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Proceedings Paper

IR ultraspectral remote sensing: efficient physical-statistical nonlinear sounding retrieval algorithms
Author(s): William Smith; Stanislav Kireev; Elisabeth Weisz; Yongxiao Jian; Melissa Yesalusky; Allen Larar; Henry Revercomb
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Paper Abstract

Two solutions to the radiative transfer equation are described for profiling the atmosphere using ultraspectral infrared radiance measurements. The sounding retrieval algorithms are fast non-linear physical-statistical algorithms. The first solution described, applied to ground-based ultraspectral radiance measurements, is a statistical matrix inverse solution of the radiative transfer equation where the optimal matrix inverse stability factor is chosen by trial and error as that value which minimizes the RMS difference between the retrieval calculated radiance spectrum and the observed radiance spectrum. The second solution, applied to satellite and aircraft ultraspectral radiance observation, is a fast non-linear "Physical Dual-Regression " method trained to produce accurate retrievals for both clear and cloudy sky conditions. The second method relies on using Eigenvector Regression (EOF) "Clear-trained" and "Cloud-trained" retrievals of: surface skin temperature, surface emissivity PC-scores, CO2 concentration, cloud top altitude, effective cloud optical depth, and atmospheric temperature, moisture, and ozone profiles above the cloud and below thin or scattered cloud (i.e., cloud effective optical depth < 1.5 and a cloud induced temperature profile attenuation < 15 K. The "Clear-trained" regression is a relation relating a "clear sky equivalent" perturbed profile from a clouded radiance spectrum (e.g., an isothermal profile below an opague cloud cover) to the observed radiance spectrum. The "Cloud-trained" regression relates the true atmospheric profile, both above and below cloud level, to the observed radiance spectrum. Results from the application of both of these algorithms are presented in this paper.

Paper Details

Date Published: 12 November 2010
PDF: 11 pages
Proc. SPIE 7857, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III, 785703 (12 November 2010); doi: 10.1117/12.869425
Show Author Affiliations
William Smith, Univ. of Wisconsin-Madison (United States)
Hampton Univ. (United States)
Stanislav Kireev, Hampton Univ. (United States)
Elisabeth Weisz, Univ. of Wisconsin-Madison (United States)
Yongxiao Jian, Hampton Univ. (United States)
Melissa Yesalusky, Hampton Univ. (United States)
Allen Larar, NASA Langley Research Ctr. (United States)
Henry Revercomb, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 7857:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III
Allen M. Larar; Hyo-Sang Chung; Makoto Suzuki, Editor(s)

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