def run_proj(year, month, dir_inter, model_grid, target_grid):
print('Add input file...')
fn_in = dir_inter + '\emis_{}_{}_hour_transform.nc'.format(year, month)
print(fn_in)
f_in = dataset.addfile(fn_in)
#set dimension
tdim = np.dimension(np.arange(24), 'Time')
ydim = np.dimension(target_grid.y_coord, 'south_north', 'Y')
xdim = np.dimension(target_grid.x_coord, 'west_east', 'X')
zdim = np.dimension(np.arange(3), 'emissions_zdim')
sdim = np.dimension(np.arange(19), 'DateStrLen')
dims = [tdim, zdim, ydim, xdim]
all_dims = [tdim, sdim, xdim, ydim, zdim]
#set global attributes
gattrs = OrderedDict()
#gattrs['Conventions'] = 'CF-1.6'
#gattrs['Tools'] = 'Created using MeteoInfo'
gattrs[
'TITLE'] = ' OUTPUT FROM * PROGRAM:WRF-Chem V4.1.5 MODEL'
gattrs['START_DATE'] = "2017-01-01_00:00:00"
gattrs['WEST-EAST_GRID_DIMENSION'] = 335
gattrs['SOUTH-NORTH_GRID_DIMENSION'] = 275
gattrs['BOTTOM-TOP_GRID_DIMENSION'] = 28
gattrs['DX'] = 15000.0
gattrs['DY'] = 15000.0
gattrs['AERCU_OPT'] = 0
gattrs['AERCU_FCT'] = 1.0
gattrs['IDEAL_CASE'] = 0
gattrs['DIFF_6TH_SLOPEOPT'] = 0
gattrs['AUTO_LEVELS_OPT'] = 2
gattrs['DIFF_6TH_THRESH'] = 0.1
gattrs['DZBOT'] = 50.0
gattrs['DZSTRETCH_S'] = 1.3
gattrs['DZSTRETCH_U'] = 1.1
gattrs['GRIDTYPE'] = "C"
gattrs['DIFF_OPT'] = 1
gattrs['KM_OPT'] = 4
gattrs['DAMP_OPT'] = 3
gattrs['DAMPCOEF'] = 0.2
gattrs['KHDIF'] = 0.0
gattrs['KVDIF'] = 0.0
gattrs['MP_PHYSICS'] = 2
gattrs['RA_LW_PHYSICS'] = 4
gattrs['RA_SW_PHYSICS'] = 4
gattrs['SF_SFCLAY_PHYSICS'] = 2
gattrs['SF_SURFACE_PHYSICS'] = 2
gattrs['BL_PBL_PHYSICS'] = 2
gattrs['CU_PHYSICS'] = 10
gattrs['SF_LAKE_PHYSICS'] = 0
gattrs['SURFACE_INPUT_SOURCE'] = 3
gattrs['SST_UPDATE'] = 0
gattrs['GRID_FDDA'] = 1
gattrs['GFDDA_INTERVAL_M'] = 0
gattrs['GFDDA_END_H'] = 0
gattrs['GRID_SFDDA'] = 0
gattrs['SGFDDA_INTERVAL_M'] = 0
gattrs['SGFDDA_END_H'] = 0
gattrs['HYPSOMETRIC_OPT'] = 2
gattrs['USE_THETA_M'] = 1
gattrs['GWD_OPT'] = 1
gattrs['SF_URBAN_PHYSICS'] = 0
gattrs['SF_SURFACE_MOSAIC'] = 0
gattrs['SF_OCEAN_PHYSICS'] = 0
gattrs['WEST-EAST_PATCH_START_UNSTAG '] = 1
gattrs['WEST-EAST_PATCH_END_UNSTAG'] = 334
gattrs['WEST-EAST_PATCH_START_STAG'] = 1
gattrs['WEST-EAST_PATCH_END_STAG'] = 335
gattrs['SOUTH-NORTH_PATCH_START_UNSTAG'] = 1
gattrs['SOUTH-NORTH_PATCH_END_UNSTAG'] = 274
gattrs['SOUTH-NORTH_PATCH_START_STAG'] = 1
gattrs['SOUTH-NORTH_PATCH_END_STAG'] = 275
