地球科学进展 ›› 2007, Vol. 22 ›› Issue (10): 989 -996. doi: 10.11867/j.issn.1001-8166.2007.10.0989

综述与评述    下一篇

数据同化在海洋生态模型中的应用和研究进展
王 辉 1,刘桂梅 2,万莉颖 3   
  1. 1.中国气象科学研究院,北京 100081;2.中国科学院海洋研究所海洋生态与环境科学重点实验室,山东 青岛 266071; 3.国家海洋环境预报中心,北京 100081
  • 收稿日期:2007-09-03 修回日期:2007-09-18 出版日期:2007-10-15
  • 通讯作者: 王辉(1962-),男,安徽濉溪人,研究员,主要从事海洋动力学和海洋生态系统动力学过程研究和模型研究.E-mail:wanghui@cams.cma.gov.cn E-mail:wanghui@cams.cma.gov.cn
  • 基金资助:

    国家自然科学基金项目“南海初级生产对季风变动影响的数值模拟研究”(编号:40531006);“黄海中华哲水蚤种群动力学模型研究”(编号:40676019);“黄东海浮游动物优势种群动态变化机制”(编号:40631008);中国科学院知识创新工程重要方向项目(编号:KSCX2-SW-132)资助.

Review on the Data Assimilation into Marine Ecosystem Model

WANG Hui 1, LIU Gui-mei 2, WAN Li-ying 3   

  1. 1.Chinese Academy of Meteorological Science, Beijing 100081,China; 2.Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology,Chinese Academy of Sciences, Qingdao 266071,China; 3.National Marine Environmental Forecasting Center, Beijing 100081,China
  • Received:2007-09-03 Revised:2007-09-18 Online:2007-10-15 Published:2007-10-10

将数据同化方法引入海洋生态系统动力学模型研究,利用现有的观测数据,获得最佳的模式参数、初始场或提高状态模拟,是当前多学科交叉研究的热门领域。本文依据国内外研究进展,主要就海洋生态模型研究中所采用的变分伴随、卡尔曼滤波、模拟退火法方法进行了介绍,总结了数据同化在我国的海洋生态系统研究中的现状和发展趋势。

Identification, understanding, and prediction of many interdisciplinary biological and oceanographic processes remain as elusive goals of ocean science. However, new ocean technologies are being effectively used to increase the variety and numbers of sampled variables and thus to fill in the gaps of the time-space continuum of interdisciplinary ocean observations. The formulation, accuracy, and efficacy of data assimilation are highly dependent upon the quantity of interdisciplinary observational data. This review aims to describe the work carried out to-data on the data assimilation into marine ecosystem model and the techniques developed for the assimilation of data into marine ecosystem model. Compared with the meteorological forecasting, the data assimilation into marine ecosystem model is in its infancy. In addition, the developments and applications of data assimilation into marine ecosystem model in China was summarized and the related problems were presented.

中图分类号: 

