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Proceedings Paper

Algorithms for nonlinear retrieval problems in atmospheric remote sensing using regularization methods
Author(s): Fabian O. Gonzalez; Miguel Velez-Reyes
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Paper Abstract

In this paper, we present a retrieval algorithm for nonlinear retrieval problems based on regularization theory. The proposed method is based on the Gauss-Newton method for nonlinear least square problems. In the proposed algorithm, Tikhonov and truncated singular value decomposition techniques are used to regularize the solution of the linearization problem used to compute the Gauss-Newton search direction. The dependency of the performance and behavior of the proposed algorithms on the initial guess, stopping criterion, and regularization parameter is studied by means of simulations. Results are presented for atmospheric temperature retrievals based on radiometry from the HIRS/2 and MSU instruments in NOAA TOVS.

Paper Details

Date Published: 14 December 1998
PDF: 12 pages
Proc. SPIE 3495, Satellite Remote Sensing of Clouds and the Atmosphere III, (14 December 1998); doi: 10.1117/12.332675
Show Author Affiliations
Fabian O. Gonzalez, Univ. of Puerto Rico/Mayaguez (United States)
Miguel Velez-Reyes, Univ. of Puerto Rico/Mayaguez (United States)

Published in SPIE Proceedings Vol. 3495:
Satellite Remote Sensing of Clouds and the Atmosphere III
Jaqueline E. Russell, Editor(s)

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