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iasiCrisAirs_inNucaps_allStandard.py
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#!/usr/bin/env python3
import configparser
pathInfo = configparser.ConfigParser()
pathInfo.read('rttov.cfg')
rttovPath = pathInfo['RTTOV']['rttovPath']
import matplotlib
matplotlib.use('Agg')
import pyrttov
import example_data as ex
import numpy as np
import os
import sys
from matplotlib import pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib.mlab import bivariate_normal
import matplotlib
import matplotlib.cm as mplcm
import matplotlib.colors as colors
from cycler import cycler
import h5py
pyRttovPath = os.path.join(rttovPath,'wrapper')
if not pyRttovPath in sys.path:
sys.path.append(pyRttovPath)
def calculateWeightingFunctions(chan_list, rttovInstance, myProfiles):
nlevels = np.asarray(rttovInstance.TauLevels).shape[2]
nprofiles = myProfiles.P.shape[0]
wf = np.zeros([nprofiles, nlevels-1, len(chan_list)])
for p in range(myProfiles.P.shape[0]):
iList = 0
for c in range(0,rttovInstance.Nchannels):
if c+1 in chan_list:
num = rttovInstance.TauLevels[p, c, 1::] - rttovInstance.TauLevels[p, c, 0:nlevels-1]
den = np.log(myProfiles.P[p, 0:nlevels-1]) - np.log(myProfiles.P[p, 1:nlevels])
wf[p,:,iList] = num/den
iList+=1
return wf
def plotWeightingFunctions(chan_list, profiles, wf, instrument, profileNumber, wavenumbers):
matplotlib.rc('xtick', labelsize=10)
plt.figure()
NUM_COLORS = len(chan_list)
cm = plt.get_cmap('brg')
cNorm = colors.Normalize(vmin=0, vmax=NUM_COLORS-1)
scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
plt.gca().set_prop_cycle(cycler('color', [scalarMap.to_rgba(i) for i in range(NUM_COLORS)]))
for i in range(wf.shape[1]):
plt.plot( wf[0:99,i], profiles.P[int(profileNumber)-1,0:99] )
plt.gca().set_yscale('log')
plt.gca().invert_yaxis()
plt.gca().set_xlim(0,0.5)
plt.yticks(np.array([1000.0, 100.0, 10.0, 1.0, 0.1]),['1000.0','100.0','10.0','1.0','0.1'])
legendList = []
for i,c in enumerate(chan_list):
legendList.append('{} {:4.3f} {:4.3f}'.format(c, wavenumbers[instrument][c-1], 10000.0/wavenumbers[instrument][c-1]))
plt.legend(legendList, fontsize=6, ncol=3)
plt.ylabel('Pressure [hPa]')
plt.xlabel('Weighting Function')
plt.savefig(instrument+'_profile_'+profileNumber+'_weighting_functions.png')
if(len(chan_list)>30): matplotlib.rc('xtick', labelsize=6)
else: matplotlib.rc('xtick', labelsize=10)
plt.close()
plt.figure()
plt.title('Weighting Function by Instrument Channel')
wf = np.asarray(wf)
#plt.pcolor(np.arange(wf.shape[0]), myProfiles.P[0,0:99], wf[:,0:99].T, norm = LogNorm( vmin = wf[:,0:99].min(), vmax = 0.3 ))#vmax = wf[:,0:99].max() ) )
plt.pcolor(np.arange(wf.shape[1]), myProfiles.P[int(profileNumber)-1,0:99], wf[0:99,:],vmin=wf[0:99,:].min(), vmax=0.5 )#vmax = wf[:,0:99].max() ) )
plt.colorbar()
plt.ylabel('Pressure [hPa]')
plt.xlabel('Instrument Channel')
plt.gca().set_yscale('log')
plt.gca().invert_yaxis()
plt.xticks(np.arange(len(chan_list)), chan_list, rotation='vertical')
plt.yticks(np.array([1000.0, 100.0, 10.0, 1.0, 0.1]),['1000.0','100.0','10.0','1.0','0.1'])
plt.tight_layout()
plt.savefig(instrument+'_profile_'+profileNumber+'_weighting_functions_pcolor.png')
plt.close()
