A Multivariate Empirical Orthogonal Function-Based Scheme for the Balanced Initial Ensemble Generation of an Ensemble Kalman Filter

作     者：Zheng Fei Zhu Jiang 

作者机构：International Center for Climate and Environment Science (ICCES) Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC) Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China 

出 版 物：《Atmospheric and Oceanic Science Letters》 (大气和海洋科学快报(英文版))

年 卷 期：2010年第3卷第3期

页      面：165-169页

学科分类：0710[理学-生物学] 0908[农学-水产] 07[理学] 0707[理学-海洋科学] 0706[理学-大气科学] 0816[工学-测绘科学与技术] 0825[工学-航空宇航科学与技术] 

基　　金：supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX1-YW-12-03) the National Basic Research Program of China (Grant No. 2010CB951901) the National Natural Science Foundation of China (Grant No. 40805033) 

主　　题：ensemble Kalman filter initial ensemble generation multivariate empirical orthogonal function 

摘      要：The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced initial perturbations for the ensemble Kalman filter (EnKF) data assimilation, with a reasonable consideration of the physical relationships between different model variables. The scheme is applied in assimilation experiments with a global spectral atmospheric model and with real observations. The proposed perturbation method is compared to the commonly used method of spatially-correlated random perturbations. The comparisons show that the model uncertainties prior to the first analysis time, which are forecasted from the balanced ensemble initial fields, maintain a much more reasonable spread and a more accurate forecast error covariance than those from the randomly perturbed initial fields. The analysis results are further improved by the balanced ensemble initialization scheme due to more accurate background information. Also, a 20-day continuous assimilation experiment shows that the ensemble spreads for each model variable are still retained in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the randomly perturbed initialization scheme decrease and collapse rapidly.