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地球科学进展  2013, Vol. 28 Issue (6): 648-656    DOI: 10.11867/j.issn.1001-8166.2013.06.0648
综述与评述     
集合—变分数据同化方法的发展与应用
熊春晖1,2,张立凤1*,关吉平1,陶恒锐3,苏佳佳4
1.解放军理工大学气象海洋学院,江苏 南京 211101;2.空降兵95903部队90分队,湖北 武汉 430331;3.空军航空大学,吉林 长春 130022;4.71521部队75分队,河南 新乡 453002
Development and Application of Ensemble-Variational Data Assimilation Methods
Xiong Chunhui1,2, Zhang Lifeng1, Guan Jiping1, Tao Hengrui3, Su Jiajia4
1.College of Meteorology and Oceanography,PLA University of Science and Technology, Nanjing 211101,China;2.Unit No.95903 of Airborne Troops of Air Force, Wuhan 430331, China;3. Aviation University of Air Force, Changchun 130022, China;4.Unit No.71521 of PLA, Xinxiang 453002, China
 全文: PDF(1200 KB)  
摘要:

近年来,集合—变分数据同化方法已成为大气数据同化领域研究的热点问题。该方法能够综合利用集合卡尔曼滤波和变分同化的优势,是实现“集合预报和数据同化一体化”的有效途径。在分析变分同化和集合卡尔曼滤波优缺点的基础上引出集合—变分数据同化的概念;按照不同实现方式,将集合—变分同化分为协方差线性组合和增加控制变量2类,介绍了相应的研究进展,并将集合—变分同化概念拓展;然后介绍了集合—变分同化在英美两国的应用;最后回顾了集合—变分同化研究的主要问题,展望了未来的发展趋势。

关键词: 数据同化变分同化集合卡尔曼滤波集合预报    
Abstract:

In recent years, ensemble-variational Data Assimilation (DA) methods have become cuttingedge issues of atmospheric data assimilation. The ensemblevariational DA methods which adopt the advantages of ensemble Kalman filter and variational DA is an effective way to the integration of ensemble prediction system and DA system in the Numerical Weather Prediction (NWP) system. Firstly, the concept of ensemble-variational DA is introduced after the comparison of advantages and disadvantages between variational DA and ensemble Kalman filter. Secondly, the ensemble-variational DA methods are divided into two categories by different ways of background error covariance generation. One is simple linear combination of static and ensemble covariance, and the other is augmentation of control variables. Moreover, the related  development is introduced and the concept of ensemblevariational DA is expanded. Then, the application of ensemblevariational DA in the Great Britain and the U.S. is introduced. Finally, the main issues of ensemblevariational DA are reviewed and the prospect of the future development trend is listed.

Key words: Ensemble-variational data assimilation    Variational data assimilation    Ensemble Kalman filter    Ensemble prediction
收稿日期: 2013-01-31 出版日期: 2013-06-10
:  P435  
基金资助:

国家自然科学基金面上项目“暴雨预报中初始误差的演变和可预报性研究”(编号:40975031);国家自然科学基金青年科学基金项目“大尺度初始扰动对暴雨可预报性的影响及在集合预报中的应用”(编号:41205074)资助.

通讯作者: 张立凤(1961-),女,河北唐山人,教授,主要从事大气动力学和数值模拟研究.E-mail:zhanglf@yeah.net     E-mail: 张立凤zhanglf@yeah.net
作者简介: 熊春晖(1984-),男,湖北枣阳人,博士研究生,主要从事大气数据同化和暴雨数值模拟研究.E-mail:chunhui0603@sina.com
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引用本文:

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.

Xiong Chunhui, Zhang Lifeng, Guan Jiping, Tao Hengrui,Su Jiajia. Development and Application of Ensemble-Variational Data Assimilation Methods. Advances in Earth Science, 2013, 28(6): 648-656.

链接本文:

http://www.adearth.ac.cn/CN/10.11867/j.issn.1001-8166.2013.06.0648        http://www.adearth.ac.cn/CN/Y2013/V28/I6/648

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