gattrs['BOTTOM-TOP_PATCH_START_UNSTAG'] = 1
gattrs['BOTTOM-TOP_PATCH_END_UNSTAG'] = 27
gattrs['BOTTOM-TOP_PATCH_START_STAG'] = 1
gattrs['BOTTOM-TOP_PATCH_END_STAG'] = 28
gattrs['GRID_ID'] = 1
gattrs['PARENT_ID'] = 0
gattrs['I_PARENT_START'] = 1
gattrs['J_PARENT_START'] = 1
gattrs['PARENT_GRID_RATIO'] = 1
gattrs['DT'] = 90.0
gattrs['CEN_LAT'] = 36.500008
gattrs['CEN_LON'] = 103.5
gattrs['TRUELAT1'] = 30.0
gattrs['TRUELAT2'] = 60.0
gattrs['MOAD_CEN_LAT'] = 36.500008
gattrs['STAND_LON'] = 103.5
gattrs['POLE_LAT'] = 90.0
gattrs['POLE_LON'] = 0.0
gattrs['GMT'] = 0.0
gattrs['JULYR'] = 2016
gattrs['JULDAY'] = 365
gattrs['MAP_PROJ'] = 1
gattrs['MAP_PROJ_CHAR'] = 'Lambert Conformal'
gattrs['MMINLU'] = 'MODIFIED_IGBP_MODIS_NOAH'
gattrs['NUM_LAND_CAT'] = 21
gattrs['ISWATER'] = 17
gattrs['ISLAKE'] = 21
gattrs['ISICE'] = 15
gattrs['ISURBAN'] = 13
gattrs['ISOILWATER'] = 14
gattrs['HYBRID_OPT'] = 2
gattrs['ETAC'] = 0.15
fn_out = dir_inter + '\emis_{}_{}_hour_proj_nc4.nc'.format(year, month)
#fn_out = dir_inter + '\emis_{}_{}_hour_proj_chunk.nc'.format(year, month)
#set variables
dimvars = []
dimvar = dataset.DimVariable()
dimvar.name = 'Times'
dimvar.dtype = np.dtype.char
dimvar.dims = [tdim, sdim]
dimvar.addattr('_ChunkSizes', np.array([1, 19], dtype=np.dtype.uint))
#dimvar.addattr('_ChunkSizes', [1, 19])
dimvars.append(dimvar)
for out_specie in out_species:
dimvar = dataset.DimVariable()
dimvar.name = out_specie
dimvar.dtype = np.dtype.float
dimvar.dims = dims
dimvar.addattr('FieldType', 104)
dimvar.addattr('MemoryOrder', "XYZ")
dimvar.addattr('description', "EMISSION_{}".format(out_specie[2:]))
if out_specie in out_species_unit:
#g/m2/s to ug/m^3 m/s
dimvar.addattr('units', 'ug/m3 m/s')
else:
#mole/m2/s to mol/km^2/hr
dimvar.addattr('units', 'mol km^-2 hr^-1')
dimvar.addattr('stagger', "")
dimvar.addattr('coordinates', "XLONG XLAT XTIME")
dimvar.addattr('_ChunkSizes',
array([1, 3, 137, 167], dtype=np.dtype.uint))
#dimvar.addattr('_ChunkSizes', [1, 3, 137, 167])
dimvars.append(dimvar)
print('Create output data file...')
print(fn_out)
ncfile = dataset.addfile(fn_out, 'c', version='netcdf4', largefile=True)
#ncfile = dataset.addfile(fn_out, 'c', largefile=True)
print('Define dimensions, global attributes and variables...')
ncfile.nc_define(all_dims, gattrs, dimvars, write_dimvars=False)
#Times
print('Write Times variable...')
