# Impact of Cloud optical depth on atmospheric transmission on Radiance

Sylvie Dagoret-Campagne
affliliation : IJCLAB/IN2P3/CNRS
creation date : 24 January 2020
update : November 7th 2023

[1]:

%load_ext autoreload
%autoreload 2

[2]:

import matplotlib.pyplot as plt
%matplotlib inline
import sys
import os
import numpy as np

[3]:

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx

[4]:

matplotlib.rcParams.update({'font.size': 15, 'lines.linewidth': 2, 'lines.markersize': 15})
matplotlib.rcParams["axes.labelsize"]="small"
matplotlib.rcParams["axes.linewidth"]=2.0
matplotlib.rcParams["xtick.major.size"]=8
matplotlib.rcParams["ytick.major.size"]=8
matplotlib.rcParams["ytick.minor.size"]=5
matplotlib.rcParams["xtick.labelsize"]="large"
matplotlib.rcParams["ytick.labelsize"]="large"
matplotlib.rcParams["figure.figsize"]=(12,6)
matplotlib.rcParams['axes.titlesize'] = 15
matplotlib.rcParams['axes.titleweight'] = 'bold'
#matplotlib.rcParams['axes.facecolor'] = 'blue'
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
matplotlib.rcParams['lines.markeredgewidth'] = 0.3 # the line width around the marker symbol
matplotlib.rcParams['lines.markersize'] = 10  # markersize, in points
matplotlib.rcParams['grid.alpha'] = 1. # transparency, between 0.0 and 1.0
matplotlib.rcParams['grid.linestyle'] = '-' # simple line
matplotlib.rcParams['grid.linewidth'] = 0.4 # in points

[5]:

from scipy.interpolate import interp1d

[6]:

import time
from datetime import datetime,date
import dateutil.parser

[7]:

today = date.today()
string_date=today.strftime("%Y-%m-%d")

[8]:

from libradtranpy import libsimulateThermal

[9]:

os.getenv('LIBRADTRANDIR')

[9]:

'/Users/dagoret/MacOSX/External/libRadtran'

[10]:

am=1.
pressure = 0.
pwv=4.0
aer=0
ozone=300.

[11]:

#cloudext=np.linspace(0.0,0.2,10)
cloudext=np.linspace(0.2,0.3,1)

[12]:

NBOBS=len(cloudext)

[13]:

jet = plt.get_cmap('jet')
cNorm = colors.Normalize(vmin=0, vmax=NBOBS)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
all_colors = scalarMap.to_rgba(np.arange( NBOBS), alpha=1)

[14]:

all_temp = []
for index in np.arange(NBOBS):

    path,thefile=libsimulateThermal.ProcessSimulation(am,pwv,ozone,pressure,
                                                      prof_str='us',proc_str='sa',cloudext=cloudext[index],thermal_output='radiance')

    data = np.loadtxt(os.path.join(path,thefile))
    wl = data[:,0]
    atm = data[:,1:]
    all_temp.append(atm)

[15]:

# this array is defined (hardcoded in radiance definition in libsimulateThermal.py
costh=np.array([1. , 0.9, 0.8,  0.7, 0.6, 0.5 , 0.4,  0.3, 0.2,  0.1 ])

[16]:

N=len(costh)

[17]:

jet = plt.get_cmap('jet')
cNorm = colors.Normalize(vmin=0, vmax=N-1)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
all_colors = scalarMap.to_rgba(np.arange(N), alpha=1)

[18]:

cldindex = str(int(cloudext[0] * 1000))
cld_str=cldindex.zfill(4)

[19]:

for index in np.arange(N):
    label = "$\cos \\theta $ =  {}".format(costh[index])
    plt.plot(wl,atm[:,index],'-',color=all_colors[index],label=label)
plt.xlim(0,25000)
plt.grid()
title = "Radiance, cloudext = {}".format(cloudext[0])
plt.title(title)
plt.xlabel("$\\lambda$ (nm)")
plt.ylabel("radiance ($W/(m^2.nm.sr)$)")
plt.legend()
figname=string_date+"_radiance_cloudext_{}.png".format(cld_str)
plt.savefig(figname)

../../../_images/notebooks_libradtranpy_thermal_SimuRT_thermal_CLD_Radiance_19_0.png

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