print(profileNumber, myProfiles.O3[int(profileNumber)-1,:].sum())
rttovPath = '../'
def plotJacobians(chan_list, profiles, jacobians, instrument, profileNumber, wavenumbers, ofWhat):
matplotlib.rc('xtick', labelsize=10)
plt.figure()
NUM_COLORS = len(chan_list)
cm = plt.get_cmap('brg')
cNorm = colors.Normalize(vmin=0, vmax=NUM_COLORS-1)
scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
plt.gca().set_prop_cycle(cycler('color', [scalarMap.to_rgba(i) for i in range(NUM_COLORS)]))
jacSub = []
for i in range(jacobians.shape[0]):
if(i+1 in chan_list):
plt.plot( jacobians[i,:], profiles.P[int(profileNumber)-1,:] )
jacSub.append(jacobians[i,:])
plt.gca().set_yscale('log')
plt.gca().invert_yaxis()
#plt.gca().set_xlim(0,0.5)
plt.yticks(np.array([1000.0, 100.0, 10.0, 1.0, 0.1]),['1000.0','100.0','10.0','1.0','0.1'])
legendList = []
for i,c in enumerate(chan_list):
legendList.append('{} {:4.3f} {:4.3f}'.format(c, wavenumbers[instrument][c-1], 10000.0/wavenumbers[instrument][c-1]))
plt.legend(legendList, fontsize=6, ncol=3)
plt.ylabel('Pressure [hPa]')
if(ofWhat.lower() =='temperature'): plt.xlabel('Jacobian [K/K]')
else: plt.xlabel('Jacobian [K/ppmv]')
plt.savefig(instrument+'_profile_'+profileNumber+'_jacobians_'+ofWhat+'.png')
if(len(chan_list)>30): matplotlib.rc('xtick', labelsize=6)
else: matplotlib.rc('xtick', labelsize=10)
plt.close()
plt.figure()
if(ofWhat.lower() == 'temperature'): plt.title(ofWhat.capitalize()+' Jacobian by Instrument Channel [K/K]')
else: plt.title(ofWhat.capitalize()+' Jacobian by Instrument Channel [K/ppmv]')
jacSub = np.asarray(jacSub)
print(profileNumber)
#plt.pcolor(np.arange(wf.shape[0]), myProfiles.P[0,0:99], wf[:,0:99].T, norm = LogNorm( vmin = wf[:,0:99].min(), vmax = 0.3 ))#vmax = wf[:,0:99].max() ) )
plt.pcolor(np.arange(len(chan_list)), myProfiles.P[int(profileNumber)-1,:], jacSub[:,:].T )
plt.colorbar()
plt.ylabel('Pressure [hPa]')
plt.xlabel('Instrument Channel')
plt.gca().set_yscale('log')
plt.gca().invert_yaxis()
plt.xticks(np.arange(len(chan_list)), chan_list, rotation='vertical')
plt.yticks(np.array([1000.0, 100.0, 10.0, 1.0, 0.1]),['1000.0','100.0','10.0','1.0','0.1'])
plt.tight_layout()
plt.savefig(instrument+'_profile_'+profileNumber+'_jacobians_'+ofWhat+'_pcolor.png')
plt.close()
print(profileNumber, myProfiles.O3[int(profileNumber)-1,:].sum())
def plotProfilesO3(profiles):
matplotlib.rc('xtick', labelsize=10)
plt.figure()
NUM_COLORS = profiles.P.shape[0]
cm = plt.get_cmap('brg')
cNorm = colors.Normalize(vmin=0, vmax=NUM_COLORS-1)
scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
plt.gca().set_prop_cycle(cycler('color', [scalarMap.to_rgba(i) for i in range(NUM_COLORS)]))
for i in range(profiles.O3.shape[0]):
plt.plot( profiles.O3[i,0:99], profiles.P[i,0:99] )
plt.gca().set_yscale('log')
plt.gca().invert_yaxis()
plt.yticks(np.array([1000.0, 100.0, 10.0, 1.0, 0.1]),['1000.0','100.0','10.0','1.0','0.1'])
legendList = []
plt.legend(np.arange(1,7), fontsize=6, ncol=1)
plt.ylabel('Pressure [hPa]')
plt.xlabel('Ozone [ppmv]')
plt.savefig('ozoneProfiles.png')
plt.close()
def plotProfilesT(profiles):
matplotlib.rc('xtick', labelsize=10)
plt.figure()
NUM_COLORS = profiles.P.shape[0]
cm = plt.get_cmap('brg')
cNorm = colors.Normalize(vmin=0, vmax=NUM_COLORS-1)
scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
plt.gca().set_prop_cycle(cycler('color', [scalarMap.to_rgba(i) for i in range(NUM_COLORS)]))
for i in range(profiles.O3.shape[0]):
plt.plot( profiles.T[i,0:99], profiles.P[i,0:99] )
plt.gca().set_yscale('log')
plt.gca().invert_yaxis()
plt.yticks(np.array([1000.0, 100.0, 10.0, 1.0, 0.1]),['1000.0','100.0','10.0','1.0','0.1'])
legendList = []
plt.legend(np.arange(1,7), fontsize=6, ncol=1)
plt.ylabel('Pressure [hPa]')
plt.xlabel('Temperature [K]')
plt.savefig('temperatureProfiles.png')
plt.close()
def readProfileH5(filename,nprofiles):
h5 = h5py.File('../rttov_test/profile-datasets-hdf/standard101lev_allgas.H5')
nprofiles = 6
groups = []
for i in range(1,nprofiles+1):
groups.append("{:04d}".format(i))
P = np.zeros([nprofiles,101])
T = np.zeros([nprofiles,101])
Q = np.zeros([nprofiles,101])
CO2 = np.zeros([nprofiles,101])
O3 = np.zeros([nprofiles,101])
for i,g in enumerate(groups):
P[i,:] = np.asarray(h5['PROFILES'][g]['P'])
Q[i,:] = np.asarray(h5['PROFILES'][g]['Q'])
T[i,:] = np.asarray(h5['PROFILES'][g]['T'])
CO2[i,:] = np.asarray(h5['PROFILES'][g]['CO2'])
O3[i,:] = np.asarray(h5['PROFILES'][g]['O3'])
GasUnits = int(np.asarray(h5['PROFILES'][g]['GAS_UNITS']))
return P, T, Q, CO2, O3, GasUnits
if __name__ == '__main__':
# This example program simulates two profiles for each of three instruments
# The example profile data are defined in example_data
# ------------------------------------------------------------------------
# Set up the profile data
# ------------------------------------------------------------------------
# Declare an instance of Profiles
nprofiles = 6
nlevels = 101
myProfiles = pyrttov.Profiles(nprofiles, nlevels)
h5ProfileFilename = os.path.join(rttovPath,'rttov_test','profile-datasets-hdf','standard101lev_allgas.H5')
myProfiles.P, myProfiles.T, myProfiles.Q, myProfiles.CO2, myProfiles.O3, myProfiles.GasUnits = readProfileH5(h5ProfileFilename, nprofiles)
# repeat first example parameter for all profiles
myProfiles.Angles = ex.angles.transpose()[0,:]*np.ones([nprofiles,ex.angles.transpose()[0,:].shape[0]])
myProfiles.S2m = ex.s2m.transpose()[0,:]*np.ones([nprofiles,ex.s2m.transpose()[0,:].shape[0]])
myProfiles.Skin = ex.skin.transpose()[0,:]*np.ones([nprofiles,ex.skin.transpose()[0,:].shape[0]])
myProfiles.SurfType = ex.surftype.transpose()[0,:]*np.ones([nprofiles,ex.surftype.transpose()[0,:].shape[0]])
myProfiles.SurfGeom = ex.surfgeom.transpose()[0,:]*np.ones([nprofiles,ex.surfgeom.transpose()[0,:].shape[0]])
myProfiles.DateTimes = ex.datetimes.transpose()[0,:]*np.ones([nprofiles,ex.datetimes.transpose()[0,:].shape[0]])
# ------------------------------------------------------------------------
# Set up Rttov instances for each instrument
# ------------------------------------------------------------------------
# Create three Rttov objects for Four instruments
iasiRttov = pyrttov.Rttov()
crisRttov = pyrttov.Rttov()
crisFsrRttov = pyrttov.Rttov()
airsRttov = pyrttov.Rttov()
fsrH5 = h5py.File('etc/cris-fsr_wavenumbers.h5','r')
crisH5 = h5py.File('etc/cris_wavenumbers.h5','r')
iasiH5 = h5py.File('etc/iasi_wavenumbers.h5','r')
airsH5 = h5py.File('etc/airs_wavenumbers.h5','r')
chan_list_fsr = fsrH5['idxNucapsOzoneInInstrument']
