diff --git a/preproc/GFAS_hourly_Parameters.csv b/preproc/GFAS_hourly_Parameters.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7b8f414d2ed167ab10262cdfea3a90e8826b63b4
--- /dev/null
+++ b/preproc/GFAS_hourly_Parameters.csv
@@ -0,0 +1,27 @@
+Name;Short_Name;Units;id
+Carbon Monoxide;co;kg m-2 s-1;param81.210.192
+Non-Methane Hydro-Carbons;nmhc;kg m-2 s-1;param83.210.192
+Nitrogen Oxides expressed as monoxide nitrogen;nox_no;kg m-2 s-1;param85.210.192
+Particulate Matter PM2.5;pm25;kg m-2 s-1;param87.210.192
+Organic Carbon;oc;kg m-2 s-1;param90.210.192
+Black Carbon;bc;kg m-2 s-1;param91.210.192
+Sulfur Dioxide;so2;kg m-2 s-1;param102.210.192
+Methanol;ch3oh;kg m-2 s-1;param103.210.192
+Higher Alkanes;hialkanes;kg m-2 s-1;param112.210.192
+Formaldehyde;ch2o;kg m-2 s-1;param113.210.192
+Acetaldehyde;c2h4o;kg m-2 s-1;param114.210.192
+Acetone;c3h6o;kg m-2 s-1;param115.210.192
+Ammonia;nh3;kg m-2 s-1;param116.210.192
+Dimethyl Sulfide;c2h6s;kg m-2 s-1;param117.210.192
+Ethane;c2h6;kg m-2 s-1;param118.210.192
+Toluene;c7h8;kg m-2 s-1;param231.210.192
+Benzene;c6h6;kg m-2 s-1;param232.210.192
+Xylene;c8h10;kg m-2 s-1;param233.210.192
+Butenes;c4h8;kg m-2 s-1;param234.210.192
+Pentenes;c5h10;kg m-2 s-1;param235.210.192
+Hexene;c6h12;kg m-2 s-1;param236.210.192
+Octene;c8h16;kg m-2 s-1;param237.210.192
+Butanes;c4h10;kg m-2 s-1;param238.210.192
+Pentanes;c5h12;kg m-2 s-1;param239.210.192
+Hexanes;c6h14;kg m-2 s-1;param240.210.192
+Heptane;c7h16;kg m-2 s-1;param241.210.192
diff --git a/preproc/gfas12_h_preproc.py b/preproc/gfas12_h_preproc.py
new file mode 100755
index 0000000000000000000000000000000000000000..9cc93fef153e21e8666355ab350c815145f1cf56
--- /dev/null
+++ b/preproc/gfas12_h_preproc.py
@@ -0,0 +1,282 @@
+#!/usr/bin/env python
+
+# Copyright 2018 Earth Sciences Department, BSC-CNS
+#
+# This file is part of HERMESv3_GR.
+#
+# HERMESv3_GR is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# HERMESv3_GR is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with HERMESv3_GR. If not, see <http://www.gnu.org/licenses/>.
+
+
+import os
+from netCDF4 import Dataset
+import cf_units
+import pandas as pd
+import datetime
+from datetime import datetime, timedelta
+
+# ============== README ======================
+"""
+downloading website: http://apps.ecmwf.int/datasets/data/cams-gfas/
+reference: https://www.biogeosciences.net/9/527/2012/
+Besides citing HERMESv3_GR, users must also acknowledge the use of the corresponding emission inventories in their works
+"""
+
+# ============== CONFIGURATION PARAMETERS ======================
+INPUT_PATH = '/esarchive/recon/ecmwf/gfas/original_files/ga_mc_sfc_gfas_ecmf/'
+INPUT_NAME = 'gfas_hourly_<date>.grb'
+OUTPUT_PATH = '/esarchive/recon/ecmwf/gfas'
+
+STARTING_DATE = datetime(year=2018, month=11, day=01)
+ENDIND_DATE = datetime(year=2018, month=11, day=01)
+
+PARAMETERS_FILE = '/esarchive/recon/ecmwf/gfas/original_files/ga_mc_sfc_gfas_ecmf/GFAS_hourly_Parameters.csv'
+# ==============================================================
+
+
+def create_bounds(coords, number_vertices=2):
+    """
+    Calculate the vertices coordinates.
+
+    :param coords: Coordinates in degrees (latitude or longitude)
+    :type coords: numpy.array
+
+    :param number_vertices: Non mandatory parameter that informs the number of vertices that must have the boundaries.
