A Local Implementation of the POD-Based Ensemble 4DVar with R-Localization  

作　　者：TIAN Xiang-Jun 

机构地区：[1]International Center for Climate and Environment Sciences,Institute of Atmospheric Physics,Chinese Academy of Sciences [2]State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences

出　　处：《Atmospheric and Oceanic Science Letters》2014年第1期11-16,共6页大气和海洋科学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.41075076);the National High Technology Research and Development Program of China (Grant No.2013AA122002);the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No.KZCX2- EW-QN207);and the National Basic Research Program of China (Grant Nos.2010CB428403 and 2009CB421407)

摘　　要：The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar) through Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The feasibility and effectiveness of the PODEn4DVar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations(OSSEs) in comparison with the original version of the PODEn4DVar with B-localization and that without localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method（PODEn4DVar） through Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The feasibility and effectiveness of the PODEn4DVar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations（OSSEs） in comparison with the original version of the PODEn4DVar with B-localization and that without localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.

关 键 词：PODEn4DVar R-localization local implementation 

分 类 号：P40[天文地球—大气科学及气象学]