chan_list_cris = crisH5['idxNucapsOzoneInInstrument']
chan_list_iasi = iasiH5['idxNucapsOzoneInInstrument']
chan_list_airs = airsH5['idxNucapsOzoneInInstrument']
wavenumbers = {}
wavenumbers['cris-fsr'] = fsrH5['wavenumbers']
wavenumbers['cris'] = crisH5['wavenumbers']
wavenumbers['iasi'] = iasiH5['wavenumbers']
wavenumbers['airs'] = airsH5['wavenumbers']
# Set the options for each Rttov instance:
# - the path to the coefficient file must always be specified
# - turn RTTOV interpolation on (because input pressure levels differ from
# coefficient file levels)
# - set the verbose_wrapper flag to true so the wrapper provides more
# information
# - enable solar simulations for iasi
# - enable CO2 simulations for cris (the CO2 profiles are ignored for
# the iasi and airs simulations)
# - enable the store_trans wrapper option for airs to provide access to
# RTTOV transmission structure
iasiRttov.FileCoef = '{}/{}'.format(rttovPath,
"rtcoef_rttov12/rttov9pred101L/rtcoef_metop_2_iasi_so2.H5")
# "rtcoef_rttov12/rttov8pred101L/rtcoef_metop_2_iasi.H5")
iasiRttov.Options.AddInterp = True
iasiRttov.Options.AddSolar = True
iasiRttov.Options.CO2Data = True
iasiRttov.Options.OzoneData = True
iasiRttov.Options.StoreTrans = True
iasiRttov.Options.VerboseWrapper = True
crisRttov.FileCoef = '{}/{}'.format(rttovPath,
"rtcoef_rttov12/rttov9pred101L/rtcoef_jpss_0_cris_so2.H5")
crisRttov.Options.AddInterp = True
crisRttov.Options.AddSolar = True
crisRttov.Options.CO2Data = True
crisRttov.Options.OzoneData = True
crisRttov.Options.StoreTrans = True
crisRttov.Options.VerboseWrapper = True
crisFsrRttov.FileCoef = '{}/{}'.format(rttovPath,
"rtcoef_rttov12/rttov9pred101L/rtcoef_jpss_0_cris-fsr_so2.H5")
crisFsrRttov.Options.AddInterp = True
crisFsrRttov.Options.AddSolar = True
crisFsrRttov.Options.CO2Data = True
crisFsrRttov.Options.OzoneData = True
crisFsrRttov.Options.StoreTrans = True
crisFsrRttov.Options.VerboseWrapper = True
airsRttov.FileCoef = '{}/{}'.format(rttovPath,
"rtcoef_rttov12/rttov9pred101L/rtcoef_eos_2_airs_so2.H5")
airsRttov.Options.AddInterp = True
airsRttov.Options.StoreTrans = True
airsRttov.Options.VerboseWrapper = True
airsRttov.Options.AddInterp = True
airsRttov.Options.AddSolar = True
airsRttov.Options.CO2Data = True
airsRttov.Options.OzoneData = True
airsRttov.Options.StoreTrans = True
airsRttov.Options.VerboseWrapper = True
# Load the instruments: for cris and airs do not supply a channel list and
# so read all channels
try:
iasiRttov.loadInst()
crisRttov.loadInst()
crisFsrRttov.loadInst()
airsRttov.loadInst()
except pyrttov.RttovError as e:
sys.stderr.write("Error loading instrument(s): {!s}".format(e))
sys.exit(1)
# Associate the profiles with each Rttov instance
iasiRttov.Profiles = myProfiles
crisRttov.Profiles = myProfiles
crisFsrRttov.Profiles = myProfiles
airsRttov.Profiles = myProfiles
try:
#iasiRttov.printOptions()
#iasiRttov.runDirect()
#crisRttov.runDirect()
#crisFsrRttov.runDirect()
#airsRttov.runDirect()
iasiRttov.runK()
crisRttov.runK()
crisFsrRttov.runK()
airsRttov.runK()
airsRttov.printOptions()
except pyrttov.RttovError as e:
sys.stderr.write("Error running RTTOV direct model: {!s}".format(e))
sys.exit(1)
print("calc weighting functions.")
