def getAtmAttPantra(cloudNetFilePath, radarFreqs):
cloudNetData = netCDF4.Dataset(cloudNetFilePath, 'r')
height_M = cloudNetData[
'model_height'][:] - 105 # added -105[m] here to Joses original file since the cloudnet mocel_height start at msl and radar data is at 105 above msl
temp = cloudNetData['temperature'][:] #[k]
speHum = cloudNetData['specific_humidity'][:] #[kg/kg]
press = cloudNetData['pressure'][:] #[Pa]
time = cloudNetData['time'][:] #[hour fraction]
att_atm_cloud = cloudNetData['radar_gas_atten'][:] #[dB]
att_liq_cloud = cloudNetData['radar_liquid_atten'][:] #[dB]
relHum = atmFunc.speHumi2RelHum(speHum, temp, press) #[%]
vaporPress = atmFunc.calcVaporPress(speHum, temp,
press) #[Pa] (!! variable name)
waterVaporDens = atmFunc.calcVaporDens(vaporPress, temp) #[kg/m^3]
dryAirDens = atmFunc.calcDryAirDens(press, waterVaporDens, temp) #[kg/m^3]
descriptorFile = getDescriptor()
pam = pyPamtra.pyPamtra()
for hyd in descriptorFile:
pam.df.addHydrometeor(hyd)
pam.nmlSet['active'] = True
pam.nmlSet['passive'] = False
pam.nmlSet["radar_attenuation"] = 'bottom-up'
pamData = dict()
heightArr = np.zeros((len(time), len(height_M))) + height_M
pamData['hgt'] = heightArr
pamData['temp'] = temp
pamData['relhum'] = relHum
pamData['press'] = press
pam.createProfile(**pamData)
pam.runParallelPamtra(radarFreqs,
pp_deltaX=1,
pp_deltaY=1,
pp_deltaF=1,
pp_local_workers=8)
return pam.r, time, height_M, temp, relHum, press
from __future__ import print_function
import pyPamtra
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
fig1 = plt.figure()
for miscalibration in [-10, -5, 0, 5, 10]:
pam = pyPamtra.pyPamtra()
#!name as_ratio liq_ice rho_ms a_ms b_ms alpha beta moment_in nbin dist_name p_1 p_2 p_3 p_4 d_1 d_2 scat_name vel_size_mod canting
pam.df.addHydrometeor(
('ice', 1.0, -1, 200, -99, -99, np.pi / 4., 2, 0, 100, 'exp', 3000,
3e8, -99.0, -99.0, 100e-6, 1000e-6, 'mie-sphere',
'heymsfield10_particles', 0.0))
pam = pyPamtra.importer.createUsStandardProfile(pam,
hgt_lev=np.arange(
1000, 1300, 200))
pam.p["airturb"][:] = 0.2
freqs = [35.5] #,80,150]
pam.set["verbose"] = 0
pam.set["pyVerbose"] = 0
pam.nmlSet["randomseed"] = 10
pam.nmlSet["radar_mode"] = "spectrum"
pam.nmlSet["radar_aliasing_nyquist_interv"] = 3
pam.nmlSet["hydro_adaptive_grid"] = False
pam.nmlSet["radar_receiver_miscalibration"] = miscalibration
import pyPamtra
import numpy as np
import matplotlib.pyplot as plt
pam = pyPamtra.pyPamtra()
pam.df.addHydrometeor(('ice', 0.5, -1, 917,917 * np.pi / 6., 3, np.pi/4., 2, 0, 10, 'exp', 3000, 3e8, -99.0, -99.0, 100e-6, 1000e-6, 'tmatrix', 'heymsfield10_particles', 90.0))
pam = pyPamtra.importer.createUsStandardProfile(pam,hgt_lev=np.arange(1000,1300,200))
freqs = [9.5]
pam.set["verbose"] = 0
pam.set["pyVerbose"] =0
pam.nmlSet["passive"] = False
pam.nmlSet["radar_mode"] = "spectrum"
pam.nmlSet["randomseed"] = 10
#pam.nmlSet["radar_dualpol"] = "both"
pam.nmlSet["radar_polarisation"] = "NN,HH,VV,HV"
pam.p["hydro_q"][:] = 0.001
pam.runPamtra(freqs,checkData=False)
#plot(pam.r["Ze"].ravel())
#plt.plot(pam.r["radar_vel"],pam.r["radar_spectra"][0,0,0,2])
plt.figure(11)
plt.clf()
for i_p in range(pam.set["radar_npol"]):
import pyPamtra
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
plt.figure()
pam = pyPamtra.pyPamtra()
#!name as_ratio liq_ice rho_ms a_ms b_ms alpha beta moment_in nbin dist_name p_1 p_2 p_3 p_4 d_1 d_2 scat_name vel_size_mod canting
pam.df.addHydrometeor(('ice', 1.0, -1, 200,-99,-99, np.pi/4., 2, 0, 100, 'exp', 3000, 3e8, -99.0, -99.0, 100e-6, 1000e-6, 'mie-sphere', 'heymsfield10_particles',0.0))
pam = pyPamtra.importer.createUsStandardProfile(pam,hgt_lev=np.arange(1000,1300,200))
pam.p["airturb"][:] = 0.2
freqs = [35.5]#,80,150]
pam.set["verbose"] = 0
pam.set["pyVerbose"] =0
pam.nmlSet["data_path"] = "/work/mmaahn/pamtra_data/"
pam.nmlSet["randomseed"] = 0
pam.nmlSet["radar_mode"] = "spectrum"
pam.nmlSet["radar_aliasing_nyquist_interv"] = 3
pam.nmlSet["hydro_adaptive_grid"] = False
pam.p["hydro_q"][:] = 0.002
pam.nmlSet["save_psd"] = True
pam.runPamtra(freqs,checkData=False)
plt.plot(pam.r["radar_vel"],pam.r["radar_spectra"][0,0,0,0,0])
import sys
import pyPamtra # main pamtra module
import os # import operating system module
# os.environ['PAMTRA_DATADIR'] = '/net/sever/mech/pamtra/data/'
pam = pyPamtra.pyPamtra() # basic empty pyPamtra object with default settings
pam.df.addHydrometeor(("ice", -99., -1, 917., 130., 3.0, 0.684, 2., 3, 1,
"mono_cosmo_ice", -99., -99., -99., -99., -99., -99.,
"mie-sphere", "heymsfield10_particles", 0.0))
pamData = dict()
pamData["temp"] = [275., 274., 273.]
pamData["relhum"] = [10., 90., 90.]
pamData["hgt"] = [400., 1500., 2500.]
pamData["press"] = [90000., 80000., 70000.]
pam.createProfile(**pamData) # create a pamtra profile.
pam.runPamtra(89.0)
