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

非线性滤波方法与陆面数据同化
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:

    国家自然科学基金项目“陆面数据同化中的贝叶斯滤波方法研究”(编号:40771036);国家自然科学基金重点项目“中国西部地区陆面数据同化系统研究”(编号:90202014)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

陆面数据同化研究近几年成为地球科学研究的新兴领域,其中以非线性滤波为代表的数据同化方法发展迅速并得到了广泛应用。在贝叶斯理论框架内,从递推贝叶斯估计理论的角度系统地分析了扩展卡尔曼滤波、无迹卡尔曼滤波、集合卡尔曼滤波、SIR粒子滤波等非线性滤波方法的异同;针对应用比较广泛的集合卡尔曼滤波和SIR粒子滤波应用中存在的问题,论述了几种提高滤波性能的实用方法,如协方差矩阵的Localization方法、协方差矩阵的Inflation方法、双集合卡尔曼滤波方法、扰动集合、扰动大气驱动和模型参数、平方根集合卡尔曼滤波以及粒子滤波算法的改进等。最后总结讨论了各种非线性滤波方法应用中的特点、难点以及各种算法在陆面数据同化中的应用前景和发展方向。

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.

中图分类号: 

[1] Li XinHuang ChunlinChe Taoet al. Development of a Chinese land data assimilation system: Its progress and prospects [J]. Progress in Natural Science2007178:881-892.[李新,黄春林,车涛,等.中国陆面数据同化系统研究的进展与前瞻 [J]. 自然科学进展,2007178:881-892.]

[2] McLaughlin D. Recent development in hydrologic data assimilation [J]. Reviews of Geophysics199533suppl.:977-984.

[3] Houser P RShuttleworth W JGupta H Vet al. Integration of soil moisture remote sensing and hydrologic modeling using data assimilation [J]. Water Resources Research19983412:3 405-3 420.

[4] Troch P APaniconi CMcLaughlin D. Catchment-scale hydrological modeling and data assimilation [J]. Advances in Water Resources2003262:131-135.

[5] Margulis S AWood E FTroch P A. Terrestrial water cycle: Modeling and data assimilation across catchment scales [J]. Journal of Hydrometeorology20067:309-311.

[6] Mitchell K ELohmann DHouser P Ret al. The multi-institution North American Land Data Assimilation SystemNLDAS: Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system [J]. Journal of Geophysical Research2004109D07:doi.10.1029/2003JD003823.

[7] Rodell MHouser P RJambor Uet al. The global land data assimilation system [J]. Bulletin of the American Meteorological Society2004853:381-394.

[8] Van den Hurk B. ELDAS Final Report [R]. KNMIECMWF2005.

[9] Qiu Chongjian. Four dimensional variational data assimilation with discontinuous models [J]. Journal of Lanzhou UniversityNatural Sciences),1997331:115-119.[邱崇践. 变分四维同化方法中的不连续问题[J]. 兰州大学学报:自然科学版,1997331:115-119.]

[10] Reichle R HWalker J PKoster R Det al. Extended versus ensemble filtering for land data assimilation [J].  Journal of Hydrometeorology20023:728-740.

[11] Anderson B D OMoore J B. Optimal Filtering [M]. Englewood CliffsNJ: Prentice-Hall1979.

[12] Verlaan MHeemink A W. Nonlinearity in data assimilation applications: A practical method for analysis [J]. Monthly Weather Review20011296:1 578-1 589.

[13] Pham D TVerron JRoubaud M C. A singular evolutive extended Kalman filter for data assimilation in oceanography [J]. Journal of Marine Systems1998163:323-340.

[14] Evensen G. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics [J]. Journal of Geophysical Research199499C5:10 143-10 162.

[15] Burgers GVan Leeuven P JEvensen G. Analysis scheme in the ensemble Kalman filter [J]. Monthly Weather Review1998126:1 719-1 724.

[16] Evensen G. The Ensemble Kalman Filter: Theoretical formulation and practical implementation [J]. Ocean Dynamics200353:343-367.

[17] Evensen G. Advanced data assimilation for strongly nonlinear dynamics [J]. Monthly Weather Review19971256:1 342-1 354.

[18] Julier S JUhlmann J K. Unscented filtering and nonlinear estimation [C]Proceedings of the IEEE Aerospace and Electronic Systems2004923:410-422.

[19] Arulampalam M SMaskell SGordon Net al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Transactions on Signal Processing2002502:174-188.