+            (by default 2)
+    :type number_vertices: int
+
+    :return: Array with as many elements as vertices for each value of coords.
+    :rtype: numpy.array
+    """
+    import numpy as np
+
+    interval = coords[1] - coords[0]
+
+    coords_left = coords - interval / 2
+    coords_right = coords + interval / 2
+    if number_vertices == 2:
+        bound_coords = np.dstack((coords_left, coords_right))
+    elif number_vertices == 4:
+        bound_coords = np.dstack((coords_left, coords_right, coords_right, coords_left))
+    else:
+        raise ValueError('The number of vertices of the boudaries must be 2 or 4')
+
+    return bound_coords
+
+
+def get_grid_area(filename):
+    """
+    Calculate the area for each cell of the grid using CDO
+
+    :param filename: Path to the file to calculate the cell area
+    :type filename: str
+
+    :return: Area of each cell of the grid.
+    :rtype: numpy.array
+    """
+    from cdo import Cdo
+    from netCDF4 import Dataset
+
+    cdo = Cdo()
+    s = cdo.gridarea(input=filename)
+    nc_aux = Dataset(s, mode='r')
+    grid_area = nc_aux.variables['cell_area'][:]
+    nc_aux.close()
+
+    return grid_area
+
+
+def write_netcdf(output_name_path, data_list, center_lats, center_lons, grid_cell_area, date):
+    """
+    Write a NetCDF with the given information.
+
+    :param output_name_path: Complete path to the output NetCDF to be stored.
+    :type output_name_path: str
+
+    :param data_list
+
+    :param center_lats: Latitudes of the center of each cell.
+    :type center_lats: numpy.array
+
+    :param center_lons: Longitudes of the center of each cell.
+    :type center_lons: numpy.array
+
+    :param grid_cell_area: Area of each cell of the grid.
+    :type grid_cell_area: numpy.array
+
+    :param date: Date of the current netCDF.
+    :type date: datetime.datetime
+
+    """
+    print output_name_path
+    # Creating NetCDF & Dimensions
+    nc_output = Dataset(output_name_path, mode='w', format="NETCDF4")
+    nc_output.createDimension('nv', 2)
+    nc_output.createDimension('lon', center_lons.shape[0])
+    nc_output.createDimension('lat', center_lats.shape[0])
+    nc_output.createDimension('time', None)
+
+    # TIME
+    time = nc_output.createVariable('time', 'd', ('time',), zlib=True)
+    # time.units = "{0} since {1}".format(tstep_units, global_atts['Start_DateTime'].strftime('%Y-%m-%d %H:%M:%S'))
+    time.units = "hours since {0}".format(date.strftime('%Y-%m-%d %H:%M:%S'))
+    time.standard_name = "time"
+    time.calendar = "gregorian"
+    time.long_name = "time"
+    time[:] = [0]
+
+    # LATITUDE
+    lat = nc_output.createVariable('lat', 'f', ('lat',), zlib=True)
+    lat.bounds = "lat_bnds"
+    lat.units = "degrees_north"
+    lat.axis = "Y"
+    lat.long_name = "latitude"
+    lat.standard_name = "latitude"
+    lat[:] = center_lats
+
+    lat_bnds = nc_output.createVariable('lat_bnds', 'f', ('lat', 'nv',), zlib=True)
+    lat_bnds[:] = create_bounds(center_lats)
+
+    # LONGITUDE
+    lon = nc_output.createVariable('lon', 'f', ('lon',), zlib=True)
+    lon.bounds = "lon_bnds"
+    lon.units = "degrees_east"
+    lon.axis = "X"
+    lon.long_name = "longitude"
+    lon.standard_name = "longitude"
+    lon[:] = center_lons
+
+    lon_bnds = nc_output.createVariable('lon_bnds', 'f', ('lon', 'nv',), zlib=True)
+    lon_bnds[:] = create_bounds(center_lons)
+
+    for var in data_list:
+        # VARIABLE
+        nc_var = nc_output.createVariable(var['name'], 'f', ('time', 'lat', 'lon',), zlib=True)
+        nc_var.units = var['units'].symbol
+        nc_var.long_name = var['long_name']
+        nc_var.coordinates = 'lat lon'
+        nc_var.grid_mapping = 'crs'
+        nc_var.cell_measures = 'area: cell_area'
+        nc_var[:] = var['data']
+
+    # CELL AREA
+    cell_area = nc_output.createVariable('cell_area', 'f', ('lat', 'lon',))
