EnKF同化的背景误差协方差矩阵局地化对比研究

1. 1.武汉大学测绘遥感信息工程国家重点实验室,湖北 武汉 430079; 2.武汉大学苏州研究院,江苏 苏州 215123
• 收稿日期:2014-06-16 出版日期:2014-10-20
• 基金资助:

江苏省苏州市科技计划项目“气象观测数据分析的时空统计软件”(编号：SYG201319); 国家自然科学基金项目“时空交互的统计建模”(编号：41171313)资助

### A Comparative Study of Background Error Covariance Localization in EnKF Data Assimilation

Han Pei, Shu Hong, Xu Jianhui

1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079,China; 2.SuZhou Institute of Wuhan University, Suzhou 215123,China
• Received:2014-06-16 Online:2014-10-20 Published:2014-10-20

In ensemble data assimilation, the estimate of background error covariance is particuarly important. In general, the use of a finite ensemble size for estimating the background error covariance matrix easily introduces spurious correlations, which leads to the underestimation of covariance and filter divergence. Covariance inflation is an empirical method of correcting the underestimation of background error covariance, but it does not help to solve the problem of long-range spurious correlations. Therefore, based on the EnKF scheme, we explored two localization methods to eliminate the spurious correlations, which were the covariance localization method and the local analysis method. We analyzed their impacts on the background error covariance matrix, gain matrix, ensemble transform matrices and data assimilation results. The experimental results have been obtained. That is, the localization method not only can remove the spurious covariance in the background error covariance matrix, but also can increase the rank of the matrix. In a “weak” assimilation, the gain matrix and ensemble transform matrices of two methods are very close, but the differences of the gain matrix and ensemble transform matrices become more evident with the increase of assimilation strength. Under the different strength of assimilation, two localization methods have their own characteristics, and relatively the covariance localization method has stronger robustness on the update of ensemble mean and ensemble anomalies. This study is very helpful for the fine analysis and estimate of the background error covariance.

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