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/nes/create_nes.py b/nes/create_nes.py
index a4e484f88f66ea2a309afd58413ebb62f79d07ca..d722ce848b29466ac5ca358a49a1b3a488776529 100644
--- a/nes/create_nes.py
+++ b/nes/create_nes.py
@@ -139,9 +139,9 @@ def from_shapefile(path, method=None, **kwargs):
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/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py
index 9cb632810e93f26f24c9e9575eeb6517e564e8b6..ad6eecd2e5ab4dd0e08a124d77aa8c2ca6ca1a71 100644
--- a/nes/nc_projections/default_nes.py
+++ b/nes/nc_projections/default_nes.py
@@ -1672,7 +1672,7 @@ 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
@@ -2584,48 +2584,58 @@ class Nes(object):
return None
- def spatial_join(self, mask, method=None):
+ def spatial_join(self, shapefile, method=None, var_list=None):
"""
Compute overlay intersection of two GeoPandasDataFrames.
Parameters
----------
- mask : GeoPandasDataFrame
- File from where the data will be obtained on the intersection.
+ shapefile : 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
+
"""
+
+ if isinstance(shapefile, str):
+ shapefile = gpd.read_file(shapefile)
+
+ if self.shapefile is None:
+ msg = 'Warning: Shapefile does not exist. It will be created now.'
+ warnings.warn(msg)
+ self.create_shapefile()
- # Nearest centroids to the mask polygons
+ # Nearest centroids to the shapefile polygons
if method == 'nearest':
# Make copy
- shapefile_aux = deepcopy(self.shapefile)
+ aux_grid = deepcopy(self.shapefile)
- # Get centroids of shapefile to mask
- shapefile_aux.geometry = shapefile_aux.centroid
+ # Get centroids of shapefile to shapefile
+ aux_grid.geometry = aux_grid.centroid
# Calculate spatial joint by distance
- shapefile_aux = gpd.sjoin_nearest(shapefile_aux, mask.to_crs(shapefile_aux.crs), distance_col='distance')
+ aux_grid = gpd.sjoin_nearest(aux_grid, shapefile.to_crs(aux_grid.crs), 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 of the shapefile there will be NaN
elif method == 'intersection':
# Get intersected areas
- inp, res = mask.sindex.query_bulk(self.shapefile.geometry, predicate='intersects')
+ inp, res = shapefile.sindex.query_bulk(self.shapefile.geometry, predicate='intersects')
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()],
+ intersection = pd.concat([self.shapefile.geometry[inp].reset_index(), shapefile.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,
+ list(shapefile.geometry[res].reset_index().rename(columns={'geometry': 'geometry_shapefile',
+ 'index': 'index_shapefile'}).columns))
+ intersection['area'] = intersection.apply(lambda x: x['geometry'].intersection(x['geometry_shapefile']).buffer(0).area,
axis=1)
intersection['fraction'] = intersection.apply(lambda x: x['area'] / x['geometry'].area, axis=1)
@@ -2634,26 +2644,30 @@ class Nes(object):
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, :])
+ # Get data from shapefile
+ del shapefile['geometry']
+ self.shapefile.loc[intersection.index, shapefile.columns] = np.array(shapefile.loc[intersection.index_shapefile, :])
- # Centroids that fall on the mask polygons, outside of the mask there will be NaN
- elif method is None:
+ # Centroids that fall on the shapefile polygons, outside of the shapefile there will be NaN
+ elif method == 'centroid':
# Make copy
- shapefile_aux = deepcopy(self.shapefile)
+ aux_grid = deepcopy(self.shapefile)
- # Get centroids of shapefile to mask
- shapefile_aux.geometry = shapefile_aux.centroid
+ # Get centroids of shapefile to shapefile
+ aux_grid.geometry = aux_grid.centroid
# Calculate spatial joint
- shapefile_aux = gpd.sjoin(shapefile_aux, mask.to_crs(shapefile_aux.crs))
+ aux_grid = gpd.sjoin(aux_grid, shapefile.to_crs(aux_grid.crs))
# 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 __gather_data_py_object(self):
diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb
index d726d988b91d86014d429d6d04067f0f5bc78e02..21eed90fa654a8c635d7de1ac741140ad847f712 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"
]
},
{
@@ -24,7 +24,9 @@
"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'"
]
},
{
@@ -414,7 +416,7 @@
" Domain: Regional\n",
" Conventions: CF-1.7\n",
" history: MONARCHv1.0 netcdf file.\n",
- " comment: Generated on marenostrum4
xarray.Dataset
time: 25
lev: 1
lat: 115
lon: 165
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",
+ " comment: Generated on marenostrum4
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.
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",
+ " dtype='datetime64[ns]')
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.
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".
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.
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.
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.
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".
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".
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".
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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".
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.
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).
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.
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".
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.
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.
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.
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".
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".
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".
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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".
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.
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).
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.
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",
+ " 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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).
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.
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.
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.
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.
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).
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.
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.
Percentage of the maximum data gap in the monthly averaged measurement UTC window filled by native resolution data, relative to the total window length.
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.
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.
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.
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.
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).
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).
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.
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.
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.
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.
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).
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.
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.
Percentage of the maximum data gap in the monthly averaged measurement UTC window filled by native resolution data, relative to the total window length.
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.
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).
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.
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).
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'.
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.
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'.
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.
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.
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.
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 ";".
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 ";".
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.
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.
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).
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.
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).
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.
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.
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",
+ " dtype='datetime64[ns]')
station
(station)
float64
0.0 1.0 2.0
units :
axis :
X
long_name :
standard_name :
station
array([0., 1., 2.])
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.
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".
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.
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.
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.
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".
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".
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".
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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".
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.
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).
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.
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".
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.
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.
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.
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".
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".
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".
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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".
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.
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).
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.
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",
+ " 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.
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.
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.
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.
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.
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.
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.