+    cell_area.long_name = "area of the grid cell"
+    cell_area.standard_name = "area"
+    cell_area.units = "m2"
+    cell_area[:] = grid_cell_area
+
+    # CRS
+    crs = nc_output.createVariable('crs', 'i')
+    crs.grid_mapping_name = "latitude_longitude"
+    crs.semi_major_axis = 6371000.0
+    crs.inverse_flattening = 0
+
+    nc_output.setncattr('title', 'GFASv1.2 inventory')
+    nc_output.setncattr('Conventions', 'CF-1.6', )
+    nc_output.setncattr('institution', 'ECMWF', )
+    nc_output.setncattr('source', 'GFAS', )
+    nc_output.setncattr('history', 'Re-writing of the GFAS input to follow the CF-1.6 conventions;\n' +
+                        '2019-01-02: Added boundaries;\n' +
+                        '2019-01-02: Added global attributes;\n' +
+                        '2019-01-02: Re-naming pollutant;\n' +
+                        '2019-01-02: Added cell_area variable;\n' +
+                        '2019-01-02: Added new varaibles;\n')
+    nc_output.setncattr('references', 'downloading website: http://apps.ecmwf.int/datasets/data/cams-gfas/\n' +
+                        'reference: https://www.biogeosciences.net/9/527/2012/', )
+    nc_output.setncattr('comment', 'Re-writing done by Carles Tena (carles.tena@bsc.es) from the BSC-CNS ' +
+                        '(Barcelona Supercomputing Center)', )
+
+    nc_output.close()
+    return True
+
+
+def do_transformation(input_file, date, output_dir, variables_list):
+    """
+    Transform the original file into a NEtCDF file that follows the conventions.
+
+    :param input_file:
+    :type input_file: str
+
+    :param date: Date of the file to do the transformation.
+    :type date: datetime.datetime
+
+    :param output_dir: Path where have to be stored the output file.
+    :type output_dir: str
+
+    :param variables_list: LIst of dictionaries with the information of each variable of the output files.
+    :type variables_list: list
+    """
+    from cdo import Cdo
+    cdo = Cdo()
+
+    nc_temp = cdo.copy(input=input_file, options='-R -r -f nc4c -z zip_4')
+
+    nc_in = Dataset(nc_temp, mode='r')
+
+    cell_area = get_grid_area(nc_temp)
+
+    lats = nc_in.variables['lat'][:]
+    lons = nc_in.variables['lon'][:]
+
+    for variable in variables_list:
+        variable['data'] = nc_in.variables[variable['original_name']][:]
+
+    nc_in.close()
+
+    out_path_aux = os.path.join(output_dir, '1hourly', 'multivar')
+    if not os.path.exists(out_path_aux):
+        os.makedirs(out_path_aux)
+    out_path_aux = os.path.join(out_path_aux, 'ga_{0}.nc'.format(date.strftime('%Y%m%d')))
+    write_netcdf(out_path_aux, variables_list, lats, lons, cell_area, date)
+
+    return True
+
+
+def do_var_list(variables_file):
+    """
+    Create the List of dictionaries
+
+    :param variables_file: CSV file with the information of each variable
+    :type variables_file: str
+
+    :return: Dictionaries list with the information of each variable.
+    :rtype: list
+    """
+    df = pd.read_csv(variables_file, sep=';')
+    list_aux = []
+    for i, element in df.iterrows():
+        # print element
+        dict_aux = {
+            'original_name': str(element.id),
+            'name': element['Short_Name'],
+            'long_name': element['Name'],
+            'units': cf_units.Unit(element['Units']),
+        }
+        list_aux.append(dict_aux)
+    return list_aux
+
+
+if __name__ == '__main__':
+
+    var_list = do_var_list(PARAMETERS_FILE)
+
+    date_aux = STARTING_DATE
+    while date_aux <= ENDIND_DATE:
+        f = os.path.join(INPUT_PATH, INPUT_NAME.replace('<date>', date_aux.strftime('%Y%m%d')))
+        if os.path.isfile(f):
+            do_transformation(f, date_aux, OUTPUT_PATH, var_list)
+        else:
+            print 'ERROR: file {0} not found'.format(f)
+
+        date_aux = date_aux + timedelta(days=1)