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Remote Sensing Technology and Application  2015, Vol. 30 Issue (6): 1182-1188    DOI: 10.11873/j.issn.1004-0323.2015.6.1182
    
Comparative Studies on Iterative Ensemble Kalman Filter Methods
Xu Baoxiong,Bai Yulong,Shao Yu,Huang Zhihui
(College of Physics and Electrical Engineering,Northwest Normal University;Key Laboratory of
Atomic and Molecular Physics & Functional Materials of Gansu province,Lanzhou 730070,China)
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Abstract  

With regard to model non-linear problems in data assimilation process,an Iterative Ensemble Kalman Filter (IEnKF) is derived by thoroughly analysis and comparison.Within the framework of Lorenz-63model,this paper compared the different performances among the following three methods,Ensemble Kalman Filter (EnKF) Iterative Ensemble Kalman Filter (IEnKF) and Iterative extended Kalman Filter (IEKF),by changing ensemble numbers,observation error variance,the inflation factors and the model steps.The final comparative studies show that the assimilation accuracy of all three algorithms can be improved when ensemble numbers increase.When we change the inflation factors,the assimilation results are becoming worse and the EnKF presents obvious multihill and multivalley phenomena.The RMSE of all three algorithms increase when observation error variance and the model steps increase,and the results of algorithms get worse as well.The results show that the IEnKF is the most optimal algorithms with a much better robust performance.

Key words:  Data assimilation      Lorenz-63 model      Ensemble Kalman Filter(EnKF)      Iterative Ensemble Kalman Filter(IEnKF);Iterative Extended Kalman Filter(IEKF)     
Received:  24 October 2014      Published:  25 January 2016
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Xu Baoxiong
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Huang Zhihui

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Xu Baoxiong,Bai Yulong,Shao Yu,Huang Zhihui. Comparative Studies on Iterative Ensemble Kalman Filter Methods. Remote Sensing Technology and Application, 2015, 30(6): 1182-1188.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2015.6.1182     OR     http://www.rsta.ac.cn/EN/Y2015/V30/I6/1182

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