...
standard_name : primary sampling type long_name : standardised primary sampling type units : unitless description : Standardised primary sampling type. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) principal_investigator_email_address
(station)
object
...
standard_name : principal investigator email address long_name : principal investigator email address units : unitless description : Email address of the principal scientific investigator for the specific reported data. array(['Brent.N.Holben@nasa.gov', 'Brent.N.Holben@nasa.gov',\n",
- " 'Brent.N.Holben@nasa.gov', ..., 'Brent.N.Holben@nasa.gov',\n",
- " 'Brent.N.Holben@nasa.gov', 'Brent.N.Holben@nasa.gov'], dtype=object) principal_investigator_institution
(station)
object
...
standard_name : principal investigator institution long_name : principal investigator institution units : unitless description : Institution of the principal scientific investigator for the specific reported data. array(['NASA', 'NASA', 'NASA', ..., 'NASA', 'NASA', 'NASA'], dtype=object) principal_investigator_name
(station)
object
...
standard_name : principal investigator name long_name : principal investigator name units : unitless description : Full name of the principal scientific investigator for the specific reported data. array(['Brent Holben', 'Brent Holben', 'Brent Holben', ..., 'Brent Holben',\n",
- " 'Brent Holben', 'Brent Holben'], dtype=object) process_warnings
(station)
object
...
standard_name : process warnings long_name : process warnings units : unitless description : Warnings accumulated through GHOST processing regarding the data that should be considered. array(['', '', '', ..., '', '', ''], dtype=object) projection
(station)
object
...
standard_name : projection long_name : projection units : unitless description : Name of the projected coordinate system of the original provided station position x, y coordinates. If the original coordinates are not projected, then this is set as 'geographic'. array(['GEOGRAPHIC', 'GEOGRAPHIC', 'GEOGRAPHIC', ..., 'GEOGRAPHIC',\n",
- " 'GEOGRAPHIC', 'GEOGRAPHIC'], dtype=object) qa
(station, time, N_qa_codes)
float32
...
standard_name : qa long_name : qa standardised data flags units : unitless description : List of derived quality assurance flag codes per measurement, informing on the quality associated with each observation, determined by multiple scientific quality control/assurance checks. Fill value code of 255. [20291040 values with dtype=float32] reported_uncertainty_per_measurement
(station, time)
float32
...
standard_name : reported measurement uncertainty per measurement long_name : reported measurement uncertainty per measurement units : unitless description : Reported measurement uncertainty (±) of methodology, for a specific measurement. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This is given in absolute terms in unitless. [263520 values with dtype=float32] representative_radius
(station)
float32
...
standard_name : representative_radius long_name : representative_radius units : km description : Radius of representativity of the measurements made (i.e. for what distance scale around the sampling point would the measurements be very similar?), given in kilometres. A quantitative version of the "measurement_scale" classification. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) retrieval_algorithm
(station)
object
...
standard_name : retrieval algorithm long_name : retrieval algorithm units : unitless description : The name of the retrieval algorithm. Remote sensing algorithms are used to retrieve the aerosol optical properties (as aerosol optical depths or single scattering albedo among others) using remote-sensing radiances for multiple wavelengths from ground stations or on satellite platforms. Each algorithm is particularly designed considering the characteristics of the sensor and other ancillary information. array(['directsun algorithm', 'directsun algorithm', 'directsun algorithm',\n",
- " ..., 'directsun algorithm', 'directsun algorithm',\n",
- " 'directsun algorithm'], dtype=object) sample_preparation_further_details
(station)
object
...
standard_name : sample preparation further details long_name : sample preparation further details units : unitless description : Further associated details regarding the specifics of the sample preparation types/instruments. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) sample_preparation_process_details
(station)
object
...
standard_name : sample preparation process details long_name : sample preparation process details units : unitless description : Miscellaneous details regarding assumptions made in the standardisation of the sample preparation types/techniques. Multiple details specific to different types are separated by ";". array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) sample_preparation_techniques
(station)
object
...
standard_name : sample preparation techniques long_name : standardised specific preparation techniques units : unitless description : Standardised sample preparation techniques utilised in the measurement process. Mutiple names are separated by ";". array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) sample_preparation_types
(station)
object
...
standard_name : sample preparation types long_name : standardised sample preparation types units : unitless description : Standardised sample preparation types utilised in the measurement process. Mutiple types are separated by ";". array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) sampling_height
(station)
float32
...
standard_name : sampling height long_name : sampling height relative to ground units : m description : Height above the ground of the inlet/instrument/sampler, in metres. array([0., 0., 0., ..., 0., 0., 0.], dtype=float32) season_code
(station, time)
uint8
...
standard_name : season code long_name : season code per measurement units : unitless description : Code decreeing if measurement was made during the Spring, Summer, Autumn or Winter Seasons. Spring=0, Summer=1, Autumn=2, Winter=3. The classification is made by evaluating which season the local-time of a mid-measurement window timestamp falls in. [263520 values with dtype=uint8] station_classification
(station)
object
...
standard_name : station classification long_name : standardised network provided station classification units : unitless description : Standardised network provided classification, categorising the type of air measured by a station. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) station_name
(station)
object
...
standard_name : station name long_name : station name units : unitless description : Name of station where the measurement was conducted. array(['Aoe_Baotou', 'Arm_Graciosa', 'Arm_Oliktok_Ak', ..., 'Zaragoza',\n",
- " 'Zinder_Airport', 'Zvenigorod'], dtype=object) station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : unitless description : reference ID for station. array(['AOEBaotou_P-D', 'ARMGraciosa_P-D', 'ARMOliktokAK_P-D', ...,\n",
- " 'Zaragoza_P-D', 'ZinderAirport_P-D', 'Zvenigorod_P-D'], dtype=object) station_timezone
(station)
object
...
standard_name : station timezone long_name : station timezone units : unitless description : Name of the local timezone that the measuring station is located in. This is automatically generated using Timezone Finder Python package (taking longitude and latitude as inputs). array(['Asia/Shanghai', 'Atlantic/Azores', 'America/Anchorage', ...,\n",
- " 'Europe/Madrid', 'Africa/Niamey', 'Europe/Moscow'], dtype=object) street_type
(station)
object
...
standard_name : street type long_name : type of street units : unitless description : Type of street where measurements are being made (if applicable). array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) street_width
(station)
float32
...
standard_name : street width long_name : width of the street units : m description : Width of the street where measurements are being made (if applicable), in metres. array([nan, nan, nan, ..., nan, nan, nan], dtype=float32) terrain
(station)
object
...
standard_name : terrain long_name : standardised network provided terrain type units : unitless description : Standardised network provided classification, describing the dominant terrain in the area of the reporting station. array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype=object) vertical_datum
(station)
object
...
standard_name : vertical datum long_name : vertical datum units : unitless description : Name of the vertical datum used to define vertical elevation on the Earth. The datum is a surface of zero elevation to which other heights can be reference against. If not explicitely stated then this is assumed to be 'tidal - mean sea level'. array(['TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', ..., 'TIDAL - MEAN SEA LEVEL',\n",
- " 'TIDAL - MEAN SEA LEVEL', 'TIDAL - MEAN SEA LEVEL'], dtype=object) weekday_weekend_code
(station, time)
uint8
...
standard_name : weekday/weekend code long_name : weekday/weekend code per measurement units : unitless description : Binary indication if measurement was made during the weekday or weekend. Weekday=0, Weekend=1. The classification is made by evaluating if the local-time of a mid-measurement window timestamp falls on a weekday or on the weekend. Classification is 255 if cannot be made. [263520 values with dtype=uint8] Attributes: (6)
title : Surface aerosol optical depth at 550nm data in the AERONET_v3_lev1.5 network in 2020-06. institution : Barcelona Supercomputing Center source : Surface observations creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es version : 1.4 "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (station: 366, time: 720, N_flag_codes: 190, N_qa_codes: 77)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] ...\n",
- "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
- "Data variables: (12/180)\n",
- " ASTER_v3_altitude (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NMVOC_emissions (station) float32 ...\n",
- " EDGAR_v4.3.2_annual_average_NOx_emissions (station) float32 ...\n",
- " ... ...\n",
- " station_timezone (station) object ...\n",
- " street_type (station) object ...\n",
- " street_width (station) float32 ...\n",
- " terrain (station) object ...\n",
- " vertical_datum (station) object ...\n",
- " weekday_weekend_code (station, time) uint8 ...\n",
- "Attributes:\n",
- " title: Surface aerosol optical depth at 550nm data in the AERONE...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Surface observations\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " version: 1.4"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset(path_obs)"
+ "# Original path: /esarchive/obs/ghost/AERONET_v3_lev1.5/1.4/hourly/od550aero/od550aero_202006.nc\n",
+ "# Points grid from AERONET network\n",
+ "path_obs = '/gpfs/projects/bsc32/models/NES_tutorial_data/obs_od550aero_202006.nc'"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -1265,10 +303,10 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 11,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -1280,7 +318,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -1306,7 +344,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1418,7 +456,7 @@
"[366 rows x 2 columns]"
]
},
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -1445,460 +483,18 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
- "path_prv_before = '/gpfs/projects/bsc32/AC_cache/recon/exp_interp/bug_1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (grid_edge: 877, station: 366, model_latitude: 181, model_longitude: 257, time: 720)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2020-06-01 ... 2020-06-30T2...\n",
- "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
- "Data variables:\n",
- " grid_edge_latitude (grid_edge) float64 -90.5 -89.5 ... -90.5 -90.5\n",
- " grid_edge_longitude (grid_edge) float64 -180.7 -180.7 ... -179.3 -180.7\n",
- " latitude (station) float64 40.85 39.09 70.5 ... 13.78 55.7\n",
- " longitude (station) float64 109.6 -28.03 -149.9 ... 8.99 36.77\n",
- " model_centre_latitude (model_latitude, model_longitude) float64 -90.0 ....\n",
- " model_centre_longitude (model_latitude, model_longitude) float64 -180.0 ...\n",
- " od550aero (station, time) float32 ...\n",
- " station_reference (station) object 'AOEBaotou_P-D' ... 'Zvenigorod_...\n",
- "Attributes:\n",
- " title: Inverse distance weighting (4 neighbours) interpolated a4...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Experiment a4s2\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.0\n",
- " history: Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
- " NCO: netCDF Operators version 4.9.2 (Homepage = http://nco.sf.... Dimensions: grid_edge : 877station : 366model_latitude : 181model_longitude : 257time : 720
Coordinates: (1)
Data variables: (8)
grid_edge_latitude
(grid_edge)
float64
...
standard_name : grid edge latitude long_name : grid edge latitude units : decimal degrees North description : Latitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner). axis : Y array([-90.5, -89.5, -88.5, ..., -90.5, -90.5, -90.5]) grid_edge_longitude
(grid_edge)
float64
...
standard_name : grid edge longitude long_name : grid edge longitude units : decimal degrees East description : Longitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner). axis : X array([-180.703125, -180.703125, -180.703125, ..., -177.890625, -179.296875,\n",
- " -180.703125]) latitude
(station)
float64
...
standard_name : latitude units : decimal degrees North long_name : latitude description : Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum. axis : Y array([40.8517 , 39.09109 , 70.4995 , ..., 41.6334 , 13.776683, 55.695 ]) longitude
(station)
float64
...
standard_name : longitude units : decimal degrees East long_name : longitude description : Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum. axis : X array([ 109.6288 , -28.02917 , -149.88 , ..., -0.88235 , 8.990233,\n",
- " 36.775 ]) model_centre_latitude
(model_latitude, model_longitude)
float64
...
standard_name : model centre latitude long_name : model centre latitude units : decimal degrees North description : 2D meshed grid centre latitudes with 181 latitudes in 257 bands of longitude axis : Y array([[-90., -90., -90., ..., -90., -90., -90.],\n",
- " [-89., -89., -89., ..., -89., -89., -89.],\n",
- " [-88., -88., -88., ..., -88., -88., -88.],\n",
- " ...,\n",
- " [ 88., 88., 88., ..., 88., 88., 88.],\n",
- " [ 89., 89., 89., ..., 89., 89., 89.],\n",
- " [ 90., 90., 90., ..., 90., 90., 90.]]) model_centre_longitude
(model_latitude, model_longitude)
float64
...
standard_name : model centre longitude long_name : model centre longitude units : decimal degrees East description : 2D meshed grid centre longitudes with 257 longitudes in 181 bands of latitude axis : X array([[-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " ...,\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ]]) od550aero
(station, time)
float32
...
long_name : aerosol optical depth at 550nm units : unitless standard_name : aerosol optical depth at 550nm descrption : Interpolated value of aerosol optical depth at 550nm from the experiment a4s2 with reference to the measurement stations in the AERONET_v3_lev1.5 network [263520 values with dtype=float32] station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : unitless description : reference ID for station. array(['AOEBaotou_P-D', 'ARMGraciosa_P-D', 'ARMOliktokAK_P-D', ...,\n",
- " 'Zaragoza_P-D', 'ZinderAirport_P-D', 'Zvenigorod_P-D'], dtype=object) Attributes: (9)
title : Inverse distance weighting (4 neighbours) interpolated a4s2 experiment data for the component od550aero with reference to the measurement stations in the AERONET_v3_lev1.5 network in 2020-06. institution : Barcelona Supercomputing Center source : Experiment a4s2 creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es conventions : CF-1.7 data_version : 1.0 history : Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc NCO : netCDF Operators version 4.9.2 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco) "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (grid_edge: 877, station: 366, model_latitude: 181, model_longitude: 257, time: 720)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2020-06-01 ... 2020-06-30T2...\n",
- "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
- "Data variables:\n",
- " grid_edge_latitude (grid_edge) float64 ...\n",
- " grid_edge_longitude (grid_edge) float64 ...\n",
- " latitude (station) float64 ...\n",
- " longitude (station) float64 ...\n",
- " model_centre_latitude (model_latitude, model_longitude) float64 ...\n",
- " model_centre_longitude (model_latitude, model_longitude) float64 ...\n",
- " od550aero (station, time) float32 ...\n",
- " station_reference (station) object ...\n",
- "Attributes:\n",
- " title: Inverse distance weighting (4 neighbours) interpolated a4...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Experiment a4s2\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.0\n",
- " history: Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
- " NCO: netCDF Operators version 4.9.2 (Homepage = http://nco.sf...."
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset(path_prv_before)"
+ "# Original path: /gpfs/projects/bsc32/AC_cache/recon/exp_interp/bug_1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc\n",
+ "# Interpolated experiment to AERONET network from MONARCH (with bug)\n",
+ "path_prv_before = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_interp_bug_od550aero_202006.nc'"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -1912,10 +508,10 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 16,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -1927,7 +523,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -1953,7 +549,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -2065,7 +661,7 @@
"[366 rows x 2 columns]"
]
},
- "execution_count": 18,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -2085,12 +681,12 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -2130,460 +726,18 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
- "path_prv_after = '/gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset>\n",
- "Dimensions: (grid_edge: 877, station: 366, model_latitude: 181, model_longitude: 257, time: 720)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2020-06-01 ... 2020-06-30T2...\n",
- "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
- "Data variables:\n",
- " grid_edge_latitude (grid_edge) float64 -90.5 -89.5 ... -90.5 -90.5\n",
- " grid_edge_longitude (grid_edge) float64 -180.7 -180.7 ... -179.3 -180.7\n",
- " latitude (station) float64 40.85 39.09 70.5 ... 13.78 55.7\n",
- " longitude (station) float64 109.6 -28.03 -149.9 ... 8.99 36.77\n",
- " model_centre_latitude (model_latitude, model_longitude) float64 -90.0 ....\n",
- " model_centre_longitude (model_latitude, model_longitude) float64 -180.0 ...\n",
- " od550aero (station, time) float32 ...\n",
- " station_reference (station) object 'AOEBaotou_P-D' ... 'Zvenigorod_...\n",
- "Attributes:\n",
- " title: Inverse distance weighting (4 neighbours) interpolated a4...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Experiment a4s2\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.0\n",
- " history: Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
- " NCO: netCDF Operators version 4.9.2 (Homepage = http://nco.sf.... Dimensions: grid_edge : 877station : 366model_latitude : 181model_longitude : 257time : 720
Coordinates: (1)
Data variables: (8)
grid_edge_latitude
(grid_edge)
float64
...
standard_name : grid edge latitude long_name : grid edge latitude units : decimal degrees North description : Latitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner). axis : Y array([-90.5, -89.5, -88.5, ..., -90.5, -90.5, -90.5]) grid_edge_longitude
(grid_edge)
float64
...
standard_name : grid edge longitude long_name : grid edge longitude units : decimal degrees East description : Longitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner). axis : X array([-180.703125, -180.703125, -180.703125, ..., -177.890625, -179.296875,\n",
- " -180.703125]) latitude
(station)
float64
...
standard_name : latitude units : decimal degrees North long_name : latitude description : Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum. axis : Y array([40.8517 , 39.09109 , 70.4995 , ..., 41.6334 , 13.776683, 55.695 ]) longitude
(station)
float64
...
standard_name : longitude units : decimal degrees East long_name : longitude description : Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum. axis : X array([ 109.6288 , -28.02917 , -149.88 , ..., -0.88235 , 8.990233,\n",
- " 36.775 ]) model_centre_latitude
(model_latitude, model_longitude)
float64
...
standard_name : model centre latitude long_name : model centre latitude units : decimal degrees North description : 2D meshed grid centre latitudes with 181 latitudes in 257 bands of longitude axis : Y array([[-90., -90., -90., ..., -90., -90., -90.],\n",
- " [-89., -89., -89., ..., -89., -89., -89.],\n",
- " [-88., -88., -88., ..., -88., -88., -88.],\n",
- " ...,\n",
- " [ 88., 88., 88., ..., 88., 88., 88.],\n",
- " [ 89., 89., 89., ..., 89., 89., 89.],\n",
- " [ 90., 90., 90., ..., 90., 90., 90.]]) model_centre_longitude
(model_latitude, model_longitude)
float64
...
standard_name : model centre longitude long_name : model centre longitude units : decimal degrees East description : 2D meshed grid centre longitudes with 257 longitudes in 181 bands of latitude axis : X array([[-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " ...,\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ],\n",
- " [-180. , -178.59375, -177.1875 , ..., 177.1875 , 178.59375,\n",
- " 180. ]]) od550aero
(station, time)
float32
...
long_name : aerosol optical depth at 550nm units : unitless standard_name : aerosol optical depth at 550nm descrption : Interpolated value of aerosol optical depth at 550nm from the experiment a4s2 with reference to the measurement stations in the AERONET_v3_lev1.5 network [263520 values with dtype=float32] station_reference
(station)
object
...
standard_name : station reference long_name : station reference identifier units : unitless description : reference ID for station. array(['AOEBaotou_P-D', 'ARMGraciosa_P-D', 'ARMOliktokAK_P-D', ...,\n",
- " 'Zaragoza_P-D', 'ZinderAirport_P-D', 'Zvenigorod_P-D'], dtype=object) Attributes: (9)
title : Inverse distance weighting (4 neighbours) interpolated a4s2 experiment data for the component od550aero with reference to the measurement stations in the AERONET_v3_lev1.5 network in 2020-06. institution : Barcelona Supercomputing Center source : Experiment a4s2 creator_name : Dene R. Bowdalo creator_email : dene.bowdalo@bsc.es conventions : CF-1.7 data_version : 1.0 history : Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc NCO : netCDF Operators version 4.9.2 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco) "
- ],
- "text/plain": [
- "\n",
- "Dimensions: (grid_edge: 877, station: 366, model_latitude: 181, model_longitude: 257, time: 720)\n",
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2020-06-01 ... 2020-06-30T2...\n",
- "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
- "Data variables:\n",
- " grid_edge_latitude (grid_edge) float64 ...\n",
- " grid_edge_longitude (grid_edge) float64 ...\n",
- " latitude (station) float64 ...\n",
- " longitude (station) float64 ...\n",
- " model_centre_latitude (model_latitude, model_longitude) float64 ...\n",
- " model_centre_longitude (model_latitude, model_longitude) float64 ...\n",
- " od550aero (station, time) float32 ...\n",
- " station_reference (station) object ...\n",
- "Attributes:\n",
- " title: Inverse distance weighting (4 neighbours) interpolated a4...\n",
- " institution: Barcelona Supercomputing Center\n",
- " source: Experiment a4s2\n",
- " creator_name: Dene R. Bowdalo\n",
- " creator_email: dene.bowdalo@bsc.es\n",
- " conventions: CF-1.7\n",
- " data_version: 1.0\n",
- " history: Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
- " NCO: netCDF Operators version 4.9.2 (Homepage = http://nco.sf...."