[20] Pham D T. Stochastic methods for sequential data assimilation in strongly nonlinear systems [J]. Monthly Weather Review20011295:1 194-1 207.

[21] Kivman G A. Sequential parameter estimation for stochastic systems [J]. Nonlinear Processes in Geophysics2003103:253-259.

[22] Xiong XNavon I MUzunoglu B. A note on the particle filter with posterior Gaussian resampling [J]. Tellus A2006584:456-460.

[23] Moradkhani HHsu K LGupta Het al. Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter [J]. Water Resources Research200541W05012doi:10.1029/2004WR003604.

[24] Zhou Y HMcLaughlin DEntekhabi D. Assessing the performance of the ensemble Kalman filter for land surface data assimilation [J]. Monthly Weather Review20061348:2 128-2 142.

[25] Van Leeuwen P J. A variance-minimizing filter for large-scale applications [J]. Monthly Weather Review20031319:2 071-2 084.

[26] Han X JLi X. An evaluation of the nonlinear/non-gaussian filters for sequential data assimilation [J]. Remote Sensing of Environment20081124:1 434-1 449.

[27] Gordon N JSalmond D JSmith A F M. Novel approach to nonlinear/non-gaussian Baysian state estimation [C]IEEE Proceedings on Radar and Signal Processing19931402:107-113.

[28] Van der Merwe R. Sigma-Point Kalman Filters for Probalistic Inference in Dynamic State-Space Models [D]. Oregon Health & Science University2004.

[29] Bergman N. Recursive Bayesian Estimation: Navigation and Tracking Applications [D]. Lin Ping: Link Ping UniversitySweden1999.

[30] Liu J SChen R. Sequential Monte Carlo methods for dynamic systems [J]. Journal of the American Statistical Association199893443:1 032-1 044.

[31] Kitagawa G. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models [J]. Journal of Computational and Graphical Statistics199651:1-25.

[32] Carpenter JClifford PFearnhead P. An improved particle filter for non-linear problems [C]IEEE Proceedings on Rader and Sonar Navigation19991461:2-7.

[33] Walker J PHouser P R. A methodology for initializing soil moisture in a global climate model: Assimilation of near-surface soil moisture observations [J]. Journal of Geophysical Research2001106D11:11 761-11 774.

[34] Huang ChunlinLi Xin. Experiments of soil moisture data assimilation system basedon ensemble Kalman filter [J]. Plateau Meteorology2006254:665-671.[黄春林,李新.基于集合卡尔曼滤波的土壤水分同化试验[J].高原气象,2006254:665-671.]

[35] Reichle R HKoster R D. Assessing the impact of horizontal error correlations in background fields on soil moisture estimation [J]. Journal of Hydrometeorology20034:1 229-1 242.

[36] Houtekamer P LMitchell H L. Data assimilation using an ensemble Kalman filter technique [J]. Monthly Weather Review1998126:796-811.

[37] Hamill T MWhitaker J SSnyder C. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter [J]. Monthly Weather Review2001129:2 776-2 790.

[38] Houtekamer P LMitchell H L. A sequential ensemble Kalman filter for atmospheric data assimilation [J]. Monthly Weather Review2001129:123-137.

[39] Hunt B RKostelich E JSzunyogh I. Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter [J]. Physica D2007230:112-126.

[40] McLaughlin DZhou Y HEntekhabi Det al. Computational issues for large-scale land surface data assimilation problems [J]. Journal of Hydrometeorology200673:494-510.

[41] Whitaker J SHamill T M. Ensemble data assimilation without perturbed observations [J]. Monthly Weather Review2002130:1 913-1 924.

[42] Anderson J. An ensemble adjustment Kalman filter for data assimilation [J]. Monthly Weather Review2001129:2 884-2 903.

[43] Bishop CEtherton BMajumdar S. Adaptive sampling with the ensemble transform Kalman filter Part I. Theoretical aspects [J]. Monthly Weather Review2001129:420-436.

[44] Evensen G. Sampling strategies and square root analysis schemes for the EnKF [J]. Ocean Dynamics200454:539-560.

[45] Musso COudjane NLeGland F. Improving Regularised Particle Filters [C]Doucet Ade Freitas NGordon N. Sequential Monte Carlo Methods in Practice.New York:Springer2001.

[46] Pan MWood E F. Data assimilation for estimating land water budget using a constrained ensemble Kalman Filter [J]. Journal of Hydrometeorology200673:534-547.

[47] Miller R NCarter E FBlue S T. Data assimilation into nonlinear stochastic models [J]. Tellus A1999512:167-194.

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