elif mode == 'convert_climatology':
# load dataset
dataset = loadGPCC_LTM(varlist=['stations','precip'],resolution=res)
# change meta-data
dataset.name = 'GPCC'
dataset.title = 'GPCC Long-term Climatology'
dataset.atts.resolution = res
# load data into memory
dataset.load()
# add landmask
addLandMask(dataset) # create landmask from precip mask
dataset.mask(dataset.landmask) # mask all fields using the new landmask
# add length and names of month
addLengthAndNamesOfMonth(dataset, noleap=False)
# figure out a different filename
filename = getFileName(grid=res, period=None, name='GPCC', filepattern=avgfile)
print('\n'+filename+'\n')
if os.path.exists(avgfolder+filename): os.remove(avgfolder+filename)
# write data and some annotation
ncset = writeNetCDF(dataset, avgfolder+filename, close=False)
# add_var(ncset,'name_of_month', name_of_month, 'time', # add names of month
# atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
# close...
ncset.close()
source = loadPCIC_LTM()
# change meta-data
source.name = 'PCIC'
source.title = 'PCIC PRISM Climatology'
# load data into memory (and ignore last time step, which is just the annual average)
# source.load(time=(0,12)) # exclusive the last index
source.load(time=(0,12)) # for testing
# make normal dataset
dataset = source.copy()
source.close()
## add new variables
# add landmask (it's not really a landmask, thought)
maskatts = dict(name='datamask', units='', long_name='Mask for Climatology Fields',
description='where this mask is non-zero, no data is available')
addLandMask(dataset, maskname='datamask',atts=maskatts) # create mask from precip mask
dataset.mask(dataset.datamask) # mask all fields using the new data mask
# add length and names of month
addLengthAndNamesOfMonth(dataset, noleap=False)
# add mean temperature
T2 = dataset.Tmin + dataset.Tmax # average temperature is just the average between min and max
T2 /= 2.
T2.name = 'T2'; T2.atts.long_name='Average 2m Temperature'
print(T2)
dataset += T2 # add to dataset
# rewrite time axis
time = dataset.time
time.load(data=np.arange(1,13))
time.units = 'month'; time.atts.long_name='Month of the Year'
print(time)
# print diagnostic
# source.load(time=(0,12)) # exclusive the last index
# N.B.: now we need to trim the files beforehand...
# make normal dataset
dataset = source.copy()
source.close()
## add new variables
# add landmask (it's not really a landmask, thought)
dataset.precip.mask(
maskValue=-9999.) # mask all fields using the missing value flag
maskatts = dict(
name='datamask',
units='',
long_name='Mask for Climatology Fields',
description='where this mask is non-zero, no data is available')
addLandMask(dataset, maskname='datamask',
atts=maskatts) # create mask from precip mask
# add length and names of month
addLengthAndNamesOfMonth(dataset, noleap=False)
# add mean temperature
T2 = dataset.Tmin + dataset.Tmax # average temperature is just the average between min and max
T2 /= 2.
T2.name = 'T2'
T2.atts.long_name = 'Average 2m Temperature'
print(T2)
dataset += T2 # add to dataset
# rewrite time axis
time = dataset.time
time.load(data=np.arange(
1, 13, dtype=time.dtype)) # 1 to 12 (incl.) for climatology
time.units = 'month'
time.atts.long_name = 'Month of the Year'
assert dataset.shape[0] == 1
elif mode == 'convert_climatology':
# load dataset
dataset = loadGPCC_LTM(varlist=['stations', 'precip'],
resolution=res)
# change meta-data
dataset.name = 'GPCC'
dataset.title = 'GPCC Long-term Climatology'
dataset.atts.resolution = res
# load data into memory
dataset.load()
# add landmask
addLandMask(dataset) # create landmask from precip mask
dataset.mask(
dataset.landmask) # mask all fields using the new landmask
# add length and names of month
addLengthAndNamesOfMonth(dataset, noleap=False)
# figure out a different filename
filename = getFileName(grid=res,
period=None,
name='GPCC',
filepattern=avgfile)
print('\n' + filename + '\n')
if os.path.exists(avgfolder + filename):
os.remove(avgfolder + filename)
# write data and some annotation
ncset = writeNetCDF(dataset, avgfolder + filename, close=False)