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "xr.open_dataset(path_prv_before)"
+ "# Original path: /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc\n",
+ "# Interpolated experiment to AERONET network from MONARCH (without bug)\n",
+ "path_prv_after = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_interp_od550aero_202006.nc'"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -2597,10 +751,10 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 22,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -2612,7 +766,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -2638,7 +792,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -2750,7 +904,7 @@
"[366 rows x 2 columns]"
]
},
- "execution_count": 24,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -2770,12 +924,12 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -2801,7 +955,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -2826,7 +980,7 @@
" dtype=float32)"
]
},
- "execution_count": 26,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -2852,14 +1006,18 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\tod550du horizontal interpolation\n"
+ "Creating Weight Matrix\n",
+ "Weight Matrix done!\n",
+ "Applying weights\n",
+ "\tod550du horizontal methods\n",
+ "Formatting\n"
]
}
],
@@ -2871,28 +1029,28 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'od550du': {'data': array([[0.5048964 , 0.63574161, 0.79751828, ..., 0.5958503 , 0.55814504,\n",
- " 0.54268227],\n",
- " [0.00756333, 0.00732328, 0.00696368, ..., 0.01266789, 0.01230407,\n",
- " 0.01215834],\n",
- " [0.01346751, 0.01355801, 0.01349525, ..., 0.01335663, 0.01338057,\n",
+ "{'od550du': {'data': array([[0.50475728, 0.63564274, 0.79752713, ..., 0.59597564, 0.5582657 ,\n",
+ " 0.54280552],\n",
+ " [0.00756297, 0.00732296, 0.00696342, ..., 0.01266858, 0.01230505,\n",
+ " 0.01215943],\n",
+ " [0.01346747, 0.01355799, 0.01349523, ..., 0.01335665, 0.01338057,\n",
" 0.01345622],\n",
" ...,\n",
- " [0.01848428, 0.01945806, 0.02101376, ..., 0.02690235, 0.02741421,\n",
- " 0.02839378],\n",
- " [0.90109581, 0.88706459, 0.87328311, ..., 0.84196177, 0.86970553,\n",
- " 0.89764859],\n",
- " [0.01314365, 0.01134606, 0.01024394, ..., 0.00430861, 0.00383769,\n",
- " 0.00348033]]), 'dimensions': ('station', 'time')}}"
+ " [0.01848554, 0.01946019, 0.02101676, ..., 0.02690396, 0.02741605,\n",
+ " 0.0283956 ],\n",
+ " [0.90090905, 0.88688139, 0.87311261, ..., 0.841753 , 0.86948918,\n",
+ " 0.89742322],\n",
+ " [0.0131431 , 0.01134514, 0.01024295, ..., 0.00430793, 0.00383704,\n",
+ " 0.00347976]]), 'dimensions': ('station', 'time')}}"
]
},
- "execution_count": 28,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -2910,7 +1068,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -2947,7 +1105,7 @@
" \n",
" 0 \n",
" POINT (109.62880 40.85170) \n",
- " 0.504896 \n",
+ " 0.504757 \n",
" \n",
" \n",
" 1 \n",
@@ -2957,7 +1115,7 @@
" \n",
" 2 \n",
" POINT (-149.88000 70.49950) \n",
- " 0.013468 \n",
+ " 0.013467 \n",
" \n",
" \n",
" 3 \n",
@@ -2967,7 +1125,7 @@
" \n",
" 4 \n",
" POINT (40.19450 -2.99600) \n",
- " 0.002980 \n",
+ " 0.002979 \n",
" \n",
" \n",
" ... \n",
@@ -2982,22 +1140,22 @@
" \n",
" 362 \n",
" POINT (126.93479 37.56443) \n",
- " 0.184427 \n",
+ " 0.184404 \n",
" \n",
" \n",
" 363 \n",
" POINT (-0.88235 41.63340) \n",
- " 0.018484 \n",
+ " 0.018486 \n",
" \n",
" \n",
" 364 \n",
" POINT (8.99023 13.77668) \n",
- " 0.901096 \n",
+ " 0.900909 \n",
" \n",
" \n",
" 365 \n",
" POINT (36.77500 55.69500) \n",
- " 0.013144 \n",
+ " 0.013143 \n",
" \n",
" \n",
"\n",
@@ -3007,22 +1165,22 @@
"text/plain": [
" geometry od550du\n",
"FID \n",
- "0 POINT (109.62880 40.85170) 0.504896\n",
+ "0 POINT (109.62880 40.85170) 0.504757\n",
"1 POINT (-28.02917 39.09109) 0.007563\n",
- "2 POINT (-149.88000 70.49950) 0.013468\n",
+ "2 POINT (-149.88000 70.49950) 0.013467\n",
"3 POINT (-97.48562 36.60518) 0.011034\n",
- "4 POINT (40.19450 -2.99600) 0.002980\n",
+ "4 POINT (40.19450 -2.99600) 0.002979\n",
".. ... ...\n",
"361 POINT (-114.37625 62.45130) 0.014462\n",
- "362 POINT (126.93479 37.56443) 0.184427\n",
- "363 POINT (-0.88235 41.63340) 0.018484\n",
- "364 POINT (8.99023 13.77668) 0.901096\n",
- "365 POINT (36.77500 55.69500) 0.013144\n",
+ "362 POINT (126.93479 37.56443) 0.184404\n",
+ "363 POINT (-0.88235 41.63340) 0.018486\n",
+ "364 POINT (8.99023 13.77668) 0.900909\n",
+ "365 POINT (36.77500 55.69500) 0.013143\n",
"\n",
"[366 rows x 2 columns]"
]
},
- "execution_count": 29,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -3042,12 +1200,12 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -3087,7 +1245,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -3096,7 +1254,7 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -3113,7 +1271,7 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -3157,9 +1315,9 @@
" 0 \n",
" POINT (109.62880 40.85170) \n",
" 0.504757 \n",
- " 0.504896 \n",
+ " 0.504757 \n",
" NaN \n",
- " 1.391183e-04 \n",
+ " -6.329158e-09 \n",
" \n",
" \n",
" 1 \n",
@@ -3167,15 +1325,15 @@
" 0.007563 \n",
" 0.007563 \n",
" NaN \n",
- " 3.650604e-07 \n",
+ " 3.951382e-11 \n",
" \n",
" \n",
" 2 \n",
" POINT (-149.88000 70.49950) \n",
" 0.013467 \n",
- " 0.013468 \n",
+ " 0.013467 \n",
" NaN \n",
- " 3.439117e-08 \n",
+ " -6.039905e-11 \n",
" \n",
" \n",
" 3 \n",
@@ -3183,15 +1341,15 @@
" 0.011034 \n",
" 0.011034 \n",
" NaN \n",
- " 4.619126e-07 \n",
+ " 2.067994e-10 \n",
" \n",
" \n",
" 4 \n",
" POINT (40.19450 -2.99600) \n",
" 0.002979 \n",
- " 0.002980 \n",
+ " 0.002979 \n",
" NaN \n",
- " 2.042769e-07 \n",
+ " -3.650107e-11 \n",
" \n",
" \n",
" ... \n",
@@ -3207,39 +1365,39 @@
" 0.014462 \n",
" 0.014462 \n",
" NaN \n",
- " -7.643789e-08 \n",
+ " 1.836392e-10 \n",
" \n",
" \n",
" 362 \n",
" POINT (126.93479 37.56443) \n",
" 0.184404 \n",
- " 0.184427 \n",
+ " 0.184404 \n",
" 0.152831 \n",
- " 2.291842e-05 \n",
+ " 5.078100e-09 \n",
" \n",
" \n",
" 363 \n",
" POINT (-0.88235 41.63340) \n",
" 0.018486 \n",
- " 0.018484 \n",
+ " 0.018486 \n",
" NaN \n",
- " -1.265558e-06 \n",
+ " -8.104343e-11 \n",
" \n",
" \n",
" 364 \n",
" POINT (8.99023 13.77668) \n",
" 0.900909 \n",
- " 0.901096 \n",
+ " 0.900909 \n",
" NaN \n",
- " 1.867402e-04 \n",
+ " -2.038381e-08 \n",
" \n",
" \n",
" 365 \n",
" POINT (36.77500 55.69500) \n",
" 0.013143 \n",
- " 0.013144 \n",
+ " 0.013143 \n",
" NaN \n",
- " 5.505649e-07 \n",
+ " 1.533717e-10 \n",
" \n",
" \n",
"\n",
@@ -3249,36 +1407,36 @@
"text/plain": [
" geometry providentia nes obs \\\n",
"FID \n",
- "0 POINT (109.62880 40.85170) 0.504757 0.504896 NaN \n",
+ "0 POINT (109.62880 40.85170) 0.504757 0.504757 NaN \n",
"1 POINT (-28.02917 39.09109) 0.007563 0.007563 NaN \n",
- "2 POINT (-149.88000 70.49950) 0.013467 0.013468 NaN \n",
+ "2 POINT (-149.88000 70.49950) 0.013467 0.013467 NaN \n",
"3 POINT (-97.48562 36.60518) 0.011034 0.011034 NaN \n",
- "4 POINT (40.19450 -2.99600) 0.002979 0.002980 NaN \n",
+ "4 POINT (40.19450 -2.99600) 0.002979 0.002979 NaN \n",
".. ... ... ... ... \n",
"361 POINT (-114.37625 62.45130) 0.014462 0.014462 NaN \n",
- "362 POINT (126.93479 37.56443) 0.184404 0.184427 0.152831 \n",
- "363 POINT (-0.88235 41.63340) 0.018486 0.018484 NaN \n",
- "364 POINT (8.99023 13.77668) 0.900909 0.901096 NaN \n",
- "365 POINT (36.77500 55.69500) 0.013143 0.013144 NaN \n",
+ "362 POINT (126.93479 37.56443) 0.184404 0.184404 0.152831 \n",
+ "363 POINT (-0.88235 41.63340) 0.018486 0.018486 NaN \n",
+ "364 POINT (8.99023 13.77668) 0.900909 0.900909 NaN \n",
+ "365 POINT (36.77500 55.69500) 0.013143 0.013143 NaN \n",
"\n",
" absolute_difference \n",
"FID \n",
- "0 1.391183e-04 \n",
- "1 3.650604e-07 \n",
- "2 3.439117e-08 \n",
- "3 4.619126e-07 \n",
- "4 2.042769e-07 \n",
+ "0 -6.329158e-09 \n",
+ "1 3.951382e-11 \n",
+ "2 -6.039905e-11 \n",
+ "3 2.067994e-10 \n",
+ "4 -3.650107e-11 \n",
".. ... \n",
- "361 -7.643789e-08 \n",
- "362 2.291842e-05 \n",
- "363 -1.265558e-06 \n",
- "364 1.867402e-04 \n",
- "365 5.505649e-07 \n",
+ "361 1.836392e-10 \n",
+ "362 5.078100e-09 \n",
+ "363 -8.104343e-11 \n",
+ "364 -2.038381e-08 \n",
+ "365 1.533717e-10 \n",
"\n",
"[366 rows x 5 columns]"
]
},
- "execution_count": 67,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -3290,12 +1448,12 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -3320,16 +1478,16 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "-0.0004256221585662301"
+ "-2.1082139034511727e-08"
]
},
- "execution_count": 35,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -3340,16 +1498,16 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.0012512493387983464"
+ "1.1416417144971547e-08"
]
},
- "execution_count": 36,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -3367,7 +1525,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
@@ -3394,6 +1552,7 @@
" geometry \n",
" providentia \n",
" nes \n",
+ " obs \n",
" absolute_difference \n",
" relative_difference \n",
" \n",
@@ -3404,6 +1563,7 @@
" \n",
" \n",
" \n",
+ " \n",
" \n",
" \n",
" \n",
@@ -3411,41 +1571,46 @@
" 0 \n",
" POINT (109.62880 40.85170) \n",
" 0.504757 \n",
- " 0.504896 \n",
- " 1.391183e-04 \n",
- " 0.027554 \n",
+ " 0.504757 \n",
+ " NaN \n",
+ " -6.329158e-09 \n",
+ " -1.253901e-06 \n",
" \n",
" \n",
" 1 \n",
" POINT (-28.02917 39.09109) \n",
" 0.007563 \n",
" 0.007563 \n",
- " 3.650604e-07 \n",
- " 0.004827 \n",
+ " NaN \n",
+ " 3.951382e-11 \n",
+ " 5.224647e-07 \n",
" \n",
" \n",
" 2 \n",
" POINT (-149.88000 70.49950) \n",
" 0.013467 \n",
- " 0.013468 \n",
- " 3.439117e-08 \n",
- " 0.000255 \n",
+ " 0.013467 \n",
+ " NaN \n",
+ " -6.039905e-11 \n",
+ " -4.484809e-07 \n",
" \n",
" \n",
" 3 \n",
" POINT (-97.48562 36.60518) \n",
" 0.011034 \n",
" 0.011034 \n",
- " 4.619126e-07 \n",
- " 0.004186 \n",
+ " NaN \n",
+ " 2.067994e-10 \n",
+ " 1.874285e-06 \n",
" \n",
" \n",
" 4 \n",
" POINT (40.19450 -2.99600) \n",
" 0.002979 \n",
- " 0.002980 \n",
- " 2.042769e-07 \n",
- " 0.006856 \n",
+ " 0.002979 \n",
+ " NaN \n",
+ " -3.650107e-11 \n",
+ " -1.225091e-06 \n",
" \n",
" \n",
" ... \n",
@@ -3454,85 +1619,91 @@
" ... \n",
" ... \n",
" ... \n",
+ " ... \n",
" \n",
" \n",
" 361 \n",
" POINT (-114.37625 62.45130) \n",
" 0.014462 \n",
" 0.014462 \n",
- " -7.643789e-08 \n",
- " -0.000529 \n",
+ " NaN \n",
+ " 1.836392e-10 \n",
+ " 1.269820e-06 \n",
" \n",
" \n",
" 362 \n",
" POINT (126.93479 37.56443) \n",
" 0.184404 \n",
- " 0.184427 \n",
- " 2.291842e-05 \n",
- " 0.012427 \n",
+ " 0.184404 \n",
+ " 0.152831 \n",
+ " 5.078100e-09 \n",
+ " 2.753794e-06 \n",
" \n",
" \n",
" 363 \n",
" POINT (-0.88235 41.63340) \n",
" 0.018486 \n",
- " 0.018484 \n",
- " -1.265558e-06 \n",
- " -0.006847 \n",
+ " 0.018486 \n",
+ " NaN \n",
+ " -8.104343e-11 \n",
+ " -4.384152e-07 \n",
" \n",
" \n",
" 364 \n",
" POINT (8.99023 13.77668) \n",
" 0.900909 \n",
- " 0.901096 \n",
- " 1.867402e-04 \n",
- " 0.020724 \n",
+ " 0.900909 \n",
+ " NaN \n",
+ " -2.038381e-08 \n",
+ " -2.262583e-06 \n",
" \n",
" \n",
" 365 \n",
" POINT (36.77500 55.69500) \n",
" 0.013143 \n",
- " 0.013144 \n",
- " 5.505649e-07 \n",
- " 0.004189 \n",
+ " 0.013143 \n",
+ " NaN \n",
+ " 1.533717e-10 \n",
+ " 1.166937e-06 \n",
" \n",
" \n",
"\n",
- "366 rows × 5 columns
\n",
+ "366 rows × 6 columns
\n",
""
],
"text/plain": [
- " geometry providentia nes absolute_difference \\\n",
- "FID \n",
- "0 POINT (109.62880 40.85170) 0.504757 0.504896 1.391183e-04 \n",
- "1 POINT (-28.02917 39.09109) 0.007563 0.007563 3.650604e-07 \n",
- "2 POINT (-149.88000 70.49950) 0.013467 0.013468 3.439117e-08 \n",
- "3 POINT (-97.48562 36.60518) 0.011034 0.011034 4.619126e-07 \n",
- "4 POINT (40.19450 -2.99600) 0.002979 0.002980 2.042769e-07 \n",
- ".. ... ... ... ... \n",
- "361 POINT (-114.37625 62.45130) 0.014462 0.014462 -7.643789e-08 \n",
- "362 POINT (126.93479 37.56443) 0.184404 0.184427 2.291842e-05 \n",
- "363 POINT (-0.88235 41.63340) 0.018486 0.018484 -1.265558e-06 \n",
- "364 POINT (8.99023 13.77668) 0.900909 0.901096 1.867402e-04 \n",
- "365 POINT (36.77500 55.69500) 0.013143 0.013144 5.505649e-07 \n",
- "\n",
- " relative_difference \n",
- "FID \n",
- "0 0.027554 \n",
- "1 0.004827 \n",
- "2 0.000255 \n",
- "3 0.004186 \n",
- "4 0.006856 \n",
- ".. ... \n",
- "361 -0.000529 \n",
- "362 0.012427 \n",
- "363 -0.006847 \n",
- "364 0.020724 \n",
- "365 0.004189 \n",
- "\n",
- "[366 rows x 5 columns]"
+ " geometry providentia nes obs \\\n",
+ "FID \n",
+ "0 POINT (109.62880 40.85170) 0.504757 0.504757 NaN \n",
+ "1 POINT (-28.02917 39.09109) 0.007563 0.007563 NaN \n",
+ "2 POINT (-149.88000 70.49950) 0.013467 0.013467 NaN \n",
+ "3 POINT (-97.48562 36.60518) 0.011034 0.011034 NaN \n",
+ "4 POINT (40.19450 -2.99600) 0.002979 0.002979 NaN \n",
+ ".. ... ... ... ... \n",
+ "361 POINT (-114.37625 62.45130) 0.014462 0.014462 NaN \n",
+ "362 POINT (126.93479 37.56443) 0.184404 0.184404 0.152831 \n",
+ "363 POINT (-0.88235 41.63340) 0.018486 0.018486 NaN \n",
+ "364 POINT (8.99023 13.77668) 0.900909 0.900909 NaN \n",
+ "365 POINT (36.77500 55.69500) 0.013143 0.013143 NaN \n",
+ "\n",
+ " absolute_difference relative_difference \n",
+ "FID \n",
+ "0 -6.329158e-09 -1.253901e-06 \n",
+ "1 3.951382e-11 5.224647e-07 \n",
+ "2 -6.039905e-11 -4.484809e-07 \n",
+ "3 2.067994e-10 1.874285e-06 \n",
+ "4 -3.650107e-11 -1.225091e-06 \n",
+ ".. ... ... \n",
+ "361 1.836392e-10 1.269820e-06 \n",
+ "362 5.078100e-09 2.753794e-06 \n",
+ "363 -8.104343e-11 -4.384152e-07 \n",
+ "364 -2.038381e-08 -2.262583e-06 \n",
+ "365 1.533717e-10 1.166937e-06 \n",
+ "\n",
+ "[366 rows x 6 columns]"
]
},
- "execution_count": 37,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -3544,22 +1715,9 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"fig_4, ax_4 = plt.subplots(1, figsize=(20, 7))\n",
"\n",
@@ -3576,113 +1734,27 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "-0.19982211142673692"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gdf_diff['relative_difference'].min()"
]
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "6.944350654874811"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gdf_diff['relative_difference'].max()"
]
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " providentia \n",
- " nes \n",
- " absolute_difference \n",
- " relative_difference \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 306 \n",
- " POINT (103.78038 1.29767) \n",
- " 0.001023 \n",
- " 0.001099 \n",
- " 0.000076 \n",
- " 6.944351 \n",
- " \n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " geometry providentia nes absolute_difference \\\n",
- "FID \n",
- "306 POINT (103.78038 1.29767) 0.001023 0.001099 0.000076 \n",
- "\n",
- " relative_difference \n",
- "FID \n",
- "306 6.944351 "
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gdf_diff.loc[gdf_diff['relative_difference'] == gdf_diff['relative_difference'].max()]"
]
@@ -3696,32 +1768,9 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 68,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plt.scatter(gdf_diff['providentia'], gdf_diff['nes'])"
]
@@ -3735,64 +1784,18 @@
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 69,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plt.scatter(gdf_diff['nes'], gdf_diff['obs'])"
]
},
{
"cell_type": "code",
- "execution_count": 71,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 71,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plt.scatter(gdf_diff['providentia'], gdf_diff['obs'])"
]
@@ -3813,123 +1816,9 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " model \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " POLYGON ((-180.70312 -90.50000, -179.29688 -90... \n",
- " 0.000144 \n",
- " \n",
- " \n",
- " 1 \n",
- " POLYGON ((-179.29688 -90.50000, -177.89062 -90... \n",
- " 0.000144 \n",
- " \n",
- " \n",
- " 2 \n",
- " POLYGON ((-177.89062 -90.50000, -176.48438 -90... \n",
- " 0.000144 \n",
- " \n",
- " \n",
- " 3 \n",
- " POLYGON ((-176.48438 -90.50000, -175.07812 -90... \n",
- " 0.000144 \n",
- " \n",
- " \n",
- " 4 \n",
- " POLYGON ((-175.07812 -90.50000, -173.67188 -90... \n",
- " 0.000144 \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 46512 \n",
- " POLYGON ((173.67188 89.50000, 175.07812 89.500... \n",
- " 0.023041 \n",
- " \n",
- " \n",
- " 46513 \n",
- " POLYGON ((175.07812 89.50000, 176.48438 89.500... \n",
- " 0.023041 \n",
- " \n",
- " \n",
- " 46514 \n",
- " POLYGON ((176.48438 89.50000, 177.89062 89.500... \n",
- " 0.023041 \n",
- " \n",
- " \n",
- " 46515 \n",
- " POLYGON ((177.89062 89.50000, 179.29688 89.500... \n",
- " 0.023041 \n",
- " \n",
- " \n",
- " 46516 \n",
- " POLYGON ((179.29688 89.50000, 180.70312 89.500... \n",
- " 0.023041 \n",
- " \n",
- " \n",
- "
\n",
- "
46517 rows × 2 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry model\n",
- "FID \n",
- "0 POLYGON ((-180.70312 -90.50000, -179.29688 -90... 0.000144\n",
- "1 POLYGON ((-179.29688 -90.50000, -177.89062 -90... 0.000144\n",
- "2 POLYGON ((-177.89062 -90.50000, -176.48438 -90... 0.000144\n",
- "3 POLYGON ((-176.48438 -90.50000, -175.07812 -90... 0.000144\n",
- "4 POLYGON ((-175.07812 -90.50000, -173.67188 -90... 0.000144\n",
- "... ... ...\n",
- "46512 POLYGON ((173.67188 89.50000, 175.07812 89.500... 0.023041\n",
- "46513 POLYGON ((175.07812 89.50000, 176.48438 89.500... 0.023041\n",
- "46514 POLYGON ((176.48438 89.50000, 177.89062 89.500... 0.023041\n",
- "46515 POLYGON ((177.89062 89.50000, 179.29688 89.500... 0.023041\n",
- "46516 POLYGON ((179.29688 89.50000, 180.70312 89.500... 0.023041\n",
- "\n",
- "[46517 rows x 2 columns]"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gdf_eval = copy.deepcopy(gdf_model)\n",
"gdf_eval.rename(columns={'od550du': 'model'}, inplace=True)\n",
@@ -3945,136 +1834,9 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " model \n",
- " obs \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 9587 \n",
- " POLYGON ((-71.01562 -53.50000, -69.60938 -53.5... \n",
- " 0.000458 \n",
- " NaN \n",
- " \n",
- " \n",
- " 9845 \n",
- " POLYGON ((-69.60938 -52.50000, -68.20312 -52.5... \n",
- " 0.000429 \n",
- " NaN \n",
- " \n",
- " \n",
- " 11388 \n",
- " POLYGON ((-68.20312 -46.50000, -66.79688 -46.5... \n",
- " 0.000508 \n",
- " NaN \n",
- " \n",
- " \n",
- " 12161 \n",
- " POLYGON ((-65.39062 -43.50000, -63.98438 -43.5... \n",
- " 0.001136 \n",
- " NaN \n",
- " \n",
- " \n",
- " 13187 \n",
- " POLYGON ((-68.20312 -39.50000, -66.79688 -39.5... \n",
- " 0.002681 \n",
- " NaN \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 41394 \n",
- " POLYGON ((-156.79688 70.50000, -155.39062 70.5... \n",
- " 0.013695 \n",
- " NaN \n",
- " \n",
- " \n",
- " 42998 \n",
- " POLYGON ((-69.60938 76.50000, -68.20312 76.500... \n",
- " 0.013201 \n",
- " NaN \n",
- " \n",
- " \n",
- " 43058 \n",
- " POLYGON ((14.76562 76.50000, 16.17188 76.50000... \n",
- " 0.017579 \n",
- " NaN \n",
- " \n",
- " \n",
- " 43569 \n",
- " POLYGON ((10.54688 78.50000, 11.95312 78.50000... \n",
- " 0.013934 \n",
- " NaN \n",
- " \n",
- " \n",
- " 43757 \n",
- " POLYGON ((-86.48438 79.50000, -85.07812 79.500... \n",
- " 0.011777 \n",
- " NaN \n",
- " \n",
- " \n",
- "
\n",
- "
366 rows × 3 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry model obs\n",
- "FID \n",
- "9587 POLYGON ((-71.01562 -53.50000, -69.60938 -53.5... 0.000458 NaN\n",
- "9845 POLYGON ((-69.60938 -52.50000, -68.20312 -52.5... 0.000429 NaN\n",
- "11388 POLYGON ((-68.20312 -46.50000, -66.79688 -46.5... 0.000508 NaN\n",
- "12161 POLYGON ((-65.39062 -43.50000, -63.98438 -43.5... 0.001136 NaN\n",
- "13187 POLYGON ((-68.20312 -39.50000, -66.79688 -39.5... 0.002681 NaN\n",
- "... ... ... ...\n",
- "41394 POLYGON ((-156.79688 70.50000, -155.39062 70.5... 0.013695 NaN\n",
- "42998 POLYGON ((-69.60938 76.50000, -68.20312 76.500... 0.013201 NaN\n",
- "43058 POLYGON ((14.76562 76.50000, 16.17188 76.50000... 0.017579 NaN\n",
- "43569 POLYGON ((10.54688 78.50000, 11.95312 78.50000... 0.013934 NaN\n",
- "43757 POLYGON ((-86.48438 79.50000, -85.07812 79.500... 0.011777 NaN\n",
- "\n",
- "[366 rows x 3 columns]"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gdf_eval = gdf_eval.sjoin(data_obs.shapefile.rename(columns={'od550aero': 'obs'}))\n",
"gdf_eval = gdf_eval.drop(columns=['index_right'])\n",
@@ -4090,163 +1852,9 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " model \n",
- " obs \n",
- " nes \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 9587 \n",
- " POLYGON ((-71.01562 -53.50000, -69.60938 -53.5... \n",
- " 0.000458 \n",
- " NaN \n",
- " 0.000420 \n",
- " \n",
- " \n",
- " 9845 \n",
- " POLYGON ((-69.60938 -52.50000, -68.20312 -52.5... \n",
- " 0.000429 \n",
- " NaN \n",
- " 0.000441 \n",
- " \n",
- " \n",
- " 11388 \n",
- " POLYGON ((-68.20312 -46.50000, -66.79688 -46.5... \n",
- " 0.000508 \n",
- " NaN \n",
- " 0.000532 \n",
- " \n",
- " \n",
- " 12161 \n",
- " POLYGON ((-65.39062 -43.50000, -63.98438 -43.5... \n",
- " 0.001136 \n",
- " NaN \n",
- " 0.001565 \n",
- " \n",
- " \n",
- " 13187 \n",
- " POLYGON ((-68.20312 -39.50000, -66.79688 -39.5... \n",
- " 0.002681 \n",
- " NaN \n",
- " 0.001954 \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 41394 \n",
- " POLYGON ((-156.79688 70.50000, -155.39062 70.5... \n",
- " 0.013695 \n",
- " NaN \n",
- " 0.013110 \n",
- " \n",
- " \n",
- " 42998 \n",
- " POLYGON ((-69.60938 76.50000, -68.20312 76.500... \n",
- " 0.013201 \n",
- " NaN \n",
- " 0.013245 \n",
- " \n",
- " \n",
- " 43058 \n",
- " POLYGON ((14.76562 76.50000, 16.17188 76.50000... \n",
- " 0.017579 \n",
- " NaN \n",
- " 0.017365 \n",
- " \n",
- " \n",
- " 43569 \n",
- " POLYGON ((10.54688 78.50000, 11.95312 78.50000... \n",
- " 0.013934 \n",
- " NaN \n",
- " 0.014070 \n",
- " \n",
- " \n",
- " 43757 \n",
- " POLYGON ((-86.48438 79.50000, -85.07812 79.500... \n",
- " 0.011777 \n",
- " NaN \n",
- " 0.011703 \n",
- " \n",
- " \n",
- "
\n",
- "
512 rows × 4 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry model obs \\\n",
- "FID \n",
- "9587 POLYGON ((-71.01562 -53.50000, -69.60938 -53.5... 0.000458 NaN \n",
- "9845 POLYGON ((-69.60938 -52.50000, -68.20312 -52.5... 0.000429 NaN \n",
- "11388 POLYGON ((-68.20312 -46.50000, -66.79688 -46.5... 0.000508 NaN \n",
