IR ultraspectral remote sensing: efficient physical-statistical nonlinear sounding retrieval algorithms

William Smith Sr.; Stanislav Kireev; Elisabeth Weisz; Yongxiao Jian; Melissa Yesalusky; Allen Larar; Henry Revercomb

doi:10.1117/12.869425

12 November 2010 IR ultraspectral remote sensing: efficient physical-statistical nonlinear sounding retrieval algorithms

William Smith Sr., Stanislav Kireev, Elisabeth Weisz, Yongxiao Jian, Melissa Yesalusky, Allen Larar, Henry Revercomb

Author Affiliations +

Proceedings Volume 7857, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III; 785703 (2010) https://doi.org/10.1117/12.869425
Event: SPIE Asia-Pacific Remote Sensing, 2010, Incheon, Korea, Republic of

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.

Citation Download Citation

William Smith Sr., Stanislav Kireev, Elisabeth Weisz, Yongxiao Jian, Melissa Yesalusky, Allen Larar, and Henry Revercomb "IR ultraspectral remote sensing: efficient physical-statistical nonlinear sounding retrieval algorithms", Proc. SPIE 7857, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III, 785703 (12 November 2010); https://doi.org/10.1117/12.869425

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