iasi_wf = calculateWeightingFunctions(chan_list_iasi, iasiRttov, myProfiles)
cris_wf = calculateWeightingFunctions(chan_list_cris, crisRttov, myProfiles)
crisFsr_wf = calculateWeightingFunctions(chan_list_fsr, crisFsrRttov, myProfiles)
airs_wf = calculateWeightingFunctions(chan_list_airs, airsRttov, myProfiles)
print("Plot weighting Functions.")
for i in range(nprofiles):
print('Plotting IASI')
plotWeightingFunctions(chan_list_iasi, myProfiles, iasi_wf[i,:,:], 'iasi','{:04d}'.format(i+1), wavenumbers)
plotJacobians(chan_list_iasi, myProfiles, iasiRttov.O3K[i,:,:], 'iasi', '{:04d}'.format(i+1), wavenumbers,'ozone')
plotJacobians(chan_list_iasi, myProfiles, iasiRttov.TK[i,:,:], 'iasi', '{:04d}'.format(i+1), wavenumbers,'temperature')
plotJacobians(chan_list_iasi, myProfiles, iasiRttov.QK[i,:,:], 'iasi', '{:04d}'.format(i+1), wavenumbers,'water')
print('Plotting CrIS')
plotWeightingFunctions(chan_list_cris, myProfiles, cris_wf[i,:,:], 'cris','{:04d}'.format(i+1),wavenumbers)
plotJacobians(chan_list_cris, myProfiles, crisRttov.O3K[i,:,:], 'cris', '{:04d}'.format(i+1), wavenumbers,'ozone')
plotJacobians(chan_list_cris, myProfiles, crisRttov.TK[i,:,:], 'cris', '{:04d}'.format(i+1), wavenumbers,'temperature')
plotJacobians(chan_list_cris, myProfiles, crisRttov.QK[i,:,:], 'cris', '{:04d}'.format(i+1), wavenumbers,'water')
print('Plotting CrIS-FSR')
plotWeightingFunctions(chan_list_fsr, myProfiles, crisFsr_wf[i,:,:], 'cris-fsr','{:04d}'.format(i+1),wavenumbers)
plotJacobians(chan_list_fsr, myProfiles, crisFsrRttov.O3K[i,:,:], 'cris-fsr', '{:04d}'.format(i+1), wavenumbers,'ozone')
plotJacobians(chan_list_fsr, myProfiles, crisFsrRttov.TK[i,:,:], 'cris-fsr', '{:04d}'.format(i+1), wavenumbers,'temperature')
plotJacobians(chan_list_fsr, myProfiles, crisFsrRttov.QK[i,:,:], 'cris-fsr', '{:04d}'.format(i+1), wavenumbers,'water')
print('Plotting AIRS')
plotWeightingFunctions(chan_list_airs, myProfiles, airs_wf[i,:,:], 'airs', '{:04d}'.format(i+1),wavenumbers)
plotJacobians(chan_list_airs, myProfiles, airsRttov.O3K[i,:,:], 'airs', '{:04d}'.format(i+1), wavenumbers,'ozone')
plotJacobians(chan_list_airs, myProfiles, airsRttov.TK[i,:,:], 'airs', '{:04d}'.format(i+1), wavenumbers,'temperature')
plotJacobians(chan_list_airs, myProfiles, airsRttov.QK[i,:,:], 'airs', '{:04d}'.format(i+1), wavenumbers,'water')
plotProfilesO3(myProfiles)
plotProfilesT(myProfiles)