- "12161 POLYGON ((-65.39062 -43.50000, -63.98438 -43.5... 0.001136 NaN \n",
- "13187 POLYGON ((-68.20312 -39.50000, -66.79688 -39.5... 0.002681 NaN \n",
- "... ... ... ... \n",
- "41394 POLYGON ((-156.79688 70.50000, -155.39062 70.5... 0.013695 NaN \n",
- "42998 POLYGON ((-69.60938 76.50000, -68.20312 76.500... 0.013201 NaN \n",
- "43058 POLYGON ((14.76562 76.50000, 16.17188 76.50000... 0.017579 NaN \n",
- "43569 POLYGON ((10.54688 78.50000, 11.95312 78.50000... 0.013934 NaN \n",
- "43757 POLYGON ((-86.48438 79.50000, -85.07812 79.500... 0.011777 NaN \n",
- "\n",
- " nes \n",
- "FID \n",
- "9587 0.000420 \n",
- "9845 0.000441 \n",
- "11388 0.000532 \n",
- "12161 0.001565 \n",
- "13187 0.001954 \n",
- "... ... \n",
- "41394 0.013110 \n",
- "42998 0.013245 \n",
- "43058 0.017365 \n",
- "43569 0.014070 \n",
- "43757 0.011703 \n",
- "\n",
- "[512 rows x 4 columns]"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gdf_eval = gdf_eval.sjoin(gdf_nes.rename(columns={'od550du': 'nes'}))\n",
"gdf_eval = gdf_eval.drop(columns=['index_right'])\n",
@@ -4262,176 +1870,9 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " model \n",
- " obs \n",
- " nes \n",
- " providentia \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 9587 \n",
- " POLYGON ((-71.01562 -53.50000, -69.60938 -53.5... \n",
- " 0.000458 \n",
- " NaN \n",
- " 0.000420 \n",
- " 0.000420 \n",
- " \n",
- " \n",
- " 9845 \n",
- " POLYGON ((-69.60938 -52.50000, -68.20312 -52.5... \n",
- " 0.000429 \n",
- " NaN \n",
- " 0.000441 \n",
- " 0.000441 \n",
- " \n",
- " \n",
- " 11388 \n",
- " POLYGON ((-68.20312 -46.50000, -66.79688 -46.5... \n",
- " 0.000508 \n",
- " NaN \n",
- " 0.000532 \n",
- " 0.000532 \n",
- " \n",
- " \n",
- " 12161 \n",
- " POLYGON ((-65.39062 -43.50000, -63.98438 -43.5... \n",
- " 0.001136 \n",
- " NaN \n",
- " 0.001565 \n",
- " 0.001565 \n",
- " \n",
- " \n",
- " 13187 \n",
- " POLYGON ((-68.20312 -39.50000, -66.79688 -39.5... \n",
- " 0.002681 \n",
- " NaN \n",
- " 0.001954 \n",
- " 0.001954 \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 41394 \n",
- " POLYGON ((-156.79688 70.50000, -155.39062 70.5... \n",
- " 0.013695 \n",
- " NaN \n",
- " 0.013110 \n",
- " 0.013110 \n",
- " \n",
- " \n",
- " 42998 \n",
- " POLYGON ((-69.60938 76.50000, -68.20312 76.500... \n",
- " 0.013201 \n",
- " NaN \n",
- " 0.013245 \n",
- " 0.013245 \n",
- " \n",
- " \n",
- " 43058 \n",
- " POLYGON ((14.76562 76.50000, 16.17188 76.50000... \n",
- " 0.017579 \n",
- " NaN \n",
- " 0.017365 \n",
- " 0.017365 \n",
- " \n",
- " \n",
- " 43569 \n",
- " POLYGON ((10.54688 78.50000, 11.95312 78.50000... \n",
- " 0.013934 \n",
- " NaN \n",
- " 0.014070 \n",
- " 0.014070 \n",
- " \n",
- " \n",
- " 43757 \n",
- " POLYGON ((-86.48438 79.50000, -85.07812 79.500... \n",
- " 0.011777 \n",
- " NaN \n",
- " 0.011703 \n",
- " 0.011703 \n",
- " \n",
- " \n",
- "
\n",
- "
954 rows × 5 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry model obs \\\n",
- "FID \n",
- "9587 POLYGON ((-71.01562 -53.50000, -69.60938 -53.5... 0.000458 NaN \n",
- "9845 POLYGON ((-69.60938 -52.50000, -68.20312 -52.5... 0.000429 NaN \n",
- "11388 POLYGON ((-68.20312 -46.50000, -66.79688 -46.5... 0.000508 NaN \n",
- "12161 POLYGON ((-65.39062 -43.50000, -63.98438 -43.5... 0.001136 NaN \n",
- "13187 POLYGON ((-68.20312 -39.50000, -66.79688 -39.5... 0.002681 NaN \n",
- "... ... ... ... \n",
- "41394 POLYGON ((-156.79688 70.50000, -155.39062 70.5... 0.013695 NaN \n",
- "42998 POLYGON ((-69.60938 76.50000, -68.20312 76.500... 0.013201 NaN \n",
- "43058 POLYGON ((14.76562 76.50000, 16.17188 76.50000... 0.017579 NaN \n",
- "43569 POLYGON ((10.54688 78.50000, 11.95312 78.50000... 0.013934 NaN \n",
- "43757 POLYGON ((-86.48438 79.50000, -85.07812 79.500... 0.011777 NaN \n",
- "\n",
- " nes providentia \n",
- "FID \n",
- "9587 0.000420 0.000420 \n",
- "9845 0.000441 0.000441 \n",
- "11388 0.000532 0.000532 \n",
- "12161 0.001565 0.001565 \n",
- "13187 0.001954 0.001954 \n",
- "... ... ... \n",
- "41394 0.013110 0.013110 \n",
- "42998 0.013245 0.013245 \n",
- "43058 0.017365 0.017365 \n",
- "43569 0.014070 0.014070 \n",
- "43757 0.011703 0.011703 \n",
- "\n",
- "[954 rows x 5 columns]"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gdf_eval = gdf_eval.sjoin(gdf_prv_after.rename(columns={'od550aero': 'providentia'}))\n",
"gdf_eval = gdf_eval.drop(columns=['index_right'])\n",
@@ -4447,189 +1888,9 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " model \n",
- " obs \n",
- " nes \n",
- " providentia \n",
- " nes-prov \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 9587 \n",
- " POLYGON ((-71.01562 -53.50000, -69.60938 -53.5... \n",
- " 0.000458 \n",
- " NaN \n",
- " 0.000420 \n",
- " 0.000420 \n",
- " 0.000568 \n",
- " \n",
- " \n",
- " 9845 \n",
- " POLYGON ((-69.60938 -52.50000, -68.20312 -52.5... \n",
- " 0.000429 \n",
- " NaN \n",
- " 0.000441 \n",
- " 0.000441 \n",
- " 0.001709 \n",
- " \n",
- " \n",
- " 11388 \n",
- " POLYGON ((-68.20312 -46.50000, -66.79688 -46.5... \n",
- " 0.000508 \n",
- " NaN \n",
- " 0.000532 \n",
- " 0.000532 \n",
- " 0.014902 \n",
- " \n",
- " \n",
- " 12161 \n",
- " POLYGON ((-65.39062 -43.50000, -63.98438 -43.5... \n",
- " 0.001136 \n",
- " NaN \n",
- " 0.001565 \n",
- " 0.001565 \n",
- " -0.011830 \n",
- " \n",
- " \n",
- " 13187 \n",
- " POLYGON ((-68.20312 -39.50000, -66.79688 -39.5... \n",
- " 0.002681 \n",
- " NaN \n",
- " 0.001954 \n",
- " 0.001954 \n",
- " 0.001149 \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 41394 \n",
- " POLYGON ((-156.79688 70.50000, -155.39062 70.5... \n",
- " 0.013695 \n",
- " NaN \n",
- " 0.013110 \n",
- " 0.013110 \n",
- " 0.000853 \n",
- " \n",
- " \n",
- " 42998 \n",
- " POLYGON ((-69.60938 76.50000, -68.20312 76.500... \n",
- " 0.013201 \n",
- " NaN \n",
- " 0.013245 \n",
- " 0.013245 \n",
- " 0.000210 \n",
- " \n",
- " \n",
- " 43058 \n",
- " POLYGON ((14.76562 76.50000, 16.17188 76.50000... \n",
- " 0.017579 \n",
- " NaN \n",
- " 0.017365 \n",
- " 0.017365 \n",
- " 0.000002 \n",
- " \n",
- " \n",
- " 43569 \n",
- " POLYGON ((10.54688 78.50000, 11.95312 78.50000... \n",
- " 0.013934 \n",
- " NaN \n",
- " 0.014070 \n",
- " 0.014070 \n",
- " -0.000013 \n",
- " \n",
- " \n",
- " 43757 \n",
- " POLYGON ((-86.48438 79.50000, -85.07812 79.500... \n",
- " 0.011777 \n",
- " NaN \n",
- " 0.011703 \n",
- " 0.011703 \n",
- " -0.000004 \n",
- " \n",
- " \n",
- "
\n",
- "
954 rows × 6 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry model obs \\\n",
- "FID \n",
- "9587 POLYGON ((-71.01562 -53.50000, -69.60938 -53.5... 0.000458 NaN \n",
- "9845 POLYGON ((-69.60938 -52.50000, -68.20312 -52.5... 0.000429 NaN \n",
- "11388 POLYGON ((-68.20312 -46.50000, -66.79688 -46.5... 0.000508 NaN \n",
- "12161 POLYGON ((-65.39062 -43.50000, -63.98438 -43.5... 0.001136 NaN \n",
- "13187 POLYGON ((-68.20312 -39.50000, -66.79688 -39.5... 0.002681 NaN \n",
- "... ... ... ... \n",
- "41394 POLYGON ((-156.79688 70.50000, -155.39062 70.5... 0.013695 NaN \n",
- "42998 POLYGON ((-69.60938 76.50000, -68.20312 76.500... 0.013201 NaN \n",
- "43058 POLYGON ((14.76562 76.50000, 16.17188 76.50000... 0.017579 NaN \n",
- "43569 POLYGON ((10.54688 78.50000, 11.95312 78.50000... 0.013934 NaN \n",
- "43757 POLYGON ((-86.48438 79.50000, -85.07812 79.500... 0.011777 NaN \n",
- "\n",
- " nes providentia nes-prov \n",
- "FID \n",
- "9587 0.000420 0.000420 0.000568 \n",
- "9845 0.000441 0.000441 0.001709 \n",
- "11388 0.000532 0.000532 0.014902 \n",
- "12161 0.001565 0.001565 -0.011830 \n",
- "13187 0.001954 0.001954 0.001149 \n",
- "... ... ... ... \n",
- "41394 0.013110 0.013110 0.000853 \n",
- "42998 0.013245 0.013245 0.000210 \n",
- "43058 0.017365 0.017365 0.000002 \n",
- "43569 0.014070 0.014070 -0.000013 \n",
- "43757 0.011703 0.011703 -0.000004 \n",
- "\n",
- "[954 rows x 6 columns]"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gdf_eval['nes-prov'] = (gdf_eval['nes'] - gdf_eval['providentia'])*100 / gdf_eval['nes']\n",
"gdf_eval"
@@ -4637,20 +1898,9 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "31.73012028502732"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Difference is higher now because we have used spatial joins (nearest 1 neighbour vs nearest 4)\n",
"gdf_eval['nes-prov'].max()"
@@ -4658,20 +1908,9 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "-46.491689814524314"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Difference is higher now because we have used spatial joins (nearest 1 neighbour vs nearest 4)\n",
"gdf_eval['nes-prov'].min()"
@@ -4686,64 +1925,18 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plt.scatter(gdf_eval['providentia'], gdf_eval['model'])"
]
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plt.scatter(gdf_eval['nes'], gdf_eval['model'])"
]
diff --git a/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb b/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..59ad844841041dadb951c235ae440d8270d26228
--- /dev/null
+++ b/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb
@@ -0,0 +1,1537 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to create shapefiles"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nes import *\n",
+ "import datetime"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. From grids that have already been created"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc\n",
+ "# Regular lat-lon grid from MONARCH\n",
+ "grid_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc'\n",
+ "grid = open_netcdf(path=grid_path, info=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Loading sconcno2 var (1/1)\n",
+ "Rank 000: Loaded sconcno2 var ((25, 1, 115, 165))\n"
+ ]
+ }
+ ],
+ "source": [
+ "grid.load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid.to_shapefile(path='regular_shp', \n",
+ " time=datetime.datetime(2019, 1, 1, 10, 0), \n",
+ " lev=0, var_list=['sconcno2'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " sconcno2 \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-11.85000 33.85000, -11.75000 33.850... \n",
+ " 0.000288 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-11.75000 33.85000, -11.65000 33.850... \n",
+ " 0.000271 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-11.65000 33.85000, -11.55000 33.850... \n",
+ " 0.000258 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-11.55000 33.85000, -11.45000 33.850... \n",
+ " 0.000254 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-11.45000 33.85000, -11.35000 33.850... \n",
+ " 0.000262 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 18970 \n",
+ " POLYGON ((4.15000 45.25000, 4.25000 45.25000, ... \n",
+ " 0.003521 \n",
+ " \n",
+ " \n",
+ " 18971 \n",
+ " POLYGON ((4.25000 45.25000, 4.35000 45.25000, ... \n",
+ " 0.003924 \n",
+ " \n",
+ " \n",
+ " 18972 \n",
+ " POLYGON ((4.35000 45.25000, 4.45000 45.25000, ... \n",
+ " 0.004454 \n",
+ " \n",
+ " \n",
+ " 18973 \n",
+ " POLYGON ((4.45000 45.25000, 4.55000 45.25000, ... \n",
+ " 0.004619 \n",
+ " \n",
+ " \n",
+ " 18974 \n",
+ " POLYGON ((4.55000 45.25000, 4.65000 45.25000, ... \n",
+ " 0.004712 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
18975 rows × 2 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry sconcno2\n",
+ "FID \n",
+ "0 POLYGON ((-11.85000 33.85000, -11.75000 33.850... 0.000288\n",
+ "1 POLYGON ((-11.75000 33.85000, -11.65000 33.850... 0.000271\n",
+ "2 POLYGON ((-11.65000 33.85000, -11.55000 33.850... 0.000258\n",
+ "3 POLYGON ((-11.55000 33.85000, -11.45000 33.850... 0.000254\n",
+ "4 POLYGON ((-11.45000 33.85000, -11.35000 33.850... 0.000262\n",
+ "... ... ...\n",
+ "18970 POLYGON ((4.15000 45.25000, 4.25000 45.25000, ... 0.003521\n",
+ "18971 POLYGON ((4.25000 45.25000, 4.35000 45.25000, ... 0.003924\n",
+ "18972 POLYGON ((4.35000 45.25000, 4.45000 45.25000, ... 0.004454\n",
+ "18973 POLYGON ((4.45000 45.25000, 4.55000 45.25000, ... 0.004619\n",
+ "18974 POLYGON ((4.55000 45.25000, 4.65000 45.25000, ... 0.004712\n",
+ "\n",
+ "[18975 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "grid.shapefile.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "grid.shapefile[:10].plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. From grids created from scratch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Regular lat-lon grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lat_orig = 41.1\n",
+ "lon_orig = 1.8\n",
+ "inc_lat = 0.1\n",
+ "inc_lon = 0.1\n",
+ "n_lat = 50\n",
+ "n_lon = 100\n",
+ "regular_grid = create_nes(comm=None, info=False, projection='regular',\n",
+ " lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, \n",
+ " n_lat=n_lat, n_lon=n_lon)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((1.80000 41.10000, 1.90000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((1.90000 41.10000, 2.00000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((2.00000 41.10000, 2.10000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((2.10000 41.10000, 2.20000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((2.20000 41.10000, 2.30000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 9995 \n",
+ " POLYGON ((11.30000 51.00000, 11.40000 51.00000... \n",
+ " \n",
+ " \n",
+ " 9996 \n",
+ " POLYGON ((11.40000 51.00000, 11.50000 51.00000... \n",
+ " \n",
+ " \n",
+ " 9997 \n",
+ " POLYGON ((11.50000 51.00000, 11.60000 51.00000... \n",
+ " \n",
+ " \n",
+ " 9998 \n",
+ " POLYGON ((11.60000 51.00000, 11.70000 51.00000... \n",
+ " \n",
+ " \n",
+ " 9999 \n",
+ " POLYGON ((11.70000 51.00000, 11.80000 51.00000... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
10000 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((1.80000 41.10000, 1.90000 41.10000, ...\n",
+ "1 POLYGON ((1.90000 41.10000, 2.00000 41.10000, ...\n",
+ "2 POLYGON ((2.00000 41.10000, 2.10000 41.10000, ...\n",
+ "3 POLYGON ((2.10000 41.10000, 2.20000 41.10000, ...\n",
+ "4 POLYGON ((2.20000 41.10000, 2.30000 41.10000, ...\n",
+ "... ...\n",
+ "9995 POLYGON ((11.30000 51.00000, 11.40000 51.00000...\n",
+ "9996 POLYGON ((11.40000 51.00000, 11.50000 51.00000...\n",
+ "9997 POLYGON ((11.50000 51.00000, 11.60000 51.00000...\n",
+ "9998 POLYGON ((11.60000 51.00000, 11.70000 51.00000...\n",
+ "9999 POLYGON ((11.70000 51.00000, 11.80000 51.00000...\n",
+ "\n",
+ "[10000 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "regular_grid.create_shapefile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((1.80000 41.10000, 1.90000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((1.90000 41.10000, 2.00000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((2.00000 41.10000, 2.10000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((2.10000 41.10000, 2.20000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((2.20000 41.10000, 2.30000 41.10000, ... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 9995 \n",
+ " POLYGON ((11.30000 51.00000, 11.40000 51.00000... \n",
+ " \n",
+ " \n",
+ " 9996 \n",
+ " POLYGON ((11.40000 51.00000, 11.50000 51.00000... \n",
+ " \n",
+ " \n",
+ " 9997 \n",
+ " POLYGON ((11.50000 51.00000, 11.60000 51.00000... \n",
+ " \n",
+ " \n",
+ " 9998 \n",
+ " POLYGON ((11.60000 51.00000, 11.70000 51.00000... \n",
+ " \n",
+ " \n",
+ " 9999 \n",
+ " POLYGON ((11.70000 51.00000, 11.80000 51.00000... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
10000 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((1.80000 41.10000, 1.90000 41.10000, ...\n",
+ "1 POLYGON ((1.90000 41.10000, 2.00000 41.10000, ...\n",
+ "2 POLYGON ((2.00000 41.10000, 2.10000 41.10000, ...\n",
+ "3 POLYGON ((2.10000 41.10000, 2.20000 41.10000, ...\n",
+ "4 POLYGON ((2.20000 41.10000, 2.30000 41.10000, ...\n",
+ "... ...\n",
+ "9995 POLYGON ((11.30000 51.00000, 11.40000 51.00000...\n",
+ "9996 POLYGON ((11.40000 51.00000, 11.50000 51.00000...\n",
+ "9997 POLYGON ((11.50000 51.00000, 11.60000 51.00000...\n",
+ "9998 POLYGON ((11.60000 51.00000, 11.70000 51.00000...\n",
+ "9999 POLYGON ((11.70000 51.00000, 11.80000 51.00000...\n",
+ "\n",
+ "[10000 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "regular_grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "regular_grid.shapefile.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "regular_grid.shapefile[:10].plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Rotated grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "centre_lat = 51\n",
+ "centre_lon = 10\n",
+ "west_boundary = -35\n",
+ "south_boundary = -27\n",
+ "inc_rlat = 0.2\n",
+ "inc_rlon = 0.2\n",
+ "rotated_grid = create_nes(comm=None, info=False, projection='rotated',\n",
+ " centre_lat=centre_lat, centre_lon=centre_lon,\n",
+ " west_boundary=west_boundary, south_boundary=south_boundary,\n",
+ " inc_rlat=inc_rlat, inc_rlon=inc_rlon)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-22.21497 16.22040, -22.05071 16.303... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-22.05071 16.30307, -21.88618 16.385... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-21.88618 16.38536, -21.72137 16.467... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-21.72137 16.46727, -21.55629 16.548... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-21.55629 16.54881, -21.39094 16.629... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 95116 \n",
+ " POLYGON ((87.25127 59.16191, 87.43401 59.01025... \n",
+ " \n",
+ " \n",
+ " 95117 \n",
+ " POLYGON ((87.43401 59.01025, 87.61561 58.85850... \n",
+ " \n",
+ " \n",
+ " 95118 \n",
+ " POLYGON ((87.61561 58.85850, 87.79608 58.70663... \n",
+ " \n",
+ " \n",
+ " 95119 \n",
+ " POLYGON ((87.79608 58.70663, 87.97545 58.55466... \n",
+ " \n",
+ " \n",
+ " 95120 \n",
+ " POLYGON ((87.97545 58.55466, 88.15372 58.40259... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
95121 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-22.21497 16.22040, -22.05071 16.303...\n",
+ "1 POLYGON ((-22.05071 16.30307, -21.88618 16.385...\n",
+ "2 POLYGON ((-21.88618 16.38536, -21.72137 16.467...\n",
+ "3 POLYGON ((-21.72137 16.46727, -21.55629 16.548...\n",
+ "4 POLYGON ((-21.55629 16.54881, -21.39094 16.629...\n",
+ "... ...\n",
+ "95116 POLYGON ((87.25127 59.16191, 87.43401 59.01025...\n",
+ "95117 POLYGON ((87.43401 59.01025, 87.61561 58.85850...\n",
+ "95118 POLYGON ((87.61561 58.85850, 87.79608 58.70663...\n",
+ "95119 POLYGON ((87.79608 58.70663, 87.97545 58.55466...\n",
+ "95120 POLYGON ((87.97545 58.55466, 88.15372 58.40259...\n",
+ "\n",
+ "[95121 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rotated_grid.create_shapefile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-22.21497 16.22040, -22.05071 16.303... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-22.05071 16.30307, -21.88618 16.385... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-21.88618 16.38536, -21.72137 16.467... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-21.72137 16.46727, -21.55629 16.548... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-21.55629 16.54881, -21.39094 16.629... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 95116 \n",
+ " POLYGON ((87.25127 59.16191, 87.43401 59.01025... \n",
+ " \n",
+ " \n",
+ " 95117 \n",
+ " POLYGON ((87.43401 59.01025, 87.61561 58.85850... \n",
+ " \n",
+ " \n",
+ " 95118 \n",
+ " POLYGON ((87.61561 58.85850, 87.79608 58.70663... \n",
+ " \n",
+ " \n",
+ " 95119 \n",
+ " POLYGON ((87.79608 58.70663, 87.97545 58.55466... \n",
+ " \n",
+ " \n",
+ " 95120 \n",
+ " POLYGON ((87.97545 58.55466, 88.15372 58.40259... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
95121 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-22.21497 16.22040, -22.05071 16.303...\n",
+ "1 POLYGON ((-22.05071 16.30307, -21.88618 16.385...\n",
+ "2 POLYGON ((-21.88618 16.38536, -21.72137 16.467...\n",
+ "3 POLYGON ((-21.72137 16.46727, -21.55629 16.548...\n",
+ "4 POLYGON ((-21.55629 16.54881, -21.39094 16.629...\n",
+ "... ...\n",
+ "95116 POLYGON ((87.25127 59.16191, 87.43401 59.01025...\n",
+ "95117 POLYGON ((87.43401 59.01025, 87.61561 58.85850...\n",
+ "95118 POLYGON ((87.61561 58.85850, 87.79608 58.70663...\n",
+ "95119 POLYGON ((87.79608 58.70663, 87.97545 58.55466...\n",
+ "95120 POLYGON ((87.97545 58.55466, 88.15372 58.40259...\n",
+ "\n",
+ "[95121 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rotated_grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rotated_grid.shapefile.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rotated_grid.shapefile[:10].plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### LCC grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lat_1 = 37\n",
+ "lat_2 = 43\n",
+ "lon_0 = -3\n",
+ "lat_0 = 40\n",
+ "nx = 100\n",
+ "ny = 200\n",
+ "inc_x = 4000\n",
+ "inc_y = 4000\n",
+ "x_0 = -807847.688\n",
+ "y_0 = -797137.125\n",
+ "lcc_grid = create_nes(comm=None, info=False, projection='lcc',\n",
+ " lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, \n",
+ " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-11.58393 32.47507, -11.54169 32.478... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-11.54169 32.47851, -11.49944 32.481... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-11.49944 32.48192, -11.45719 32.485... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-11.45719 32.48533, -11.41494 32.488... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-11.41494 32.48871, -11.37268 32.492... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 19995 \n",
+ " POLYGON ((-8.03489 39.88059, -7.98792 39.88262... \n",
+ " \n",
+ " \n",
+ " 19996 \n",
+ " POLYGON ((-7.98792 39.88262, -7.94094 39.88464... \n",
+ " \n",
+ " \n",
+ " 19997 \n",
+ " POLYGON ((-7.94094 39.88464, -7.89397 39.88663... \n",
+ " \n",
+ " \n",
+ " 19998 \n",
+ " POLYGON ((-7.89397 39.88663, -7.84698 39.88860... \n",
+ " \n",
+ " \n",
+ " 19999 \n",
+ " POLYGON ((-7.84698 39.88860, -7.80000 39.89055... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
20000 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n",
+ "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n",
+ "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n",
+ "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n",
+ "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n",
+ "... ...\n",
