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

被引：1

作者：

Bogoslovskiy, Nikolay N. ^{[1
]}

Erin, Sergei I. ^{[1
]}

Borodina, Irina A. ^{[1
]}

Kizhner, Lubov I. ^{[1
]}

Alipova, Kseniya A. ^{[1
]}

机构：

[1] Tomsk State Univ, 36 Lenin Ave, Tomsk 634050, Russia

来源：

22ND INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS: ATMOSPHERIC PHYSICS | 2016年 / 10035卷

关键词：

data assimilation; surface layer soil moisture; satellite observations; SOIL-MOISTURE; PARAMETERIZATION;

D O I：

10.1117/12.2249275

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

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.

引用

页数：6

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