atmrad

A framework for solving forward and inverse problems of atmospheric microwave radiometry based on International Telecommunication Union Recommendations ITU-R P.676-3, ITU-R P.676-12, ITU-R P.840-8, etc.

Atmrad includes two subpackages, which implement the same interface. CPU package uses only standard Python tools and numpy. GPU package additionally involves TensorFlow to operate with NVIDIA CUDA.

See also https://github.com/dobribobri/meteo-

 

Usage examples

• Forward simulation of brightness temperatures in a sub-satellite point
 

Code:

import numpy as np
from matplotlib import pyplot as plt

from cpu.atmosphere import Atmosphere
from cpu.surface import SmoothWaterSurface
import cpu.satellite as satellite
from cpu.cloudiness import CloudinessColumn


# A model of the Earth's atmosphere with standard altitude profiles of
# thermodynamic temperature, pressure and air humidity
# up to H = 20 km height with discretization of 500 nodes.
# Clear sky, liquid water content (LWC) is zero
atmosphere = Atmosphere.Standard(H=20., dh=20./500)
# The integration method and other parameters can be additionally specified
atmosphere.integration_method = 'boole'

# Introduce a smooth water surface as an underlying one
surface = SmoothWaterSurface()
# Specify surface temperature and salinity
surface.temperature = 15.
surface.salinity = 0.

# Set a frequency range e.g. from 10 to 150 GHz
frequencies = np.linspace(10, 150, 500)
# Compute "underlying surface - atmosphere" system outgoing radiation
# brightness temperatures at the specified frequencies
brt = satellite.brightness_temperatures(frequencies, atmosphere, surface, cosmic=True)

# Make a plot
plt.figure()
plt.xlabel(r'Frequencies $\nu$, GHz')
plt.ylabel(r'Brightness temperature $T_b$, K')
# Draw curve #1
plt.plot(frequencies, brt, ls='-', lw=2, label='Standard atmosphere, clear sky, LWC is zero')

# Consider a horizontally-homogeneous cloudiness of 1 km power with base altitude of 1.2 km.
# Find the corresponding liquid water altitude profile using Mazin's model.
# Liquid water amount is taken to be zero outside the cloud layer
liquid_water_distribution = CloudinessColumn(20., 500, clouds_bottom=1.2).liquid_water(height=1.)
# Assign it to the atmosphere object
atmosphere.liquid_water = liquid_water_distribution[0, 0, :]

# Repeat the brightness temperature calculation
brt = satellite.brightness_temperatures(frequencies, atmosphere, surface, cosmic=True)
# Draw curve #2
plt.plot(frequencies, brt, ls='--', lw=2,
         label='Cloud layer of 1 km power, LWC is {:.2f}'.format(np.round(atmosphere.W, decimals=2)) + ' kg/m$^2$')


# And one more time
liquid_water_distribution = CloudinessColumn(20., 500, clouds_bottom=1.2).liquid_water(height=3.)
atmosphere.liquid_water = liquid_water_distribution[0, 0, :]
brt = satellite.brightness_temperatures(frequencies, atmosphere, surface, cosmic=True)
# Draw curve #3
plt.plot(frequencies, brt, ls='-.', lw=2,
         label='Cloud layer of 3 km power, LWC is {:.2f}'.format(np.round(atmosphere.W, decimals=2)) + ' kg/m$^2$')

plt.grid(ls=':', alpha=0.5)
plt.legend(loc='best', frameon=False)
plt.tight_layout()
plt.savefig('example1.png', dpi=300)
plt.show()

 

Result:



• The inverse problem of total water vapor (TWV) and liquid water content (LWC) radiometric retrieval in a sub-satellite point. Common interface for this functionality is still under development.
 

