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.东京大学工学部土木工学科，日本 东京 1138656
• 收稿日期:2002-12-17 修回日期:2003-05-19 出版日期:2003-12-20
• 通讯作者: 李新 E-mail:lixin@ns.lzb.ac.cn
• 基金资助:

国家自然科学基金项目“中国西部地区陆面数据同化系统”(编号:90202014)和“青藏高原积雪和冻土的被动微波遥感监测研究”(编号:49971060);中国科学院知识创新工程重大项目“青藏铁路工程与多年冻土相互作用及其环境效应”(编号:KZCX1-SW-04)资助·所使用的数据来自于中日GAME-Tibet项目.

AN ALGORITHM FOR LAND DATA ASSIMILATION BY USING SIMULATED ANNEALING METHOD

Li Xin 1,Toshio Koike 2,Cheng Guodong 1

1. 1.Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;2.Department of Civil Engineering, the University of Tokyo, Tokyo 113-8656, Japan

The high nonlinearity and discontinuity of landsurface model and radiative transfer model hinder further practical operation of some advanced four dimensional data assimilation methods such as the Kalman filter and the variational method in the land data assimilation system. Accordingly, we develop a new data assimilation algorithm by employing a heuristic optimization approach named simulated annealing, which is capable of minimizing the four dimensional cost function without using the adjoint model. The method has advantages in dealing with the strong nonlinearity and discontinuity, and in finding the global minimal in the hilly structure of the cost function. Additionally, all the processes in the model operator and the observation operator can be kept because the method is independent on the cost function. The disadvantage of this method, when compared to the variational method, is its low efficiency. Therefore, we make efforts to improve the method by incorporating very fast simulated re-annealing(VFSA) algorithms into the data assimilation cycle.
Based on the VFSA algorithm, we design a research purpose land surface data assimilation system which assimilates the in situ monitoring of soil moisture into a land surface scheme. The modified SiB2 with frozen soil parameterization is used as the model operator. We have implemented one dimensional offline test of the algorithm with the GAME-Tibet observations. The algorithm is compared with a control run without assimilating the observations. The results show that the cost value calculated from the optimized initial values is much smaller. In addition, the bias from observations is also significantly reduced.

