关于集合Kalman滤波的理论和方法的发展

刘成思; 薛纪善

关于集合Kalman滤波的理论和方法的发展

中国气象科学研究院数值预报研究中心, 北京100081

基金项目: 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

计量
- 文章访问数: 1299
- HTML全文浏览量: 8
- PDF下载量: 2962
- 被引次数: 0
出版历程
- 收稿日期: 2005-02-17
- 修回日期: 2005-03-28

THE ENSEMBLE KALMAN FILTER THEORY AND METHOD DEVELOPMENT

Chinese Academy of Meteorological Sciences, Beijing 100081, China

摘要

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。
- 同化 /
- Kalman滤波 /
- 集合Kalman滤波
Abstract: 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.
- assimilation /
- Kalman filter /
- ensemble Kalman filter

HTML全文

参考文献(1)

[1]

Panel on model-assimilated Data sets (D.R. Johnson, J.T. Bates, G.P. Brasseur, M.Ghil, A.Hollingsworth, R.L.Jenne ,K.Miyakoda, E. Rasmusson, E.S.Sarachik, and T.T.Warner).1991:Four-Dimensional Model Assimilation of Data: A Strategyfor the Earth System Sciences, National Academy Press, Washington, D.C.,78 pp[2] PANOFSKY H. Objective weather-map analysis[J]. J Appl Meteor, 1949, 6:386-392.[3] CRESSMAN. An operational objective analysis system[J]. Mon Wea Rev, 1959, 87(10): 367-374.[4] GANDIN L. Objective analysis of meteorological fields (Leningrad: Gridromet). English translation(Jerusalem: IsraelProblem for Scientific Translation), 1965.[5] JONES, ROBERT W. On Improving Initial Data for Numerical Forecasts of Hurricane Trajectories by the Steering Method[J]. Journal of Applied Meteorology, 1964, 3(3): 277–284.[6] NAGLE, ROLAND E, CLARK, et al. Formulation and testing of a program for the objective assembly of meteorologicalsatellite cloud observations[J]. Monthly Weather Review, 1967, 95(4): 171–187.[7] DALEY. Atmospheric data analysis，Cambridge Univ. Press, 1991.[8] EPSTEIN E S. Stochastic dynamic prediction[J]. Tellus Ser A. 1969, 21(4): 739-759.[9] GEIR EVENSEN. Sequential data assimilation with a nonlinear quasi-geostrophic model using Montre Carlo methods toforecast error statistics[J]. J Geophys Res, 1994, 99(10): 143-162.[10] 高山红, 吴增茂. Kalman 滤波在气象数据同化中的发展与应用[J]. 地球科学进展, 2000, 5(4): 571-575.[11] HMAILL T M. Ensemble-Based Data Assimilation, Workshop on Predictability ECMWF , 2002.8.83-105.[12] WHITAKER J S, HAMILL T M. Ensemble Data Assimilation without perturbed observations[J]. Mon Wea Rev, 130(7):1913–1924.[13] HOUTEKAMER P L, MITCHELL H L. Data assimilation using an ensemble Kalman filter technique[J]. Mon Wea Rev,1998, 126(3): 796-811.[14] MITCHELL H L, HOUTEKAMER P L, PELLERIN G. Ensemble size, and model-error representation in an EnsembleKalman Filter[J]. Mon Wea Rev, 2002, 130(11): 2791-2808.[15] MITCHELL H L, HOUTEKAMER P L. An adaptive ensemble Kalman filter[J]. Mon Wea Rev, 2000, 128(2): 426-433.[16] HOUTEKAMER P L, HERSCHEL L, MITCHELL. A Sequential Ensemble Kalman Filter for Atmospheric Data Assimi-lation[J]. Mon Wea Rev, 2001, 129(1): 123-137.[17] LORENC A. (Met Office). Relative merits of 4DVar and Ensemble methods. ECMWF Seminar on Recent developmentsin data assimilation for atmosphere and ocean, 8 to 12 September 2003.[18] DERBER J. (NOAA). Flow dependent Jb in a grid-point 3D-Var Ensemble data assimilation. Seminar on Recent develop-ments in data assimilation for atmosphere and ocean. 8 to 12 September 2003.[19] KALNAY E. Atmospheric modeling, data assimilation and predictability. Cambridge university press, 2003. 181[20] HAMILL T M, MULLEN S L, SNYDER C, et al. Baumhefner D. P. Ensemble forecasting in the short to medium range:Report from a workshop[J]. Bull Amer Meteor Soc, 2000, 81(11): 2653–2664.[21] NOAA-CIRES Climate Diagnostics Center. CDC’s 2001 Science Review[D].[22] HAMILL T M, SNYDER C. Using improved background error covariances from an ensemble Kalman filter for adaptiveobservations[J]. Monthly Weather Review, 2002, 130(6): 1552-1572.[23] BISHOP C H, ETHERTON B J, MAJUMDAR S J. Adaptive sampling with the ensemble transform Kalman filter. Part I:Theoretical aspects[J]. Monthly Weather Review, 2001, 129(3): 420--436.

施引文献

资源附件(0)

访问统计