Algorithms for nonlinear retrieval problems in atmospheric remote sensing using regularization methods

Fabian O. Gonzalez; Miguel Velez-Reyes

doi:10.1117/12.332675

14 December 1998 Algorithms for nonlinear retrieval problems in atmospheric remote sensing using regularization methods

Fabian O. Gonzalez, Miguel Velez-Reyes

Proceedings Volume 3495, Satellite Remote Sensing of Clouds and the Atmosphere III; (1998) https://doi.org/10.1117/12.332675
Event: Remote Sensing, 1998, Barcelona, Spain

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.

Citation Download Citation

Fabian O. Gonzalez and Miguel Velez-Reyes "Algorithms for nonlinear retrieval problems in atmospheric remote sensing using regularization methods", Proc. SPIE 3495, Satellite Remote Sensing of Clouds and the Atmosphere III, (14 December 1998); https://doi.org/10.1117/12.332675

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