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Proceedings Paper

Hybrid inversion algorithm for nonlinear retrieval problems and its use for water vapor profiling based on microwave sounder data
Author(s): Markus J. Rieder; Gottfried Kirchengast
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Paper Abstract

It is shown that geophysical inversion problems can be solved by usage of a hybrid algorithm, which combines optimal estimation with further optimization techniques. We employ a Bayesian approach to nonlinear inversion and discuss several extensions of this method. Especially, a sensible guess of a priori information, the shape of the probability density functions, the utility of Monte Carlo methods, and the advantages of simulated annealing have been investigated. All these techniques furnish capability of retrieving state vectors, which depend on the data in a highly nonlinear manner. A combination of these powerful tools can provide solutions to questions that cannot be tackled with standard inversion methods properly. As a moderately nonlinear optimization problem, profiling of water vapor based on downlooking microwave sounder data is a typical geophysical problem that could not be treated with standard inversion algebra adequately. Based on synthetic state vector data, we show the potential and the characteristics features of all of the hybrid algorithm's components contributing to the retrieval of the state. The hybrid algorithm has been employed in a way that it is able to provide humidity profiles in a numerically stable and computationally efficient manner. Using this example application, the benefit of a hybrid approach is demonstrated.

Paper Details

Date Published: 19 August 1998
PDF: 12 pages
Proc. SPIE 3503, Microwave Remote Sensing of the Atmosphere and Environment, (19 August 1998); doi: 10.1117/12.319503
Show Author Affiliations
Markus J. Rieder, Univ. of Graz (Austria)
Gottfried Kirchengast, Univ. of Graz (Austria)


Published in SPIE Proceedings Vol. 3503:
Microwave Remote Sensing of the Atmosphere and Environment
Tadahiro Hayasaka; Dong Liang Wu; Yaqiu Jin; JingShang Jiang, Editor(s)

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