Kalman滤波在气象数据同化中的发展与应用

doi:10.11867/j.issn.1001-8166.2000.05.0571

地球科学进展 ›› 2000, Vol. 15 ›› Issue (5): 571 -575. doi: 10.11867/j.issn.1001-8166.2000.05.0571

高山红,吴增茂,谢红琴

青岛海洋大学物理海洋研究所,山东青岛 266003

收稿日期:1999-11-17 修回日期:2000-03-06 出版日期:2000-10-01
通讯作者: 高山红(1972-),男,湖北省汉川市人,博士研究生,主要从事海岸气象学和大气中尺度数值模拟研究。
基金资助:
国家“九五”重点科技攻关专题“近岸带灾害动力环境的数值模拟技术和优化评估技术研究”(编号:96-922-03-03)和山东省自然科学基金“近岸风场中尺度结构分析”(编号:Y-97E03080)联合资助。

THE DEVELOPMENTS AND APPLICATIONS OF KALMAN FILTERS IN METEOROLOGICAL DATA ASSIMILATION

GAO Shan-hong,WU Zeng-mao,XIE Hong-qin

Institute of Physical Oceanography,Ocean University of Qingdao,Qingdao 266003,China

Received:1999-11-17 Revised:2000-03-06 Online:2000-10-01 Published:2000-10-01

下载PDF ( 993 )

气象学领域各种观测(特别是遥感遥测等非常规观测)数据的大量增多和数值天气预报模式的不断进步,推动气象数据同化技术不断发展。回顾了Kalman滤波在气象数据同化中的引入和几个发展阶段;介绍了Kalman滤波(尤其是简化Kalman滤波和总体Kalman滤波)在气象数据同化中的重要地位和应用进展。

关键词: 气象, 数据同化, Kalman滤波, 伴随变分法

Meteorological data assimilation techniques are motivated forward by the advance of numerical weather prediction models and the increasing rapidly observations, including the great part of uncon-ventional data obtained by remote measurement methods. There are mainly two general concepts that have been discussed repeatedly for data assimilation in meteorology. The variational (especially adjoint variational) method has been the popular and most used scheme, which, however, has a drawback that model errors (system noise) are not taken into account. Another class of methods are those described as sequential data assimilation, which are represented by Kalman filters. The introduction of Kalman filters and their developmental stages in the meteorological data assimilation field are presented in this paper, as well as that the importance and applications of Kalman filters, particularly simplified Kalman filters and ensemble Kalman filters. Due to that they have the ability to consider model errors and let assimilation results not drift away from observations, Kalman filters are paid more and more attentions, though they need
much of computational load. Compared with the current advance abroad, the developments and applications of Kalman filters in China are lagged. However, there will be a bright prospect for them with the improvements of computational conditions.

Key words: Meteorology, Data assimilation, Kalman filters, Adjoint variational method.

中图分类号:

P732.6

[1] Smedstad O M, O' Brien J J. Variational data assimilation and parameter estimation in an equatorial Pacific Ocean model [J].Prog Oceanog, 1992, 26:179～241.
[2] Panofsky, H. Objective weather-map analysis[J]. J Appl Meteor, 1949, 6:386～92.
[3] Gichrist B, Cressman G. An experiment in objective analysis[J]. Tellus, 1954, 6:309～318.
[4] Daley R. Atmospheric Data Analysis[M]. Cambridge: Cambridge University Press, 1991.
[5] Chao W C, Chang L P. Development of a four-dimensional variational analysis system using the adjoint method at GLA,Part 1: dynamics [J]. Mon Wea Rev, 1992, 26:1 661～1 673.
[6] Courtier P, Derber J C, Ron Errico,et al. Important literature on the use of adjoint, variational methods and the Kalman filter in meteorology [J]. Telus, 1993, 45A: 342～357.
[7] Hoffman B N, Jean-Francois Louis, Thomas Nehrkorn. A Method for Implementing Adjoint Calculations in the Discrete case [R]. ECMWF Research Dept Tech Memo 183, 1992.
[8] Jarvinen H, Jean-Noel Thepant and Phillippe Courtier. Quasi-continuous Variational Data Assimilation[R]. ECMWF Research Dept Tech Memo 210, 1995.
[9] Rabier F, Mahfouf J-F, Fisher M,et al. Recent Experimentation on 4D-Var and First Results from a Simplified Kalman Filter [R]. ECMWF Research Dept Tech Memo 240, 1997.
[10] Lorenc A C. Analysis methods for numerical weather prediction[J]. Quart J R Met Soc, 1986, 112:1 177～1 194.
[11] Ehrendorfer M. Four-dimensional data assimilation: comparison of variational and sequential algorithms[J]. Quart J R Met Soc, 1992, 118: 673～713.
[12] Evensen G. Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model[J]. J Geophys Res,1992, 97:17 904～17 905.
[13] Kalman R E. A new approach to linear filtering and prediction problems[J]. Transaction of the ASME, Journal of Basic Engineering, 1960, 82D: 34～45.
[14] Kalman R E, Bucy R. New results in linear filtering and prediction[J]. Transaction of the ASME, Journal of Basic Engineering, 1961, 83D: 95～108.
[15] Jones R H. Optimal estimation of initial conditions for numerical prediction[J]. J Atmos Sci, 1965, 22:658～663.
[16] Epstein E S. Stochastic dynamic prediction[J]. Tellus,1969, 21:739～759.
[17] Petersen D P. On the concepts and implementation of se-quential analysis for linear random fields[J]. Tellus, 1968,20: 673～686.
[18] Petersen D P. Transient suppression in optimal sequential analysis[J]. J Atmos Sci, 1973, 12: 437～440.
[19] Petersen D P. A comparison of the performance of quasi-optimal and conventional objective analysis schemes[J]. J Atmos Sci, 1973, 12: 1 093～1 101.
[20] Phillips N A. The spatial statistics of random geostrophic modes and first-guess errors[J]. Tellus, 1986, 38A: 314～322.
[21] Dee D P. Simplification of the Kalman filter for meteorological data assimilation[J]. Quart J R Met Soc, 1991, 117:365～384.
[22] Cohn S E, Parrish D P. The behavior of forecast error covariances for a Kalman filter in two dimensions[J]. Mon Wea Rev, 1991, 119: 1 757～1 785.
[23] Leith C E. Theoretical skill of Monte Carlo forecasts[J].Mon Wea Rev, 1974,102: 409～418.
[24] Evensen G. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics [J]. J Geophys Res, 1994, 99(C5):10 143～10 612.
[25] Burgers G, Van Leeuwen P J, Evensen G. Analysis scheme in the ensemble Kalman filter[J]. Mon Wea Rev, 1998,126: 1 719～1 724.
[26] Ghil M, Malanotte-Rizzoli P. Data assimilation in meteorology and oceanography[J]. Adv Geophys, 1991, 33:141～266.
[27] Anderson B D O, Moore J B. Optional Filtering[M].Cliffs: Prentice-Hall, Englewood, 1979.
[28] Houtekamer P L, Mitchell H L. Data assimilation using an ensemble Kalman filter technique[J]. Mon Wea Rev, 1998,126: 796～811.
[29] Evensen G, Van Leeuwen P J. Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model [J]. Mon Wea Rev,1996, 124: 85～96.
[30] Fisher M. Development of a Simplified Kalman Filter[R].ECMWF Research Dept Tech Memo 260, 1998.
[31] 路如华,何于班.卡尔曼滤波方法在天气预报中的应用[J].气象,1994,20(9):41～43.
[32] 黄嘉佑,谢庄.卡尔曼滤波方法在天气预报中的应用[J].气象,1993,19(4):3～7.
[33] 路如华,徐传玉,张玲,等.卡尔曼滤波的初值计算方法及其应用[J].应用气象学报,1997,8(1):34～42.
[34] 王莉,黄嘉佑. Kalman滤波的试验应用研究[J].应用气象学报,1999,10(3):276～282.
[35] 刘春霞,周家斌.用卡尔曼滤波预报南海热带气旋路径的试验[J].热带气象学报,1988,13(4):349～356.