+ "19995 POLYGON ((-8.03489 39.88059, -7.98792 39.88262...\n",
+ "19996 POLYGON ((-7.98792 39.88262, -7.94094 39.88464...\n",
+ "19997 POLYGON ((-7.94094 39.88464, -7.89397 39.88663...\n",
+ "19998 POLYGON ((-7.89397 39.88663, -7.84698 39.88860...\n",
+ "19999 POLYGON ((-7.84698 39.88860, -7.80000 39.89055...\n",
+ "\n",
+ "[20000 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lcc_grid.create_shapefile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-11.58393 32.47507, -11.54169 32.478... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-11.54169 32.47851, -11.49944 32.481... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-11.49944 32.48192, -11.45719 32.485... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-11.45719 32.48533, -11.41494 32.488... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-11.41494 32.48871, -11.37268 32.492... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 19995 \n",
+ " POLYGON ((-8.03489 39.88059, -7.98792 39.88262... \n",
+ " \n",
+ " \n",
+ " 19996 \n",
+ " POLYGON ((-7.98792 39.88262, -7.94094 39.88464... \n",
+ " \n",
+ " \n",
+ " 19997 \n",
+ " POLYGON ((-7.94094 39.88464, -7.89397 39.88663... \n",
+ " \n",
+ " \n",
+ " 19998 \n",
+ " POLYGON ((-7.89397 39.88663, -7.84698 39.88860... \n",
+ " \n",
+ " \n",
+ " 19999 \n",
+ " POLYGON ((-7.84698 39.88860, -7.80000 39.89055... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
20000 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n",
+ "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n",
+ "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n",
+ "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n",
+ "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n",
+ "... ...\n",
+ "19995 POLYGON ((-8.03489 39.88059, -7.98792 39.88262...\n",
+ "19996 POLYGON ((-7.98792 39.88262, -7.94094 39.88464...\n",
+ "19997 POLYGON ((-7.94094 39.88464, -7.89397 39.88663...\n",
+ "19998 POLYGON ((-7.89397 39.88663, -7.84698 39.88860...\n",
+ "19999 POLYGON ((-7.84698 39.88860, -7.80000 39.89055...\n",
+ "\n",
+ "[20000 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lcc_grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lcc_grid.shapefile.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lcc_grid.shapefile[:10].plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Mercator grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lat_ts = -1.5\n",
+ "lon_0 = -18.0\n",
+ "nx = 100\n",
+ "ny = 50\n",
+ "inc_x = 50000\n",
+ "inc_y = 50000\n",
+ "x_0 = -126017.5\n",
+ "y_0 = -5407460.0\n",
+ "mercator_grid = create_nes(comm=None, info=False, projection='mercator',\n",
+ " lat_ts=lat_ts, lon_0=lon_0, nx=nx, ny=ny, \n",
+ " inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-19.13242 -43.82824, -18.68311 -43.8... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-18.68311 -43.82824, -18.23380 -43.8... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-18.23380 -43.82824, -17.78449 -43.8... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-17.78449 -43.82824, -17.33518 -43.8... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-17.33518 -43.82824, -16.88587 -43.8... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 4995 \n",
+ " POLYGON ((23.55209 -25.82217, 24.00140 -25.822... \n",
+ " \n",
+ " \n",
+ " 4996 \n",
+ " POLYGON ((24.00140 -25.82217, 24.45071 -25.822... \n",
+ " \n",
+ " \n",
+ " 4997 \n",
+ " POLYGON ((24.45071 -25.82217, 24.90002 -25.822... \n",
+ " \n",
+ " \n",
+ " 4998 \n",
+ " POLYGON ((24.90002 -25.82217, 25.34933 -25.822... \n",
+ " \n",
+ " \n",
+ " 4999 \n",
+ " POLYGON ((25.34933 -25.82217, 25.79864 -25.822... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
5000 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-19.13242 -43.82824, -18.68311 -43.8...\n",
+ "1 POLYGON ((-18.68311 -43.82824, -18.23380 -43.8...\n",
+ "2 POLYGON ((-18.23380 -43.82824, -17.78449 -43.8...\n",
+ "3 POLYGON ((-17.78449 -43.82824, -17.33518 -43.8...\n",
+ "4 POLYGON ((-17.33518 -43.82824, -16.88587 -43.8...\n",
+ "... ...\n",
+ "4995 POLYGON ((23.55209 -25.82217, 24.00140 -25.822...\n",
+ "4996 POLYGON ((24.00140 -25.82217, 24.45071 -25.822...\n",
+ "4997 POLYGON ((24.45071 -25.82217, 24.90002 -25.822...\n",
+ "4998 POLYGON ((24.90002 -25.82217, 25.34933 -25.822...\n",
+ "4999 POLYGON ((25.34933 -25.82217, 25.79864 -25.822...\n",
+ "\n",
+ "[5000 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mercator_grid.create_shapefile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-19.13242 -43.82824, -18.68311 -43.8... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-18.68311 -43.82824, -18.23380 -43.8... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-18.23380 -43.82824, -17.78449 -43.8... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-17.78449 -43.82824, -17.33518 -43.8... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-17.33518 -43.82824, -16.88587 -43.8... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 4995 \n",
+ " POLYGON ((23.55209 -25.82217, 24.00140 -25.822... \n",
+ " \n",
+ " \n",
+ " 4996 \n",
+ " POLYGON ((24.00140 -25.82217, 24.45071 -25.822... \n",
+ " \n",
+ " \n",
+ " 4997 \n",
+ " POLYGON ((24.45071 -25.82217, 24.90002 -25.822... \n",
+ " \n",
+ " \n",
+ " 4998 \n",
+ " POLYGON ((24.90002 -25.82217, 25.34933 -25.822... \n",
+ " \n",
+ " \n",
+ " 4999 \n",
+ " POLYGON ((25.34933 -25.82217, 25.79864 -25.822... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
5000 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-19.13242 -43.82824, -18.68311 -43.8...\n",
+ "1 POLYGON ((-18.68311 -43.82824, -18.23380 -43.8...\n",
+ "2 POLYGON ((-18.23380 -43.82824, -17.78449 -43.8...\n",
+ "3 POLYGON ((-17.78449 -43.82824, -17.33518 -43.8...\n",
+ "4 POLYGON ((-17.33518 -43.82824, -16.88587 -43.8...\n",
+ "... ...\n",
+ "4995 POLYGON ((23.55209 -25.82217, 24.00140 -25.822...\n",
+ "4996 POLYGON ((24.00140 -25.82217, 24.45071 -25.822...\n",
+ "4997 POLYGON ((24.45071 -25.82217, 24.90002 -25.822...\n",
+ "4998 POLYGON ((24.90002 -25.82217, 25.34933 -25.822...\n",
+ "4999 POLYGON ((25.34933 -25.82217, 25.79864 -25.822...\n",
+ "\n",
+ "[5000 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mercator_grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mercator_grid.shapefile.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mercator_grid.shapefile[:10].plot()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8ebefeef0f2536b9f35f135f3a3cdc3085aa81d7
--- /dev/null
+++ b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb
@@ -0,0 +1,738 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to make spatial joins"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nes import *\n",
+ "import geopandas as gpd\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "pd.options.mode.chained_assignment = None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "shapefile_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. From grids that have already been created"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n",
+ "# Rotated grid from MONARCH\n",
+ "original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid = open_netcdf(original_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Spatial join"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2666: UserWarning: Shapefile does not exist. It will be created now.\n",
+ " warnings.warn(msg)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Method can be centroid, nearest and intersection\n",
+ "grid.spatial_join(shapefile_path, method='centroid')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " tzid \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-22.21497 16.22040, -22.05072 16.303... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-22.05072 16.30307, -21.88617 16.385... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-21.88617 16.38537, -21.72137 16.467... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-21.72137 16.46728, -21.55630 16.548... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-21.55630 16.54881, -21.39093 16.629... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 95116 \n",
+ " POLYGON ((87.25128 59.16191, 87.43402 59.01026... \n",
+ " Asia/Tomsk \n",
+ " \n",
+ " \n",
+ " 95117 \n",
+ " POLYGON ((87.43402 59.01025, 87.61560 58.85850... \n",
+ " Asia/Tomsk \n",
+ " \n",
+ " \n",
+ " 95118 \n",
+ " POLYGON ((87.61560 58.85850, 87.79608 58.70663... \n",
+ " Asia/Tomsk \n",
+ " \n",
+ " \n",
+ " 95119 \n",
+ " POLYGON ((87.79608 58.70663, 87.97543 58.55466... \n",
+ " Asia/Tomsk \n",
+ " \n",
+ " \n",
+ " 95120 \n",
+ " POLYGON ((87.97543 58.55466, 88.15372 58.40260... \n",
+ " Asia/Krasnoyarsk \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
95121 rows × 2 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry tzid\n",
+ "FID \n",
+ "0 POLYGON ((-22.21497 16.22040, -22.05072 16.303... NaN\n",
+ "1 POLYGON ((-22.05072 16.30307, -21.88617 16.385... NaN\n",
+ "2 POLYGON ((-21.88617 16.38537, -21.72137 16.467... NaN\n",
+ "3 POLYGON ((-21.72137 16.46728, -21.55630 16.548... NaN\n",
+ "4 POLYGON ((-21.55630 16.54881, -21.39093 16.629... NaN\n",
+ "... ... ...\n",
+ "95116 POLYGON ((87.25128 59.16191, 87.43402 59.01026... Asia/Tomsk\n",
+ "95117 POLYGON ((87.43402 59.01025, 87.61560 58.85850... Asia/Tomsk\n",
+ "95118 POLYGON ((87.61560 58.85850, 87.79608 58.70663... Asia/Tomsk\n",
+ "95119 POLYGON ((87.79608 58.70663, 87.97543 58.55466... Asia/Tomsk\n",
+ "95120 POLYGON ((87.97543 58.55466, 88.15372 58.40260... Asia/Krasnoyarsk\n",
+ "\n",
+ "[95121 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Add data of last timestep"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid.keep_vars('O3')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid.load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid.shapefile['O3'] = grid.variables['O3']['data'][-1, -1, :].ravel()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " tzid \n",
+ " O3 \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-22.21497 16.22040, -22.05072 16.303... \n",
+ " NaN \n",
+ " 0.025778 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-22.05072 16.30307, -21.88617 16.385... \n",
+ " NaN \n",
+ " 0.025487 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-21.88617 16.38537, -21.72137 16.467... \n",
+ " NaN \n",
+ " 0.025579 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-21.72137 16.46728, -21.55630 16.548... \n",
+ " NaN \n",
+ " 0.025752 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-21.55630 16.54881, -21.39093 16.629... \n",
+ " NaN \n",
+ " 0.025959 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 95116 \n",
+ " POLYGON ((87.25128 59.16191, 87.43402 59.01026... \n",
+ " Asia/Tomsk \n",
+ " 0.032334 \n",
+ " \n",
+ " \n",
+ " 95117 \n",
+ " POLYGON ((87.43402 59.01025, 87.61560 58.85850... \n",
+ " Asia/Tomsk \n",
+ " 0.032313 \n",
+ " \n",
+ " \n",
+ " 95118 \n",
+ " POLYGON ((87.61560 58.85850, 87.79608 58.70663... \n",
+ " Asia/Tomsk \n",
+ " 0.032317 \n",
+ " \n",
+ " \n",
+ " 95119 \n",
+ " POLYGON ((87.79608 58.70663, 87.97543 58.55466... \n",
+ " Asia/Tomsk \n",
+ " 0.032341 \n",
+ " \n",
+ " \n",
+ " 95120 \n",
+ " POLYGON ((87.97543 58.55466, 88.15372 58.40260... \n",
+ " Asia/Krasnoyarsk \n",
+ " 0.032367 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
95121 rows × 3 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry tzid \\\n",
+ "FID \n",
+ "0 POLYGON ((-22.21497 16.22040, -22.05072 16.303... NaN \n",
+ "1 POLYGON ((-22.05072 16.30307, -21.88617 16.385... NaN \n",
+ "2 POLYGON ((-21.88617 16.38537, -21.72137 16.467... NaN \n",
+ "3 POLYGON ((-21.72137 16.46728, -21.55630 16.548... NaN \n",
+ "4 POLYGON ((-21.55630 16.54881, -21.39093 16.629... NaN \n",
+ "... ... ... \n",
+ "95116 POLYGON ((87.25128 59.16191, 87.43402 59.01026... Asia/Tomsk \n",
+ "95117 POLYGON ((87.43402 59.01025, 87.61560 58.85850... Asia/Tomsk \n",
+ "95118 POLYGON ((87.61560 58.85850, 87.79608 58.70663... Asia/Tomsk \n",
+ "95119 POLYGON ((87.79608 58.70663, 87.97543 58.55466... Asia/Tomsk \n",
+ "95120 POLYGON ((87.97543 58.55466, 88.15372 58.40260... Asia/Krasnoyarsk \n",
+ "\n",
+ " O3 \n",
+ "FID \n",
+ "0 0.025778 \n",
+ "1 0.025487 \n",
+ "2 0.025579 \n",
+ "3 0.025752 \n",
+ "4 0.025959 \n",
+ "... ... \n",
+ "95116 0.032334 \n",
+ "95117 0.032313 \n",
+ "95118 0.032317 \n",
+ "95119 0.032341 \n",
+ "95120 0.032367 \n",
+ "\n",
+ "[95121 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot timezones in grid domain"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1, figsize=(20, 7))\n",
+ "\n",
+ "gdf_grid = grid.shapefile\n",
+ "gdf_grid.plot(ax=ax, column='tzid', cmap='seismic', legend=True, legend_kwds={'ncol': 5})\n",
+ "leg = ax.get_legend()\n",
+ "leg.set_bbox_to_anchor((1.05, -0.05))\n",
+ "\n",
+ "ax.margins(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot ozone for available timezones in grid domain"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1, figsize=(20, 7))\n",
+ "\n",
+ "gdf_grid = grid.shapefile.dropna()\n",
+ "gdf_grid.plot(ax=ax, column='O3', cmap='jet', legend=True)\n",
+ "\n",
+ "ax.margins(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot ozone in Spain, Portugal and France"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1, figsize=(20, 7))\n",
+ "\n",
+ "gdf_grid = grid.shapefile[grid.shapefile['tzid'].isin(['Europe/Madrid', 'Europe/Lisbon', 'Europe/Paris'])]\n",
+ "gdf_grid.plot(ax=ax, column='O3', cmap='jet', legend=True)\n",
+ "\n",
+ "ax.set_xlim([-11, 11])\n",
+ "ax.set_ylim([35, 52])\n",
+ "ax.margins(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. With new grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "projection = 'regular'\n",
+ "lat_orig = 35\n",
+ "lon_orig = -11\n",
+ "inc_lat = 0.2\n",
+ "inc_lon = 0.2\n",
+ "n_lat = 100\n",
+ "n_lon = 150"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Spatial join"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Method can be centroid, nearest and intersection\n",
+ "regular_grid = from_shapefile(shapefile_path, method='centroid', projection=projection, \n",
+ " lat_orig=lat_orig, lon_orig=lon_orig, \n",
+ " inc_lat=inc_lat, inc_lon=inc_lon, \n",
+ " n_lat=n_lat, n_lon=n_lon)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " tzid \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-11.00000 35.00000, -10.80000 35.000... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-10.80000 35.00000, -10.60000 35.000... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-10.60000 35.00000, -10.40000 35.000... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-10.40000 35.00000, -10.20000 35.000... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-10.20000 35.00000, -10.00000 35.000... \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 22495 \n",
+ " POLYGON ((18.00000 64.80000, 18.20000 64.80000... \n",
+ " Europe/Stockholm \n",
+ " \n",
+ " \n",
+ " 22496 \n",
+ " POLYGON ((18.20000 64.80000, 18.40000 64.80000... \n",
+ " Europe/Stockholm \n",
+ " \n",
+ " \n",
+ " 22497 \n",
+ " POLYGON ((18.40000 64.80000, 18.60000 64.80000... \n",
+ " Europe/Stockholm \n",
+ " \n",
+ " \n",
+ " 22498 \n",
+ " POLYGON ((18.60000 64.80000, 18.80000 64.80000... \n",
+ " Europe/Stockholm \n",
+ " \n",
+ " \n",
+ " 22499 \n",
+ " POLYGON ((18.80000 64.80000, 19.00000 64.80000... \n",
+ " Europe/Stockholm \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
22500 rows × 2 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry tzid\n",
+ "FID \n",
+ "0 POLYGON ((-11.00000 35.00000, -10.80000 35.000... NaN\n",
+ "1 POLYGON ((-10.80000 35.00000, -10.60000 35.000... NaN\n",
+ "2 POLYGON ((-10.60000 35.00000, -10.40000 35.000... NaN\n",
+ "3 POLYGON ((-10.40000 35.00000, -10.20000 35.000... NaN\n",
+ "4 POLYGON ((-10.20000 35.00000, -10.00000 35.000... NaN\n",
+ "... ... ...\n",
+ "22495 POLYGON ((18.00000 64.80000, 18.20000 64.80000... Europe/Stockholm\n",
+ "22496 POLYGON ((18.20000 64.80000, 18.40000 64.80000... Europe/Stockholm\n",
+ "22497 POLYGON ((18.40000 64.80000, 18.60000 64.80000... Europe/Stockholm\n",
+ "22498 POLYGON ((18.60000 64.80000, 18.80000 64.80000... Europe/Stockholm\n",
+ "22499 POLYGON ((18.80000 64.80000, 19.00000 64.80000... Europe/Stockholm\n",
+ "\n",
+ "[22500 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "regular_grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot timezones in Spain, Portugal and France"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1, figsize=(20, 7))\n",
+ "\n",
+ "gdf_grid = regular_grid.shapefile[regular_grid.shapefile['tzid'].isin(['Europe/Madrid', \n",
+ " 'Europe/Lisbon', \n",
+ " 'Europe/Paris'])]\n",
+ "gdf_grid.plot(ax=ax, column='tzid', cmap='viridis', legend=True)\n",
+ "\n",
+ "ax.set_xlim([-11, 11])\n",
+ "ax.set_ylim([35, 52])\n",
+ "ax.margins(0)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6585bf6674c4011093d085abf74a94b0f67628c1
--- /dev/null
+++ b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb
@@ -0,0 +1,1481 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to read and add coordinates bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import datetime\n",
+ "import numpy as np\n",
+ "from nes import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. File with existing bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.1. Read dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/rotated_nes.py:166: UserWarning: There is no variable called rotated_pole, projection has not been defined.\n",
+ " warnings.warn(msg)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Original path: /gpfs/scratch/bsc32/bsc32538/HERMESv3/OUT_Complete_single/GFAS_p13h/HERMESv3_GR_GFAS_d01_2022050100.nc\n",
+ "# Rotated grid from HERMES\n",
+ "path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/HERMESv3_GR_GFAS_d01_2022050100.nc'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nessy_1 = open_netcdf(path=path_1, info=True)\n",
+ "nessy_1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.2. Explore spatial bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[[16.27358055114746, 16.335533142089844, 16.46847152709961,\n",
+ " 16.40639305114746],\n",
+ " [16.335533142089844, 16.397274017333984, 16.530336380004883,\n",
+ " 16.46847152709961],\n",
+ " [16.397274017333984, 16.45880126953125, 16.59198760986328,\n",
+ " 16.530336380004883],\n",
+ " ...,\n",
+ " [16.45880126953125, 16.397274017333984, 16.530336380004883,\n",
+ " 16.59198760986328],\n",
+ " [16.397274017333984, 16.335533142089844, 16.46847152709961,\n",
+ " 16.530336380004883],\n",
+ " [16.335533142089844, 16.27358055114746, 16.40639305114746,\n",
+ " 16.46847152709961]],\n",
+ " \n",
+ " [[16.40639305114746, 16.46847152709961, 16.601383209228516,\n",
+ " 16.539180755615234],\n",
+ " [16.46847152709961, 16.530336380004883, 16.663372039794922,\n",
+ " 16.601383209228516],\n",
+ " [16.530336380004883, 16.59198760986328, 16.725149154663086,\n",
+ " 16.663372039794922],\n",
+ " ...,\n",
+ " [16.59198760986328, 16.530336380004883, 16.663372039794922,\n",
+ " 16.725149154663086],\n",
+ " [16.530336380004883, 16.46847152709961, 16.601383209228516,\n",
+ " 16.663372039794922],\n",
+ " [16.46847152709961, 16.40639305114746, 16.539180755615234,\n",
+ " 16.601383209228516]],\n",
+ " \n",
+ " [[16.539180755615234, 16.601383209228516, 16.734270095825195,\n",
+ " 16.67194175720215],\n",
+ " [16.601383209228516, 16.663372039794922, 16.796384811401367,\n",
+ " 16.734270095825195],\n",
+ " [16.663372039794922, 16.725149154663086, 16.858285903930664,\n",
+ " 16.796384811401367],\n",
+ " ...,\n",
+ " [16.725149154663086, 16.663372039794922, 16.796384811401367,\n",
+ " 16.858285903930664],\n",
+ " [16.663372039794922, 16.601383209228516, 16.734270095825195,\n",
+ " 16.796384811401367],\n",
+ " [16.601383209228516, 16.539180755615234, 16.67194175720215,\n",
+ " 16.734270095825195]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[58.31517791748047, 58.429019927978516, 58.50811004638672,\n",
+ " 58.3941650390625],\n",
+ " [58.429019927978516, 58.54280090332031, 58.62199783325195,\n",
+ " 58.50811004638672],\n",
+ " [58.54280090332031, 58.65652084350586, 58.7358283996582,\n",
+ " 58.62199783325195],\n",
+ " ...,\n",
+ " [58.65652084350586, 58.54280090332031, 58.62199783325195,\n",
+ " 58.7358283996582],\n",
+ " [58.54280090332031, 58.429019927978516, 58.50811004638672,\n",
+ " 58.62199783325195],\n",
+ " [58.429019927978516, 58.31517791748047, 58.3941650390625,\n",
+ " 58.50811004638672]],\n",
+ " \n",
+ " [[58.3941650390625, 58.50811004638672, 58.586734771728516,\n",
+ " 58.472686767578125],\n",
+ " [58.50811004638672, 58.62199783325195, 58.70072937011719,\n",
+ " 58.586734771728516],\n",
+ " [58.62199783325195, 58.7358283996582, 58.814666748046875,\n",
+ " 58.70072937011719],\n",
+ " ...,\n",
+ " [58.7358283996582, 58.62199783325195, 58.70072937011719,\n",
+ " 58.814666748046875],\n",
+ " [58.62199783325195, 58.50811004638672, 58.586734771728516,\n",
+ " 58.70072937011719],\n",
+ " [58.50811004638672, 58.3941650390625, 58.472686767578125,\n",
+ " 58.586734771728516]],\n",
+ " \n",
+ " [[58.472686767578125, 58.586734771728516, 58.664894104003906,\n",
+ " 58.550743103027344],\n",
+ " [58.586734771728516, 58.70072937011719, 58.77899169921875,\n",
+ " 58.664894104003906],\n",
+ " [58.70072937011719, 58.814666748046875, 58.89303207397461,\n",
+ " 58.77899169921875],\n",
+ " ...,\n",
+ " [58.814666748046875, 58.70072937011719, 58.77899169921875,\n",
+ " 58.89303207397461],\n",
+ " [58.70072937011719, 58.586734771728516, 58.664894104003906,\n",
+ " 58.77899169921875],\n",
+ " [58.586734771728516, 58.472686767578125, 58.550743103027344,\n",
+ " 58.664894104003906]]],\n",
+ " mask=[[[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]]],\n",
+ " fill_value=1e+20,\n",
+ " dtype=float32)}"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_1.lat_bnds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[[-22.16550636291504, -22.04222297668457, -22.114652633666992,\n",
+ " -22.238161087036133],\n",
+ " [-22.04222297668457, -21.918787002563477, -21.990989685058594,\n",
+ " -22.114652633666992],\n",