Code:

import numpy as np
from cpu.atmosphere import Atmosphere, avg
from cpu.cloudiness import CloudinessColumn
from cpu.surface import SmoothWaterSurface
import cpu.satellite as satellite
from cpu.weight_funcs import krho
from cpu.core.static.weight_funcs import kw


# Solve the forward problem firstly.
# See the previous example
atmosphere = Atmosphere.Standard(H=20., dh=20./500)
atmosphere.integration_method = 'boole'
liquid_water_distribution = CloudinessColumn(20., 500, clouds_bottom=1.2).liquid_water(height=1.)
atmosphere.liquid_water = liquid_water_distribution[0, 0, :]
surface = SmoothWaterSurface()

# We use further the dual-frequency method for TWV and LWC retrieval from the known brightness temperatures
frequency_pair = [22.2, 27.2]

# Obtain brightness temperatures for the specified frequency pair
brts = []
for nu in frequency_pair:
    brts.append(satellite.brightness_temperature(nu, atmosphere, surface, cosmic=True))
brts = np.asarray(brts)


# Let's proceed to the inverse problem.
# Make some precomputes and model estimates
# Cosmic relict background
T_cosmic = 2.72548

sa = Atmosphere.Standard(H=20., dh=20./500)
# Water vapor specific attenuation coefficient (weighting function)
k_rho = [krho(sa, nu) for nu in frequency_pair]
# Liquid water specific attenuation coefficient (weighting function).
# Cloud effective temperature is taken to be 0 deg. Celsius here
k_w = [kw(nu, t=0.) for nu in frequency_pair]

M = np.asarray([k_rho, k_w]).T

# Total opacity coefficient in dry oxygen
tau_oxygen = np.asarray([sa.opacity.oxygen(nu) for nu in frequency_pair])
# Average "absolute" temperature of standard atmosphere downwelling radiation
T_avg_down = np.asarray([avg.downward.T(sa, nu) for nu in frequency_pair])
# Average "absolute" temperature of standard atmosphere upwelling radiation
T_avg_up = np.asarray([avg.upward.T(sa, nu) for nu in frequency_pair])
# Surface reflectivity index
R = np.asarray([surface.reflectivity(nu) for nu in frequency_pair])
# Surface emissivity under conditions of thermodynamic equilibrium
kappa = 1 - R

A = (T_avg_down - T_cosmic) * R
B = T_avg_up - T_avg_down * R - np.asarray(surface.temperature + 273.15) * kappa

D = B * B - 4 * A * (brts - T_avg_up)


# Compute total opacity from the known brightness temperatures and the model estimates
tau_experiment = -np.log((-B + np.sqrt(D)) / (2 * A))

# The dual-frequency method consists in resolving the following system of linear equations
sol = np.linalg.solve(M, tau_experiment - tau_oxygen)

# Display the retrieved total water vapor and liquid water content values
print('TWV is {:.2f} g/cm2, \t\t'.format(np.round(sol[0], decimals=2)) +
      'LWC is {:.2f} kg/m2'.format(np.round(sol[1], decimals=2)))

 

Result:

  TWV is 1.55 g/cm2, 		LWC is 0.12 kg/m2

• Forward simulation of brightness temperature (map) outgoing from an atmspheric cell filled with cumuli
 

Code:

import numpy as np
from matplotlib import pyplot as plt

# Using GPU acceleration
from gpu.atmosphere import Atmosphere
from gpu.surface import SmoothWaterSurface
import gpu.satellite as satellite
from cpu.cloudiness import Plank3D

frequency = 36      # GHz

# Standard atmosphere model with a smooth water surface as an underlying one
atmosphere = Atmosphere.Standard(H=20., dh=20./500)
atmosphere.integration_method = 'boole'
surface = SmoothWaterSurface()

