地球科学进展 ›› 2011, Vol. 26 ›› Issue (8): 795 -804. doi: 10.11867/j.issn.1001-8166.2011.08.0795

综述与评述    下一篇

陆面数据同化系统误差问题研究综述
摆玉龙 1, 2, 李新 1, 韩旭军 1   
  1. 1.中国科学院寒区旱区环境与工程研究所,甘肃兰州730000;2. 西北师范大学物理与电子工程学院,甘肃兰州730070
  • 收稿日期:2011-01-25 修回日期:2011-07-03 出版日期:2011-08-10
  • 通讯作者: 摆玉龙 E-mail:yulongbai@gmail.com
  • 基金资助:

    国家高技术研究发展计划重点项目“全球陆表特征参量产品生成与应用研究”子课题“陆面模拟与同化系统示范研究”(编号:2009AA122104);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”(编号:41061038);国家杰出青年科学基金项目“流域尺度陆面数据同化系统研究”(编号:40925004)资助.

A Review of Error Problems for Land Data Assimilation Systems

Bai Yulong 1, 2, Li Xin 1, Han Xujun 1   

  1. 1. Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences,Lanzhou730000,China;2. College of Physics and Electrical Engineering,Northwest Normal University,Lanzhou730070,China
  • Received:2011-01-25 Revised:2011-07-03 Online:2011-08-10 Published:2011-08-10

同化系统中的误差问题一直被认为是制约数据同化性能的瓶颈问题。从分析陆面数据同化系统的误差问题研究现状出发,统一定义了同化系统的误差来源及误差表现,简要综述了顺序同化方法及连续同化方法中的误差定义和相关理论问题。从误差估计的角度,重点介绍了目前研究中各种误差估计的方法和面临的困难。针对误差处理方法的研究,介绍了在集合数据同化中为减小误差常用的乘数放大法、附加放大法和松弛先验法等模型误差参数化方案,并且介绍了在实际数据同化系统中为减小系统偏差常采用状态增广法。最后总结讨论了各种误差估计与处理方法的特点及其在陆面数据同化中的应用前景和发展方向。

As an important methodology for optimally merging Earth observation information and geophysical model output information, data assimilation has played an important role in the area of Earth observation. At present, great progress has been made in the theoretical and methodological exploration and foundation of the operational land data assimilation system. Due to the complexity of research objectives, error problems are thought to be the bottleneck for improving the performance of data assimilation systems. Firstly, the research statuses of error problems of Land Data Assimilation Systems are reviewed. Based on the mathematical descriptions of land surface process model and measurement process, error sources and error characteristic are unifying defined. In a word, data assimilation systems include model errors, observation errors and the algorithm errors. Secondly, with respect to the sequential and variational data assimilation methods, error definitions and the related theoretical problems of those methods are briefly introduced with the emphasis on the error sources and the fundamental error parameterization methods. Moreover, from the perspective of error estimation, several novel methods for estimating model errors are reviewed from three parts: the model input error estimation, the model parameters error estimation and the model structure error estimations. As for the observation errors, the error sources can be divided with the observation algorithm errors, the representative errors and the instrument errors. Beside some exiting methods, there are no more effectively methods to deal with those kinds of error. Meanwhile, the difficulties for implementing all those methods are clarified. Thirdly, in order to reduce the errors for ensemble data assimilation systems, the common error parameterization methods, such as multiplicative inflation methods, additive inflation methods and the relax-to-prior methods, are employed. All these methods for dealing with model errors are meant to ameliorate the bias error in ensemble second moment. As far as the model bias is concerned, the state augmentation methods are discussed. A new scheme to obtain the optimal estimation of the state and model bias variable simultaneously is reviewed. Finally, the characteristic of all kinds of error estimation and processing methods and the surveys for the future implementation of all above methods in land data assimilation are given. 

中图分类号: 

[1]Li Xin, Huang Chunlin, Che Tao,et al. Development of a Chinese land data assimilation system: Its process and prospects[J].Process in Natural Science,2007,17(8): 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,2007, 17(2): 163-173.]

[2]Evensen G.Data Assimilation: The Ensemble Kalman Filter[M].Berlin, Heidelberg: Springer, 2007: 279.

[3]Reichle R H. Data assimilation methods in the Earth science[J].Advances in Water Resources,2008, 31: 1 411-1 418.

[4]Mitchell K E, Lohmann D, Houser P R,et al. The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system[J].Journal of Geophysical Research,2004, 109(D07):32:doi.10.1029/2003JD003823.

[5]McLaughlin D.An integrated approach to hydrologic data assimilation:Interpolation, smoothing and filtering[J].Advances in Water Resources,2002, 25: 1 275-1 286.

[6]Bai Y L, Li X. Evolutionary algorithmbased error parameterization methods for data assimilation[J].Monthly Weather Review,2011,139(8):2 668-2 685, doi: 10.1175/2011MWR3641.1

[7]Han Xujun. Algorithm Development and Application of the Land Data Assimilation at Catchment Scale[D]. Beijing: Graduate University of the Chinese Academy of Sciences,2008.[韩旭军. 流域尺度陆面数据同化方法及其应用研究[D]. 北京:中国科学院研究生院,2008.]

