当前位置： 首页»科研成果» 2013 郑小谷等在ADVANCES IN ATMOSPHERIC SCIENCES发表论文 全球院

 

ZHENG Xiaogu¹, WU Guocan^1*, ZHANG Shupeng¹, LIANG Xiao², DAI Yongjiu¹, and LI Yong³

 

¹College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875;

²National Meteorological Information Center, China Meteorological Administration, Beijing 100081;

³School of Mathematical Sciences, Beijing Normal University, Beijing 100875.

 

ABSTRACT

Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed.

The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model, both of which are associated with spatially correlated observational systems. The experiments showed that by introducing the proposed structure of the forecast error covariance matrix and applying its adaptive estimation procedure, the assimilation results were further improved.

 

KEY WORDS: data assimilation, ensemble Kalman filter, error covariance inflation, adaptive estimation, maximum likelihood estimation

 

PUBLISHED IN: ADVANCES IN ATMOSPHERIC SCIENCES, 2013, 30 (5): 1303-1312.

 

DOWNLOAD PDF: http://link.springer.com/content/pdf/10.1007/s00376-012-2133-5.pdf