in2out_category = {'F1': 'F', 'F2': 'F', 'F3': 'F', 'F4': 'F'}
# output variables are written in the following format using species and
# category after applying the mapping
varname_format = '{species}_{category}_{source_type}'
# output path and filename
output_path = os.path.join('oae-art-example', '{online}', 'tno')
output_name = 'tno-art.nc'
# Output grid is European domain (rotated pole coordinates)
cosmo_grid = COSMOGrid(
nx=192,
ny=164,
dx=0.12,
dy=0.12,
xmin=-16.08,
ymin=-9.54,
pollon=-170.0,
pollat=43.0,
)
# resolution of shapefile used for country mask
shpfile_resolution = "10m"
# number of processes
nprocs = 16
# metadata added as global attributes to netCDF output file
nc_metadata = {
"DESCRIPTION": "Gridded annual emissions",
"DATAORIGIN": "TNO-CAMS",
# mapping from input to output species (input is used for missing keys)
in2out_category = {}
# output variables are written in the following format using species and
# category after applying mapping as well as source_type (AREA or POINT) for
# TNO inventories
varname_format = '{species}_{category}' # not providing source_type will add up
# point and area sources
# COSMO domain
output_grid = COSMOGrid(
nx=450, #900
ny=300, #600
dx=0.02, #0.01
dy=0.02, #0.01
xmin=-4.92,
ymin=-3.18,
pollon=-170.0,
pollat=43.0,
)
# output path and filename
output_path = os.path.join('outputs', '{online}')
output_name = "tno.nc"
# resolution of shape file used for country mask
shpfile_resolution = "10m"
# number of processes computing the mapping inventory->COSMO-grid
nprocs = 18
import os
from emiproc.grids import COSMOGrid, TNOGrid
inv_1 = os.path.join('outputs', '{online}', 'tno.nc')
inv_name_1 = '_TNO'
inv_2 = os.path.join('outputs', '{online}', 'carbocount.nc')
inv_name_2 = '_Carbocount'
inv_out = os.path.join('outputs', '{online}', 'all_emissions.nc')
# COSMO domain
cosmo_grid = COSMOGrid(
nx=900,
ny=600,
dx=0.01,
dy=0.01,
xmin=-4.92,
ymin=-3.18,
pollon=-170.0,
pollat=43.0,
)
nc_metadata = {
"DESCRIPTION": "Gridded annual emissions",
"DATAORIGIN": "TNO and carbocount-CH",
"CREATOR": "Jean-Matthieu Haussaire",
"EMAIL": "*****@*****.**",
"AFFILIATION": "Empa Duebendorf, Switzerland",
"DATE CREATED": time.ctime(time.time()),
}
# mapping from input to output species (input is used for missing keys)
# All the categories will be summed.
# There is no mapping between these catgories and GNFR yet
in2out_category = {}
# output variables are written in the following format using species
varname_format = '{species}_EDGAR'
# Domain
# CHE_Europe domain
output_grid = COSMOGrid(
nx=760,
ny=610,
dx=0.05,
dy=0.05,
xmin=-17,
ymin=-11,
pollon=-170,
pollat=43,
)
# output path and filename
output_path = os.path.join('outputs', 'EDGAR','{online}')
output_name = "edgar.nc"
# resolution of shape file used for country mask
shpfile_resolution = "10m"
# number of processes computing the mapping inventory->COSMO-grid
nprocs = 18
"K": 'Others',
"L": 'Others',
}
# output variables are written in the following format using species and
# category after applying mapping as well as source_type (AREA or POINT) for
# TNO inventories
varname_format = '{species}_{category}_{source_type}' # not providing source_type will add up
# point and area sources
# COSMO domain
cosmo_grid = COSMOGrid(
nx=700,
ny=600,
dx=0.01,
dy=0.01,
xmin=0.0,
ymin=1.5,
pollon=-170.0,
pollat=43.0,
)
# output path and filename
output_path = os.path.join('outputs', '{online}')
output_name = "smartcarbExt_TNO6.nc"
# resolution of shape file used for country mask
shpfile_resolution = "110m"
# number of processes computing the mapping inventory->COSMO-grid
nprocs = 8
# mapping from input to output species (input is used for missing keys)
in2out_species = {'co2_ff': 'CO2', 'co2_bf': 'CO2'}
# mapping from input to output category (input is used for missing keys)
in2out_category = {'F1': 'F', 'F2': 'F', 'F3': 'F'}
# output variables are written in the following format using species and
# category after applying the mapping
varname_format = '{species}_{category}_{source_type}'
# COSMO domain
output_grid = COSMOGrid(
nx=90,
ny=60,
dx=0.1,
dy=0.1,
xmin=-4.92,
ymin=-3.18,
pollon=-170.0,
pollat=43.0,
)
# output path and filename
output_path = os.path.join("TNO-test", '{online}')
output_name = "test-tno.nc"
# resolution of shapefile used for country mask
shpfile_resolution = "110m"
# number of processes computing the mapping inventory->COSMO-grid
nprocs = 18
"K": 'Others',
"L": 'Others',
}
# output variables are written in the following format using species and
# category after applying mapping as well as source_type (AREA or POINT) for
# TNO inventories
varname_format = '{species}_{category}_{source_type}' # not providing source_type will add up
# point and area sources
# COSMO domain
cosmo_grid = COSMOGrid(
nx=70,
ny=60,
dx=0.1,
dy=0.1,
xmin=-1.4,
ymin=2.5,
pollon=-170.0,
pollat=43.0,
)
# output path and filename
output_path = os.path.join('outputs', '{online}')
output_name = "smartcarb_coarse_TNO6.nc"
# resolution of shape file used for country mask
shpfile_resolution = "110m"
# number of processes computing the mapping inventory->COSMO-grid
nprocs = 8
varname_format = '{species}_{category}_ch'
# Online or offline emissions (offline emissions have grid with 2-cell boundary)
offline = False
# Europe domain (rotated pole coordinates)
xmin = -16.08
ymin = -9.54
nx = 192
ny = 164
output_grid = COSMOGrid(
nx=nx,
ny=ny,
dx=0.12,
dy=0.12,
xmin=xmin,
ymin=ymin,
pollon=-170.0,
pollat=43.0,
)
# output filename
output_path = os.path.join('oae-art-example', '{online}', 'swiss')
output_name = 'swiss-art.nc'
# resolution of shape file used for country mask
shpfile_resolution = "10m"
# number of processes used
nprocs = 16