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LMR_calibrate.py
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"""
Module containing definitions pertaining to linear PSM calibration sources.
Revisions:
- Addition of the GPCC precipitation dataset as a possible calibration
moisture source.
[R. Tardif, U. of Washington, February 2016]
- Addition of the Dai PDSI dataset as a possible calibration
moisture source.
[R. Tardif, U. of Washington, May 2016]
- Addition of the SPEI dataset as a possible calibration moisture source.
[R. Tardif, U. of Washington, December 2016]
- Added parameter explicitely defining a reference period in the calculation
of anomalies in functions tasked with uploading instrumental-era calibration
datasets.
Parameter now defined in configuration.
[R. Tardif, U. of Washington, February 2018]
"""
# -------------------------------------------------------------------------------
# *** Calibration type assignment ----------------------------------------------
# -------------------------------------------------------------------------------
# All logic for proxy object assignment
def calibration_assignment(icalib):
if icalib == 'GISTEMP':
calib_object = calibration_GISTEMP()
elif icalib == 'HadCRUT':
calib_object = calibration_HadCRUT()
elif icalib == 'BerkeleyEarth':
calib_object = calibration_BerkeleyEarth()
elif icalib == 'MLOST':
calib_object = calibration_MLOST()
elif icalib == 'NOAAGlobalTemp':
calib_object = calibration_NOAAGlobalTemp()
elif icalib == 'GPCC':
calib_object = calibration_precip_GPCC()
elif icalib == 'DaiPDSI':
calib_object = calibration_precip_DaiPDSI()
elif icalib == 'SPEI':
calib_object = calibration_precip_SPEI()
else:
print('Error in calibration data specification! Exiting ...')
exit(1)
return calib_object
# -------------------------------------------------------------------------------
# *** Master class for calibration ----------------------------------------------
# -------------------------------------------------------------------------------
class calibration_master(object):
'''
This is the master calibration class. Turn this into a metaclass so one cannot instantiate directly;
it is an abstract class.
'''
pass
# -------------------------------------------------------------------------------
# *** GISTEMP class --------------------------------------------------
# -------------------------------------------------------------------------------
class calibration_GISTEMP(calibration_master):
source = 'GISTEMP'
dataformat_calib = 'NCD'
calib_vars = ['Tsfc']
outfreq = 'monthly'
# read the data
def read_calibration(self):
from load_gridded_data import read_gridded_data_GISTEMP
[self.time,self.lat,self.lon,self.temp_anomaly] = read_gridded_data_GISTEMP(self.datadir_calib,
self.datafile_calib,
self.calib_vars,
self.outfreq,
self.anom_reference_period)
# -------------------------------------------------------------------------------
# *** HadCRUT class --------------------------------------------------
# -------------------------------------------------------------------------------
class calibration_HadCRUT(calibration_master):
source = 'HadCRUT'
dataformat_calib = 'NCD'
calib_vars = ['Tsfc']
outfreq = 'monthly'
# read the data
def read_calibration(self):
from load_gridded_data import read_gridded_data_HadCRUT
[self.time,self.lat,self.lon,self.temp_anomaly] = read_gridded_data_HadCRUT(self.datadir_calib,
self.datafile_calib,
self.calib_vars,
self.outfreq,
self.anom_reference_period)
# -------------------------------------------------------------------------------
# *** BerkeleyEarth class --------------------------------------------------
# -------------------------------------------------------------------------------
class calibration_BerkeleyEarth(calibration_master):
source = 'BerkeleyEarth'
dataformat_calib = 'NCD'
calib_vars = ['Tsfc']
outfreq = 'monthly'
# read the data
def read_calibration(self):
from load_gridded_data import read_gridded_data_BerkeleyEarth
[self.time,self.lat,self.lon,self.temp_anomaly] = read_gridded_data_BerkeleyEarth(self.datadir_calib,
self.datafile_calib,
self.calib_vars,
self.outfreq,
self.anom_reference_period)
# -------------------------------------------------------------------------------
# *** MLOST class --------------------------------------------------
# -------------------------------------------------------------------------------
class calibration_MLOST(calibration_master):
source = 'MLOST'
dataformat_calib = 'NCD'
calib_vars = ['air']
outfreq = 'monthly'
# read the data
def read_calibration(self):
from load_gridded_data import read_gridded_data_MLOST
[self.time,self.lat,self.lon,self.temp_anomaly] = read_gridded_data_MLOST(self.datadir_calib,
self.datafile_calib,
self.calib_vars,
self.outfreq,
self.anom_reference_period)
# -------------------------------------------------------------------------------
# *** NOAAGlobalTemp class ----------------------------------------
# -------------------------------------------------------------------------------
class calibration_NOAAGlobalTemp(calibration_master):
source = 'NOAAGlobalTemp'
dataformat_calib = 'NCD'
calib_vars = ['air']
outfreq = 'monthly'
# read the data
def read_calibration(self):
from load_gridded_data import read_gridded_data_MLOST
[self.time,self.lat,self.lon,self.temp_anomaly] = read_gridded_data_MLOST(self.datadir_calib,
self.datafile_calib,
self.calib_vars,
self.outfreq,
self.anom_reference_period)
# -------------------------------------------------------------------------------
# *** GPCC class --------------------------------------------------
# -------------------------------------------------------------------------------
class calibration_precip_GPCC(calibration_master):
source = 'GPCC'
dataformat_calib = 'NCD'
calib_vars = ['precip']
outfreq = 'monthly'
# read_calibration() to return anomalies w.r.t. a reference period (True or False)
out_anomalies = True
# read the data
def read_calibration(self):
from load_gridded_data import read_gridded_data_GPCC
[self.time,self.lat,self.lon,self.temp_anomaly] = read_gridded_data_GPCC(self.datadir_calib,
self.datafile_calib,
self.calib_vars,
self.out_anomalies,
self.anom_reference_period,
self.outfreq)
# -------------------------------------------------------------------------------
# *** DaiPDSI class -----------------------------------------------
# -------------------------------------------------------------------------------
class calibration_precip_DaiPDSI(calibration_master):
source = 'DaiPDSI'
dataformat_calib = 'NCD'
calib_vars = ['pdsi']
outfreq = 'monthly'
# read_calibration() to return anomalies w.r.t. a reference period (True or False)
out_anomalies = True
# read the data
def read_calibration(self):
from load_gridded_data import read_gridded_data_DaiPDSI
[self.time,self.lat,self.lon,self.temp_anomaly] = read_gridded_data_DaiPDSI(self.datadir_calib,
self.datafile_calib,
self.calib_vars,
self.out_anomalies,
self.anom_reference_period,
self.outfreq)
# -------------------------------------------------------------------------------
# *** SPEI class --------------------------------------------------
# -------------------------------------------------------------------------------
class calibration_precip_SPEI(calibration_master):
source = 'SPEI'
dataformat_calib = 'NCD'
calib_vars = ['spei']
outfreq = 'monthly'
# read_calibration() to return anomalies w.r.t. a reference period (True or False)
out_anomalies = True
# read the data
def read_calibration(self):
from load_gridded_data import read_gridded_data_SPEI
[self.time,self.lat,self.lon,self.temp_anomaly] = read_gridded_data_SPEI(self.datadir_calib,
self.datafile_calib,
self.calib_vars,
self.out_anomalies,
self.anom_reference_period,
self.outfreq)