[8]Jin Rui, Li Xin. Improve the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating in situ observations and SSM/I data[J].Science in China(Series D),2009, 52(11): 1 732-1 745.[晋锐, 李新. 同化站点观测和SSM/I亮温改善冻土活动层状态变量的模拟精度[J]. 中国科学:D辑, 2009, 39(9): 1 220-1 231.]

[9]Han Lijuan. Estimation of Evapotranspiration by Assimilating MODIS LST Product into the CLM[D]. Beijing: Beijing Normal University,2006.[韩丽娟. 同化MODIS 地表温度产品和陆面过程模型研究地表蒸散[D]. 北京:北京师范大学,2006.]

[10]Talagrand O.Assimilation of observations, an introduction[J].Journal of the Meteorological Society of Japan,1997, 75(1B):191-209.

[11]Evensen G.The ensemble Kalman filter:Theoretical formulation and practical implementation[J].Ocean Dynamics,2003, 53:343-367.

[12]Huang C L,Li X, Lu L,et al. Experiments of onedimensional soil moisture assimilation system based on ensemble Kalman filter[J].Remote Sensing of Environment,2008, 112(3): 888-900.

[13]Ide K,Courtier P, Ghil M,et al. Unified notation for data assimilation: Operational, sequential and variational[J].Journal of the Meteorological Society of Japan, 1997, 75(1B):181-189.

[14]Li X,Koike T, Mahadevan P. A Very Fast Simulated re-Annealing (VFSA) approach for land data assimilation[J].Computers and Geosciences,2004, 30(3): 239-248.

[15]Daley R.Atmospheric Data Analysis[M]. New York: Cambridge University Press, 1991. 

[16]Li Xin, Bai Yulong. A Bayesian filter framework for sequential data assimilation[J].Advances in Earth Science,2010, 25(5):515-523.

[李新, 摆玉龙. 顺序数据同化的Bayes框架[J]. 地球科学进展, 2010, 25(5):515-523.]

[17]Kalman R E. A new approach to linear filtering and prediction problems[J].Transactions of the ASME Journal of Basic Engineering, 1960, 82 (Series D):35-46.

[18]Li Hong. Local Ensemble Transform Kalman Filter with Realistic Observations[D]. Maryland: University of Maryland, 2007.

[19]Crow W T, Van Loon E. Impact of incorrect model error assumption on the sequential assimilation of remotely sensed surface soil moisture[J].Journal of Hydrometeorology,2006,7:421-432.

[20]Kumar P, Kaleita A L. Assimilation of nearsurface temperature using extended Kalman filter[J].Advances in Water Resources,2003, 26: 79-93.

[21]Pham D T, Verron J, Roubaud M C. A singular evolutive extended Kalman filter for data assimilation in oceanography[J].Journal of Marine Systems,1998, 16(3):323-340.

[22]Chai Lin, Yuan Jianping, Luo Jianjun,et al. New development in nonlinear systems estimation[J].Journal of Astronautics,2005, 26(3):380-384.[柴霖, 袁建平, 罗建军,等. 非线性估计理论的最新进展[J]. 宇航学报, 2005, 26(3): 380-384.]

[23]Han Xujun, Li Xin. Review of nonlinear filters in the land data assimilation[J].Advances in Earth Science,2008, 23(8):813-820.[韩旭军,李新. 非线性滤波方法与陆面数据同化[J]. 地球科学进展, 2008, 23(8): 813-820.]

[24]Courtier P. Variational methods[J].Journal of the Meteorological Society of Japan,1997, 75(1B):211-218.

[25]Kalnay E, Li H, Miyoshi T, et al. 4D-Var or ensemble Kalman filter?[J].Fellus A,2007, 59A(5):758-773.

[26]Bannister R N. A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariance[J].Quarterly Journal of the Royal Meteorological Society,2008, 134(11):1 951-1 970.

[27]Hamill T M, Whitaker J S. Accounting for the error due to unresolved scales in ensemble data assimilation:A comparison of different approaches[J].Monthly Weather Review, 2005, 133(11):3 132-3 147.

[28]Qiu Chongjian.Four dimensional variational data assimilation with discontinuous models[J].Journal of Lanzhou University (Natural Sciences),1997, 33(1): 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, 1997, 33(1):115-119.]

[29]Reichle R H, Walker J P, Koster R D,et al. Extended versus ensemble filtering for land data assimilation[J].Journal of Hydrometeorology,2002, 3(12): 728-740.

[30]Goodrich D C, Faures J, Woolhiser D A,et al. Measurement and analysis of small-scale convective storm rainfall variability[J].Journal of Hydrology,1995, 173(4):283-308.

[31]Han X J, Li X. On the representation of spatial uncertainty with stochastic simulation in land data assimilation[C]∥The 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Vol.1. Shanghai: World Academic Press, 2008:221-227.

[32]Beven K J, Binley A. The future of distributed models: Model calibration and uncertainty prediction[J].Hydrological Processes,1992, 6: 279-298.