+ " [-21.918787002563477, -21.795194625854492, -21.867170333862305,\n",
+ " -21.990989685058594],\n",
+ " ...,\n",
+ " [41.795196533203125, 41.918785095214844, 41.990989685058594,\n",
+ " 41.86717224121094],\n",
+ " [41.918785095214844, 42.0422248840332, 42.114654541015625,\n",
+ " 41.990989685058594],\n",
+ " [42.0422248840332, 42.165504455566406, 42.2381591796875,\n",
+ " 42.114654541015625]],\n",
+ " \n",
+ " [[-22.238161087036133, -22.114652633666992, -22.18718147277832,\n",
+ " -22.310914993286133],\n",
+ " [-22.114652633666992, -21.990989685058594, -22.06329345703125,\n",
+ " -22.18718147277832],\n",
+ " [-21.990989685058594, -21.867170333862305, -21.939247131347656,\n",
+ " -22.06329345703125],\n",
+ " ...,\n",
+ " [41.86717224121094, 41.990989685058594, 42.06329345703125,\n",
+ " 41.939247131347656],\n",
+ " [41.990989685058594, 42.114654541015625, 42.18718338012695,\n",
+ " 42.06329345703125],\n",
+ " [42.114654541015625, 42.2381591796875, 42.310916900634766,\n",
+ " 42.18718338012695]],\n",
+ " \n",
+ " [[-22.310914993286133, -22.18718147277832, -22.259811401367188,\n",
+ " -22.383769989013672],\n",
+ " [-22.18718147277832, -22.06329345703125, -22.135696411132812,\n",
+ " -22.259811401367188],\n",
+ " [-22.06329345703125, -21.939247131347656, -22.011423110961914,\n",
+ " -22.135696411132812],\n",
+ " ...,\n",
+ " [41.939247131347656, 42.06329345703125, 42.13569641113281,\n",
+ " 42.01142501831055],\n",
+ " [42.06329345703125, 42.18718338012695, 42.25981140136719,\n",
+ " 42.13569641113281],\n",
+ " [42.18718338012695, 42.310916900634766, 42.38376998901367,\n",
+ " 42.25981140136719]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[-67.64056396484375, -67.50543212890625, -67.74915313720703,\n",
+ " -67.88362121582031],\n",
+ " [-67.50543212890625, -67.36968231201172, -67.61406707763672,\n",
+ " -67.74915313720703],\n",
+ " [-67.36968231201172, -67.23330688476562, -67.47835540771484,\n",
+ " -67.61406707763672],\n",
+ " ...,\n",
+ " [87.23330688476562, 87.36968231201172, 87.61406707763672,\n",
+ " 87.47835540771484],\n",
+ " [87.36968231201172, 87.50543212890625, 87.74915313720703,\n",
+ " 87.61406707763672],\n",
+ " [87.50543212890625, 87.64056396484375, 87.88362121582031,\n",
+ " 87.74915313720703]],\n",
+ " \n",
+ " [[-67.88362121582031, -67.74915313720703, -67.99396514892578,\n",
+ " -68.12776184082031],\n",
+ " [-67.74915313720703, -67.61406707763672, -67.85955047607422,\n",
+ " -67.99396514892578],\n",
+ " [-67.61406707763672, -67.47835540771484, -67.72451782226562,\n",
+ " -67.85955047607422],\n",
+ " ...,\n",
+ " [87.47835540771484, 87.61406707763672, 87.85955047607422,\n",
+ " 87.72451782226562],\n",
+ " [87.61406707763672, 87.74915313720703, 87.99396514892578,\n",
+ " 87.85955047607422],\n",
+ " [87.74915313720703, 87.88362121582031, 88.12776184082031,\n",
+ " 87.99396514892578]],\n",
+ " \n",
+ " [[-68.12776184082031, -67.99396514892578, -68.23987579345703,\n",
+ " -68.37299346923828],\n",
+ " [-67.99396514892578, -67.85955047607422, -68.10614776611328,\n",
+ " -68.23987579345703],\n",
+ " [-67.85955047607422, -67.72451782226562, -67.9718017578125,\n",
+ " -68.10614776611328],\n",
+ " ...,\n",
+ " [87.72451782226562, 87.85955047607422, 88.10614776611328,\n",
+ " 87.9718017578125],\n",
+ " [87.85955047607422, 87.99396514892578, 88.23987579345703,\n",
+ " 88.10614776611328],\n",
+ " [87.99396514892578, 88.12776184082031, 88.37299346923828,\n",
+ " 88.23987579345703]]],\n",
+ " mask=[[[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]]],\n",
+ " fill_value=1e+20,\n",
+ " dtype=float32)}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_1.lon_bnds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.3. Write file with bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Creating bounds_file_1.nc\n",
+ "Rank 000: NetCDF ready to write\n",
+ "Rank 000: Dimensions done\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable NO was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable NO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable HONO was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable CO was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable CO_GFAS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable SO2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable SO2_GFAS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable NH3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ALD2 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ALDX was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable BENZENE was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ETH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ETHA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ETOH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable FORM was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable IOLE was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ISOP was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable MEOH was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable OLE was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PAR was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable SESQ was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable TERP was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable TOL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable XYL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable DMS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable DMS_GFAS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable HCL was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable POA was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable POA_GFAS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PEC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PEC_GFAS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PNO3 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PSO4 was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PMFINE was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PMFINE_GFAS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PMC was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable EPOA_biomass was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable EPOA_anthro was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable OPOA_biomass was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable OPOA_anthro was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable SOAP_bb was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable SOAP_anthro was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ECres was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ECtot was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable TPM_GFAS was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n",
+ "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable cell_area was not loaded. It will not be written.\n",
+ " warnings.warn(msg)\n"
+ ]
+ }
+ ],
+ "source": [
+ "nessy_1.to_netcdf('bounds_file_1.nc', info=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.4. Reopen with NES"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_2 = open_netcdf('bounds_file_1.nc', info=True)\n",
+ "nessy_2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.5. Explore spatial bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[[16.27358055114746, 16.335533142089844, 16.46847152709961,\n",
+ " 16.40639305114746],\n",
+ " [16.335533142089844, 16.397274017333984, 16.530336380004883,\n",
+ " 16.46847152709961],\n",
+ " [16.397274017333984, 16.45880126953125, 16.59198760986328,\n",
+ " 16.530336380004883],\n",
+ " ...,\n",
+ " [16.45880126953125, 16.397274017333984, 16.530336380004883,\n",
+ " 16.59198760986328],\n",
+ " [16.397274017333984, 16.335533142089844, 16.46847152709961,\n",
+ " 16.530336380004883],\n",
+ " [16.335533142089844, 16.27358055114746, 16.40639305114746,\n",
+ " 16.46847152709961]],\n",
+ " \n",
+ " [[16.40639305114746, 16.46847152709961, 16.601383209228516,\n",
+ " 16.539180755615234],\n",
+ " [16.46847152709961, 16.530336380004883, 16.663372039794922,\n",
+ " 16.601383209228516],\n",
+ " [16.530336380004883, 16.59198760986328, 16.725149154663086,\n",
+ " 16.663372039794922],\n",
+ " ...,\n",
+ " [16.59198760986328, 16.530336380004883, 16.663372039794922,\n",
+ " 16.725149154663086],\n",
+ " [16.530336380004883, 16.46847152709961, 16.601383209228516,\n",
+ " 16.663372039794922],\n",
+ " [16.46847152709961, 16.40639305114746, 16.539180755615234,\n",
+ " 16.601383209228516]],\n",
+ " \n",
+ " [[16.539180755615234, 16.601383209228516, 16.734270095825195,\n",
+ " 16.67194175720215],\n",
+ " [16.601383209228516, 16.663372039794922, 16.796384811401367,\n",
+ " 16.734270095825195],\n",
+ " [16.663372039794922, 16.725149154663086, 16.858285903930664,\n",
+ " 16.796384811401367],\n",
+ " ...,\n",
+ " [16.725149154663086, 16.663372039794922, 16.796384811401367,\n",
+ " 16.858285903930664],\n",
+ " [16.663372039794922, 16.601383209228516, 16.734270095825195,\n",
+ " 16.796384811401367],\n",
+ " [16.601383209228516, 16.539180755615234, 16.67194175720215,\n",
+ " 16.734270095825195]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[58.31517791748047, 58.429019927978516, 58.50811004638672,\n",
+ " 58.3941650390625],\n",
+ " [58.429019927978516, 58.54280090332031, 58.62199783325195,\n",
+ " 58.50811004638672],\n",
+ " [58.54280090332031, 58.65652084350586, 58.7358283996582,\n",
+ " 58.62199783325195],\n",
+ " ...,\n",
+ " [58.65652084350586, 58.54280090332031, 58.62199783325195,\n",
+ " 58.7358283996582],\n",
+ " [58.54280090332031, 58.429019927978516, 58.50811004638672,\n",
+ " 58.62199783325195],\n",
+ " [58.429019927978516, 58.31517791748047, 58.3941650390625,\n",
+ " 58.50811004638672]],\n",
+ " \n",
+ " [[58.3941650390625, 58.50811004638672, 58.586734771728516,\n",
+ " 58.472686767578125],\n",
+ " [58.50811004638672, 58.62199783325195, 58.70072937011719,\n",
+ " 58.586734771728516],\n",
+ " [58.62199783325195, 58.7358283996582, 58.814666748046875,\n",
+ " 58.70072937011719],\n",
+ " ...,\n",
+ " [58.7358283996582, 58.62199783325195, 58.70072937011719,\n",
+ " 58.814666748046875],\n",
+ " [58.62199783325195, 58.50811004638672, 58.586734771728516,\n",
+ " 58.70072937011719],\n",
+ " [58.50811004638672, 58.3941650390625, 58.472686767578125,\n",
+ " 58.586734771728516]],\n",
+ " \n",
+ " [[58.472686767578125, 58.586734771728516, 58.664894104003906,\n",
+ " 58.550743103027344],\n",
+ " [58.586734771728516, 58.70072937011719, 58.77899169921875,\n",
+ " 58.664894104003906],\n",
+ " [58.70072937011719, 58.814666748046875, 58.89303207397461,\n",
+ " 58.77899169921875],\n",
+ " ...,\n",
+ " [58.814666748046875, 58.70072937011719, 58.77899169921875,\n",
+ " 58.89303207397461],\n",
+ " [58.70072937011719, 58.586734771728516, 58.664894104003906,\n",
+ " 58.77899169921875],\n",
+ " [58.586734771728516, 58.472686767578125, 58.550743103027344,\n",
+ " 58.664894104003906]]],\n",
+ " mask=[[[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]]],\n",
+ " fill_value=1e+20)}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_2.lat_bnds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[[-22.16550636291504, -22.04222297668457, -22.114652633666992,\n",
+ " -22.238161087036133],\n",
+ " [-22.04222297668457, -21.918787002563477, -21.990989685058594,\n",
+ " -22.114652633666992],\n",
+ " [-21.918787002563477, -21.795194625854492, -21.867170333862305,\n",
+ " -21.990989685058594],\n",
+ " ...,\n",
+ " [41.795196533203125, 41.918785095214844, 41.990989685058594,\n",
+ " 41.86717224121094],\n",
+ " [41.918785095214844, 42.0422248840332, 42.114654541015625,\n",
+ " 41.990989685058594],\n",
+ " [42.0422248840332, 42.165504455566406, 42.2381591796875,\n",
+ " 42.114654541015625]],\n",
+ " \n",
+ " [[-22.238161087036133, -22.114652633666992, -22.18718147277832,\n",
+ " -22.310914993286133],\n",
+ " [-22.114652633666992, -21.990989685058594, -22.06329345703125,\n",
+ " -22.18718147277832],\n",
+ " [-21.990989685058594, -21.867170333862305, -21.939247131347656,\n",
+ " -22.06329345703125],\n",
+ " ...,\n",
+ " [41.86717224121094, 41.990989685058594, 42.06329345703125,\n",
+ " 41.939247131347656],\n",
+ " [41.990989685058594, 42.114654541015625, 42.18718338012695,\n",
+ " 42.06329345703125],\n",
+ " [42.114654541015625, 42.2381591796875, 42.310916900634766,\n",
+ " 42.18718338012695]],\n",
+ " \n",
+ " [[-22.310914993286133, -22.18718147277832, -22.259811401367188,\n",
+ " -22.383769989013672],\n",
+ " [-22.18718147277832, -22.06329345703125, -22.135696411132812,\n",
+ " -22.259811401367188],\n",
+ " [-22.06329345703125, -21.939247131347656, -22.011423110961914,\n",
+ " -22.135696411132812],\n",
+ " ...,\n",
+ " [41.939247131347656, 42.06329345703125, 42.13569641113281,\n",
+ " 42.01142501831055],\n",
+ " [42.06329345703125, 42.18718338012695, 42.25981140136719,\n",
+ " 42.13569641113281],\n",
+ " [42.18718338012695, 42.310916900634766, 42.38376998901367,\n",
+ " 42.25981140136719]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[-67.64056396484375, -67.50543212890625, -67.74915313720703,\n",
+ " -67.88362121582031],\n",
+ " [-67.50543212890625, -67.36968231201172, -67.61406707763672,\n",
+ " -67.74915313720703],\n",
+ " [-67.36968231201172, -67.23330688476562, -67.47835540771484,\n",
+ " -67.61406707763672],\n",
+ " ...,\n",
+ " [87.23330688476562, 87.36968231201172, 87.61406707763672,\n",
+ " 87.47835540771484],\n",
+ " [87.36968231201172, 87.50543212890625, 87.74915313720703,\n",
+ " 87.61406707763672],\n",
+ " [87.50543212890625, 87.64056396484375, 87.88362121582031,\n",
+ " 87.74915313720703]],\n",
+ " \n",
+ " [[-67.88362121582031, -67.74915313720703, -67.99396514892578,\n",
+ " -68.12776184082031],\n",
+ " [-67.74915313720703, -67.61406707763672, -67.85955047607422,\n",
+ " -67.99396514892578],\n",
+ " [-67.61406707763672, -67.47835540771484, -67.72451782226562,\n",
+ " -67.85955047607422],\n",
+ " ...,\n",
+ " [87.47835540771484, 87.61406707763672, 87.85955047607422,\n",
+ " 87.72451782226562],\n",
+ " [87.61406707763672, 87.74915313720703, 87.99396514892578,\n",
+ " 87.85955047607422],\n",
+ " [87.74915313720703, 87.88362121582031, 88.12776184082031,\n",
+ " 87.99396514892578]],\n",
+ " \n",
+ " [[-68.12776184082031, -67.99396514892578, -68.23987579345703,\n",
+ " -68.37299346923828],\n",
+ " [-67.99396514892578, -67.85955047607422, -68.10614776611328,\n",
+ " -68.23987579345703],\n",
+ " [-67.85955047607422, -67.72451782226562, -67.9718017578125,\n",
+ " -68.10614776611328],\n",
+ " ...,\n",
+ " [87.72451782226562, 87.85955047607422, 88.10614776611328,\n",
+ " 87.9718017578125],\n",
+ " [87.85955047607422, 87.99396514892578, 88.23987579345703,\n",
+ " 88.10614776611328],\n",
+ " [87.99396514892578, 88.12776184082031, 88.37299346923828,\n",
+ " 88.23987579345703]]],\n",
+ " mask=[[[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]]],\n",
+ " fill_value=1e+20)}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_2.lon_bnds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. File without existing bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.1. Read dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\n",
+ "# Rotated grid from MONARCH\n",
+ "path_3 = '/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc'\n",
+ "nessy_3 = open_netcdf(path=path_3, info=True)\n",
+ "nessy_3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2. Create spatial bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nessy_3.create_spatial_bounds()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.3. Explore spatial bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Loading O3_all var (1/1)\n",
+ "Rank 000: Loaded O3_all var ((1, 24, 271, 351))\n"
+ ]
+ }
+ ],
+ "source": [
+ "nessy_3.load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([[[16.2203979 , 16.30306824, 16.48028979, 16.39739715],\n",
+ " [16.30306855, 16.3853609 , 16.56280424, 16.48029011],\n",
+ " [16.38536121, 16.46727425, 16.64493885, 16.56280455],\n",
+ " ...,\n",
+ " [16.46727269, 16.38535964, 16.56280298, 16.64493728],\n",
+ " [16.3853609 , 16.30306855, 16.48029011, 16.56280424],\n",
+ " [16.30306824, 16.2203979 , 16.39739715, 16.48028979]],\n",
+ " \n",
+ " [[16.39739783, 16.48029047, 16.65746762, 16.57435251],\n",
+ " [16.48029079, 16.56280491, 16.74020402, 16.65746794],\n",
+ " [16.56280523, 16.64493952, 16.82256006, 16.74020434],\n",
+ " ...,\n",
+ " [16.64493796, 16.56280366, 16.74020276, 16.82255849],\n",
+ " [16.56280491, 16.48029079, 16.65746794, 16.74020402],\n",
+ " [16.48029047, 16.39739783, 16.57435251, 16.65746762]],\n",
+ " \n",
+ " [[16.57435149, 16.65746661, 16.83459876, 16.751261 ],\n",
+ " [16.65746692, 16.74020301, 16.91755729, 16.83459908],\n",
+ " [16.74020332, 16.82255904, 17.00013494, 16.91755761],\n",
+ " ...,\n",
+ " [16.82255748, 16.74020175, 16.91755603, 17.00013337],\n",
+ " [16.74020301, 16.65746692, 16.83459908, 16.91755729],\n",
+ " [16.65746661, 16.57435149, 16.751261 , 16.83459876]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[58.19210948, 58.34380497, 58.44964444, 58.29776032],\n",
+ " [58.34380555, 58.49539321, 58.6014247 , 58.44964502],\n",
+ " [58.49539378, 58.64687141, 58.75309835, 58.60142528],\n",
+ " ...,\n",
+ " [58.64686852, 58.49539089, 58.60142239, 58.75309546],\n",
+ " [58.49539321, 58.34380555, 58.44964502, 58.6014247 ],\n",
+ " [58.34380497, 58.19210948, 58.29776032, 58.44964444]],\n",
+ " \n",
+ " [[58.29776072, 58.44964485, 58.55466327, 58.40259426],\n",
+ " [58.44964543, 58.6014251 , 58.7066318 , 58.55466385],\n",
+ " [58.60142568, 58.75309876, 58.85849715, 58.70663238],\n",
+ " ...,\n",
+ " [58.75309587, 58.60142279, 58.70662948, 58.85849425],\n",
+ " [58.6014251 , 58.44964543, 58.55466385, 58.7066318 ],\n",
+ " [58.44964485, 58.29776072, 58.40259426, 58.55466327]],\n",
+ " \n",
+ " [[58.40259366, 58.55466267, 58.65885172, 58.50660166],\n",
+ " [58.55466325, 58.7066312 , 58.81100467, 58.6588523 ],\n",
+ " [58.70663178, 58.85849655, 58.96305787, 58.81100525],\n",
+ " ...,\n",
+ " [58.85849365, 58.70662888, 58.81100235, 58.96305497],\n",
+ " [58.7066312 , 58.55466325, 58.6588523 , 58.81100467],\n",
+ " [58.55466267, 58.40259366, 58.50660166, 58.65885172]]])}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_3.lat_bnds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([[[-22.21497021, -22.05071303, -22.14733617, -22.31199395],\n",
+ " [-22.0507124 , -21.88618013, -21.9824008 , -22.14733554],\n",
+ " [-21.8861795 , -21.72137239, -21.81718872, -21.98240017],\n",
+ " ...,\n",
+ " [ 41.72137553, 41.88618264, 41.98240332, 41.81719187],\n",
+ " [ 41.88618013, 42.0507124 , 42.14733554, 41.9824008 ],\n",
+ " [ 42.05071303, 42.21497021, 42.31199395, 42.14733617]],\n",
+ " \n",
+ " [[-22.31199432, -22.14733654, -22.24413665, -22.4091946 ],\n",
+ " [-22.14733591, -21.98240117, -22.07879923, -22.24413602],\n",
+ " [-21.98240054, -21.81718908, -21.91318321, -22.0787986 ],\n",
+ " ...,\n",
+ " [ 41.81719223, 41.98240369, 42.07880176, 41.91318637],\n",
+ " [ 41.98240117, 42.14733591, 42.24413602, 42.07879923],\n",
+ " [ 42.14733654, 42.31199432, 42.4091946 , 42.24413665]],\n",
+ " \n",
+ " [[-22.40919405, -22.2441361 , -22.34111548, -22.50657316],\n",
+ " [-22.24413547, -22.07879868, -22.17537644, -22.34111485],\n",
+ " [-22.07879805, -21.91318266, -22.00935688, -22.1753758 ],\n",
+ " ...,\n",
+ " [ 41.91318582, 42.07880121, 42.17537897, 42.00936005],\n",
+ " [ 42.07879868, 42.24413547, 42.34111485, 42.17537644],\n",
+ " [ 42.2441361 , 42.40919405, 42.50657316, 42.34111548]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[-67.50645709, -67.32583243, -67.64966627, -67.82912696],\n",
+ " [-67.32583174, -67.14410165, -67.46910621, -67.64966558],\n",
+ " [-67.14410095, -66.96124932, -67.28743133, -67.46910552],\n",
+ " ...,\n",
+ " [ 86.96125282, 87.14410443, 87.46910897, 87.28743481],\n",
+ " [ 87.14410165, 87.32583174, 87.64966558, 87.46910621],\n",
+ " [ 87.32583243, 87.50645709, 87.82912696, 87.64966627]],\n",
+ " \n",
+ " [[-67.82912819, -67.64966751, -67.97544812, -68.1537229 ],\n",
+ " [-67.64966682, -67.46910745, -67.79608108, -67.97544744],\n",
+ " [-67.46910676, -67.28743258, -67.6156063 , -67.79608039],\n",
+ " ...,\n",
+ " [ 87.28743606, 87.46911022, 87.79608382, 87.61560976],\n",
+ " [ 87.46910745, 87.64966682, 87.97544744, 87.79608108],\n",
+ " [ 87.64966751, 87.82912819, 88.1537229 , 87.97544812]],\n",
+ " \n",
+ " [[-68.15372103, -67.97544625, -68.30317799, -68.48024479],\n",
+ " [-67.97544557, -67.7960792 , -68.12502637, -68.30317732],\n",
+ " [-67.79607851, -67.61560442, -67.94577447, -68.12502569],\n",
+ " ...,\n",
+ " [ 87.61560787, 87.79608195, 88.1250291 , 87.9457779 ],\n",
+ " [ 87.7960792 , 87.97544557, 88.30317732, 88.12502637],\n",
+ " [ 87.97544625, 88.15372103, 88.48024479, 88.30317799]]])}"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_3.lon_bnds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.4. Write file with bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rank 000: Creating bounds_file_3.nc\n",
+ "Rank 000: NetCDF ready to write\n",
+ "Rank 000: Dimensions done\n",
+ "Rank 000: Writing O3_all var (1/1)\n",
+ "Rank 000: Var O3_all created (1/1)\n",
+ "Rank 000: Filling O3_all)\n",
+ "Rank 000: Var O3_all data (1/1)\n",
+ "Rank 000: Var O3_all completed (1/1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "nessy_3.to_netcdf('bounds_file_3.nc', info=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.5. Reopen with NES"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_4 = open_netcdf('bounds_file_3.nc', info=True)\n",
+ "nessy_4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.6. Explore spatial bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[[16.220397902213396, 16.303068236068505, 16.480289793933082,\n",
+ " 16.397397154910678],\n",
+ " [16.30306855071067, 16.38536089516572, 16.56280423641698,\n",
+ " 16.48029010942229],\n",
+ " [16.385361208363484, 16.467274252013816, 16.64493884522746,\n",
+ " 16.56280455045976],\n",
+ " ...,\n",
