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

Nikolay N. Bogoslovskiy; Sergei I. Erin; Irina A. Borodina; Lubov I. Kizhner; Kseniya A. Alipova

doi:10.1117/12.2249275

29 November 2016 Satellite data assimilation in global numerical weather prediction model using Kalman filter

Nikolay N. Bogoslovskiy, Sergei I. Erin, Irina A. Borodina, Lubov I. Kizhner, Kseniya A. Alipova

Proceedings Volume 10035, 22nd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 100356Z (2016) https://doi.org/10.1117/12.2249275
Event: XXII International Symposium Atmospheric and Ocean Optics. Atmospheric Physics, 2016, Tomsk, Russian Federation

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.

Citation Download Citation

Nikolay N. Bogoslovskiy, Sergei I. Erin, Irina A. Borodina, Lubov I. Kizhner, and Kseniya A. Alipova "Satellite data assimilation in global numerical weather prediction model using Kalman filter", Proc. SPIE 10035, 22nd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 100356Z (29 November 2016); https://doi.org/10.1117/12.2249275

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