[33]Yin Xiongrui, Xia Jun, Zhang Xiang,et al.  Recent progress and prospect of the study on uncertainties in hydrological modeling and forecasting[J].Water Power,2006, 32(10): 27-31.[尹雄锐,夏军,张翔,等.水文模拟与预测的不确定性研究与展望[J]. 水力发电,2006,32(10): 27-31.]

[34]Dee D P, Todling R. Data assimilation in the presence of forecast bias:The GEOS moisture analysis[J].Monthly Weather Review,2000, 128(9): 3 268-3 282.

[35]Zhang Peng, Yang Jun,  Dong Chaohua,et al. General introduction on payloads, ground segment and data application of Fengyun 3A[J].Front Earth Science China,2009, 132: 1 238-1 253.

[36]Hamill T M, Whitaker J S. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter[J].Monthly Weather Review,2001, 129(11):2 776-2 790.

[37]Whitaker J S, Hamill T M. Ensemble data assimilation with the NCEP Global forecast system[J].Monthly Weather Review,2007, 136(2): 463-482. [38]Qin J, Liang S, Yang K,et al. Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal[J].Journal of Geophysical Research,2009, 114, D15103, doi: 10.1029/2008JD011358.

[39]Anderson J L, Anderson S L. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilation and forecast [J].Monthly Weather Review,1999, 127(12):2 741-2 758.

[40]Houtekamer P L,Mitchell H L. Ensemble Kalman filer[J].Quarterly Journal of the Royal Meteorological Society,2005, 131:3 269-3 289.

[41]Zhang F, Snyder C, Sun J. Impacts if initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter[J].Monthly Weather Review,2004, 132(5): 1 238-1 253.

[42]Meng Z, Zhang F. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation Part II:Imperfect model experiments[J].Monthly Weather Review,2007, 135(4): 1 403-1 423.

[43]Li H, Kalnay E, Miyoshi T. Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter[J].Quarterly Journal of the Royal Meteorological Society,2009,135: 523-533,doi: 10.1002/qj.371.

[44]Li H, Kalnay E, Miyoshi T,et al. Accounting for model errors in ensemble data assimilation[J].Monthly Weather Review,2009, 137(10): 3 407-3 419.

[45]Jazwinski A H. Stochastic Processes and Filtering Theory[M].New York: Academic Press, 1970.

[46]Dee D P, Gaspari G, Redder C,et al. Maximumlikelihood estimation of forecast and observation error covariance parameters. Part II:Applications[J].Monthly Weather Review,1999, 127(8):1 835-1 849.

[47]Dee D P, da Silva A M. Maximumlikelihood estimation of forecast and observation error covariance parameters. Part I: Methodology[J]. Monthly Weather Review,1999, 127(8):1 822-1 834.[48]Martin M J, Bell M J, Nichols N K. Estimation of systematic error in an equatorial ocean model using data assimilation[J].International Journal for Numerical Methods in Fluids,2002,40(3): 435-444.

[49]Carton J A, Chepurin G, Cao X,et al. A simple ocean data assimilation analysis of the global upper ocean 1950-95, Part I:Methodology[J]. Journal of Physical Oceanography,2000, 30(2): 294-309.

[50]Baek S J, Hunt B R, Kalnay E, et al. Local ensemble Kalman filtering in the presence of model bias[J].Tellus,2006,58A:293-306.

[51]Zupanski D, Zupanski M. Model error estimation employing an ensemble data assimilation approach[J]. Monthly Weather Review,2006, 134(5):1 337-1 354.

[52]Tremolet Yannick. Modelerror estimation in 4D-Var[J].Quarterly Journal of the Royal Meteorological Society,2007, 133:1 367-1 380.

[53]Carrassi A, Vannitsem S, Nicolis C. Model error and sequential data assimilation:A deterministic formulation[J].Quarterly Journal of the Royal Meteorological Society,2008, 134:1 297-1 313.

[54]Houtekamer P L, Mitchell H L,Deng Xingxiu,et al. Model error representation in an operational ensemble kalman filter[J].Monthly Weather Review, 2009, 137(7):2 126-2 143.

[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] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 李得勤,段云霞,张述文. 土壤湿度观测、模拟和估算研究[J]. 地球科学进展, 2012, 27(4): 424-434.
[12] 李新,摆玉龙. 顺序数据同化的Bayes滤波框架[J]. 地球科学进展, 2010, 25(5): 515-522.
[13] 邢雅娟,刘东生,王鹏新. 遥感信息与作物生长模型的耦合应用研究进展[J]. 地球科学进展, 2009, 24(4): 444-451.
[14] 周剑,根绪,李新,杨永民,潘小多. 数据同化算法在青藏高原高寒生态系统能量—水分平衡分析中的应用[J]. 地球科学进展, 2008, 23(9): 965-973.
[15] 韩旭军,李新. 非线性滤波方法与陆面数据同化[J]. 地球科学进展, 2008, 23(8): 813-820.
阅读次数
全文


摘要