A new sequential data assimilation method  

作　　者：HAN YueQi ZHANG YaoCun WANG YunFeng YE Song FANG HanXian 

机构地区：[1]Department of Atmospheric Sciences,Nanjing University,Nanjing 210093,China [2]Institute of Meteorology,PLA University of Science and Technology,Nanjing 211101,China [3]Institute of Heavy Rain,China Meteorological Administration,Wuhan 430074,China

出　　处：《Science China(Technological Sciences)》2009年第4期1027-1038,共12页中国科学（技术科学英文版）

基　　金：Supported by the National Natural Science Foundation of China (Grant Nos. 40275032, 40505005 and 40405019) ;Opening Foundation of Institute of Heavy Rain, CMA (Grant No. IHR2006G13)

摘　　要：A new sequential data assimilation method named "Monte Carlo H ∞ filter" is introduced based on H ∞ filter technique and Monte Carlo method in this paper. This method applies to nonlinear systems in condition of lacking the statistical properties of observational errors. In order to compare the as- similation capability of Monte Carlo H ∞ filter with that of the ensemble Kalman filter (EnKF) in solving practical problems caused by temporal correlation or spatial correlation of observational errors, two numerical experiments are performed by using Lorenz (1963) system and shallow-water equations re- spectively. The result is that the assimilation capability of the new method is better than that of EnKF method. It is also shown that Monte Carlo H ∞ filter assimilation method is effective and suitable to nonlinear systems in that it does not depend on the statistical properties of observational errors and has better robustness than EnKF method when the statistical properties of observational errors are varying. In addition, for the new method, the smallest level factor founded by search method is flow-dependent.A new sequential data assimilation method named “Monte Carlo H ∞ filter” is introduced based on H ∞ filter technique and Monte Carlo method in this paper. This method applies to nonlinear systems in condition of lacking the statistical properties of observational errors. In order to compare the assimilation capability of Monte Carlo H ∞ filter with that of the ensemble Kalman filter (EnKF) in solving practical problems caused by temporal correlation or spatial correlation of observational errors, two numerical experiments are performed by using Lorenz (1963) system and shallow-water equations respectively. The result is that the assimilation capability of the new method is better than that of EnKF method. It is also shown that Monte Carlo H ∞ filter assimilation method is effective and suitable to nonlinear systems in that it does not depend on the statistical properties of observational errors and has better robustness than EnKF method when the statistical properties of observational errors are varying. In addition, for the new method, the smallest level factor founded by search method is flow-dependent.

关 键 词：SEQUENTIAL data ASSIMILATION MONTE Carlo H∞ filter correlation of OBSERVATIONAL ERRORS ROBUSTNESS flow-dependent 

分 类 号：TN713[电子电信—电路与系统]