def main(coefficientPath, sensor_id):
thisDir = os.path.dirname(os.path.abspath(__file__))
cases = os.listdir(os.path.join(thisDir, 'data'))
cases.sort()
# create 4 profiles for each of the 4 cases
profiles = profilesCreate(4, 92, nClouds=0, nAerosols=0)
storedTb = []
storedEmis = []
# populate the cases, and previously calculated Tb from crtm test program.
for i, c in enumerate(cases):
h5 = h5py.File(os.path.join(thisDir, 'data', c), 'r')
profiles.Angles[i, 0] = h5['zenithAngle'][()]
profiles.Angles[i, 1] = 999.9
profiles.Angles[i, 2] = 100.0 # 100 degrees zenith below horizon.
profiles.Angles[i, 3] = 0.0 # zero solar azimuth
profiles.Angles[i, 4] = h5['scanAngle'][()]
profiles.DateTimes[i, 0] = 2001
profiles.DateTimes[i, 1] = 1
profiles.DateTimes[i, 2] = 1
profiles.Pi[i, :] = np.asarray(h5['pressureLevels'])
profiles.P[i, :] = np.asarray(h5['pressureLayers'][()])
profiles.T[i, :] = np.asarray(h5['temperatureLayers'])
profiles.Q[i, :] = np.asarray(h5['humidityLayers'])
profiles.O3[i, :] = np.asarray(h5['ozoneConcLayers'])
profiles.climatology[i] = h5['climatology'][()]
profiles.surfaceFractions[i, :] = h5['surfaceFractions']
profiles.surfaceTemperatures[i, :] = h5['surfaceTemperatures']
profiles.S2m[i,
1] = 33.0 # just use salinity out of S2m for the moment.
profiles.windSpeed10m[i] = 5.0
profiles.LAI[i] = h5['LAI'][()]
profiles.windDirection10m[i] = h5['windDirection10m'][()]
# land, soil, veg, water, snow, ice
profiles.surfaceTypes[i, 0] = h5['landType'][()]
profiles.surfaceTypes[i, 1] = h5['soilType'][()]
profiles.surfaceTypes[i, 2] = h5['vegType'][()]
profiles.surfaceTypes[i, 3] = h5['waterType'][()]
profiles.surfaceTypes[i, 4] = h5['snowType'][()]
profiles.surfaceTypes[i, 5] = h5['iceType'][()]
storedTb.append(np.asarray(h5['Tb_cris_no_clouds']))
storedEmis.append(np.asarray(h5['emissivity_cris_no_clouds']))
h5.close()
crtmOb = pyCRTM()
crtmOb.profiles = profiles
crtmOb.coefficientPath = pathInfo['CRTM']['coeffs_dir']
crtmOb.sensor_id = sensor_id
crtmOb.nThreads = 4
crtmOb.loadInst()
crtmOb.runDirect()
forwardTb = crtmOb.Bt
forwardEmissivity = crtmOb.surfEmisRefl[0, :]
crtmOb.surfEmisRefl = []
crtmOb.runK()
kTb = crtmOb.Bt
kEmissivity = crtmOb.surfEmisRefl[0, :]
if (all(
np.abs(forwardTb.flatten() -
np.asarray(storedTb).flatten()) <= 1e-5) and all(
np.abs(kTb.flatten() -
np.asarray(storedTb).flatten()) <= 1e-5)):
print(
"Yay! all values are close enough to what CRTM test program produced!"
)
else:
print("Boo! something failed. Look at cris plots")
wavenumbers = np.zeros([4, 1305])
wavenumbers[0:4, :] = np.linspace(1, 1306, 1305)
plt.figure()
plt.plot(wavenumbers.T, forwardTb.T - np.asarray(storedTb).T)
plt.legend(['1', '2', '3', '4'])
plt.savefig(os.path.join(thisDir, 'cris' + '_spectrum_forward.png'))
plt.figure()
plt.plot(wavenumbers.T, forwardEmissivity.T - np.asarray(storedEmis).T)
plt.savefig(os.path.join(thisDir, 'cris' + '_emissivity_forward.png'))
plt.figure()
plt.plot(wavenumbers.T, kTb.T - np.asarray(storedTb).T)
plt.savefig(os.path.join(thisDir, 'cris' + '_spectrum_k.png'))
plt.figure()
plt.plot(wavenumbers.T, kEmissivity.T - np.asarray(storedEmis).T)
plt.savefig(os.path.join(thisDir, 'cris' + '_emissivity_k.png'))
#for i,c in enumerate(cases):
# h5 = h5py.File(os.path.join(thisDir,'data',c) , 'r+')
# h5.create_dataset('Tb_cris_no_clouds', data = forwardTb[i,:])
# h5.create_dataset('emissivity_cris_no_clouds', data = forwardEmissivity[i,:])
sys.exit("Boo! didn't pass tolerance with CRTM test program.")