s_out = []
for i in range(24):
s = '{}-{:0>2d}-01_{:0>2d}:00:00'.format(year, month, i)
s_out.append(s)
s_out = np.array(s_out, dtype=np.dtype.char)
ncfile.write('Times', s_out)
print('Write variable data except times...')
for out_specie in out_species:
if out_specie in f_in.varnames():
print(out_specie)
data = f_in[out_specie][:]
#Conversion
data = transform(data, model_grid, target_grid)
#Set the fourth dimension
data = data.reshape(24, 1, 274, 334)
#Set default values
data[data == np.nan] = 0
ncfile.write(out_specie, data)
ncfile.close()
print('Conversion projection finished!')
def run(year, month, dir_inter, emission, model_grid):
"""
Process emission data by spatial allocation, temporal allocation
and chemical speciation except VOC pollution.
:param year: (*int*) Year.
:param month: (*int*) Month.
:param dir_inter: (*string*) Data output path.
:param emission: (*module*) Emission module.
:param model_grid: (*GridDesc*) Model data grid describe.
"""
#Set profile files
temp_profile_fn = os.path.join(ge_data_dir, 'amptpro.m3.default.us+can.txt')
temp_ref_fn = os.path.join(ge_data_dir, 'amptref.m3.us+can.cair.txt')
spec_profile_fn = os.path.join(ge_data_dir, 'gspro.cmaq.radm2p25_rev.txt')
spec_ref_fn = os.path.join(ge_data_dir, 'gsref.cmaq.radm2p25.txt')
#Set dimensions
tdim = np.dimension(np.arange(24), 'hour')
ydim = np.dimension(model_grid.y_coord, 'lat', 'Y')
xdim = np.dimension(model_grid.x_coord, 'lon', 'X')
dims = [tdim, ydim, xdim]
#Set sectors and pollutants
sectors = [SectorEnum.INDUSTRY, SectorEnum.AGRICULTURE, SectorEnum.ENERGY,
SectorEnum.RESIDENTIAL, SectorEnum.TRANSPORT]
pollutants = [PollutantEnum.BC, PollutantEnum.CO, PollutantEnum.NH3, \
PollutantEnum.NOx, PollutantEnum.OC, PollutantEnum.PM2_5, \
PollutantEnum.SO2, PollutantEnum.PMcoarse, PollutantEnum.PM10more]
out_species = [SpeciesEnum.PEC, SpeciesEnum.CO, SpeciesEnum.NH3, \
None, SpeciesEnum.POA, None, SpeciesEnum.SO2, SpeciesEnum.PMC, \
SpeciesEnum.PMC]
#Loop
for sector in sectors:
print('####################################')
print(sector)
print('####################################')
#Get SCC
scc = emis_util.get_scc(sector)
#Get pollutant profiles
pollutant_profiles = emips.chem_spec.read_file(spec_ref_fn, spec_profile_fn, scc)
for pollutant, out_spec in zip(pollutants, out_species):
print(pollutant)
print('Read emission data...')
emis_data = emission.read_emis(sector, pollutant, month)
#### Spatial allocation
print('Convert emission data untis from Mg/grid/month to g/m2/month...')
emis_data = emis_data * 1e6 / emission.grid_areas
print('Spatial allocation of emission grid to model grid...')
emis_data = transform(emis_data, emission.emis_grid, model_grid)
#### Temporal allocation
print('Temporal allocation...')
month_profile, week_profile, diurnal_profile, diurnal_profile_we = \
emips.temp_alloc.read_file(temp_ref_fn, temp_profile_fn, scc)
print('To daily emission (g/m2/day)...')
weekday_data, weekend_data = emips.temp_alloc.week_allocation(emis_data, week_profile, year, month)
print('To hourly emission (g/m2/s)...')
