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2016•Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Satellite data assimilation in global numerical weather prediction model using Kalman filter

Nikolay N. Bogoslovskiy, Sergei I. Erin, И. А. Бородина, Lubov I. Kizhner, Kseniya A. Alipova

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Abstract

This paper examines the application of the Kalman filter for assimilation of satellite soil moisture measurement data into the SL-AV global numerical weather prediction (NWP) model. This technique allows to consider soil moisture data in areas with available satellite observations. Single-assimilation numerical experiments based on the Kalman filter revealed a reduction of errors in the initial surface layer soil moisture data.

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What this paper is about

This paper examines the application of the Kalman filter for assimilation of satellite soil moisture measurement data into the SL-AV global numerical weather prediction (NWP) model. This technique allows to consider soil moisture data in areas with available satellite observations. Single-assimilation numerical experiments based on the Kalman filter revealed a reduction of errors in the initial surface layer soil moisture data.

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Available abstract

This paper examines the application of the Kalman filter for assimilation of satellite soil moisture measurement data into the SL-AV global numerical weather prediction (NWP) model. This technique allows to consider soil moisture data in areas with available satellite observations. Single-assimilation numerical experiments based on the Kalman filter revealed a reduction of errors in the initial surface layer soil moisture data.

Key concepts: Data assimilation, Numerical weather prediction, Kalman filter, Ensemble Kalman filter, Satellite, Assimilation (phonology), Meteorology, Environmental science

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