 [1] Daley R. Atmospheric Data Analysis[M]. New York: Cambridge University Press, 1991. [2] Morel P. An overview of meteorological data assimilation[A].In: Bengtsson L, Ghil M, Kallen E, eds. Dynamic Meteorology: Data Assimilation Methods[C]. New York: Springer-Verlag, 1981. 5-16. [3] Talagrand O. Assimilation of observations, an introduction[J]. Journal of Meteorological Society of Japan, 1997, 75 (1B): 191-209. [4] McLaughlin D. Recent development in hydrologic data assimilation[J]. Reviews of Geophysics,1995,(Suppl.): 977-984. [5] Entekhabi D, Galantowicz J F, Njoku E G. Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations[J]. IEEE Transactions on Geoscience and Remote Sensing,1994, 32(2): 438-448. [6] Galantowicz J F, Entekhabi D, Njoku E G. Test of sequential data assimilation for retrieving profile soil moisture and temperature from observed L-band radio brightness[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37 (4): 1 860-1 870. [7] Hoeben R, Troch P A. Assimilation of active microwave observation data for soil moisture profile estimation[J]. Water Resources Research, 2000, 36 (10): 2 805-2 819. [8] Houser P R, Shuttleworth W J, Famiglietti J S, et al. Integration of soil moisture remote sensing and hydrologic modeling using data assimilation[J]. Water Resources Research, 1998, 34 (12):3 405-3 420. [9] Walker J P, Willgoose G R, Kalma J D. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: A comparison of retrieval algorithm[J]. Advances in Water Resources,2001,24: 631-650. [10] Kruger J. Simulated annealing—A tool for data assimilation into an almost steady model state[J]. Journal of Physical Oceanography, 1993, 23 (4): 679-688. [11] Bennett A F, Chua B S. Open-ocean modeling as an inverse problem: The primitive equations[J]. Monthly Weather Review, 1994, 122 (6): 1 326-1 336. [12] Evensen G. Advanced data assimilation for strongly nonlinear dynamics[J]. Monthly Weather Review, 1997, 125 (6): 1 342-1 354. [13] Ingber L. Very fast simulated re-annealing[J]. Mathematical Computer Modelling, 1989, 12 (8): 967-973. [14] Zupanski M, Kalnay E. Principles of data assimilation[A]. In: Browning K A, Gurney R J, eds. Global Energy and Water Cycles[C]. New York: Cambridge University Press, 1999. [15] Li Xin, Koike T. A new frozen soil parameterization in land surface scheme[A]. In: Matsuno T, Kida H, eds. Present and Future of Modeling Global Environmental Change: Toward Integrated Modeling[C]. Tokyo: TERRAPUB, 2001. 405-414. [16] Kirkpatrick S, Gelatt C D Jr, Vecchi M P. Optimization by simulated annealing[J]. Science, 1983, 220(4 598): 671-680. [17] Szu H, Hartley R. Fast simulated annealing[J]. Physics Letters A, 1987, 122 (3/4): 157-162. [18] Sellers P J, Los S O, Tucker C J, et al. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part II: Thegeneration of global fields of terrestrial biophysical parameters from satellite data[J]. Journal of Climate, 1996, 9: 706-737. [19] Li Xin, Koike T. Frozen soil parameterization in SiB2 and its validation with GAME-Tibet observations[J]. Cold Region Science and Technology,2003,36(1/3):165-182. [20] Evensen G. Inverse methods and data assimilation in nonlinear ocean models[J]. Physica D, 1994, 77 (1/3): 108-129.
 [1] 周彦昭, 李新. 涡动相关能量闭合问题的研究进展[J]. 地球科学进展, 2018, 33(9): 898-913. [2] 赵文智, 周宏, 刘鹄. 干旱区包气带土壤水分运移及其对地下水补给研究进展[J]. 地球科学进展, 2017, 32(9): 908-918. [3] 邵明安, 贾小旭, 王云强, 朱元骏. 黄土高原土壤干层研究进展与展望[J]. 地球科学进展, 2016, 31(1): 14-22. [4] 高江波, 吴绍洪, 戴尔阜, 侯文娟. 西南喀斯特地区地表水热过程研究进展与展望[J]. 地球科学进展, 2015, 30(6): 647-653. [5] 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. [6] 王磊, 李秀萍, 周璟, 刘文彬, 阳坤. 青藏高原水文模拟的现状及未来[J]. 地球科学进展, 2014, 29(6): 674-682. [7] 蔡福, 明惠青, 纪瑞鹏, 冯锐, 米娜, 赵先丽, 张玉书. 玉米冠层辐射传输参数优化对陆气通量模拟的影响[J]. 地球科学进展, 2014, 29(5): 598-607. [8] 李大治, 晋锐, 车涛, 高莹, 耶楠, 王树果. 联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究 *[J]. 地球科学进展, 2014, 29(2): 295-305. [9] 朱忠礼,林柳莺，徐同仁. 海河流域不同下垫面土壤水分动态模拟研究[J]. 地球科学进展, 2012, 27(7): 778-787. [10] 张添，黄春林，沈焕锋. 土壤水分对土壤参数的敏感性及其参数优化方法研究[J]. 地球科学进展, 2012, 27(6): 678-685. [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] 陈书林，刘元波，温作民. 卫星遥感反演土壤水分研究综述[J]. 地球科学进展, 2012, 27(11): 1192-1203. [13] 文军，蓝永超，苏中波，田辉，史小康，张宇，王欣，刘蓉，张堂堂，康悦，吕少宁，张静辉. 黄河源区陆面过程观测和模拟研究进展[J]. 地球科学进展, 2011, 26(6): 575-586. [14] 蔡福，周广胜，李荣平，明惠青. 陆面过程模型对下垫面参数动态变化的敏感性分析[J]. 地球科学进展, 2011, 26(3): 300-310. [15] 蒋维楣,苗世光,张宁,刘红年,胡非,李磊,王咏薇,王成刚. 城市气象环境与边界层数值模拟研究[J]. 地球科学进展, 2010, 25(5): 463-473.