hour_data = emips.temp_alloc.diurnal_allocation(weekday_data, diurnal_profile) / 3600
#### Chemical speciation
poll_prof = emips.chem_spec.get_pollutant_profile(pollutant_profiles, pollutant)
if (pollutant == PollutantEnum.NOx) and (poll_prof is None):
poll_prof = PollutantProfile(pollutant)
poll_prof.append(SpeciesProfile(pollutant, Species('NO'), 0.9, 1.0, 0.9))
poll_prof.append(SpeciesProfile(pollutant, Species('NO2'), 0.1, 1.0, 0.1))
outfn = os.path.join(dir_inter, \
'{}_emis_{}_{}_{}_hour.nc'.format(pollutant.name, sector.name, year, month))
print outfn
if poll_prof is None:
#### Save hourly emission data
print('Save hourly emission data...')
if out_spec.molar_mass is None:
attrs = dict(units='g/m2/s')
else:
attrs = dict(units='mole/m2/s')
print('To (mole/m2/s)')
hour_data = hour_data / out_spec.molar_mass
dataset.ncwrite(outfn, hour_data, out_spec.name, dims, attrs)
else:
print('Chemical speciation...')
specs = poll_prof.get_species()
gattrs = dict(Conventions='CF-1.6', Tools='Created using MeteoInfo')
dimvars = []
for spec in specs:
dimvar = dataset.DimVariable()
dimvar.name = spec.name
dimvar.dtype = np.dtype.float
dimvar.dims = dims
if spec.molar_mass is None:
dimvar.addattr('units', 'g/m2/s')
else:
dimvar.addattr('units', 'mole/m2/s')
dimvars.append(dimvar)
ncfile = dataset.addfile(outfn, 'c')
ncfile.nc_define(dims, gattrs, dimvars)
for spec_prof,dimvar,spec in zip(poll_prof.species_profiles, dimvars, specs):
print(dimvar.name)
spec_data = hour_data * spec_prof.mass_fraction
if not spec.molar_mass is None:
print('To (mole/m2/s)')
spec_data = spec_data / spec.molar_mass
ncfile.write(dimvar.name, spec_data)
ncfile.close()
def run(year, month, dir_inter, emission, model_grid):
"""
Process VOC emission data by spatial allocation, temporal allocation
and chemical speciation.
:param year: (*int*) Year.
:param month: (*int*) Month.
:param dir_inter: (*string*) Data output path.
:param emission: (*module*) Emission module.
:param model_grid: (*GridDesc*) Model data grid describe.
"""
#Set profile files
temp_profile_fn = os.path.join(ge_data_dir,
'amptpro.m3.default.us+can.txt')
temp_ref_fn = os.path.join(ge_data_dir, 'amptref.m3.us+can.cair.txt')
#Set dimensions
tdim = np.dimension(np.arange(24), 'hour')
ydim = np.dimension(model_grid.y_coord, 'lat', 'Y')
xdim = np.dimension(model_grid.x_coord, 'lon', 'X')
dims = [tdim, ydim, xdim]
#Set sectors and pollutants
sectors = [
SectorEnum.INDUSTRY, SectorEnum.AGRICULTURE, SectorEnum.ENERGY,
SectorEnum.RESIDENTIAL, SectorEnum.TRANSPORT
]
fn_sectors = ['inc', 'agr', 'pow', 'res', 'tra']
pollutant = PollutantEnum.NMVOC
pollutant.units = Units(Weight.MG, Area.GRID, Period.MONTH)
#Loop
for sector, fn_sector in zip(sectors, fn_sectors):
print('####################################')
print(sector)
print('####################################')
#Get SCC
scc = emis_util.get_scc(sector)
print('Read emission data...')
emis_data = emission.read_emis(sector, pollutant, month)
#### Spatial allocation
print(
'Convert emission data untis from Mg/grid/month to g/m2/month...')
emis_data = emis_data * 1e6 / emission.grid_areas
print('Spatial allocation of emission grid to model grid...')
emis_data = transform(emis_data, emission.emis_grid, model_grid)
#### Temporal allocation
print('Temporal allocation...')
month_profile, week_profile, diurnal_profile, diurnal_profile_we = \
emips.temp_alloc.read_file(temp_ref_fn, temp_profile_fn, scc)
print('To daily emission (g/m2/day)...')
