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Atmosphere
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Radiative Transfer Models of Atmospheric and Cloud Properties
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Radiative Transfer Models of Atmospheric and Cloud Properties

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (15 June 2020) | Viewed by 25114

Special Issue Editor

Dr. Stephan Havemann

E-Mail Website
Guest Editor
Met Office, Foundation and Weather Science, Exeter EX1 3PB, UK
Interests: atmospheric radiative transfer; satellite; airborne and ground-based remote sensing; retrieval of atmospheric and surface properties; electromagnetic scattering theory; cirrus; operational satellite data assimilation; numerical methods; big data; machine learning techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Versatile state-of-the-art atmospheric radiative transfer models are of paramount importance in the research of the effect of atmospheric greenhouse gases, clouds, and aerosols in climate simulations. These radiative transfer models must make use of the latest available information on gaseous absorption properties as well as on the optical properties of cloud and aerosol particles. Numerical weather prediction and climate models require accurate and fast radiative transfer codes for the simulation of vertical profiles of atmospheric heating/cooling rates. Another area with ever increasing demand on the radiative transfer codes is the assimilation of satellite data with new missions like the polar-orbiting IASI-NG, and the geostationary MTG-IRS, to be launched in a couple of years. Future missions will further extend the range of the electromagnetic spectrum that is monitored from space. The sub-millimetre and far-infrared part of the spectrum promise more information on cirrus clouds in particular. Fast radiative transfer models are only beginning to make use of dimensionality reduction and machine learning techniques. This is equally true for the treatment of the inverse problem in remote sensing and the analysis of information content. Clouds remain difficult to model. Studies that consider the importance of 3D effects, polarization, and cloud overlap, as well as schemes that can model these effects fast while remaining sufficiently accurate, are therefore also welcome.

Dr. Stephan Havemann
Guest Editor

Manuscript Submission Information

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Keywords

  • Radiative transfer
  • Atmospheric greenhouse gases and climate
  • 3D cloudy radiative transfer
  • Approximate treatments of clouds including overlap
  • Polarized radiative transfer of clouds and surfaces
  • Fast forward models
  • Dimensionality reduction
  • Machine learning
  • Satellite, airborne, and ground-based remote sensing
  • Inverse problems in remote sensing
  • New satellite missions

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Published Papers (4 papers)

