地球科学进展 ›› 2008, Vol. 23 ›› Issue (8): 813 -820. doi: 10.11867/j.issn.1001-8166.2008.08.0813

综述与评述 上一篇    下一篇

韩旭军 1,2,李 新 1   
  1. 1.中国科学院寒区旱区环境与工程研究所遥感与地理信息科学研究室,甘肃 兰州 730000;2.中国科学院深圳先进技术研究院,广东 深圳 518054
  • 收稿日期:2007-10-31 修回日期:2008-06-20 出版日期:2008-08-10
  • 通讯作者: 韩旭军 E-mail:xj.han@sub.siat.ac.cn
  • 基金资助:


Review of the Nonlinear Filters in the Land Data Assimilation

Han Xujun 1,2,Li Xin 1   

  1. 1.Laboratory of Remote Sensing and Geospatial Science, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;2.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518054,China
  • Received:2007-10-31 Revised:2008-06-20 Online:2008-08-10 Published:2008-08-10


The land data assimilation research has become the emerging domain in the geoscience, the data assimilation algorithm obtained rapid development and widespread application taking the nonlinear filter as representative's. The extended Kalman filter, unscented Kalman filter, the ensemble Kalman filter and the SIR particle filter are discussed in the Bayesian theory framework from the viewpoint of the recursive Bayesian estimation. Towards the problems in the application of the ensemble Kalman filter and the SIR particle filter, some techniques that can improve the filter performances are also reviewed, such as the covariance localization, the covariance inflation, the double ensemble Kalman filter, the perturbations in the ensembles, the model forcing and parameters, the ensemble square root Kalman filter and the improved variants of the particle filters. The advantages and disadvantages of each filter as well as the applied perspective and the future research directions are discussed.


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