weekday_data, weekend_data = emips.temp_alloc.week_allocation(
emis_data, week_profile, year, month)
print('To hourly emission (g/m2/s)...')
hour_data = emips.temp_alloc.diurnal_allocation(
weekday_data, diurnal_profile) / 3600
#### Chemical speciation
print('Chemical speciation...')
outfn = os.path.join(dir_inter, \
'{}_emis_{}_{}_{}_hour.nc'.format(pollutant.name, sector, year, month))
print('Output file: {}'.format(outfn))
print('Set grid speciation data...')
fn = r'Z:\chen\MEIC_data\Grid_speciation_data(VOC)\retro_nmvoc_ratio_{}_2000_0.1deg.nc'.format(
fn_sector)
print('Grid speciation file: {}'.format(fn))
f = dataset.addfile(fn)
#Create output netcdf file and define dimensions, global attributes and variables
gattrs = dict(Conventions='CF-1.6', Tools='Created using MeteoInfo')
dimvars = []
for var in f.variables():
if var.ndim == 2:
dimvar = dataset.DimVariable()
dimvar.name = var.name
dimvar.dtype = np.dtype.float
dimvar.dims = dims
dimvar.addattr('units', 'g/m2/s')
dimvars.append(dimvar)
ncfile = dataset.addfile(outfn, 'c')
ncfile.nc_define(dims, gattrs, dimvars)
#Write variable values
ratio_grid = GridDesc(x_orig=0.05,
x_cell=0.1,
x_num=3600,
y_orig=-89.95,
y_cell=0.1,
y_num=1800)
for dimvar in dimvars:
print(dimvar.name)
rdata = f[dimvar.name][:]
rdata = transform(rdata, ratio_grid, model_grid)
spec_data = hour_data * rdata
ncfile.write(dimvar.name, spec_data)
#Close output netcdf file
ncfile.close()
def run_proj(year, month, dir_inter, model_grid, target_grid, out_species,
out_species_aer, global_attributes, z):
"""
Write Times variable, add global attributes, convert data's projection.
io_style_emissions = 2
:param year: (*int*) Year.
:param month: (*int*) Month.
:param dir_inter: (*string*) The directory where data is stored.
:param model_grid: (*GridDesc*) Model data grid describe.
:param target_grid: (*GridDesc*) Target data grid describe.
:param out_species: (*list*) The name of the output species(gases and aerosol).
:param out_species_aer: (*list*) The name of the output species(aerosol).
:param global_attributes: (*OrderedDict*) The global attributes of the output file.
:param z: (*int*) The zdim of the output data.
"""
print('Add input file...')
fn_in = dir_inter + '\emis_{}_{}_hour_transform.nc'.format(year, month)
print(fn_in)
f_in = dataset.addfile(fn_in)
#set dimension
tdim = np.dimension(np.arange(24), 'Time')
ydim = np.dimension(target_grid.y_coord, 'south_north', 'Y')
xdim = np.dimension(target_grid.x_coord, 'west_east', 'X')
zdim = np.dimension(np.arange(z), 'emissions_zdim')
sdim = np.dimension(np.arange(19), 'DateStrLen')
dims = [tdim, zdim, ydim, xdim]
all_dims = [tdim, sdim, xdim, ydim, zdim]
fn_out = dir_inter + '\wrfchemi_d01_{}-{:0>2d}'.format(year, month)
#set variables
dimvars = []
dimvar = dataset.DimVariable()
dimvar.name = 'Times'
dimvar.dtype = np.dtype.char
dimvar.dims = [tdim, sdim]
#dimvar.addattr('_ChunkSizes', [1, 19])
dimvars.append(dimvar)
for out_specie in out_species:
dimvar = dataset.DimVariable()
dimvar.name = out_specie
dimvar.dtype = np.dtype.float
dimvar.dims = dims
dimvar.addattr('FieldType', 104)
dimvar.addattr('MemoryOrder', "XYZ")
dimvar.addattr('description', "EMISSION_{}".format(out_specie[2:]))
if out_specie in out_species_aer:
#g/m2/s to ug/m^3 m/s
dimvar.addattr('units', 'ug/m3 m/s')
else:
#mole/m2/s to mol/km^2/hr
dimvar.addattr('units', 'mol km^-2 hr^-1')
dimvar.addattr('stagger', "")
dimvar.addattr('coordinates', "XLONG XLAT XTIME")
#dimvar.addattr('_ChunkSizes', [1, 3, 137, 167])
dimvars.append(dimvar)
print('Create output data file...')
