Notes and correspondence on ensemble-based three-dimensional variational filters  

作　　者：Hong-ze LENG Jun-qiang SONG Fu-kang YIN Xiao-qun CAO 

机构地区：[1]College of Computer,National University of Defense Technology

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2013年第8期634-641,共8页浙江大学学报C辑（计算机与电子（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (No. 41105063);the Special Fund for Meteorological Scientific Research in the Public Interest (No. GYHY20100615)

摘　　要：Several ensemble-based three-dimensional variational (3D-Var) filters are compared. These schemes replace the static background error covariance of the traditional 3D-Var with the ensemble forecast error covariance, but generate analysis ensemble anomalies (perturbations) in different ways. However, it is demonstrated in this paper that they are all theoretically equivalent to the ensemble transformation Kalman filter (ETKF). Furthermore, a new method named EnPSAS is presented. The analysis shows that EnPSAS has a small condition number and can apply covariance localization more easily than other ensemble-based 3D-Var methods.Several ensemble-based three-dimensional variational （3D-Var） filters are compared. These schemes replace the static background error covariance of the traditional 3D-Var with the ensemble forecast error covariance, but generate analysis ensemble anomalies （perturbations） in different ways. However, it is demonstrated in this paper that they are all theoretically equivalent to the ensemble transformation Kalman filter （ETKF）. Furthermore, a new method named EnPSAS is presented. The analysis shows that EnPSAS has a small condition number and can apply covariance localization more easily than other ensemble-based 3D-Var methods.

关 键 词：3D-VAR Ensemble Kalman filter (EnKF) Ensemble transformation Kalman filter (ETKF) Physical space analysis system (PSAS) Ensemble data assimilation 

分 类 号：TN713[电子电信—电路与系统]