关于集合Kalman滤波的理论和方法的发展  

THE ENSEMBLE KALMAN FILTER THEORY AND METHOD DEVELOPMENT

作　　者：刘成思[1] 薛纪善[1] 

机构地区：[1]中国气象科学研究院数值预报研究中心,北京100081

出　　处：《热带气象学报》2005年第6期628-633,共6页Journal of Tropical Meteorology

基　　金：国家"十五"重点科技攻关项目<中国气象数值预报系统技术创新研究>(2001BA607B02);国家自然科学资金重点项目<中国强降水天气数值预报的研究>(40233036)联合资助

摘　　要：随着同化方法的不断的发展,一种新的资料同化方法——集合Kalman滤波正在兴起。简单地回顾了同化方法的发展,探讨了集合Kalman滤波的特点。同时,还介绍了集合Kalman滤波发展的过程以及指出目前所面临的问题和未来的发展趋势。Nowadays, data assimilation has played an important role in research of atmosphere and ocean. Four dimension variation may be considered a better data assimilation method. But with data assimilation method developing, a new data assimilation method—— ensemble Kalman filter is becoming popular. As a sequential data assimilation method, ensemble Kalman filter is similar to Kalman filter that has been presented by Kalman in 1960 but hard to apply to atmospheric data assimilation in operation for large calculating cost. Ensemble method makes Kalman filter available and has made a great progress in past ten years. After review development of data assimilation and ensemble Kalman filter, the virtue of ensemble Kalman filter is discussed. Getting a flow-dependent background error covariance may be a most attractive character of ensemble Kalman filter. Also, the problem of ensemble Kalman filter applied is discussed in this paper. Since we can just use finite ensemble in ensemble Kalman filter, simple error is unavoidable and will bring some severe problems, for instance, filter divergence. At the end, the future of ensemble Kalman filter is expected. Although no operational center has yet implemented ensemble Kalman filter, Canada has plan to do so. Besides, hybrid variation and four dimension variation may be mainstream of numeric weather prediction.

关 键 词：同化 KALMAN滤波 集合KALMAN滤波 

分 类 号：P468.0[天文地球—大气科学及气象学]