print(fn_out)
ncfile = dataset.addfile(fn_out, 'c', largefile=True)
print('Define dimensions, global attributes and variables...')
ncfile.nc_define(all_dims, global_attributes, dimvars, write_dimvars=False)
#Times
print('Write Times variable...')
s_out = []
for i in range(24):
s = '{}-{:0>2d}-01_{:0>2d}:00:00'.format(year, month, i)
s_out.append(s)
s_out = np.array(s_out, dtype=np.dtype.char)
ncfile.write('Times', s_out)
print('Write variable data except times...')
for out_specie in out_species:
data = np.zeros((tdim.length, zdim.length, ydim.length, xdim.length))
if out_specie in f_in.varnames():
print(out_specie)
dd = f_in[out_specie][:]
#Conversion proj
dd = transform(dd, model_grid, target_grid)
#Set default values
dd[dd == np.nan] = 0
data[:, :, :, :] = dd
##########test############
#data[:, 1:8, :, :] = 0
##########test############
ncfile.write(out_specie, data)
f_in.close()
ncfile.close()
print('Convert projection finished!')
def run(year, month, dir_inter, emission, model_grid):
"""
Process emission data by spatial allocation, temporal allocation
and chemical speciation except VOC pollution.
:param year: (*int*) Year.
:param month: (*int*) Month.
:param dir_inter: (*string*) Data output path.
:param emission: (*module*) Emission module.
:param model_grid: (*GridDesc*) Model data grid describe.
"""
#Set profile files
temp_profile_fn = os.path.join(ge_data_dir,
'amptpro.m3.default.us+can.txt')
temp_ref_fn = os.path.join(ge_data_dir, 'amptref.m3.us+can.cair.txt')
spec_profile_fn = os.path.join(ge_data_dir, 'gspro.cmaq.radm2p25_rev.txt')
spec_ref_fn = os.path.join(ge_data_dir, 'gsref.cmaq.radm2p25.txt')
#Set data dimensions
tdim = np.dimension(np.arange(24), 'hour')
ydim = np.dimension(model_grid.y_coord, 'lat', 'Y')
xdim = np.dimension(model_grid.x_coord, 'lon', 'X')
dims = [tdim, ydim, xdim]
#Set sectors and pollutants
sectors = [SectorEnum.INDUSTRY, SectorEnum.AGRICULTURE, SectorEnum.ENERGY, \
SectorEnum.RESIDENTIAL, SectorEnum.TRANSPORT, SectorEnum.SHIPS, \
SectorEnum.AIR]
pollutants = [PollutantEnum.BC, PollutantEnum.CH4, PollutantEnum.CO, \
PollutantEnum.NH3, PollutantEnum.NOx, PollutantEnum.OC, PollutantEnum.PM2_5, \
PollutantEnum.SO2, PollutantEnum.PM10]
out_species = [SpeciesEnum.PEC, SpeciesEnum.CH4, SpeciesEnum.CO, SpeciesEnum.NH3, \
None, SpeciesEnum.POA, None, SpeciesEnum.SO2, SpeciesEnum.PMC]
#Read Mongolia shape and mask data
lmongolia = geolib.shaperead('mongolia.shp')
#Loop
for sector in sectors:
print('####################################')
print(sector)
print('####################################')
#Get SCC
scc = emis_util.get_scc(sector)
#Get pollutant profiles
pollutant_profiles = emips.chem_spec.read_file(spec_ref_fn,
spec_profile_fn, scc)
for pollutant, out_spec in zip(pollutants, out_species):
print(pollutant)
print('Read emission data (kg/m2/s)...')