[1]Tang Qisheng, Fan Yuanbing, Lin Hai. Initial inquiring into the developmental strategy of Chinese ocean ecosystem dynamics research[J]. Advances in Earth Science,1996,11(2):160-168.[唐启升,范元炳,林海.中国海洋生态系统动力学发展战略初探[J]. 地球科学进展, 1996,11(2):160-168.]
[2]Wang H. NSFC strategy on funding global change programs with the special reference of GLOBEC[J]. Journal of Ocean University of Qingdao,1999, 29(1): 75-78.
[3]Tang Qisheng, Su Jilan. Study on marine ecosystem dynamics and living resources sustainable utilization[J].Advances in Earth Science,2001, 16(1): 5-11. [唐启升,苏纪兰. 海洋生态系统动力学研究与海洋生物资源可持续利用[J]. 地球科学进展, 2001,16(1): 5-11.]
[4]Courtier P, Derber J C, Ron Errico, et al.Important literature on the use of adjonit variational methods and the Kalman filter in meteorology[J]. Tellus, 1993, 45A: 342-357.
[5]Houtekamer P L, et al.Data assimilation using an ensemble Kalman filter technique[J].Monthly Weather Review,1998, 124: 85-96.
[6]Han Guijun, Li Dong, Ma Jirui,et al.A study on the application of data assimilation in numerical products and prediction of oceanography[J]. Marine Science Bulletin,1999, 18(5): 54-62. [韩桂军,李冬,马继瑞,等.数据同化在海洋数值产品制作及预报中的应用研究[J]. 海洋通报, 1999, 18(5): 54-62.]
[7]Han Guijun, He Bairong, Ma Jirui, et al.A study on data assimilation for the vertical distribution of sea temperature[J].Acta Oceanologica Sinica,2000, 22(4):1-7.[韩桂军,何柏荣,马继瑞,等. 海洋水温垂直分布数据同化方法研究[J]. 海洋学报, 2000, 22(4):1-7.]
[8]Zhu Jiang, Kamachi M. An adaptive variational method for data assimilation with imperfect models[J].Tellus,2000, 52A: 265-279.
[9]Zhu Jiang, Kamachi M. The role of time size in numerical stability of tangent linear models[J]. Monthly Weather Review,2000, 128:1 562-1 572.
[10]Gao Shanhong, Wu Zengmao, Xie Hongqin.The development and applications of Kalman filters in meterrological data assimilation[J]. Advance in Earth Sciences,2000,15(5): 571-575.[高山红,吴增茂,谢红琴. Kalman滤波在气象数据同化中的发展与应用[J].地球科学进展,2000,15(5): 571-575.]
[11]Zhu Jiang, Kamachi M, Wang Dongxiao. Estimation of air-sea heat flux from ocean measurements: an ill-posed problem[J].Journal of Geophysic Research,2002,107(C10):3 159-3 174.
[12]Zhu Jiang, Wang Hui, Kamachi M. The improvement made by a modified TLM in 4DVAR with a Geophysical Boundary Layer Model[J].Advances in Atmospheric Sciences,2002,19:563-582.
[13]Marsili-Libelli, S. Parameter estimation of ecological models[J].Ecology Modeling,1992, 62, 233-258.
[14]Lawson, et al. A data assimilation technique applied to a predator-prey model [J]. Bulletin of Mathematial Biology,1995,57: 593-617.
[15]Fasham M J R, Evans G T. The use of optimization techniques to model marine ecosystem dynamics at the JGOFS station[J].Philosophical Transactions of the Royal Society of London(Series B),1995, 348: 203-209.
[16]Matear R J. Parameter optimization and analysis of ecosystem models using simulated annealing: A case study at Station P[J].Journal of Marine Research,1995, 53: 571-607.
[17]Lawson, et al.Time series sampling and data assimilation in a simple marine ecosystem model [J].Deep Sea Research II,1996, 43: 625-651.
[18]McGillicuddy, et al. An adjoint data assimilation approach to diagnosis of physical and biological control on Pseudocalanus spp.in the Gulf of Maine-Georges Bank region[J].Fisher Oceanography, 1998, 7(3/4): 205-218.
[19]Gunson J A. Oschlies and V. Gar on. Sensitivity of ecosystem parameters to simulated satellite ocean color data using a couple physical-biological model of the North Atlantic[J].Journal of Marine  Research,1999,57:613-639.
[20]Friedrichs M A M. Assimilation of JGOFS EqPac and SeaWiFS data into a marine ecosystem model of the central equatorial Pacific Ocean[J].Deep Sea Reasearch II,2002, 49: 289-319.
[21]Besiktepe S T, Lermusiaux P F J, Robinson A R. Coupled physical and biogeochemical data-driven simulations of Massachusetts Bay in late summer: Real-time and postcruise data assimilation [J]. Journal of Marine Systems, 2003,40/41: 171-212.
[22]Vallino J J. Improving marine ecosystem models:Use of data assimilation and mesocosm experiments[J].Journal of Marine Research,2000, 58: 117-164.
[23]Robinson A R, Lermusiaux P F J.Overview of Data Assimilation, Harvard University Reports in Physical/Interdisciplinary Ocean Science #62, Harvard University,2000.
[24]Ussif A A M, Sandal L K, Steinshamn S I. A new approach of fitting biomass dynamics models to data [J]. Mathematical Biosciences, 2003, 182: 67-69.
[25]Spits, et al.Data assimilation and a pelagic ecosystem model: parameterization using time series observations [J].Journal of Marine Systems,1998, 16: 51-68.
