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遥感技术与应用  2013, Vol. 28 Issue (2): 276-282    DOI: 10.11873/j.issn.1004-0323.2013.2.276
遥感应用     
基于Lorenz-96模型的顺序数据同化方法比较研究
摆玉龙1,高海沙1,柴乾隆1,黄春林2
(1.西北师范大学物理与电子工程学院,甘肃 兰州 730070;
2.中国科学院寒区旱区环境与工程研究所,甘肃 兰州 730000)
Comparative Studies of Sequential Data Assimilation Methods based on Lorenz-96 Models
Bai Yulong1,Gao Haisha1,Chai Qianlong1,Huang Chunlin2
(1.College of Physics and Electrical Engineering,Northwest Normal University,Lanzhou 730070,China;
2.Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of
Sciences,Lanzhou 730000,China)
 全文: PDF(1462 KB)  
摘要:

顺序数据同化方法在数据同化系统中得到了广泛的应用,其性能各有优缺。选择3种典型的顺序数据同化算法,即集合Kalman滤波,集合转换Kalman滤波和确定性Kalman滤波,使用经典的Lorenz-96模型进行敏感性实验,研究不同的关键参数变化,如集合数目变化、观测数变化、误差放大因子变化和定位半径变化时对同化效果的影响。实验表明:集合数目和观测数目的多少直接影响3种方法的同化效果;协方差放大因子和定位半径的选择会提高同化精度。综合比较,确定性集合Kalman滤波算法是一种具有较强鲁棒性的滤波算法,能够在集合数较小的情况下达到较好的同化效果。

关键词: 数据同化集合Kalman滤波集合转换Kalman滤波确定性集合Kalman滤波    
Abstract:

Sequential data assimilation methods have been widely applied in many data assimilation systems and each method has its own characteristics.In this paper,we introduce three typical assimilation methods,for example,Ensemble Kalman Filter,Ensemble Transform Kalman Filter and Deterministic Ensemble Kalman Filter.Based on the classical nonlinear model (eg,Lorenz-96 model),the numerical experiments were developed to test the sensitivity of all these methods.Different key parameters were investigated with respect to four aspects,which were the number of ensembles,the number of observations,the inflation factor and the localization radius.The results show:the number of ensembles and observations will directly influence the assimilation results; the optimal selection of the inflation factors and the localization radius will improve the accuracy of the assimilation.According to the final comparative studies,the deterministic EnKF is a method that has a better robust performance.It can achieve a better assimilation effect,especially in the occasion of the small ensemble numbers.

Key words: Data assimilation    Ensemble Kalman Filter    Ensemble Transform Kalman Filter    Deterministic Ensemble Kalman Filter
收稿日期: 2012-07-09 出版日期: 2013-06-24
:  TP 79  
基金资助:

国家自然科学基金项目“基于鲁棒滤波方法的陆面数据同化系统误差估计与处理”(41061038 ),中国科学院百人计划项目“寒旱区地表水文关键要素的多源遥感数据同化研究”(Y127D01002 )资助。

 

作者简介: 摆玉龙(1973-),男,甘肃会宁人,博士,教授,主要从事数据同化和参数估计方面的研究。Email:yulongbai@gmail.com。
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引用本文:

摆玉龙,高海沙,柴乾隆,黄春林. 基于Lorenz-96模型的顺序数据同化方法比较研究[J]. 遥感技术与应用, 2013, 28(2): 276-282.

Bai Yulong,Gao Haisha,Chai Qianlong,Huang Chunlin. Comparative Studies of Sequential Data Assimilation Methods based on Lorenz-96 Models. Remote Sensing Technology and Application, 2013, 28(2): 276-282.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2013.2.276        http://www.rsta.ac.cn/CN/Y2013/V28/I2/276

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