Nonlinear Measurement Function in the Ensemble Kalman Filter

查看全文 作　　者：Youmin [1,2]TANG;Jaison [1,3]AMBANDAN;Dake [2]CHEN

机构地区：[1]Environmental Science and Engineering,University of Northern British,Columbia,Prince George,Canada,V2N 4Z9;[2]State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography,State Oceanic Administration;[3]International Max Planck Research School on Earth System Modelling,Max Planck Institute for Meteorology,Hamburg,Germany 20146;

出　　处：《Advances in Atmospheric Sciences》索引2014年第31卷第3期,共8页

基　　金：supported by research grants from the NSERC (Natural Sciences and Engineering Research Council of Canada) Discovery Program;the National Natural Science Foundation of China (Grant Nos.41276029 and 40730843);the National Basic Research Program (Grant No.2007CB816005) 摘　　要：The optimal Kalman gain was analyzed in a rigorous statistical framework. Emphasis was placed on a comprehensive understanding and interpretation of the current algorithm, especially when the measurement function is nonlinear. It is argued that when the measurement function is nonlinear, the current ensemble Kalman Filter algorithm seems to contain implicit assumptions: the forecast of the measurement function is unbiased or the nonlinear measurement function is linearized.While the forecast of the model state is assumed to be unbiased, the two assumptions are actually equivalent.On the above basis, we present two modified Kalman gain algorithms. Compared to the current Kalman gain algorithm,the modified ones remove the above assumptions, thereby leading to smaller estimated errors. This outcome was confirmed experimentally, in which we used the simple Lorenz 3-component model as the test-bed. It was found that in such a simple nonlinear dynamical system, the modified Kalman gain can perform better than the current one. However, the application of the modified schemes to realistic models involving nonlinear measurement functions needs to be further investigated. 关键词：卡尔曼滤波算法非线性动力系统测量功能集合增益算法 KALMAN 组件模型统计分析

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Nonlinear Measurement Function in the Ensemble Kalman Filter

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