+ " [16.467272693272037, 16.385359642374645, 16.5628029802458,\n",
+ " 16.64493728227078],\n",
+ " [16.38536089516572, 16.30306855071067, 16.48029010942229,\n",
+ " 16.56280423641698],\n",
+ " [16.303068236068505, 16.220397902213396, 16.397397154910678,\n",
+ " 16.480289793933082]],\n",
+ " \n",
+ " [[16.397397830024588, 16.48029046989534, 16.657467620465567,\n",
+ " 16.574352506965674],\n",
+ " [16.48029078538455, 16.5628049132256, 16.740204020393392,\n",
+ " 16.657467936802462],\n",
+ " [16.562805227268388, 16.644939522880435, 16.82256005992405,\n",
+ " 16.740204335281874],\n",
+ " ...,\n",
+ " [16.644937959923737, 16.56280365705441, 16.740202760839427,\n",
+ " 16.822558492749014],\n",
+ " [16.5628049132256, 16.48029078538455, 16.657467936802462,\n",
+ " 16.740204020393392],\n",
+ " [16.48029046989534, 16.397397830024588, 16.574352506965674,\n",
+ " 16.657467620465567]],\n",
+ " \n",
+ " [[16.574351494551546, 16.657466606777945, 16.834598762498768,\n",
+ " 16.75126100486087],\n",
+ " [16.657466923114836, 16.740203005435227, 16.917557294312953,\n",
+ " 16.83459907968401],\n",
+ " [16.7402033203237, 16.822559043698337, 17.00013494374161,\n",
+ " 16.9175576100478],\n",
+ " ...,\n",
+ " [16.822557476523315, 16.740201745881272, 16.91755603137349,\n",
+ " 17.000133372344752],\n",
+ " [16.740203005435227, 16.657466923114836, 16.83459907968401,\n",
+ " 16.917557294312953],\n",
+ " [16.657466606777945, 16.574351494551546, 16.75126100486087,\n",
+ " 16.834598762498768]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[58.19210947966698, 58.34380497289048, 58.44964444401728,\n",
+ " 58.29776031793852],\n",
+ " [58.34380555135856, 58.49539320689951, 58.60142470084916,\n",
+ " 58.44964502321138],\n",
+ " [58.495393784952064, 58.646871410321715, 58.7530983546572,\n",
+ " 58.60142527964072],\n",
+ " ...,\n",
+ " [58.64686852217872, 58.49539089468928, 58.601422385682845,\n",
+ " 58.75309546275335],\n",
+ " [58.49539320689951, 58.34380555135856, 58.44964502321138,\n",
+ " 58.60142470084916],\n",
+ " [58.34380497289048, 58.19210947966698, 58.29776031793852,\n",
+ " 58.44964444401728]],\n",
+ " \n",
+ " [[58.297760719410846, 58.44964484620209, 58.55466326772292,\n",
+ " 58.402594256433474],\n",
+ " [58.4496454253962, 58.60142510375937, 58.70663179672086,\n",
+ " 58.554663847628724],\n",
+ " [58.60142568255093, 58.753098758305896, 58.8584971477072,\n",
+ " 58.70663237623712],\n",
+ " ...,\n",
+ " [58.75309586640205, 58.60142278859304, 58.70662947865572,\n",
+ " 58.85849425211415],\n",
+ " [58.60142510375937, 58.4496454253962, 58.554663847628724,\n",
+ " 58.70663179672086],\n",
+ " [58.44964484620209, 58.297760719410846, 58.402594256433474,\n",
+ " 58.55466326772292]],\n",
+ " \n",
+ " [[58.40259365892516, 58.55466266916757, 58.65885171699879,\n",
+ " 58.50660166161425],\n",
+ " [58.554663249073386, 58.70663119709912, 58.81100467268263,\n",
+ " 58.65885229760164],\n",
+ " [58.706631776615396, 58.85849654699951, 58.96305787144377,\n",
+ " 58.81100525290894],\n",
+ " ...,\n",
+ " [58.85849365140645, 58.70662887903401, 58.8110023517774,\n",
+ " 58.963054972234914],\n",
+ " [58.70663119709912, 58.554663249073386, 58.65885229760164,\n",
+ " 58.81100467268263],\n",
+ " [58.55466266916757, 58.40259365892516, 58.50660166161425,\n",
+ " 58.65885171699879]]],\n",
+ " mask=[[[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]]],\n",
+ " fill_value=1e+20)}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_4.lat_bnds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[[-22.21497020844106, -22.05071302765748, -22.14733616935655,\n",
+ " -22.31199395171342],\n",
+ " [-22.050712400541215, -21.886180131800984, -21.98240080355447,\n",
+ " -22.147335540708582],\n",
+ " [-21.88617950363573, -21.72137238587186, -21.817188715117368,\n",
+ " -21.982400173850465],\n",
+ " ...,\n",
+ " [41.72137553193767, 41.886182644461655, 41.982403322370715,\n",
+ " 41.817191868913255],\n",
+ " [41.886180131800984, 42.050712400541215, 42.14733554070858,\n",
+ " 41.982400803554526],\n",
+ " [42.05071302765748, 42.21497020844106, 42.31199395171342,\n",
+ " 42.14733616935655]],\n",
+ " \n",
+ " [[-22.311994322164878, -22.147336538280683, -22.244136652959696,\n",
+ " -22.40919460454836],\n",
+ " [-22.14733590963266, -21.982401170944172, -22.078799234822952,\n",
+ " -22.244136022781618],\n",
+ " [-21.982400541240054, -21.817189080965306, -21.91318320664743,\n",
+ " -22.078798603581617],\n",
+ " ...,\n",
+ " [41.81719223476125, 41.982403689760304, 42.07880175978829,\n",
+ " 41.913186368165896],\n",
+ " [41.98240117094417, 42.14733590963266, 42.24413602278162,\n",
+ " 42.07879923482295],\n",
+ " [42.14733653828068, 42.311994322164935, 42.40919460454836,\n",
+ " 42.244136652959696]],\n",
+ " \n",
+ " [[-22.40919404785444, -22.24413609855435, -22.341115482516443,\n",
+ " -22.50657316411582],\n",
+ " [-22.24413546837627, -22.078798682716865, -22.175376436459942,\n",
+ " -22.34111485080996],\n",
+ " [-22.078798051475587, -21.913182656851575, -22.009356878042922,\n",
+ " -22.17537580368287],\n",
+ " ...,\n",
+ " [41.91318581837004, 42.078801207682204, 42.175378967568065,\n",
+ " 42.00936004727629],\n",
+ " [42.078798682716865, 42.24413546837627, 42.34111485080996,\n",
+ " 42.17537643645994],\n",
+ " [42.24413609855435, 42.40919404785444, 42.50657316411582,\n",
+ " 42.3411154825165]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[-67.50645709267866, -67.3258324302019, -67.64966626624692,\n",
+ " -67.82912695633217],\n",
+ " [-67.32583173907523, -67.14410164962646, -67.46910620827077,\n",
+ " -67.64966557957331],\n",
+ " [-67.14410095425234, -66.9612493170024, -67.28743133275196,\n",
+ " -67.46910551737545],\n",
+ " ...,\n",
+ " [86.9612528154201, 87.1441044311228, 87.469108971852,\n",
+ " 87.28743480864733],\n",
+ " [87.14410164962646, 87.32583173907523, 87.64966557957331,\n",
+ " 87.46910620827077],\n",
+ " [87.3258324302019, 87.50645709267866, 87.82912695633217,\n",
+ " 87.64966626624692]],\n",
+ " \n",
+ " [[-67.82912819088853, -67.64966750528527, -67.97544812209401,\n",
+ " -68.15372289526101],\n",
+ " [-67.64966681861165, -67.46910745181754, -67.79608107785475,\n",
+ " -67.97544743995786],\n",
+ " [-67.46910676092222, -67.28743258083347, -67.61560630362294,\n",
+ " -67.79608039152396],\n",
+ " ...,\n",
+ " [87.28743605672872, 87.46911021539864, 87.79608382317804,\n",
+ " 87.61560975656079],\n",
+ " [87.46910745181754, 87.64966681861165, 87.97544743995786,\n",
+ " 87.79608107785481],\n",
+ " [87.64966750528527, 87.82912819088853, 88.15372289526101,\n",
+ " 87.97544812209401]],\n",
+ " \n",
+ " [[-68.15372103240003, -67.97544625238419, -68.30317799462358,\n",
+ " -68.4802447924423],\n",
+ " [-67.97544557024798, -67.79607920125443, -68.12502637415002,\n",
+ " -68.30317731710966],\n",
+ " [-67.79607851492364, -67.61560442009056, -67.94577446945578,\n",
+ " -68.12502569246982],\n",
+ " ...,\n",
+ " [87.61560787302852, 87.79608194657783, 88.12502910087062,\n",
+ " 87.94577789899824],\n",
+ " [87.79607920125449, 87.97544557024798, 88.30317731710971,\n",
+ " 88.12502637415002],\n",
+ " [87.97544625238413, 88.15372103240003, 88.48024479244236,\n",
+ " 88.30317799462364]]],\n",
+ " mask=[[[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " ...,\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]],\n",
+ " \n",
+ " [[False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " ...,\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False],\n",
+ " [False, False, False, False]]],\n",
+ " fill_value=1e+20)}"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy_4.lon_bnds"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb b/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..17eb7d511622982f0250d6e4ea302c6ec060f9b8
--- /dev/null
+++ b/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb
@@ -0,0 +1,740 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Calculate the area of each cell of a grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nes import *\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Create grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lat_1 = 37\n",
+ "lat_2 = 43\n",
+ "lon_0 = -3\n",
+ "lat_0 = 40\n",
+ "nx = 10\n",
+ "ny = 15\n",
+ "inc_x = 4000\n",
+ "inc_y = 4000\n",
+ "x_0 = -807847.688\n",
+ "y_0 = -797137.125\n",
+ "lcc_grid = create_nes(comm=None, info=False, projection='lcc',\n",
+ " lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, \n",
+ " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-11.58393 32.47507, -11.54169 32.478... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-11.54169 32.47851, -11.49944 32.481... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-11.49944 32.48192, -11.45719 32.485... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-11.45719 32.48533, -11.41494 32.488... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-11.41494 32.48871, -11.37268 32.492... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 145 \n",
+ " POLYGON ((-11.42882 32.99135, -11.38628 32.994... \n",
+ " \n",
+ " \n",
+ " 146 \n",
+ " POLYGON ((-11.38628 32.99473, -11.34373 32.998... \n",
+ " \n",
+ " \n",
+ " 147 \n",
+ " POLYGON ((-11.34373 32.99809, -11.30118 33.001... \n",
+ " \n",
+ " \n",
+ " 148 \n",
+ " POLYGON ((-11.30118 33.00143, -11.25863 33.004... \n",
+ " \n",
+ " \n",
+ " 149 \n",
+ " POLYGON ((-11.25863 33.00476, -11.21607 33.008... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
150 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n",
+ "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n",
+ "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n",
+ "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n",
+ "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n",
+ ".. ...\n",
+ "145 POLYGON ((-11.42882 32.99135, -11.38628 32.994...\n",
+ "146 POLYGON ((-11.38628 32.99473, -11.34373 32.998...\n",
+ "147 POLYGON ((-11.34373 32.99809, -11.30118 33.001...\n",
+ "148 POLYGON ((-11.30118 33.00143, -11.25863 33.004...\n",
+ "149 POLYGON ((-11.25863 33.00476, -11.21607 33.008...\n",
+ "\n",
+ "[150 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lcc_grid.create_shapefile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Calculate grid area with NES"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lcc_grid.calculate_grid_area()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': array([[15830419.37602491, 15830657.31464759, 15830893.98187641,\n",
+ " 15831129.37902908, 15831363.50737192, 15831596.36818251,\n",
+ " 15831827.96271216, 15832058.29223234, 15832287.3580128 ,\n",
+ " 15832515.16128843],\n",
+ " [15832888.43944364, 15833125.4618577 , 15833361.21722112,\n",
+ " 15833595.70682441, 15833828.93193695, 15834060.89385057,\n",
+ " 15834291.59384502, 15834521.03319032, 15834749.2131148 ,\n",
+ " 15834976.13488906],\n",
+ " [15835346.79240354, 15835582.8966068 , 15835817.73809013,\n",
+ " 15836051.31816116, 15836283.63810613, 15836514.69920246,\n",
+ " 15836744.50270247, 15836973.04988706, 15837200.34204102,\n",
+ " 15837426.38040528],\n",
+ " [15837794.42326163, 15838029.60727211, 15838263.53292125,\n",
+ " 15838496.20149222, 15838727.61427387, 15838957.77256741,\n",
+ " 15839186.6776418 , 15839414.33076289, 15839640.73319616,\n",
+ " 15839865.88622452],\n",
+ " [15840231.32048458, 15840465.58229638, 15840698.59009752,\n",
+ " 15840930.34519075, 15841160.84885779, 15841390.10239228,\n",
+ " 15841618.10708416, 15841844.86421011, 15842070.37502717,\n",
+ " 15842294.64079978],\n",
+ " [15842657.47252579, 15842890.8101492 , 15843122.89812364,\n",
+ " 15843353.73774392, 15843583.33033225, 15843811.67716125,\n",
+ " 15844038.77951487, 15844264.63868577, 15844489.2559287 ,\n",
+ " 15844712.63252152],\n",
+ " [15845072.86781775, 15845305.27923895, 15845536.44539522,\n",
+ " 15845766.36759449, 15845995.04712501, 15846222.4852872 ,\n",
+ " 15846448.68336277, 15846673.64263782, 15846897.36439707,\n",
+ " 15847119.84990175],\n",
+ " [15847477.49487919, 15847708.97812176, 15847939.22047551,\n",
+ " 15848168.22324328, 15848395.9877448 , 15848622.51527306,\n",
+ " 15848847.8071165 , 15849071.86456124, 15849294.68888149,\n",
+ " 15849516.28136931],\n",
+ " [15849871.34221948, 15850101.89526329, 15850331.21182007,\n",
+ " 15850559.293196 , 15850786.14069547, 15851011.75562783,\n",
+ " 15851236.13929178, 15851459.2929683 , 15851681.21793849,\n",
+ " 15851901.91547563],\n",
+ " [15852254.39832998, 15852484.01919013, 15852712.40796391,\n",
+ " 15852939.56596235, 15853165.49449526, 15853390.19487267,\n",
+ " 15853613.66838107, 15853835.91632964, 15854056.94000432,\n",
+ " 15854276.74067481],\n",
+ " [15854626.65180506, 15854855.33845814, 15855082.79744119,\n",
+ " 15855309.03006979, 15855534.03765641, 15855757.82150882,\n",
+ " 15855980.3829386 , 15856201.72323178, 15856421.84367417,\n",
+ " 15856640.74556485],\n",
+ " [15856988.09116465, 15857215.84163662, 15857442.36884046,\n",
+ " 15857667.67411114, 15857891.75877949, 15858114.62415727,\n",
+ " 15858336.27153077, 15858556.70220796, 15858775.91748785,\n",
+ " 15858993.91865496],\n",
+ " [15859338.70502081, 15859565.51727597, 15859791.11070865,\n",
+ " 15860015.48665859, 15860238.64643876, 15860460.59134635,\n",
+ " 15860681.3227187 , 15860900.84184616, 15861119.15002387,\n",
+ " 15861336.24853621],\n",
+ " [15861678.48197006, 15861904.35401631, 15862129.01168405,\n",
+ " 15862352.45630977, 15862574.68920346, 15862795.71170322,\n",
+ " 15863015.52510921, 15863234.13072645, 15863451.52986768,\n",
+ " 15863667.72381984],\n",
+ " [15864007.41061145, 15864232.34043731, 15864456.06035349,\n",
+ " 15864678.57167045, 15864899.87571356, 15865119.97382447,\n",
+ " 15865338.86731228, 15865556.55748565, 15865773.04563914,\n",
+ " 15865988.33307546]])}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lcc_grid.cell_measures['cell_area']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Write"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lcc_grid.to_netcdf('lcc_grid_nes_area.nc')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Reopen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nessy = open_netcdf('lcc_grid_nes_area.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'data': masked_array(\n",
+ " data=[[15830419.376024913, 15830657.314647589, 15830893.981876407,\n",
+ " 15831129.379029078, 15831363.50737192, 15831596.368182508,\n",
+ " 15831827.962712158, 15832058.292232336, 15832287.358012801,\n",
+ " 15832515.161288435],\n",
+ " [15832888.43944364, 15833125.461857699, 15833361.217221119,\n",
+ " 15833595.706824414, 15833828.931936948, 15834060.89385057,\n",
+ " 15834291.593845021, 15834521.033190317, 15834749.2131148,\n",
+ " 15834976.134889057],\n",
+ " [15835346.792403538, 15835582.896606795, 15835817.738090131,\n",
+ " 15836051.318161163, 15836283.638106134, 15836514.699202463,\n",
+ " 15836744.502702465, 15836973.049887061, 15837200.342041023,\n",
+ " 15837426.380405283],\n",
+ " [15837794.423261626, 15838029.607272115, 15838263.532921247,\n",
+ " 15838496.201492224, 15838727.614273867, 15838957.77256741,\n",
+ " 15839186.677641798, 15839414.330762891, 15839640.733196164,\n",
+ " 15839865.88622452],\n",
+ " [15840231.320484577, 15840465.582296379, 15840698.590097519,\n",
+ " 15840930.345190752, 15841160.848857794, 15841390.102392279,\n",
+ " 15841618.107084159, 15841844.864210112, 15842070.375027169,\n",
+ " 15842294.640799781],\n",
+ " [15842657.472525794, 15842890.810149202, 15843122.898123644,\n",
+ " 15843353.737743918, 15843583.330332253, 15843811.677161248,\n",
+ " 15844038.779514872, 15844264.638685772, 15844489.255928697,\n",
+ " 15844712.63252152],\n",
+ " [15845072.867817752, 15845305.279238949, 15845536.445395218,\n",
+ " 15845766.367594492, 15845995.047125012, 15846222.485287195,\n",
+ " 15846448.683362775, 15846673.642637823, 15846897.364397066,\n",
+ " 15847119.849901745],\n",
+ " [15847477.494879192, 15847708.978121761, 15847939.220475506,\n",
+ " 15848168.223243283, 15848395.987744803, 15848622.51527306,\n",
+ " 15848847.8071165, 15849071.864561237, 15849294.68888149,\n",
+ " 15849516.28136931],\n",
+ " [15849871.342219478, 15850101.895263294, 15850331.211820066,\n",
+ " 15850559.293195998, 15850786.140695473, 15851011.755627826,\n",
+ " 15851236.139291776, 15851459.2929683, 15851681.217938488,\n",
+ " 15851901.915475631],\n",
+ " [15852254.398329977, 15852484.019190125, 15852712.40796391,\n",
+ " 15852939.565962346, 15853165.494495256, 15853390.194872674,\n",
+ " 15853613.668381073, 15853835.916329645, 15854056.940004325,\n",
+ " 15854276.740674812],\n",
+ " [15854626.65180506, 15854855.33845814, 15855082.797441188,\n",
+ " 15855309.030069793, 15855534.037656406, 15855757.82150882,\n",
+ " 15855980.3829386, 15856201.723231781, 15856421.843674172,\n",
+ " 15856640.745564846],\n",
+ " [15856988.091164649, 15857215.841636622, 15857442.368840463,\n",
+ " 15857667.674111139, 15857891.758779489, 15858114.624157269,\n",
+ " 15858336.271530768, 15858556.702207962, 15858775.917487847,\n",
+ " 15858993.918654963],\n",
+ " [15859338.705020806, 15859565.517275967, 15859791.110708648,\n",
+ " 15860015.48665859, 15860238.646438757, 15860460.59134635,\n",
+ " 15860681.322718704, 15860900.841846164, 15861119.150023872,\n",
+ " 15861336.248536212],\n",
+ " [15861678.48197006, 15861904.354016308, 15862129.01168405,\n",
+ " 15862352.456309775, 15862574.68920346, 15862795.711703217,\n",
+ " 15863015.52510921, 15863234.130726451, 15863451.529867679,\n",
+ " 15863667.72381984],\n",
+ " [15864007.410611445, 15864232.340437308, 15864456.060353493,\n",
+ " 15864678.571670454, 15864899.875713555, 15865119.973824475,\n",
+ " 15865338.867312277, 15865556.55748565, 15865773.045639142,\n",
+ " 15865988.333075456]],\n",
+ " mask=[[False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False],\n",
+ " [False, False, False, False, False, False, False, False, False,\n",
+ " False]],\n",
+ " fill_value=1e+20)}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy.cell_measures['cell_area']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Calculate cell area with CDO"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lcc_grid.variables['aux'] = {'data': np.ones((1, 1, len(lcc_grid.y['data']), len(lcc_grid.x['data'])))}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lcc_grid.to_netcdf('lcc_grid.nc')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the terminal:\n",
+ "\n",
+ "```\n",
+ "module load CDO\n",
+ "\n",
+ "cdo gridarea lcc_grid.nc lcc_grid_cdo_area.nc\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 6. Compare results from NES and CDO"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "shape = lcc_grid.shapefile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " shape['area_cdo'] = xr.open_dataset('lcc_grid_cdo_area.nc').cell_area.values.ravel()\n",
+ "except:\n",
+ " print('Error: Calculate grid area using CDO and save it as lcc_grid_cdo_area.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "shape['area_nes'] = lcc_grid.cell_measures['cell_area']['data'].ravel()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " area_cdo \n",
+ " area_nes \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-11.58393 32.47507, -11.54169 32.478... \n",
+ " 1.583042e+07 \n",
+ " 1.583042e+07 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-11.54169 32.47851, -11.49944 32.481... \n",
+ " 1.583066e+07 \n",
+ " 1.583066e+07 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-11.49944 32.48192, -11.45719 32.485... \n",
+ " 1.583089e+07 \n",
+ " 1.583089e+07 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-11.45719 32.48533, -11.41494 32.488... \n",
+ " 1.583113e+07 \n",
+ " 1.583113e+07 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-11.41494 32.48871, -11.37268 32.492... \n",
+ " 1.583136e+07 \n",
+ " 1.583136e+07 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 145 \n",
+ " POLYGON ((-11.42882 32.99135, -11.38628 32.994... \n",
+ " 1.586512e+07 \n",
+ " 1.586512e+07 \n",
+ " \n",
+ " \n",
+ " 146 \n",
+ " POLYGON ((-11.38628 32.99473, -11.34373 32.998... \n",
+ " 1.586534e+07 \n",
+ " 1.586534e+07 \n",
+ " \n",
+ " \n",
+ " 147 \n",
+ " POLYGON ((-11.34373 32.99809, -11.30118 33.001... \n",
+ " 1.586556e+07 \n",
+ " 1.586556e+07 \n",
+ " \n",
+ " \n",
+ " 148 \n",
+ " POLYGON ((-11.30118 33.00143, -11.25863 33.004... \n",
+ " 1.586577e+07 \n",
+ " 1.586577e+07 \n",
+ " \n",
+ " \n",
+ " 149 \n",
+ " POLYGON ((-11.25863 33.00476, -11.21607 33.008... \n",
+ " 1.586599e+07 \n",
+ " 1.586599e+07 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
150 rows × 3 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry area_cdo \\\n",
+ "FID \n",
+ "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478... 1.583042e+07 \n",
+ "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481... 1.583066e+07 \n",
+ "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485... 1.583089e+07 \n",
+ "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488... 1.583113e+07 \n",
+ "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492... 1.583136e+07 \n",
+ ".. ... ... \n",
+ "145 POLYGON ((-11.42882 32.99135, -11.38628 32.994... 1.586512e+07 \n",
+ "146 POLYGON ((-11.38628 32.99473, -11.34373 32.998... 1.586534e+07 \n",
+ "147 POLYGON ((-11.34373 32.99809, -11.30118 33.001... 1.586556e+07 \n",
+ "148 POLYGON ((-11.30118 33.00143, -11.25863 33.004... 1.586577e+07 \n",
+ "149 POLYGON ((-11.25863 33.00476, -11.21607 33.008... 1.586599e+07 \n",
+ "\n",
+ " area_nes \n",
+ "FID \n",
+ "0 1.583042e+07 \n",
+ "1 1.583066e+07 \n",
+ "2 1.583089e+07 \n",
+ "3 1.583113e+07 \n",
+ "4 1.583136e+07 \n",
+ ".. ... \n",
+ "145 1.586512e+07 \n",
+ "146 1.586534e+07 \n",
+ "147 1.586556e+07 \n",
+ "148 1.586577e+07 \n",
+ "149 1.586599e+07 \n",
+ "\n",
+ "[150 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "FID\n",
+ "0 0.0\n",
+ "1 0.0\n",
+ "2 0.0\n",
+ "3 0.0\n",
+ "4 0.0\n",
+ " ... \n",
+ "145 0.0\n",
+ "146 0.0\n",
+ "147 0.0\n",
+ "148 0.0\n",
+ "149 0.0\n",
