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Proceedings Paper

Satellite data assimilation in global numerical weather prediction model using Kalman filter
Author(s): Nikolay N. Bogoslovskiy; Sergei I. Erin; Irina A. Borodina; Lubov I. Kizhner; Kseniya A. Alipova
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Paper 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.

Paper Details

Date Published: 29 November 2016
PDF: 6 pages
Proc. SPIE 10035, 22nd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 100356Z (29 November 2016); doi: 10.1117/12.2249275
Show Author Affiliations
Nikolay N. Bogoslovskiy, Tomsk State Univ. (Russian Federation)
Sergei I. Erin, Tomsk State Univ. (Russian Federation)
Irina A. Borodina, Tomsk State Univ. (Russian Federation)
Lubov I. Kizhner, Tomsk State Univ. (Russian Federation)
Kseniya A. Alipova, Tomsk State Univ. (Russian Federation)

Published in SPIE Proceedings Vol. 10035:
22nd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics
Gennadii G. Matvienko; Oleg A. Romanovskii, Editor(s)

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