emis_data = emission.read_emis(sector, pollutant, year, month)
if emis_data is None: #No emission of a pollutant for some sectors
continue
#### Decrease PM2.5 emission of Energy sector in Mongolia
if sector == SectorEnum.ENERGY and pollutant == PollutantEnum.PM2_5:
mdata = emis_data.maskout(lmongolia.shapes())
mdata[mdata != np.nan] = 0.05
mdata[mdata == np.nan] = 1
emis_data = emis_data * mdata
#### Spatial allocation
print('Spatial allocation of emission grid to model grid...')
emis_data = transform(emis_data, emission.emis_grid, model_grid)
#### Temporal allocation
print('Temporal allocation...')
month_profile, week_profile, diurnal_profile, diurnal_profile_we = \
emips.temp_alloc.read_file(temp_ref_fn, temp_profile_fn, scc)
if pollutant == PollutantEnum.CH4 or sector == SectorEnum.AIR or \
sector == SectorEnum.SHIPS:
print('To (kg/m2/year)')
emis_data = emis_data * 3600 * 24 * emis_util.get_year_days(
year)
print('To monthly emission (kg/m2/month)...')
emis_data = emips.temp_alloc.month_allocation(
emis_data, month_profile)
emis_data = emis_data[month - 1]
else:
print('To (kg/m2/month)')
emis_data = emis_data * 3600 * 24 * emis_util.get_month_days(
year, month)
print('To daily emission (kg/m2/day)...')
weekday_data, weekend_data = emips.temp_alloc.week_allocation(
emis_data, week_profile, year, month)
print('To hourly emission (g/m2/s)...')
hour_data = emips.temp_alloc.diurnal_allocation(
weekday_data, diurnal_profile) / 3.6
#### Chemical speciation
poll_prof = emips.chem_spec.get_pollutant_profile(
pollutant_profiles, pollutant)
if (pollutant == PollutantEnum.NOx) and (poll_prof is None):
poll_prof = PollutantProfile(pollutant)
poll_prof.append(
SpeciesProfile(pollutant, SpeciesEnum.NO, 0.9, 1.0, 0.9))
poll_prof.append(
SpeciesProfile(pollutant, SpeciesEnum.NO2, 0.1, 1.0, 0.1))
#### Set output netcdf file path
outfn = os.path.join(dir_inter, \
'{}_emis_{}_{}_{}_hour.nc'.format(pollutant.name, sector.name, year, month))
print outfn
if poll_prof is None:
#### Save hourly emission data
print('Save hourly emission data...')
if out_spec.molar_mass is None:
attrs = dict(units='g/m2/s')
else:
attrs = dict(units='mole/m2/s')
hour_data = hour_data / out_spec.molar_mass
dataset.ncwrite(outfn, hour_data, out_spec.name, dims, attrs)
else:
print('Chemical speciation...')
specs = poll_prof.get_species()
gattrs = dict(Conventions='CF-1.6',
Tools='Created using MeteoInfo')
dimvars = []
for spec in specs:
dimvar = dataset.DimVariable()
dimvar.name = spec.name
dimvar.dtype = np.dtype.float
dimvar.dims = dims
if spec.molar_mass is None:
dimvar.addattr('units', 'g/m2/s')
else:
dimvar.addattr('units', 'mole/m2/s')
dimvars.append(dimvar)
ncfile = dataset.addfile(outfn, 'c')
ncfile.nc_define(dims, gattrs, dimvars)
for spec_prof, dimvar, spec in zip(poll_prof.species_profiles,
dimvars, specs):
print(dimvar.name)
if spec.molar_mass is None:
spec_data = hour_data * spec_prof.mass_fraction
else:
spec_data = hour_data * spec_prof.mass_fraction / spec.molar_mass
ncfile.write(dimvar.name, spec_data)
ncfile.close()