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)
# station density
atts = dict(long_name='Station Density', units='#')
add_var(outdata, 'stns', dims, values=climdata['stns'].filled(fill_value), atts=atts, fill_value=fill_value)
# land mask
atts = dict(long_name='Land Mask', units='')
tmp = ma.masked_array(ma.ones((datashape[1],datashape[2])), mask=dataMask)
add_var(outdata, 'landmask', ('lat','lon'), values=tmp.filled(0))
# ## dataset feedback and diagnostics
# # print dataset meta data
# print('\n\n')
# print(outdata)
# # print dimensions meta data
# for dimobj in outdata.dimensions.values():
# print dimobj
# 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 monthly PRISM data'
outdata.creator = 'Andre R. Erler'
# copy_ncatts(outdata,indata['rain'],prefix='GPCC_')
# create new lat/lon dimensions and coordinate variables
add_coord(outdata, 'lat', values=lat, atts=None)
add_coord(outdata, 'lon', values=lon, atts=None)
# create climatology variables
fill_value = -9999
atts = dict(long_name='Precipitation', units='mm/day')
add_var(outdata, 'rain', ('time','lat','lon'), values=ppt.filled(fill_value), atts=atts, fill_value=fill_value)
atts = dict(long_name='Minimum Temperature', units='deg. C')
add_var(outdata, 'Tmin', ('time','lat','lon'), values=Tmin.filled(fill_value), atts=atts, fill_value=fill_value)
atts = dict(long_name='Average Temperature', units='deg. C')
add_var(outdata, 'T2', ('time','lat','lon'), values=Tavg.filled(fill_value), atts=atts, fill_value=fill_value)
atts = dict(long_name='Maximum Temperature', units='deg. C')
add_var(outdata, 'Tmax', ('time','lat','lon'), values=Tmax.filled(fill_value), atts=atts, fill_value=fill_value)
# dataset feedback and diagnostics
# print dataset meta data
print outdata
# print dimensions meta data
for dimobj in outdata.dimensions.values():
print dimobj
# print variable meta data
for varobj in outdata.variables.values():
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)
# 2m mean Temperature
atts = dict(long_name='Temperature at 2m', units='K')
add_var(outdata, 'T2', dims, values=climdata['T2'].filled(fill_value), atts=atts, fill_value=fill_value)
# 2m maximum Temperature
atts = dict(long_name='Maximum 2m Temperature', units='K')
add_var(outdata, 'Tmax', dims, values=climdata['Tmax'].filled(fill_value), atts=atts, fill_value=fill_value)
# 2m minimum Temperature
atts = dict(long_name='Minimum 2m Temperature', units='K')
add_var(outdata, 'Tmin', dims, values=climdata['Tmin'].filled(fill_value), atts=atts, fill_value=fill_value)
# 2m water vapor
atts = dict(long_name='Water Vapor Pressure at 2m', units='hPa')
add_var(outdata, 'Q2', dims, values=climdata['Q2'].filled(fill_value), atts=atts, fill_value=fill_value)
# land mask
atts = dict(long_name='Land Mask', units='')
tmp = ma.masked_array(ma.ones((datashape[1],datashape[2])), mask=dataMask)
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)
# station density
atts = dict(long_name='Station Density', units='#')
add_var(outdata,
'stns',
dims,
values=climdata['stns'].filled(fill_value),
atts=atts,
fill_value=fill_value)
# land mask
atts = dict(long_name='Land Mask', units='')
tmp = ma.masked_array(ma.ones((datashape[1], datashape[2])), mask=dataMask)
add_var(outdata, 'landmask', ('lat', 'lon'), values=tmp.filled(0))