Comparative Studies on Iterative Ensemble Kalman Filter Methods
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 Lorenz63model,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.
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万方数据
维普
