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首页»科研成果» 2013
A new structure for error covariancematrices and their adaptive estimation in EnKF assimilation
吴国灿、郑小谷、张树鹏、梁晓
A new structure for error covariancematrices and their adaptive estimation in EnKF assimilation
Guocan Wu,a Xiaogu Zheng,a LiqunWang,b Shupeng Zhang,a Xiao Lianga and Yong Lic
a College of Global Change and Earth System Science, Beijing Normal University, Beijing, China;
b Department of Statistics, University of Manitoba, Winnipeg, Canada;
c School of mathematical sciences, Beijing Normal University, Beijing, China.
ABSTRACT
Correct estimation of the forecast and observational error covariance matrices is crucial for the accuracy of a data assimilation algorithm. In this article we propose a new structure for the forecast error covariance matrix to account for limited ensemble size and model error. An adaptive procedure combined with a second-order least squares method is applied to estimate the inflated forecast and adjusted observational error covariance matrices. The proposed estimation methods and new structure for the forecast error covariance matrix are tested on the well-known Lorenz-96 model, which is associated with spatially correlated observational systems. Our experiments show that the new structure for the forecast error covariance matrix and the adaptive estimation procedure lead to improvement of the assimilation results.
KEY WORDS: data assimilation; ensemble Kalman filter; error covariance inflation; second-order least squares estimation; adaptive estimation
PUBLISHED IN: QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2013, 139: 795–804.
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