+ "Length: 150, dtype: float64"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(shape['area_nes']-shape['area_cdo'])*100/shape['area_cdo']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "((shape['area_nes']-shape['area_cdo'])*100/shape['area_cdo']).mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "((shape['area_nes']-shape['area_cdo'])*100/shape['area_cdo']).max()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "((shape['area_nes']-shape['area_cdo'])*100/shape['area_cdo']).min()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb b/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a1bf2e7f9be2f5fa7aa5b5e77fe8fdd8456d829c
--- /dev/null
+++ b/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb
@@ -0,0 +1,304 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Calculate the area of each cell of a set of geometries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nes import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Get geometries from grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lat_1 = 37\n",
+ "lat_2 = 43\n",
+ "lon_0 = -3\n",
+ "lat_0 = 40\n",
+ "nx = 10\n",
+ "ny = 15\n",
+ "inc_x = 4000\n",
+ "inc_y = 4000\n",
+ "x_0 = -807847.688\n",
+ "y_0 = -797137.125\n",
+ "lcc_grid = create_nes(comm=None, info=False, projection='lcc',\n",
+ " lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, \n",
+ " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-11.58393 32.47507, -11.54169 32.478... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-11.54169 32.47851, -11.49944 32.481... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-11.49944 32.48192, -11.45719 32.485... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-11.45719 32.48533, -11.41494 32.488... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-11.41494 32.48871, -11.37268 32.492... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 145 \n",
+ " POLYGON ((-11.42882 32.99135, -11.38628 32.994... \n",
+ " \n",
+ " \n",
+ " 146 \n",
+ " POLYGON ((-11.38628 32.99473, -11.34373 32.998... \n",
+ " \n",
+ " \n",
+ " 147 \n",
+ " POLYGON ((-11.34373 32.99809, -11.30118 33.001... \n",
+ " \n",
+ " \n",
+ " 148 \n",
+ " POLYGON ((-11.30118 33.00143, -11.25863 33.004... \n",
+ " \n",
+ " \n",
+ " 149 \n",
+ " POLYGON ((-11.25863 33.00476, -11.21607 33.008... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
150 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n",
+ "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n",
+ "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n",
+ "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n",
+ "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n",
+ ".. ...\n",
+ "145 POLYGON ((-11.42882 32.99135, -11.38628 32.994...\n",
+ "146 POLYGON ((-11.38628 32.99473, -11.34373 32.998...\n",
+ "147 POLYGON ((-11.34373 32.99809, -11.30118 33.001...\n",
+ "148 POLYGON ((-11.30118 33.00143, -11.25863 33.004...\n",
+ "149 POLYGON ((-11.25863 33.00476, -11.21607 33.008...\n",
+ "\n",
+ "[150 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lcc_grid.create_shapefile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\n",
+ "[,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ...\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ]\n",
+ "Length: 150, dtype: geometry"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "geometry_list = lcc_grid.shapefile['geometry'].values\n",
+ "geometry_list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Calculate area of each polygon"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([15830419.37602491, 15830657.31464759, 15830893.98187641,\n",
+ " 15831129.37902908, 15831363.50737192, 15831596.36818251,\n",
+ " 15831827.96271216, 15832058.29223234, 15832287.3580128 ,\n",
+ " 15832515.16128843, 15832888.43944364, 15833125.4618577 ,\n",
+ " 15833361.21722112, 15833595.70682441, 15833828.93193695,\n",
+ " 15834060.89385057, 15834291.59384502, 15834521.03319032,\n",
+ " 15834749.2131148 , 15834976.13488906, 15835346.79240354,\n",
+ " 15835582.8966068 , 15835817.73809013, 15836051.31816116,\n",
+ " 15836283.63810613, 15836514.69920246, 15836744.50270247,\n",
+ " 15836973.04988706, 15837200.34204102, 15837426.38040528,\n",
+ " 15837794.42326163, 15838029.60727211, 15838263.53292125,\n",
+ " 15838496.20149222, 15838727.61427387, 15838957.77256741,\n",
+ " 15839186.6776418 , 15839414.33076289, 15839640.73319616,\n",
+ " 15839865.88622452, 15840231.32048458, 15840465.58229638,\n",
+ " 15840698.59009752, 15840930.34519075, 15841160.84885779,\n",
+ " 15841390.10239228, 15841618.10708416, 15841844.86421011,\n",
+ " 15842070.37502717, 15842294.64079978, 15842657.47252579,\n",
+ " 15842890.8101492 , 15843122.89812364, 15843353.73774392,\n",
+ " 15843583.33033225, 15843811.67716125, 15844038.77951487,\n",
+ " 15844264.63868577, 15844489.2559287 , 15844712.63252152,\n",
+ " 15845072.86781775, 15845305.27923895, 15845536.44539522,\n",
+ " 15845766.36759449, 15845995.04712501, 15846222.4852872 ,\n",
+ " 15846448.68336277, 15846673.64263782, 15846897.36439707,\n",
+ " 15847119.84990175, 15847477.49487919, 15847708.97812176,\n",
+ " 15847939.22047551, 15848168.22324328, 15848395.9877448 ,\n",
+ " 15848622.51527306, 15848847.8071165 , 15849071.86456124,\n",
+ " 15849294.68888149, 15849516.28136931, 15849871.34221948,\n",
+ " 15850101.89526329, 15850331.21182007, 15850559.293196 ,\n",
+ " 15850786.14069547, 15851011.75562783, 15851236.13929178,\n",
+ " 15851459.2929683 , 15851681.21793849, 15851901.91547563,\n",
+ " 15852254.39832998, 15852484.01919013, 15852712.40796391,\n",
+ " 15852939.56596235, 15853165.49449526, 15853390.19487267,\n",
+ " 15853613.66838107, 15853835.91632964, 15854056.94000432,\n",
+ " 15854276.74067481, 15854626.65180506, 15854855.33845814,\n",
+ " 15855082.79744119, 15855309.03006979, 15855534.03765641,\n",
+ " 15855757.82150882, 15855980.3829386 , 15856201.72323178,\n",
+ " 15856421.84367417, 15856640.74556485, 15856988.09116465,\n",
+ " 15857215.84163662, 15857442.36884046, 15857667.67411114,\n",
+ " 15857891.75877949, 15858114.62415727, 15858336.27153077,\n",
+ " 15858556.70220796, 15858775.91748785, 15858993.91865496,\n",
+ " 15859338.70502081, 15859565.51727597, 15859791.11070865,\n",
+ " 15860015.48665859, 15860238.64643876, 15860460.59134635,\n",
+ " 15860681.3227187 , 15860900.84184616, 15861119.15002387,\n",
+ " 15861336.24853621, 15861678.48197006, 15861904.35401631,\n",
+ " 15862129.01168405, 15862352.45630977, 15862574.68920346,\n",
+ " 15862795.71170322, 15863015.52510921, 15863234.13072645,\n",
+ " 15863451.52986768, 15863667.72381984, 15864007.41061145,\n",
+ " 15864232.34043731, 15864456.06035349, 15864678.57167045,\n",
+ " 15864899.87571356, 15865119.97382447, 15865338.86731228,\n",
+ " 15865556.55748565, 15865773.04563914, 15865988.33307546])"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "geometry_area = calculate_geometry_area(geometry_list)\n",
+ "geometry_area"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/5.Others/5.2.Add_Coordinates_Bounds.ipynb b/tutorials/5.Others/5.2.Add_Coordinates_Bounds.ipynb
deleted file mode 100644
index 9ff645c78a988572e4135c1adf164a8ffad8ab72..0000000000000000000000000000000000000000
--- a/tutorials/5.Others/5.2.Add_Coordinates_Bounds.ipynb
+++ /dev/null
@@ -1,236 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# How to add coordinates bounds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import xarray as xr\n",
- "import datetime\n",
- "import numpy as np\n",
- "from nes import *"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. Set coordinates bounds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "test_path = \"/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\"\n",
- "nessy = open_netcdf(path=test_path, info=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "nessy.create_spatial_bounds()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rank 000: Loading O3_all var (1/1)\n",
- "Rank 000: Loaded O3_all var ((1, 24, 271, 351))\n"
- ]
- }
- ],
- "source": [
- "nessy.load()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Explore variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[[16.2203979 , 16.30306824, 16.48028979, 16.39739715],\n",
- " [16.30306855, 16.3853609 , 16.56280424, 16.48029011],\n",
- " [16.38536121, 16.46727425, 16.64493885, 16.56280455],\n",
- " ...,\n",
- " [16.46727269, 16.38535964, 16.56280298, 16.64493728],\n",
- " [16.3853609 , 16.30306855, 16.48029011, 16.56280424],\n",
- " [16.30306824, 16.2203979 , 16.39739715, 16.48028979]],\n",
- "\n",
- " [[16.39739783, 16.48029047, 16.65746762, 16.57435251],\n",
- " [16.48029079, 16.56280491, 16.74020402, 16.65746794],\n",
- " [16.56280523, 16.64493952, 16.82256006, 16.74020434],\n",
- " ...,\n",
- " [16.64493796, 16.56280366, 16.74020276, 16.82255849],\n",
- " [16.56280491, 16.48029079, 16.65746794, 16.74020402],\n",
- " [16.48029047, 16.39739783, 16.57435251, 16.65746762]],\n",
- "\n",
- " [[16.57435149, 16.65746661, 16.83459876, 16.751261 ],\n",
- " [16.65746692, 16.74020301, 16.91755729, 16.83459908],\n",
- " [16.74020332, 16.82255904, 17.00013494, 16.91755761],\n",
- " ...,\n",
- " [16.82255748, 16.74020175, 16.91755603, 17.00013337],\n",
- " [16.74020301, 16.65746692, 16.83459908, 16.91755729],\n",
- " [16.65746661, 16.57435149, 16.751261 , 16.83459876]],\n",
- "\n",
- " ...,\n",
- "\n",
- " [[58.19210948, 58.34380497, 58.44964444, 58.29776032],\n",
- " [58.34380555, 58.49539321, 58.6014247 , 58.44964502],\n",
- " [58.49539378, 58.64687141, 58.75309835, 58.60142528],\n",
- " ...,\n",
- " [58.64686852, 58.49539089, 58.60142239, 58.75309546],\n",
- " [58.49539321, 58.34380555, 58.44964502, 58.6014247 ],\n",
- " [58.34380497, 58.19210948, 58.29776032, 58.44964444]],\n",
- "\n",
- " [[58.29776072, 58.44964485, 58.55466327, 58.40259426],\n",
- " [58.44964543, 58.6014251 , 58.7066318 , 58.55466385],\n",
- " [58.60142568, 58.75309876, 58.85849715, 58.70663238],\n",
- " ...,\n",
- " [58.75309587, 58.60142279, 58.70662948, 58.85849425],\n",
- " [58.6014251 , 58.44964543, 58.55466385, 58.7066318 ],\n",
- " [58.44964485, 58.29776072, 58.40259426, 58.55466327]],\n",
- "\n",
- " [[58.40259366, 58.55466267, 58.65885172, 58.50660166],\n",
- " [58.55466325, 58.7066312 , 58.81100467, 58.6588523 ],\n",
- " [58.70663178, 58.85849655, 58.96305787, 58.81100525],\n",
- " ...,\n",
- " [58.85849365, 58.70662888, 58.81100235, 58.96305497],\n",
- " [58.7066312 , 58.55466325, 58.6588523 , 58.81100467],\n",
- " [58.55466267, 58.40259366, 58.50660166, 58.65885172]]])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nessy.lat_bnds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[[-22.21497021, -22.05071303, -22.14733617, -22.31199395],\n",
- " [-22.0507124 , -21.88618013, -21.9824008 , -22.14733554],\n",
- " [-21.8861795 , -21.72137239, -21.81718872, -21.98240017],\n",
- " ...,\n",
- " [ 41.72137553, 41.88618264, 41.98240332, 41.81719187],\n",
- " [ 41.88618013, 42.0507124 , 42.14733554, 41.9824008 ],\n",
- " [ 42.05071303, 42.21497021, 42.31199395, 42.14733617]],\n",
- "\n",
- " [[-22.31199432, -22.14733654, -22.24413665, -22.4091946 ],\n",
- " [-22.14733591, -21.98240117, -22.07879923, -22.24413602],\n",
- " [-21.98240054, -21.81718908, -21.91318321, -22.0787986 ],\n",
- " ...,\n",
- " [ 41.81719223, 41.98240369, 42.07880176, 41.91318637],\n",
- " [ 41.98240117, 42.14733591, 42.24413602, 42.07879923],\n",
- " [ 42.14733654, 42.31199432, 42.4091946 , 42.24413665]],\n",
- "\n",
- " [[-22.40919405, -22.2441361 , -22.34111548, -22.50657316],\n",
- " [-22.24413547, -22.07879868, -22.17537644, -22.34111485],\n",
- " [-22.07879805, -21.91318266, -22.00935688, -22.1753758 ],\n",
- " ...,\n",
- " [ 41.91318582, 42.07880121, 42.17537897, 42.00936005],\n",
- " [ 42.07879868, 42.24413547, 42.34111485, 42.17537644],\n",
- " [ 42.2441361 , 42.40919405, 42.50657316, 42.34111548]],\n",
- "\n",
- " ...,\n",
- "\n",
- " [[-67.50645709, -67.32583243, -67.64966627, -67.82912696],\n",
- " [-67.32583174, -67.14410165, -67.46910621, -67.64966558],\n",
- " [-67.14410095, -66.96124932, -67.28743133, -67.46910552],\n",
- " ...,\n",
- " [ 86.96125282, 87.14410443, 87.46910897, 87.28743481],\n",
- " [ 87.14410165, 87.32583174, 87.64966558, 87.46910621],\n",
- " [ 87.32583243, 87.50645709, 87.82912696, 87.64966627]],\n",
- "\n",
- " [[-67.82912819, -67.64966751, -67.97544812, -68.1537229 ],\n",
- " [-67.64966682, -67.46910745, -67.79608108, -67.97544744],\n",
- " [-67.46910676, -67.28743258, -67.6156063 , -67.79608039],\n",
- " ...,\n",
- " [ 87.28743606, 87.46911022, 87.79608382, 87.61560976],\n",
- " [ 87.46910745, 87.64966682, 87.97544744, 87.79608108],\n",
- " [ 87.64966751, 87.82912819, 88.1537229 , 87.97544812]],\n",
- "\n",
- " [[-68.15372103, -67.97544625, -68.30317799, -68.48024479],\n",
- " [-67.97544557, -67.7960792 , -68.12502637, -68.30317732],\n",
- " [-67.79607851, -67.61560442, -67.94577447, -68.12502569],\n",
- " ...,\n",
- " [ 87.61560787, 87.79608195, 88.1250291 , 87.9457779 ],\n",
- " [ 87.7960792 , 87.97544557, 88.30317732, 88.12502637],\n",
- " [ 87.97544625, 88.15372103, 88.48024479, 88.30317799]]])"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nessy.lon_bnds"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- },
- "vscode": {
- "interpreter": {
- "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials/5.Others/5.3.Create_Shapefiles.ipynb b/tutorials/5.Others/5.3.Create_Shapefiles.ipynb
deleted file mode 100644
index acfe4eb4062cfc9c8389115d3bc3b35f662e1ee8..0000000000000000000000000000000000000000
--- a/tutorials/5.Others/5.3.Create_Shapefiles.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# How to create shapefiles"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from nes import *\n",
- "import datetime"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. From grids that have already been created"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "grid_path = '/gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc'\n",
- "grid = open_netcdf(path=grid_path, info=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create shapefile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rank 000: Loading sconcno2 var (1/1)\n",
- "Rank 000: Loaded sconcno2 var ((25, 1, 115, 165))\n"
- ]
- }
- ],
- "source": [
- "grid.load()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rank 000: Loading sconcno2 var (1/1)\n",
- "Rank 000: Loaded sconcno2 var ((1, 1, 115, 165))\n"
- ]
- }
- ],
- "source": [
- "grid.to_shapefile('regular', \n",
- " time=datetime.datetime(2019, 1, 1, 10, 0), \n",
- " lev=0, var_list=['sconcno2'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " sconcno2 \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " POLYGON ((-11.85000 33.85000, -11.75000 33.850... \n",
- " 0.000288 \n",
- " \n",
- " \n",
- " 1 \n",
- " POLYGON ((-11.75000 33.85000, -11.65000 33.850... \n",
- " 0.000271 \n",
- " \n",
- " \n",
- " 2 \n",
- " POLYGON ((-11.65000 33.85000, -11.55000 33.850... \n",
- " 0.000258 \n",
- " \n",
- " \n",
- " 3 \n",
- " POLYGON ((-11.55000 33.85000, -11.45000 33.850... \n",
- " 0.000254 \n",
- " \n",
- " \n",
- " 4 \n",
- " POLYGON ((-11.45000 33.85000, -11.35000 33.850... \n",
- " 0.000262 \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 18970 \n",
- " POLYGON ((4.15000 45.25000, 4.25000 45.25000, ... \n",
- " 0.003521 \n",
- " \n",
- " \n",
- " 18971 \n",
- " POLYGON ((4.25000 45.25000, 4.35000 45.25000, ... \n",
- " 0.003924 \n",
- " \n",
- " \n",
- " 18972 \n",
- " POLYGON ((4.35000 45.25000, 4.45000 45.25000, ... \n",
- " 0.004454 \n",
- " \n",
- " \n",
- " 18973 \n",
- " POLYGON ((4.45000 45.25000, 4.55000 45.25000, ... \n",
- " 0.004619 \n",
- " \n",
- " \n",
- " 18974 \n",
- " POLYGON ((4.55000 45.25000, 4.65000 45.25000, ... \n",
- " 0.004712 \n",
- " \n",
- " \n",
- "
\n",
- "
18975 rows × 2 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry sconcno2\n",
- "FID \n",
- "0 POLYGON ((-11.85000 33.85000, -11.75000 33.850... 0.000288\n",
- "1 POLYGON ((-11.75000 33.85000, -11.65000 33.850... 0.000271\n",
- "2 POLYGON ((-11.65000 33.85000, -11.55000 33.850... 0.000258\n",
- "3 POLYGON ((-11.55000 33.85000, -11.45000 33.850... 0.000254\n",
- "4 POLYGON ((-11.45000 33.85000, -11.35000 33.850... 0.000262\n",
- "... ... ...\n",
- "18970 POLYGON ((4.15000 45.25000, 4.25000 45.25000, ... 0.003521\n",
- "18971 POLYGON ((4.25000 45.25000, 4.35000 45.25000, ... 0.003924\n",
- "18972 POLYGON ((4.35000 45.25000, 4.45000 45.25000, ... 0.004454\n",
- "18973 POLYGON ((4.45000 45.25000, 4.55000 45.25000, ... 0.004619\n",
- "18974 POLYGON ((4.55000 45.25000, 4.65000 45.25000, ... 0.004712\n",
- "\n",
- "[18975 rows x 2 columns]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "grid.shapefile"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. From grids created from scratch"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create shapefile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "centre_lat = 51\n",
- "centre_lon = 10\n",
- "west_boundary = -35\n",
- "south_boundary = -27\n",
- "inc_rlat = 0.2\n",
- "inc_rlon = 0.2\n",
- "new_grid = create_nes(comm=None, info=False, projection='rotated',\n",
- " centre_lat=centre_lat, centre_lon=centre_lon,\n",
- " west_boundary=west_boundary, south_boundary=south_boundary,\n",
- " inc_rlat=inc_rlat, inc_rlon=inc_rlon)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "new_grid.to_shapefile('rotated')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "new_grid.shapefile"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- },
- "vscode": {
- "interpreter": {
- "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials/5.Others/5.4.Spatial_Join.ipynb b/tutorials/5.Others/5.4.Spatial_Join.ipynb
deleted file mode 100644
index c766d39605bb897a5d083b5aae3022b6b5ab5d67..0000000000000000000000000000000000000000
--- a/tutorials/5.Others/5.4.Spatial_Join.ipynb
+++ /dev/null
@@ -1,593 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# How to make spatial joins"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from nes import *\n",
- "import xarray as xr\n",
- "import geopandas as gpd\n",
- "import pandas as pd\n",
- "\n",
- "pd.options.mode.chained_assignment = None"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Path to mask and definition of grid"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "path = '/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "projection = 'regular'\n",
- "lat_orig = 41.1\n",
- "lon_orig = 1.8\n",
- "inc_lat = 0.2\n",
- "inc_lon = 0.2\n",
- "n_lat = 10\n",
- "n_lon = 10"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Method 1: Centroids"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2633: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
- "\n",
- " shapefile_aux.geometry = shapefile_aux.centroid\n"
- ]
- }
- ],
- "source": [
- "coordinates = from_shapefile(path, method=None, projection=projection, \n",
- " lat_orig=lat_orig, lon_orig=lon_orig, \n",
- " inc_lat=inc_lat, inc_lon=inc_lon, \n",
- " n_lat=n_lat, n_lon=n_lon)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " tzid \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " POLYGON ((1.80000 41.10000, 2.00000 41.10000, ... \n",
- " Europe/Madrid \n",
- " \n",
- " \n",
- " 1 \n",
- " POLYGON ((2.00000 41.10000, 2.20000 41.10000, ... \n",
- " Europe/Madrid \n",
- " \n",
- " \n",
- " 2 \n",
- " POLYGON ((2.20000 41.10000, 2.40000 41.10000, ... \n",
- " Europe/Madrid \n",
- " \n",
- " \n",
- " 3 \n",
- " POLYGON ((2.40000 41.10000, 2.60000 41.10000, ... \n",
- " NaN \n",
- " \n",
- " \n",
- " 4 \n",
- " POLYGON ((2.60000 41.10000, 2.80000 41.10000, ... \n",
- " NaN \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 95 \n",
- " POLYGON ((2.80000 42.90000, 3.00000 42.90000, ... \n",
- " Europe/Paris \n",
- " \n",
- " \n",
- " 96 \n",
- " POLYGON ((3.00000 42.90000, 3.20000 42.90000, ... \n",
- " Europe/Paris \n",
- " \n",
- " \n",
- " 97 \n",
- " POLYGON ((3.20000 42.90000, 3.40000 42.90000, ... \n",
- " Europe/Paris \n",
- " \n",
- " \n",
- " 98 \n",
- " POLYGON ((3.40000 42.90000, 3.60000 42.90000, ... \n",
- " NaN \n",
- " \n",
- " \n",
- " 99 \n",
- " POLYGON ((3.60000 42.90000, 3.80000 42.90000, ... \n",
- " NaN \n",
- " \n",
- " \n",
- "
\n",
- "
100 rows × 2 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry tzid\n",
- "FID \n",
- "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ... Europe/Madrid\n",
- "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ... Europe/Madrid\n",
- "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ... Europe/Madrid\n",
- "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ... NaN\n",
- "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ... NaN\n",
- ".. ... ...\n",
- "95 POLYGON ((2.80000 42.90000, 3.00000 42.90000, ... Europe/Paris\n",
- "96 POLYGON ((3.00000 42.90000, 3.20000 42.90000, ... Europe/Paris\n",
- "97 POLYGON ((3.20000 42.90000, 3.40000 42.90000, ... Europe/Paris\n",
- "98 POLYGON ((3.40000 42.90000, 3.60000 42.90000, ... NaN\n",
- "99 POLYGON ((3.60000 42.90000, 3.80000 42.90000, ... NaN\n",
- "\n",
- "[100 rows x 2 columns]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "coordinates.shapefile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "coordinates.shapefile.to_file('spatial_join_method_1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Method 2: Nearest centroids"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2591: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
- "\n",
- " shapefile_aux.geometry = shapefile_aux.centroid\n",
- "/gpfs/projects/bsc32/software/suselinux/11/software/geopandas/0.10.2-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/geopandas/array.py:348: UserWarning: Geometry is in a geographic CRS. Results from 'sjoin_nearest' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
- "\n",
- " stacklevel=stacklevel,\n"
- ]
- }
- ],
- "source": [
- "coordinates = from_shapefile(path, method='nearest', projection=projection, \n",
- " lat_orig=lat_orig, lon_orig=lon_orig, \n",
- " inc_lat=inc_lat, inc_lon=inc_lon, \n",
- " n_lat=n_lat, n_lon=n_lon)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " tzid \n",
- " distance \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " POLYGON ((1.80000 41.10000, 2.00000 41.10000, ... \n",