[1]	常明恒, 左洪超, 摆玉龙, 段济开. 两种耦合模糊控制的局地化方法研究[J]. 地球科学进展, 2021, 36(2): 185-197.
[2]	刘元波, 吴桂平, 赵晓松, 范兴旺, 潘鑫, 甘国靖, 刘永伟, 郭瑞芳, 周晗, 王颖, 王若男, 崔逸凡. 流域水文遥感的科学问题与挑战[J]. 地球科学进展, 2020, 35(5): 488-496.
[3]	闫昕旸,张强,闫晓敏,王胜,任雪塬,赵福年. 全球干旱区分布特征及成因机制研究进展[J]. 地球科学进展, 2019, 34(8): 826-841.
[4]	黄亦鹏,李万彪,赵玉春,白兰强. 基于雷达与卫星的对流触发观测研究和临近预报技术进展[J]. 地球科学进展, 2019, 34(12): 1273-1287.
[5]	周彦昭, 李新. 涡动相关能量闭合问题的研究进展[J]. 地球科学进展, 2018, 33(9): 898-913.
[6]	刘娜, 王辉, 凌铁军, 祖子清. 全球业务化海洋预报进展与展望[J]. 地球科学进展, 2018, 33(2): 131-140.
[7]	兰鑫宇, 郭子祺, 田野, 雷霞, 王婕. 土壤湿度遥感估算同化研究综述[J]. 地球科学进展, 2015, 30(6): 668-679.
[8]	毛伏平, 张述文, 叶丹, 杨茜茜. 模式时间关联误差对集合平方根滤波估算土壤湿度的影响[J]. 地球科学进展, 2015, 30(6): 700-708.
[9]	张强, 姚玉璧, 李耀辉, 罗哲贤, 张存杰, 李栋梁, 王润元, 王劲松, 陈添宇, 肖国举, 张书余, 王式功, 郭铌, 白虎志, 谢金南, 杨兴国, 董安祥, 邓振镛, 柯晓新, 徐国昌. 中国西北地区干旱气象灾害监测预警与减灾技术研究进展及其展望[J]. 地球科学进展, 2015, 30(2): 196-211.
[10]	尹剑, 占车生, 顾洪亮, 王飞宇. 基于水文模型的蒸散发数据同化实验研究[J]. 地球科学进展, 2014, 29(9): 1075-1084.
[11]	刘彦华，张述文，毛璐，薛宏宇. 评估两类模式对陆面状态的模拟和估算[J]. 地球科学进展, 2013, 28(8): 913-922.
[12]	熊春晖，张立凤，关吉平，陶恒锐，苏佳佳. 集合—变分数据同化方法的发展与应用[J]. 地球科学进展, 2013, 28(6): 648-656.
[13]	陈大可,雷小途,王伟,王桂华,韩桂军,周磊. 上层海洋对台风的响应和调制机理[J]. 地球科学进展, 2013, 28(10): 1077-1086.
[14]	马建文，秦思娴. 数据同化算法研究现状综述[J]. 地球科学进展, 2012, 27(7): 747-757.
[15]	李新，刘绍民，马明国，肖青，柳钦火，晋锐，车涛，王维真，祁元，李弘毅，朱高峰，郭建文，冉有华. 黑河流域生态—水文过程综合遥感观测联合试验总体设计[J]. 地球科学进展, 2012, 27(5): 481-498.

阅读次数

全文

摘要