The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System:Single-point Experiment  

作　　者：刘娟娟 王斌 王曙东 

机构地区：[1]State Key Laboratory of Numerical Modeling for Atmospheric Sciences &Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences [2]Graduate University of the Chinese Academy of Sciences

出　　处：《Advances in Atmospheric Sciences》2010年第6期1303-1310,共8页大气科学进展（英文版）

基　　金：We acknowledge the Ministry of Science and Technology of China (Grant No.2006BAC03B01);the Ministry of Science and Technology of China for funding the 973 project (Grant No.2005CB321703)

摘　　要：A four dimensional variational data assimilation （4DVar） based on a dimension-reduced projection （DRP-4DVar） has been developed as a hybrid of the 4DVar and Ensemble Kalman filter （EnKF） concepts. Its good flow-dependent features are demonstrated in single-point experiments through comparisons with adjointbased 4DVar and three-dimensional variational data （3DVar） assimilations using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model （MM5）. The results reveal that DRP-4DVar can reasonably generate a background error covariance matrix （simply B-matrix） during the assimilation window from an initial estimation using a number of initial condition dependent historical forecast samples. In contrast, flow-dependence in the B-matrix of MM5 4DVar is barely detectable. It is argued that use of diagonal estimation in the B-matrix of the MM5 4DVar method at the initial time leads to this failure. The experiments also show that the increments produced by DRP-4DVar are anisotropic and no longer symmetric with respect to observation location due to the effects of the weather trends captured in its B-matrix. This differs from the MM5 3DVar which does not consider the influence of heterogeneous forcing on the correlation structure of the B-matrix, a condition that is realistic for many situations. Thus, the MM5 3DVar assimilation could only present an isotropic and homogeneous structure in its increments.A four dimensional variational data assimilation （4DVar） based on a dimension-reduced projection （DRP-4DVar） has been developed as a hybrid of the 4DVar and Ensemble Kalman filter （EnKF） concepts. Its good flow-dependent features are demonstrated in single-point experiments through comparisons with adjointbased 4DVar and three-dimensional variational data （3DVar） assimilations using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model （MM5）. The results reveal that DRP-4DVar can reasonably generate a background error covariance matrix （simply B-matrix） during the assimilation window from an initial estimation using a number of initial condition dependent historical forecast samples. In contrast, flow-dependence in the B-matrix of MM5 4DVar is barely detectable. It is argued that use of diagonal estimation in the B-matrix of the MM5 4DVar method at the initial time leads to this failure. The experiments also show that the increments produced by DRP-4DVar are anisotropic and no longer symmetric with respect to observation location due to the effects of the weather trends captured in its B-matrix. This differs from the MM5 3DVar which does not consider the influence of heterogeneous forcing on the correlation structure of the B-matrix, a condition that is realistic for many situations. Thus, the MM5 3DVar assimilation could only present an isotropic and homogeneous structure in its increments.

关 键 词：DRP-4DVar data assimilation flow dependence single-point experiment 

分 类 号：P433[天文地球—大气科学及气象学]