- " Europe/Madrid \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 1 \n",
- " POLYGON ((2.00000 41.10000, 2.20000 41.10000, ... \n",
- " Europe/Madrid \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 2 \n",
- " POLYGON ((2.20000 41.10000, 2.40000 41.10000, ... \n",
- " Europe/Madrid \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 3 \n",
- " POLYGON ((2.40000 41.10000, 2.60000 41.10000, ... \n",
- " Europe/Madrid \n",
- " 0.064566 \n",
- " \n",
- " \n",
- " 4 \n",
- " POLYGON ((2.60000 41.10000, 2.80000 41.10000, ... \n",
- " Europe/Madrid \n",
- " 0.169196 \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 95 \n",
- " POLYGON ((2.80000 42.90000, 3.00000 42.90000, ... \n",
- " Europe/Paris \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 96 \n",
- " POLYGON ((3.00000 42.90000, 3.20000 42.90000, ... \n",
- " Europe/Paris \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 97 \n",
- " POLYGON ((3.20000 42.90000, 3.40000 42.90000, ... \n",
- " Europe/Paris \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 98 \n",
- " POLYGON ((3.40000 42.90000, 3.60000 42.90000, ... \n",
- " Europe/Paris \n",
- " 0.062909 \n",
- " \n",
- " \n",
- " 99 \n",
- " POLYGON ((3.60000 42.90000, 3.80000 42.90000, ... \n",
- " Europe/Paris \n",
- " 0.106836 \n",
- " \n",
- " \n",
- "
\n",
- "
100 rows × 3 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry tzid \\\n",
- "FID \n",
- "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ... Europe/Madrid \n",
- "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ... Europe/Madrid \n",
- "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ... Europe/Madrid \n",
- "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ... Europe/Madrid \n",
- "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ... Europe/Madrid \n",
- ".. ... ... \n",
- "95 POLYGON ((2.80000 42.90000, 3.00000 42.90000, ... Europe/Paris \n",
- "96 POLYGON ((3.00000 42.90000, 3.20000 42.90000, ... Europe/Paris \n",
- "97 POLYGON ((3.20000 42.90000, 3.40000 42.90000, ... Europe/Paris \n",
- "98 POLYGON ((3.40000 42.90000, 3.60000 42.90000, ... Europe/Paris \n",
- "99 POLYGON ((3.60000 42.90000, 3.80000 42.90000, ... Europe/Paris \n",
- "\n",
- " distance \n",
- "FID \n",
- "0 0.000000 \n",
- "1 0.000000 \n",
- "2 0.000000 \n",
- "3 0.064566 \n",
- "4 0.169196 \n",
- ".. ... \n",
- "95 0.000000 \n",
- "96 0.000000 \n",
- "97 0.000000 \n",
- "98 0.062909 \n",
- "99 0.106836 \n",
- "\n",
- "[100 rows x 3 columns]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "coordinates.shapefile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "coordinates.shapefile.to_file('spatial_join_method_2')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Method 3: Areas intersection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rank 000: 92 intersected areas found\n"
- ]
- }
- ],
- "source": [
- "coordinates = from_shapefile(path, method='intersection', projection=projection, \n",
- " lat_orig=lat_orig, lon_orig=lon_orig, \n",
- " inc_lat=inc_lat, inc_lon=inc_lon, \n",
- " n_lat=n_lat, n_lon=n_lon)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " geometry \n",
- " tzid \n",
- " \n",
- " \n",
- " FID \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " POLYGON ((1.80000 41.10000, 2.00000 41.10000, ... \n",
- " Europe/Madrid \n",
- " \n",
- " \n",
- " 1 \n",
- " POLYGON ((2.00000 41.10000, 2.20000 41.10000, ... \n",
- " Europe/Madrid \n",
- " \n",
- " \n",
- " 2 \n",
- " POLYGON ((2.20000 41.10000, 2.40000 41.10000, ... \n",
- " Europe/Madrid \n",
- " \n",
- " \n",
- " 3 \n",
- " POLYGON ((2.40000 41.10000, 2.60000 41.10000, ... \n",
- " Europe/Madrid \n",
- " \n",
- " \n",
- " 4 \n",
- " POLYGON ((2.60000 41.10000, 2.80000 41.10000, ... \n",
- " NaN \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 95 \n",
- " POLYGON ((2.80000 42.90000, 3.00000 42.90000, ... \n",
- " Europe/Paris \n",
- " \n",
- " \n",
- " 96 \n",
- " POLYGON ((3.00000 42.90000, 3.20000 42.90000, ... \n",
- " Europe/Paris \n",
- " \n",
- " \n",
- " 97 \n",
- " POLYGON ((3.20000 42.90000, 3.40000 42.90000, ... \n",
- " Europe/Paris \n",
- " \n",
- " \n",
- " 98 \n",
- " POLYGON ((3.40000 42.90000, 3.60000 42.90000, ... \n",
- " Europe/Paris \n",
- " \n",
- " \n",
- " 99 \n",
- " POLYGON ((3.60000 42.90000, 3.80000 42.90000, ... \n",
- " Europe/Paris \n",
- " \n",
- " \n",
- "
\n",
- "
100 rows × 2 columns
\n",
- "
"
- ],
- "text/plain": [
- " geometry tzid\n",
- "FID \n",
- "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ... Europe/Madrid\n",
- "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ... Europe/Madrid\n",
- "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ... Europe/Madrid\n",
- "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ... Europe/Madrid\n",
- "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ... NaN\n",
- ".. ... ...\n",
- "95 POLYGON ((2.80000 42.90000, 3.00000 42.90000, ... Europe/Paris\n",
- "96 POLYGON ((3.00000 42.90000, 3.20000 42.90000, ... Europe/Paris\n",
- "97 POLYGON ((3.20000 42.90000, 3.40000 42.90000, ... Europe/Paris\n",
- "98 POLYGON ((3.40000 42.90000, 3.60000 42.90000, ... Europe/Paris\n",
- "99 POLYGON ((3.60000 42.90000, 3.80000 42.90000, ... Europe/Paris\n",
- "\n",
- "[100 rows x 2 columns]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "coordinates.shapefile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "coordinates.shapefile.to_file('spatial_join_method_3')"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- },
- "vscode": {
- "interpreter": {
- "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials/5.Others/5.5.Selecting.ipynb b/tutorials/5.Others/5.5.Selecting.ipynb
deleted file mode 100644
index 3a7761479c822cac47bd71f57e2957065aacc159..0000000000000000000000000000000000000000
--- a/tutorials/5.Others/5.5.Selecting.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Select function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from nes import *"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: Change path\n",
- "file_path = \"/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Time"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "ename": "FileNotFoundError",
- "evalue": "/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnessy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mnessy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeep_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'O3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnessy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnessy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnessy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# %time nessy.load()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/load_nes.py\u001b[0m in \u001b[0;36mopen_netcdf\u001b[0;34m(path, comm, xarray, info, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, balanced)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mxarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mFileNotFoundError\u001b[0m: /gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc"
- ]
- }
- ],
- "source": [
- "nessy = open_netcdf(file_path)\n",
- "nessy.keep_vars('O3')\n",
- "\n",
- "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n",
- "# %time nessy.load()\n",
- "\"\"\"\n",
- "CPU times: user 1.17 s, sys: 6.79 s, total: 7.96 s\n",
- "Wall time: 31.4 s\n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Hours filter"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nessy = open_netcdf(file_path)\n",
- "nessy.keep_vars('O3')\n",
- "\n",
- "nessy.sel(hours_start=12, hours_end=73)\n",
- "\n",
- "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n",
- "# %time nessy.load()\n",
- "\"\"\"\n",
- "CPU times: user 295 ms, sys: 1.47 s, total: 1.76 s\n",
- "Wall time: 6.77 s\n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Time filter"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from datetime import datetime\n",
- "\n",
- "nessy = open_netcdf(file_path)\n",
- "nessy.keep_vars('O3')\n",
- "\n",
- "nessy.sel(time_min=datetime(year=2022, month=7, day=5, hour=0), \n",
- " time_max=datetime(year=2022, month=7, day=5, hour=23))\n",
- "\n",
- "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n",
- "# %time nessy.load()\n",
- "\"\"\"\n",
- "CPU times: user 274 ms, sys: 1.44 s, total: 1.71 s\n",
- "Wall time: 7.53 s\n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Level"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nessy = open_netcdf(file_path)\n",
- "nessy.keep_vars('O3')\n",
- "\n",
- "nessy.sel(lev_min=23)\n",
- "\n",
- "print(len(nessy.lev['data']))\n",
- "# %time nessy.load()\n",
- "\"\"\"\n",
- "CPU times: user 77.3 ms, sys: 248 ms, total: 325 ms\n",
- "Wall time: 17.1 s\n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Coordinates"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nessy = open_netcdf(file_path)\n",
- "nessy.keep_vars('O3')\n",
- "\n",
- "nessy.sel(lat_min=30, lat_max=31, lon_min=-1, lon_max=1)\n",
- "%time nessy.load()\n",
- "\"\"\"\n",
- "CPU times: user 13.9 ms, sys: 74.9 ms, total: 88.8 ms\n",
- "Wall time: 16.3 s\n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "nessy.to_netcdf(\"test_sel.nc\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials/5.Others/5.1.Add_Time_Bounds.ipynb b/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb
similarity index 88%
rename from tutorials/5.Others/5.1.Add_Time_Bounds.ipynb
rename to tutorials/6.Others/6.1.Add_Time_Bounds.ipynb
index a093109f800ffc77ebe3ce70b8dcbde9aa0dbcf4..77238010f589b7edb7564631e0d45168b1b44538 100644
--- a/tutorials/5.Others/5.1.Add_Time_Bounds.ipynb
+++ b/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb
@@ -13,7 +13,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import xarray as xr\n",
"import datetime\n",
"import numpy as np\n",
"from nes import *"
@@ -32,7 +31,9 @@
"metadata": {},
"outputs": [],
"source": [
- "test_path = \"/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\"\n",
+ "# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\n",
+ "# Rotated grid from MONARCH\n",
+ "test_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc'\n",
"nessy = open_netcdf(path=test_path, info=True)"
]
},
diff --git a/tutorials/6.Others/6.2.Selecting.ipynb b/tutorials/6.Others/6.2.Selecting.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f4c28b4885ab6a533b76ca5b3666551e24dee4b6
--- /dev/null
+++ b/tutorials/6.Others/6.2.Selecting.ipynb
@@ -0,0 +1,301 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Select function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nes import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n",
+ "# Rotated grid from MONARCH\n",
+ "file_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2022-11-15 12:00:00 37 2022-11-17 00:00:00\n",
+ "CPU times: user 244 ms, sys: 1.35 s, total: 1.59 s\n",
+ "Wall time: 3.33 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'\\nCPU times: user 1.17 s, sys: 6.79 s, total: 7.96 s\\nWall time: 31.4 s\\n'"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy = open_netcdf(file_path)\n",
+ "nessy.keep_vars('O3')\n",
+ "\n",
+ "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n",
+ "%time nessy.load()\n",
+ "\"\"\"\n",
+ "CPU times: user 244 ms, sys: 1.35 s, total: 1.59 s\n",
+ "Wall time: 3.33 s\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Hours filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2022-11-16 00:00:00 9 2022-11-16 08:00:00\n",
+ "CPU times: user 70.8 ms, sys: 363 ms, total: 433 ms\n",
+ "Wall time: 1.57 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'\\nCPU times: user 295 ms, sys: 1.47 s, total: 1.76 s\\nWall time: 6.77 s\\n'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy = open_netcdf(file_path)\n",
+ "nessy.keep_vars('O3')\n",
+ "\n",
+ "nessy.sel(hours_start=12, hours_end=16)\n",
+ "\n",
+ "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n",
+ "%time nessy.load()\n",
+ "\"\"\"\n",
+ "CPU times: user 70.8 ms, sys: 363 ms, total: 433 ms\n",
+ "Wall time: 1.57 s\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Time filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2022-11-15 12:00:00 12 2022-11-15 23:00:00\n",
+ "CPU times: user 95.1 ms, sys: 490 ms, total: 585 ms\n",
+ "Wall time: 1.47 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'\\nCPU times: user 274 ms, sys: 1.44 s, total: 1.71 s\\nWall time: 7.53 s\\n'"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "\n",
+ "nessy = open_netcdf(file_path)\n",
+ "nessy.keep_vars('O3')\n",
+ "\n",
+ "nessy.sel(time_min=datetime(year=2022, month=11, day=15, hour=0), \n",
+ " time_max=datetime(year=2022, month=11, day=15, hour=23))\n",
+ "\n",
+ "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n",
+ "%time nessy.load()\n",
+ "\"\"\"\n",
+ "CPU times: user 95.1 ms, sys: 490 ms, total: 585 ms\n",
+ "Wall time: 1.47 s\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Level"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1\n",
+ "CPU times: user 14.4 ms, sys: 70 ms, total: 84.4 ms\n",
+ "Wall time: 5.55 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'\\nCPU times: user 77.3 ms, sys: 248 ms, total: 325 ms\\nWall time: 17.1 s\\n'"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy = open_netcdf(file_path)\n",
+ "nessy.keep_vars('O3')\n",
+ "\n",
+ "nessy.sel(lev_min=23)\n",
+ "\n",
+ "print(len(nessy.lev['data']))\n",
+ "%time nessy.load()\n",
+ "\"\"\"\n",
+ "CPU times: user 14.4 ms, sys: 70 ms, total: 84.4 ms\n",
+ "Wall time: 5.55 s\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Coordinates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 4.11 ms, sys: 22 ms, total: 26.2 ms\n",
+ "Wall time: 4.2 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'\\nCPU times: user 13.9 ms, sys: 74.9 ms, total: 88.8 ms\\nWall time: 16.3 s\\n'"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy = open_netcdf(file_path)\n",
+ "nessy.keep_vars('O3')\n",
+ "\n",
+ "nessy.sel(lat_min=30, lat_max=31, lon_min=-1, lon_max=1)\n",
+ "%time nessy.load()\n",
+ "\"\"\"\n",
+ "CPU times: user 4.11 ms, sys: 22 ms, total: 26.2 ms\n",
+ "Wall time: 4.2 s\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nessy.to_netcdf(\"test_sel.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/6.Others/6.3.Plot.ipynb b/tutorials/6.Others/6.3.Plot.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c9b3ec986fefbd215613537ad4aeb12688eb1ba6
--- /dev/null
+++ b/tutorials/6.Others/6.3.Plot.ipynb
@@ -0,0 +1,481 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to plot data from NES"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nes import *\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "file_path = \"/gpfs/projects/bsc32/models/NES_tutorial_data/sconcno2-000_2019010100.nc\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nessy = open_netcdf(file_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nessy.load()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Plot using contourf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = plt.axes()\n",
+ "plt.contourf(nessy.lon['data'], nessy.lat['data'], nessy.variables['sconcno2']['data'][0,0], cmap='jet')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Plot using pcolormesh"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.1. With coordinates (warning)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = plt.axes()\n",
+ "plt.pcolormesh(nessy.lon['data'], nessy.lat['data'], nessy.variables['sconcno2']['data'][0,0], \n",
+ " shading='auto', cmap='jet')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2. With coordinates bounds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nessy.create_spatial_bounds()\n",
+ "lon_bnds, lat_bnds = nessy.get_spatial_bounds_mesh_format()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = plt.axes()\n",
+ "plt.pcolormesh(lon_bnds, lat_bnds, nessy.variables['sconcno2']['data'][0,0], cmap='jet')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.3. Creating a shapefile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-12.00844 33.41901, -11.88982 33.428... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-11.88982 33.42830, -11.77116 33.437... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-11.77116 33.43746, -11.65248 33.446... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-11.65248 33.44650, -11.53377 33.455... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-11.53377 33.45539, -11.41505 33.464... \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 17056 \n",
+ " POLYGON ((5.70038 45.40981, 5.84126 45.39939, ... \n",
+ " \n",
+ " \n",
+ " 17057 \n",
+ " POLYGON ((5.84126 45.39939, 5.98210 45.38881, ... \n",
+ " \n",
+ " \n",
+ " 17058 \n",
+ " POLYGON ((5.98210 45.38881, 6.12288 45.37808, ... \n",
+ " \n",
+ " \n",
+ " 17059 \n",
+ " POLYGON ((6.12288 45.37808, 6.26361 45.36719, ... \n",
+ " \n",
+ " \n",
+ " 17060 \n",
+ " POLYGON ((6.26361 45.36719, 6.40430 45.35615, ... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
17061 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "FID \n",
+ "0 POLYGON ((-12.00844 33.41901, -11.88982 33.428...\n",
+ "1 POLYGON ((-11.88982 33.42830, -11.77116 33.437...\n",
+ "2 POLYGON ((-11.77116 33.43746, -11.65248 33.446...\n",
+ "3 POLYGON ((-11.65248 33.44650, -11.53377 33.455...\n",
+ "4 POLYGON ((-11.53377 33.45539, -11.41505 33.464...\n",
+ "... ...\n",
+ "17056 POLYGON ((5.70038 45.40981, 5.84126 45.39939, ...\n",
+ "17057 POLYGON ((5.84126 45.39939, 5.98210 45.38881, ...\n",
+ "17058 POLYGON ((5.98210 45.38881, 6.12288 45.37808, ...\n",
+ "17059 POLYGON ((6.12288 45.37808, 6.26361 45.36719, ...\n",
+ "17060 POLYGON ((6.26361 45.36719, 6.40430 45.35615, ...\n",
+ "\n",
+ "[17061 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy.create_shapefile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " geometry \n",
+ " sconcno2 \n",
+ " \n",
+ " \n",
+ " FID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " POLYGON ((-12.00844 33.41901, -11.88982 33.428... \n",
+ " 0.000896 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " POLYGON ((-11.88982 33.42830, -11.77116 33.437... \n",
+ " 0.000930 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " POLYGON ((-11.77116 33.43746, -11.65248 33.446... \n",
+ " 0.000961 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " POLYGON ((-11.65248 33.44650, -11.53377 33.455... \n",
+ " 0.000989 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " POLYGON ((-11.53377 33.45539, -11.41505 33.464... \n",
+ " 0.001015 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 17056 \n",
+ " POLYGON ((5.70038 45.40981, 5.84126 45.39939, ... \n",
+ " 0.009605 \n",
+ " \n",
+ " \n",
+ " 17057 \n",
+ " POLYGON ((5.84126 45.39939, 5.98210 45.38881, ... \n",
+ " 0.008114 \n",
+ " \n",
+ " \n",
+ " 17058 \n",
+ " POLYGON ((5.98210 45.38881, 6.12288 45.37808, ... \n",
+ " 0.006802 \n",
+ " \n",
+ " \n",
+ " 17059 \n",
+ " POLYGON ((6.12288 45.37808, 6.26361 45.36719, ... \n",
+ " 0.008189 \n",
+ " \n",
+ " \n",
+ " 17060 \n",
+ " POLYGON ((6.26361 45.36719, 6.40430 45.35615, ... \n",
+ " 0.009688 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
17061 rows × 2 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry sconcno2\n",
+ "FID \n",
+ "0 POLYGON ((-12.00844 33.41901, -11.88982 33.428... 0.000896\n",
+ "1 POLYGON ((-11.88982 33.42830, -11.77116 33.437... 0.000930\n",
+ "2 POLYGON ((-11.77116 33.43746, -11.65248 33.446... 0.000961\n",
+ "3 POLYGON ((-11.65248 33.44650, -11.53377 33.455... 0.000989\n",
+ "4 POLYGON ((-11.53377 33.45539, -11.41505 33.464... 0.001015\n",
+ "... ... ...\n",
+ "17056 POLYGON ((5.70038 45.40981, 5.84126 45.39939, ... 0.009605\n",
+ "17057 POLYGON ((5.84126 45.39939, 5.98210 45.38881, ... 0.008114\n",
+ "17058 POLYGON ((5.98210 45.38881, 6.12288 45.37808, ... 0.006802\n",
+ "17059 POLYGON ((6.12288 45.37808, 6.26361 45.36719, ... 0.008189\n",
+ "17060 POLYGON ((6.26361 45.36719, 6.40430 45.35615, ... 0.009688\n",
+ "\n",
+ "[17061 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nessy.shapefile['sconcno2'] = nessy.variables['sconcno2']['data'][0, 0, :].ravel()\n",
+ "nessy.shapefile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1, figsize=(10, 5))\n",
+ "gdf_grid = nessy.shapefile.dropna()\n",
+ "gdf_grid.plot(ax=ax, column='sconcno2', cmap='jet', legend=True)\n",
+ "ax.margins(0)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8 (default, Aug 12 2021, 07:06:15) \n[GCC 8.4.1 20200928 (Red Hat 8.4.1-1)]"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/Jupyter_bash_nord3v2.cmd b/tutorials/Jupyter_bash_nord3v2.cmd
index eefd0f7219bcc337065fb5eef61df954e423919e..4a8edb2c431e0f3030224c9f763f83c9a665fc0d 100644
--- a/tutorials/Jupyter_bash_nord3v2.cmd
+++ b/tutorials/Jupyter_bash_nord3v2.cmd
@@ -1,9 +1,9 @@
#!/bin/bash
#SBATCH --ntasks 1
#SBATCH --time 02:00:00
-#SBATCH --job-name NES
-#SBATCH --output log_jupyter-notebook-%J.out
-#SBATCH --error log_jupyter-notebook-%J.err
+#SBATCH --job-name NES-tutorial
+#SBATCH --output log_NES-tutorial-%J.out
+#SBATCH --error log_NES-tutorial-%J.err
#SBATCH --exclusive
#SBATCH --qos debug
@@ -25,19 +25,9 @@ localhost:${port} (prefix w/ https:// if using password)
# load modules or conda environments here
module load jupyterlab/3.0.9-foss-2019b-Python-3.7.4
-module load Python/3.7.4-GCCcore-8.3.0
-module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4
-module load xarray/0.19.0-foss-2019b-Python-3.7.4
-module load cftime/1.0.1-foss-2019b-Python-3.7.4
-module load cfunits/1.8-foss-2019b-Python-3.7.4
-module load filelock/3.7.1-foss-2019b-Python-3.7.4
-module load pyproj/2.5.0-foss-2019b-Python-3.7.4
-module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4
-module load geopandas/0.10.2-foss-2019b-Python-3.7.4
-module load Shapely/1.7.1-foss-2019b-Python-3.7.4
-#module load NES/0.9.0-foss-2019b-Python-3.7.4
+# TODO change module when installed
+module load NES/1.0.0-nord3-v2-foss-2019b-Python-3.7.4
-export PYTHONPATH=/esarchive/scratch/avilanova/software/NES:${PYTHONPATH}
# DON'T USE ADDRESS BELOW.
# DO USE TOKEN BELOW