def main(coefficientPath, sensor_id, fin, experiment):
# the profile from grb2 has 45 levels
gfs = xr.open_dataset(fin)
lat = gfs.lat_0
lon = gfs.lon_0
profiles = profilesCreate(len(lat) * len(lon), 45, nAerosols=0, nClouds=4)
angles = gfs.xangles.stack(z=("lat_0", "lon_0")).transpose()
#angles[:,:]=30.0 # np.where(angles>60, 60, angles)
#angles=np.where(angles<-60, -60, angles)
# profiles.Angles[:,:] = angles[:,:]
profiles.Angles[:, 0] = angles[:, 0] #h5['zenithAngle'][()]
profiles.Angles[:, 1] = angles[:, 1]
profiles.Angles[:, 2] = angles[:, 3] # 100 degrees zenith below horizon.
profiles.Angles[:, 3] = angles[:, 4] # zero solar azimuth
profiles.Angles[:, 4] = angles[:, 2] # h5['scanAngle'][()]
# date time not used.
datetimes = gfs.valid_time.stack(z=("lat_0", "lon_0")).transpose()
profiles.DateTimes[:, :] = datetimes[:, :]
profiles.Pi[:, :] = gfs.plevel.broadcast_like(
gfs.player).stack(z=("lat_0", "lon_0")).transpose() / 100.0
profiles.P[:, :] = gfs.player[1:, :, :].stack(
z=("lat_0", "lon_0")).transpose() / 100.0
profiles.T[:, :] = gfs.temp[1:, :, :].stack(z=("lat_0",
"lon_0")).transpose()
Q = gfs.moisture[1:, :, :].stack(z=("lat_0", "lon_0")).transpose()
profiles.Q[:, :] = xr.where(Q > 0, Q, 0)
#profiles.O3[:,:]=gfs.o3[1:,:,:].stack(z=("lat_0","lon_0")).transpose()
cld = gfs.water_cloud[1:, :, :].stack(z=("lat_0", "lon_0")).transpose()
cld = np.where(cld < 0, 0, cld)
profiles.clouds[:, :, 0, 0] = cld
profiles.clouds[:, :, 0, 1] = 10.0
ice = gfs.ice_cloud[1:, :, :].stack(z=("lat_0", "lon_0")).transpose()
ice = np.where(ice < 0, 0, ice)
profiles.clouds[:, :, 1, 0] = ice
profiles.clouds[:, :, 1, 1] = 75
rain = gfs.rain_cloud[1:, :, :].stack(z=("lat_0", "lon_0")).transpose()
rain = np.where(rain < 0, 0, rain)
profiles.clouds[:, :, 2, 0] = rain
profiles.clouds[:, :, 2, 1] = 50
snow = gfs.snow_cloud[1:, :, :].stack(z=("lat_0", "lon_0")).transpose()
snow = np.where(snow < 0, 0, snow)
profiles.clouds[:, :, 3, 0] = snow
profiles.clouds[:, :, 3, 1] = 50
#graupel=gfs.graupel_cloud[1:,:,:].stack(z=("lat_0","lon_0")).transpose()
#graupel=np.where(graupel>0,0,-graupel)
#profiles.clouds[:,:,4,0]=graupel
#profiles.clouds[:,:,4,1]=50
profiles.cloudType[:, 0] = 1
profiles.cloudType[:, 1] = 2
profiles.cloudType[:, 2] = 3
profiles.cloudType[:, 3] = 4
#profiles.cloudType[:,4]=5
profiles.climatology[:] = 6 #US_STANDARD_ATMOSPHERE #6 # place holder don't know
lands = gfs.landmask.stack(z=("lat_0", "lon_0")).transpose()
snow = gfs.snow_cover.stack(z=("lat_0", "lon_0")).transpose()
#ice=gfs.ice_cover.stack(z=("lat_0","lon_0")).transpose()
profiles.surfaceFractions[:, 0] = np.where(lands == 1, int(1), int(0))
profiles.surfaceFractions[:, 1] = np.where(
(lands == 0) | (lands == np.nan), int(1), int(0))
profiles.surfaceFractions[:, 2] = 0
profiles.surfaceFractions[:, 3] = 0
profiles.surfaceTemperatures[:,
0] = gfs.sfctemp.stack(z=("lat_0", "lon_0"))
profiles.surfaceTemperatures[:,
1] = gfs.sfctemp.stack(z=("lat_0", "lon_0"))
profiles.surfaceTemperatures[:,
2] = gfs.sfctemp.stack(z=("lat_0", "lon_0"))
profiles.surfaceTemperatures[:,
3] = gfs.sfctemp.stack(z=("lat_0", "lon_0"))
profiles.S2m[:, 1] = 33.0 # just use salinity out of S2m for the moment.
