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A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations

Liu, Chao and Yao, Bin and Natraj, Vijay and Weng, Fuzhong and Le, Tianhao and Shia, Run-Lie and Yung, Yuk L. (2020) A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations. Journal of the Atmospheric Sciences, 77 (6). pp. 2055-2066. ISSN 0022-4928. doi:10.1175/jas-d-19-0238.1.

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With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models.

Item Type:Article
Related URLs:
URLURL TypeDescription
Liu, Chao0000-0001-7049-493X
Natraj, Vijay0000-0003-3154-9429
Le, Tianhao0000-0002-6600-8270
Shia, Run-Lie0000-0003-1911-3120
Yung, Yuk L.0000-0002-4263-2562
Additional Information:© 2020 American Meteorological Society. Published-online: 26 May 2020; Print Publication: 01 Jun 2020. This research was supported in part by the National Natural Science Foundation of China (NSFC) (41975025) and the Young Elite Scientists Sponsorship Program of CAST (2017NRC001). A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). V. N. acknowledges support from the NASA Earth Science U.S. Participating Investigator program (Solicitation NNH16ZDA001N-ESUSPI).
Funding AgencyGrant Number
National Natural Science Foundation of China41975025
China Association for Science and Technology2017NRC001
Subject Keywords:Radiative transfer; Remote sensing
Issue or Number:6
Record Number:CaltechAUTHORS:20201218-085922809
Persistent URL:
Official Citation:Liu, C., Yao, B., Natraj, V., Weng, F., Le, T., Shia, R., & Yung, Y. L. (2020). A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations, Journal of the Atmospheric Sciences, 77(6), 2055-2066; DOI: 10.1175/jas-d-19-0238.1
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107186
Deposited By: Tony Diaz
Deposited On:18 Dec 2020 17:52
Last Modified:16 Nov 2021 19:00

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