A new sequential data assimilation method
A new sequential data assimilation method作者机构:Department of Atmospheric SciencesNanjing UniversityNanjing 210093China Institute of MeteorologyPLA University of Science and TechnologyNanjing 211101China Institute of Heavy RainChina Meteorological AdministrationWuhan 430074China
出 版 物:《Science China(Technological Sciences)》 (中国科学(技术科学英文版))
年 卷 期:2009年第52卷第4期
页 面:1027-1038页
核心收录:
学科分类:080902[工学-电路与系统] 0809[工学-电子科学与技术(可授工学、理学学位)] 08[工学]
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)
主 题:sequential data assimilation Monte Carlo H∞ filter correlation of observational errors robustness flow-dependent
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.