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Research

21 pages, 1377 KiB  
Article
Inversion Models for the Retrieval of Total and Tropospheric NO2 Columns
by Song Liu
Atmosphere 2019, 10(10), 607; https://doi.org/10.3390/atmos10100607 - 9 Oct 2019
Cited by 2 | Viewed by 3319
Abstract
Inversion models for retrieving the total and tropospheric nitrogen dioxide ( NO 2 ) columns from spaceborne remote sensing data are presented. For total column retrieval, we propose the so-called differential radiance models with internal and external closure and solve the underlying nonlinear [...] Read more.
Inversion models for retrieving the total and tropospheric nitrogen dioxide ( NO 2 ) columns from spaceborne remote sensing data are presented. For total column retrieval, we propose the so-called differential radiance models with internal and external closure and solve the underlying nonlinear equations by using the method of Tikhonov regularization and the iteratively regularized Gauss–Newton method. For tropospheric column retrieval, we design a nonlinear and a linear model by using the results of the total column retrieval and the value of the stratospheric NO 2 column delivered by a stratosphere–troposphere separation method. We also analyze the fundamentals of the commonly used differential optical absorption spectroscopy (DOAS) model and outline its relationship to the proposed inversion models. By a numerical analysis, we analyze the accuracy of the inversion models to retrieve total and tropospheric NO 2 columns. Full article
(This article belongs to the Special Issue Radiative Transfer Models of Atmospheric and Cloud Properties)
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30 pages, 710 KiB  
Article
Linearizations of the Spherical Harmonic Discrete Ordinate Method (SHDOM)
by Adrian Doicu and Dmitry S. Efremenko
Atmosphere 2019, 10(6), 292; https://doi.org/10.3390/atmos10060292 - 28 May 2019
Cited by 10 | Viewed by 3673
Abstract
Linearizations of the spherical harmonic discrete ordinate method (SHDOM) by means of a forward and a forward-adjoint approach are presented. Essentially, SHDOM is specialized for derivative calculations and radiative transfer problems involving the delta-M approximation, the TMS correction, and the adaptive grid splitting, [...] Read more.
Linearizations of the spherical harmonic discrete ordinate method (SHDOM) by means of a forward and a forward-adjoint approach are presented. Essentially, SHDOM is specialized for derivative calculations and radiative transfer problems involving the delta-M approximation, the TMS correction, and the adaptive grid splitting, while practical formulas for computing the derivatives in the spherical harmonics space are derived. The accuracies and efficiencies of the proposed methods are analyzed for several test problems. Full article
(This article belongs to the Special Issue Radiative Transfer Models of Atmospheric and Cloud Properties)
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26 pages, 1566 KiB  
Article
Py4CAtS—PYthon for Computational ATmospheric Spectroscopy
by Franz Schreier, Sebastián Gimeno García, Philipp Hochstaffl and Steffen Städt
Atmosphere 2019, 10(5), 262; https://doi.org/10.3390/atmos10050262 - 10 May 2019
Cited by 27 | Viewed by 12712
Abstract
Radiation is a key process in the atmosphere. Numerous radiative transfer codes have been developed spanning a large range of wavelengths, complexities, speeds, and accuracies. In the infrared and microwave, line-by-line codes are crucial esp. for modeling and analyzing high-resolution spectroscopic observations. Here [...] Read more.
Radiation is a key process in the atmosphere. Numerous radiative transfer codes have been developed spanning a large range of wavelengths, complexities, speeds, and accuracies. In the infrared and microwave, line-by-line codes are crucial esp. for modeling and analyzing high-resolution spectroscopic observations. Here we present Py4CAtS—PYthon scripts for Computational ATmospheric Spectroscopy, a Python re-implemen-tation of the Fortran Generic Atmospheric Radiation Line-by-line Code GARLIC, where computationally-intensive code sections use the Numeric/Scientific Python modules for highly optimized array processing. The individual steps of an infrared or microwave radiative transfer computation are implemented in separate scripts (and corresponding functions) to extract lines of relevant molecules in the spectral range of interest, to compute line-by-line cross sections for given pressure(s) and temperature(s), to combine cross sections to absorption coefficients and optical depths, and to integrate along the line-of-sight to transmission and radiance/intensity. Py4CAtS can be used in three ways: in the (Unix/Windows/Mac) console/terminal, inside the (I)Python interpreter, or Jupyter notebook. The basic design of the package, numerical and computational aspects relevant for optimization, and a sketch of the typical workflow are presented. In conclusion, Py4CAtS provides a versatile environment for “interactive” (and batch) line-by-line radiative transfer modeling. Full article
(This article belongs to the Special Issue Radiative Transfer Models of Atmospheric and Cloud Properties)
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15 pages, 620 KiB  
Article
Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band
by Ana del Águila, Dmitry S. Efremenko, Víctor Molina García and Jian Xu
Atmosphere 2019, 10(3), 142; https://doi.org/10.3390/atmos10030142 - 16 Mar 2019
Cited by 14 | Viewed by 3655
Abstract
The new generation of atmospheric composition sensors such as TROPOMI is capable of providing spectra of high spatial and spectral resolution. To process this vast amount of spectral information, fast radiative transfer models (RTMs) are required. In this regard, we analyzed the efficiency [...] Read more.
The new generation of atmospheric composition sensors such as TROPOMI is capable of providing spectra of high spatial and spectral resolution. To process this vast amount of spectral information, fast radiative transfer models (RTMs) are required. In this regard, we analyzed the efficiency of two acceleration techniques based on the principal component analysis (PCA) for simulating the Hartley-Huggins band spectra. In the first one, the PCA is used to map the data set of optical properties of the atmosphere to a lower-dimensional subspace, in which the correction function for an approximate but fast RTM is derived. The second technique is based on the dimensionality reduction of the data set of spectral radiances. Once the empirical orthogonal functions are found, the whole spectrum can be reconstructed by performing radiative transfer computations only for a specific subset of spectral points. We considered a clear-sky atmosphere where the optical properties are defined by Rayleigh scattering and trace gas absorption. Clouds can be integrated into the model as Lambertian reflectors. High computational performance is achieved by combining both techniques without losing accuracy. We found that for the Hartley-Huggins band, the combined use of these techniques yields an accuracy better than 0.05% while the speedup factor is about 20. This innovative combination of both PCA-based techniques can be applied in future works as an efficient approach for simulating the spectral radiances in other spectral regions. Full article
(This article belongs to the Special Issue Radiative Transfer Models of Atmospheric and Cloud Properties)
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