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Proceedings Paper

Water vapor profiling with SSM/T-2 data employing an extended optimal estimation approach
Author(s): Markus J. Rieder; Gottfried Kirchengast
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Paper Abstract

The feasibility of retrieving water vapor profiles from SSM/T-2 data is demonstrated by usage of an extended Bayesian inversion algorithm. The SSM/T-2 downlooking sounder data consisting of brightness temperature measurements in five microwave bands sensitive to water vapor absorption can be used, together with total water vapor content data, in order to compute water vapor profiles of about 3-5 km vertical resolution. The corresponding radiative transfer equation yields a nonlinear mapping of state space into measurement space. This is reflected in a significant nonlinearity in the cost functional which has to be minimized, and necessitates several extensions of the well known optimal estimation inversion. We supplemented the optimal estimation by simulated annealing and iterative a priori lightweighting. The resulting a hybrid algorithm furnishes capability for acting as an important source of height-resolved meteorological information. Retrievals based on SSM/T-2 data were compared to atmospheric analyses of the European Centre for Medium-range Weather Forecasts. A statistical validation for the retrieved profiles is presented. The comparisons indicate an approximate accuracy of about 15 to 20 percent for relative humidity. The developed algorithm can be readily extended to include all sensible sources of additional information, on the state as well as from additional measurements.

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: 10.1117/12.319504
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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