wind_speed = gfs.wind_speed.stack(z=("lat_0", "lon_0")).transpose()
profiles.windSpeed10m[:] = wind_speed[:] # wind_speed
# lai not affecting
profiles.LAI[:] = 0.57 # h5['LAI'][()]
wind_dir = gfs.wind_dir.stack(z=("lat_0", "lon_0")).transpose()
wind_dir = np.where(wind_dir < 0, wind_dir + 360, wind_dir)
profiles.windDirection10m[:] = wind_dir[:] # h5['windDirection10m'][()]
landtype = gfs.land_type[:, :, 0].stack(z=("lat_0", "lon_0")).transpose()
landtype = np.where(landtype > 14, 16.0, landtype)
landtype = np.where(landtype < 1.0, 1.0, landtype)
profiles.surfaceTypes[:, 0] = landtype
profiles.surfaceTypes[:, 1] = 6
profiles.surfaceTypes[:, 2] = 2
profiles.surfaceTypes[:, 3] = 1
profiles.surfaceTypes[:, 4] = 3
profiles.surfaceTypes[:, 5] = 1
profiles = experiment(profiles)
crtmOb = pyCRTM()
crtmOb.profiles = profiles
crtmOb.coefficientPath = pathInfo['CRTM']['coeffs_dir']
crtmOb.sensor_id = sensor_id
crtmOb.nThreads = 1
print("hello 002")
crtmOb.loadInst()
crtmOb.runDirect()
forwardTb = crtmOb.Bt
# forwardEmissivity = crtmOb.surfEmisRefl[0,:]
# #make K matrix run surfEmisRefl again.
# crtmOb.surfEmisRefl = []
# crtmOb.runK()
# kTb = crtmOb.Bt
# kEmissivity = crtmOb.surfEmisRefl[0,:]
print("hello 003")
result = xr.DataArray(np.reshape(forwardTb.T, (10, len(lat), len(lon))),
dims=("channel", "lat_0", "lon_0"),
coords=(np.arange(0, 10), gfs.lat_0, gfs.lon_0))
#fig,ax=plt.subplots(5,2)
# result[:,30,30].plot()
# for i in range(5):
# for j in range(2):
# ax[i,j].pcolor(result[i*2+j,:,:].where(result[i*2+j,:,:]>-100), vmin=201, vmax=300)
#
# prepare for interpolation
ds_in = result.rename({"lat_0": 'lat', "lon_0": 'lon'})
fo = xr.open_dataset(fobs)
lato = fo.lat.data
lono = fo.lon.data
ds_out = xr.Dataset({
'lat': (['lat'], lato),
'lon': (['lon'], lono),
})
regridder = xe.Regridder(ds_in, ds_out, 'bilinear', reuse_weights=True)
dr_out = regridder(result)
out = dr_out.where(fo.obs.data > 0)
fout = fin[0:len(fin) - 3] + "_output_" + experiment.__name__ + ".nc"
if os.path.exists(fout):
os.remove(fout)
result.to_netcdf(fout, "w")
# plt.pcolor(0.5*(out[7]+out[8]),vmin=230,vmax=290)
#
# plt.show()
print(result.max())
print(result.min())