# Let the atmospheric cell has sizes 50x50x20 km (Ox x Oy x Oz).
# We introduce a 3D computational grid of 300x300x500 nodes.
# Then fill the cell with cumuli distributed according to the "L2" case from (Plank, 1969), see tbl. 3
# For this case, the Plank model parameters are as follows:
alpha = 1.44    # a parameter depending on the time of day and various local climatic conditions (km^-1)
Dm = 4.026      # maximum effective cloud diameter in a population (km)
dm = 0.02286    # minimum effective cloud diameter (km)
eta = 0.93      # influences on cloud power (n.d.)
beta = 0.3      # also influences on cloud power (n.d.)
xi = -np.exp(-alpha * Dm) * (((alpha * Dm) ** 2) / 2 + alpha * Dm + 1) + \
    np.exp(-alpha * dm) * (((alpha * dm) ** 2) / 2 + alpha * dm + 1)
p = 0.65      # total sky cover ratio
K = 2 * np.power(alpha, 3) * (50 * 50 * p) / (np.pi * xi)     # effective number density
cloud_base = 1.2192      # cloud base altitude

# Generate the corresponding liquid water 3D-distribution
liquid_water_distribution = Plank3D(kilometers=(50., 50., 20.),
                                    nodes=(300, 300, 500),
                                    clouds_bottom=cloud_base).liquid_water(
    alpha=alpha, Dm=Dm, dm=dm, eta=eta, beta=beta, K=K,
)
atmosphere.liquid_water = liquid_water_distribution

# Obtain the brightness temperature map at the specified frequency
brt = satellite.brightness_temperature(frequency, atmosphere, surface, cosmic=True)

# Display it
plt.figure()
plt.imshow(brt.numpy())
plt.xlabel('nodes (Ox direction)')
plt.ylabel('nodes (Oy direction)')
plt.colorbar(label=r'$T_b$, K')
plt.tight_layout()
plt.savefig('example3.png', dpi=300)
plt.show()

 

Result:

References

1. B.G. Kutuza, M.V. Danilychev and O.I. Yakovlev, Satellite Monitoring of the Earth: Microwave Radiometry of Atmosphere and Surface [in Russian]. Moscow, Russia: Lenand Publ., 2016, 336 p.
2. V.G. Plank, The size distribution of cumulus clouds in representative Florida populations, J. Appl. Met., vol. 8, no. 1, pp. 46-67, 1969.
3. Reference standard atmospheres, document Recommendation ITU-R P.835-6, International Telecommunication Union, 2017.
4. Attenuation by atmospheric gases (Question ITU-R 201/3), document Recommendation ITU-R P.676-12, International Telecommunication Union, 2019.
5. Attenuation due to clouds and fog, document Recommendation ITU-R P.840, International Telecommunication Union, 2019.
6. D.H. Staelin, Measurements and interpretation of the microwave spectrum of the terrestrial atmosphere near 1‐centimeter wavelength, J. Geophys. Res., vol. 71, iss. 12, pp. 2875-2881, 1966.
7. Ed R. Westwater, The accuracy of water vapor and cloud liquid determination by dual-frequency ground-based microwave radiometry, Radio Science, vol. 13, no. 4, pp. 677-685, 1978.
8. D.P. Egorov and B.G. Kutuza, Atmospheric brightness temperature fluctuations in the resonance absorption band of water vapor 18-27.2 GHz, IEEE Trans. Geosci. Remote Sens., vol. 59, iss. 9, pp. 7627-7634, 2021.
9. B.G. Kutuza and M.T. Smirnov, The influence of clouds on radio-thermal radiance of atmosphere - ocean surface system, Issled. Zemli Kosm. [in Russian], no. 3, pp. 76-83, 1980.
10. S.P. Gagarin and B.G. Kutuza, Influence of sea roughness and atmospheric inhomogeneities on microwave radiation of the atmosphere -- ocean system, IEEE J. Ocean., vol. OE-8, no. 2, pp. 62-70, 1983.
11. I.P. Mazin and S.M. Shmeter, Clouds, Structure and Formation Physics [in Russian]. Leningrad, USSR: Gidromteoizdat, 1983, 279 p.