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Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method

del Águila, Ana and Efremenko, Dmitry S. (2021) Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method. Remote Sensing, 13 (434), pp. 1-17. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs13030434. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/13/3/434

Abstract

Fast radiative transfer models (RTMs) are required to process a great amount of satellite-based atmospheric composition data. Specifically designed acceleration techniques can be incorporated in RTMs to simulate the reflected radiances with a fine spectral resolution, avoiding time-consuming computations on a fine resolution grid. In particular, in the cluster low-streams regression (CLSR) method, the computations on a fine resolution grid are performed by using the fast two-stream RTM, and then the spectra are corrected by using regression models between the two-stream and multi-stream RTMs. The performance enhancement due to such a scheme can be of about two orders of magnitude. In this paper, we consider a modification of the CLSR method (which is referred to as the double CLSR method), in which the single-scattering approximation is used for the computations on a fine resolution grid, while the two-stream spectra are computed by using the regression model between the two-stream RTM and the single-scattering approximation. Once the two-stream spectra are known, the CLSR method is applied the second time to restore the multi-stream spectra. Through a numerical analysis, it is shown that the double CLSR method yields an acceleration factor of about three orders of magnitude as compared to the reference multi-stream fine-resolution computations. The error of such an approach is below 0.05%. In addition, it is analysed how the CLSR method can be adopted for efficient computations for atmospheric scenarios containing aerosols. In particular, it is discussed how the precomputed data for clear sky conditions can be reused for computing the aerosol spectra in the framework of the CLSR method. The simulations are performed for the Hartley–Huggins, O2 A-, water vapour and CO2 weak absorption bands and five aerosol models from the optical properties of aerosols and clouds (OPAC) database.

Item URL in elib:https://elib.dlr.de/140727/
Document Type:Article
Title:Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
del Águila, AnaAna.delAguilaPerez (at) dlr.dehttps://orcid.org/0000-0001-9006-9631
Efremenko, Dmitry S.dmitry.efremenko (at) dlr.dehttps://orcid.org/0000-0002-7449-5072
Date:January 2021
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:13
DOI :10.3390/rs13030434
Page Range:pp. 1-17
Editors:
EditorsEmailEditor's ORCID iD
Ruhtz, ThomasUNSPECIFIEDUNSPECIFIED
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:hyper-spectral resolution; fast radiative transfer model; gaseous absorption; low-streams regression; aerosols
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):Vorhaben Spectroscopic Methods in Remote Sensing (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: del Aguila Perez, Ana
Deposited On:12 Feb 2021 12:58
Last Modified:12 Feb 2021 12:58

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