copy_dims(mean, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# global attributes
copy_ncatts(mean, wrfout, prefix="WRF_") # copy all attributes and save with prefix WRF
mean.description = "WRF monthly means"
mean.begin_date = begindate
mean.end_date = enddate
mean.experiment = exp
mean.creator = "Andre R. Erler"
# create climatology output file
clim = Dataset(folder + climfile % ndom, "w", format="NETCDF4")
add_coord(clim, "time", values=mons, dtype="i4", atts=dict(units="month of the year")) # month of the year
copy_dims(clim, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# variable with proper names of the months
clim.createDimension("tstrlen", size=9)
coord = clim.createVariable("month", "S1", ("time", "tstrlen"))
for m in xrange(nmons):
for n in xrange(9):
coord[m, n] = months[m][n]
# global attributes
copy_ncatts(clim, wrfout, prefix="WRF_") # copy all attributes and save with prefix WRF
clim.description = "climatology of WRF monthly means"
clim.begin_date = begindate
clim.end_date = enddate
clim.experiment = exp
clim.creator = "Andre R. Erler"
# check variable list
for var in varlist:
if not wrfout.variables.has_key(varmap.get(var, var)):
print("\nWARNING: variable %s not found in source file!\n" % (var,))
## convert values to mm/day
days_per_month = np.array([31,28.25,31,30,31,30,31,31,30,31,30,31])
climdata['rain'] /= days_per_month.reshape((len(days_per_month),1,1)) # convert to mm/day
## initialize netcdf dataset structure
print('\nWriting data to disk: %s'%(test+outfile,))
# create groups for different resolution
outdata = Dataset(GPCCroot+test+outfile, 'w', format='NETCDF4') # outgrp.createGroup('fineres')
# new time dimensions
months = ['January ', 'February ', 'March ', 'April ', 'May ', 'June ', #
'July ', 'August ', 'September', 'October ', 'November ', 'December ']
# create time dimensions and coordinate variables
add_coord(outdata,'time',np.arange(1,ntime+1),dtype='i4')
outdata.createDimension('tstrlen', 9) # name of month string
outdata.createVariable('ndays','i4',('time',))[:] = days_per_month
# names of months (as char array)
coord = outdata.createVariable('month','S1',('time','tstrlen'))
for m in xrange(ntime):
for n in xrange(9): coord[m,n] = months[m][n]
# global attributes
outdata.description = 'Climatology of GPCC monthly precipitation, averaged from %04i to %04i'%(debyr,finyr)
outdata.creator = 'Andre R. Erler'
copy_ncatts(outdata,indata['rain'],prefix='GPCC_')
# create old lat/lon dimensions and coordinate variables
copy_dims(outdata, indata['rain'], dimlist=dimlist.keys(), namemap=dimlist, copy_coords=True)
# create climatology variables
dims = ('time','lat','lon'); fill_value = -9999
# precipitation
atts = dict(long_name='Precipitation', units='mm/day')
add_var(outdata, 'rain', dims, values=climdata['rain'].filled(fill_value), atts=atts, fill_value=fill_value)
# create climatology output file
clim = Dataset(folder + climfile % ndom, 'w', format='NETCDF4')
add_coord(clim,
'time',
values=mons,
dtype='i4',
atts=dict(units='month of the year')) # month of the year
copy_dims(clim,
wrfout,
dimlist=dimlist,
namemap=dimmap,
copy_coords=False) # don't have coordinate variables
# variable with proper names of the months
clim.createDimension('tstrlen', size=9)
coord = clim.createVariable('month', 'S1', ('time', 'tstrlen'))
for m in xrange(nmons):
for n in xrange(9):
coord[m, n] = months[m][n]
# global attributes
copy_ncatts(
clim, wrfout,
prefix='WRF_') # copy all attributes and save with prefix WRF
clim.description = 'climatology of WRF monthly means'
clim.begin_date = begindate
clim.end_date = enddate
clim.experiment = exp
clim.creator = 'Andre R. Erler'
# check variable list
for var in varlist:
add_coord(mean, 'time', values=None, dtype='i4', atts=dict(units='month since '+begindate)) # unlimited time dimension
copy_dims(mean, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# global attributes
copy_ncatts(mean, wrfout, prefix='WRF_') # copy all attributes and save with prefix WRF
mean.description = 'WRF monthly means'
mean.begin_date = begindate; mean.end_date = enddate
mean.experiment = exp
mean.creator = 'Andre R. Erler'
# create climatology output file
clim = Dataset(folder+climfile%ndom, 'w', format='NETCDF4')
add_coord(clim, 'time', values=mons, dtype='i4', atts=dict(units='month of the year')) # month of the year
copy_dims(clim, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# variable with proper names of the months
clim.createDimension('tstrlen', size=9)
coord = clim.createVariable('month','S1',('time','tstrlen'))
for m in xrange(nmons):
for n in xrange(9): coord[m,n] = months[m][n]
# global attributes
copy_ncatts(clim, wrfout, prefix='WRF_') # copy all attributes and save with prefix WRF
clim.description = 'climatology of WRF monthly means'
clim.begin_date = begindate; clim.end_date = enddate
clim.experiment = exp
clim.creator = 'Andre R. Erler'
# check variable list
for var in varlist:
if not wrfout.variables.has_key(varmap.get(var,var)):
print('\nWARNING: variable %s not found in source file!\n'%(var,))
del var # remove variable if not present in soruce file
# copy variables to new datasets
climdata['rain'] /= days_per_month.reshape((len(days_per_month),1,1)) # convert to mm/day
climdata['Tmin'] += 273.15 # convert to Kelvin
climdata['T2'] += 273.15 # convert to Kelvin
climdata['Tmax'] += 273.15 # convert to Kelvin
## initialize netcdf dataset structure
print('\nWriting data to disk:')
# create groups for different resolution
outdata = Dataset(CRUroot+test+outfile, 'w', format='NETCDF4') # outgrp.createGroup('fineres')
# new time dimensions
months = ['January ', 'February ', 'March ', 'April ', 'May ', 'June ', #
'July ', 'August ', 'September', 'October ', 'November ', 'December ']
# create time dimensions and coordinate variables
add_coord(outdata,'time',np.arange(1,ntime+1),dtype='i4')
outdata.createDimension('tstrlen', 9) # name of month string
outdata.createVariable('ndays','i4',('time',))[:] = days_per_month
# names of months (as char array)
coord = outdata.createVariable('month','S1',('time','tstrlen'))
for m in xrange(ntime):
for n in xrange(9): coord[m,n] = months[m][n]
# global attributes
outdata.description = 'Climatology of CRU monthly climate data, averaged from %04i to %04i'%(debyr,finyr)
outdata.creator = 'Andre R. Erler'
copy_ncatts(outdata,indata['rain'],prefix='CRU_')
# create old lat/lon dimensions and coordinate variables
copy_dims(outdata, indata['rain'], dimlist=dimlist.keys(), namemap=dimlist, copy_coords=True)
# create climatology variables
dims = ('time','lat','lon'); fill_value = -9999
# precipitation
atts = dict(long_name='Precipitation', units='mm/day')
add_var(outdata, 'rain', dims, values=climdata['rain'].filled(fill_value), atts=atts, fill_value=fill_value)