[26]Garcia-Gorriz E, Hoepffner N, Ouberdous M. Assimilation of SeaWiFS data in a coupled physical-biological model of the Adriatic sea[J].Journal of Marine Systems, 2003, 40/41: 233-252.
[27]Hemmings J C P, et al.Assimilating satellite ocean-colour observations into oceanic ecosystem models. The Royal Society. 2002, 10.1098/rsta,2002:1104.
[28]Tjiputra J F, Dierk Polzin, Winguth A M E. Assimilation of seasonal chlorophyll and nutrient data into an adjoint three-dimensional ocean carbon cycle model: Sensitivity analysis and ecosystem parameter optimization[J].Global Biogeochemical Cycles,2007, 21, GB1001, doi:10.1029/2006GB002745.
[29]Kalman R E. A new approach to linear filtering and prediction problems. Transaction of the ASME [J].Journal of Basic Engineering,1960, 82D:34-45.
[30]Kalman R E, Bucy R. New results in linear filtering and prediction Transaction of the ASME[J].Journal of Basic Engineering,1961, 83D: 95-108.
[31]Ghil M, Malanotte-Rizzoli P. Data assimilation in meteorology and oceanography [J]. Adv. Geophys, 1991, 33: 141-266.
[32]Pham D, Verron J, Roubaud M. Singular evolutive Kalman filter with of initialization for data assimilation in oceanography[J].Journal of Marine Systems,1997, 16: 323-340.
[33]Pham D, Verron J, Gourdeau L. Singular evolutive Kalman filters for data assimilation in oceanography[J].Comptes Rendus Academic des Sciences Paris,1998, 326: 255-260.
[34]Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J].Journal of Geophysical research,1994,99:10 143-10 162.
[35]Carmillet V, Brankart J M, Brasseur P. et al. A singular evolutive extended Kalman filter to assimilate ocean color data in a coupled physical-biochemical model of the North Atlantic Ocean[J]. Ocean Modelling,2001,3:167-192.
[36]Fasham M J R, Ducklow W, McKelvie S M.A nitrogen-based model of plankton dynamics in the ocean mixed layer[J].Journal of Marine Research,1990, 48: 591-639.
[37]Triantafyllou, et al.A singular evolutive interpolated Kalman filter for efficient data assimilation in a 3-D complex physical-biogeochemical model of the Cretan Sea [J]. Journal of Marine Systems,2003, 40/41: 213-231.
[38]Allen J I, Eknes M, Evensen G. An ensemble Kalman filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea [J].Annales Geophysicae,2002, 20: 1-13.
[39]Eknes M, Evensen G. An ensemble Kalman filter with a 1-D marine ecosystem model[J].Journal of Marine Systems,2002, 36: 75-100.
[40]Natvik L J, Evensen G. Assimilation of ocean colour data into a biochemical model of the North Atlantic I: Data assimilation experiments[J].Journal of Marine Systems,2003, 40/41: 127-153.
[41]Kruger J. Simulated Annealing: A tool for data assimilation into an almost steady model state[J].Journal of Physicat Oceanography,1993,23:679-688.
[42]Goffe W L, Ferrier G D,Rogers J. Global optimization of statistical functions with simulated annealing[J].Journal of Econometrics,1994, 60: 65-99.
[43]Hurtt G C, Armstrong R. A pelagic ecosystem model calibrated with BATS data [J].Deep Sea Research II,1996, 43: 653-683.
[44]Wan Zhenwen, Yuan Yeli, Qiao Fangli. Study on optimization of the parameters of a marine ecosystem dynamics model for red tide [J].Oceanologica Et Limnologia Sinica,2000,31(2),205-209.[万振文,袁业立,乔方立.海洋赤潮生态模型参数优化研究 [J]. 海洋与湖沼, 2000,31(2),205-209.]
[45]Zhao Liang. Amodeling study of the phytoplankton dynamic in the Bohai Sea[D]. Qindao: Ocean University of China,2003:84-113.[赵亮.渤海浮游植物生态动力学模型研究[D].青岛:中国海洋大学,2003:84-113.]
[46]Wu Zengmao, Xie Hongqin, Zhang Zhinan, et al.Analysis of the complexity and the research methods of the marine ecosystem dynamics prediction[J]. Advances in Earth Science,2004,19(1):81-86.[吴增茂,谢红琴,张志南,等. 海洋生态预报的复杂性与研究方法的讨论[J]. 地球科学进展, 2004,19(1):81-86.]
[47]Pan Delu, Doerffer R, Mao Tianming,et al.A study on the radiance imagery of satellite ocean color remote sensing [J].Acta Oceanologica Sinica,1997, 19(6): 43-55. [潘德炉,Doerffer R,毛天明,等.海洋水色卫星的辐射模拟图像研究[J]. 海洋学报, 1997, 19(6): 43-55.]
[48]Pan Delu, Li Shujing. A study on the signal characteristic scale of satellite ocean color remote sensing[J].Journal of Remote Sensing,1998, 2(1): 26-31. [潘德炉,李淑菁. 卫星海洋水色遥感信息特征量的研究[J]. 遥感学报,1998, 2(1): 26-31.]
[49]Fei Zunle,Trees C C,Li Baohua. Satellite remote sensing for oceanic primary productivity[J].Journal of Oceanography of Huanghai & Bohai Seas,1997,15(1):35-47.[费尊乐,Trees C C,李宝华. 利用叶绿素资料计算初级生产力[J].黄渤海海洋,1997,15(1):35-47.]
[50]Shang Shaolin, Hong Huasheng, Zhang Caiyun,et al. Distribution feature of the Taiwan Straits region in winter,1998 as observed by SeaWiFS[J].Marine Science Bulletin,2001,20(2):25-29.[商少凌,洪华生,张彩云,等.1998年冬季台湾海峡遥测叶绿素分布特征[J].海洋通报,2001,20(2):25-29.]
[51]Zhao Dongzhi,Zhao Ling, Zhang Fengshou. Type of formation,distribution and temporal trend of red tides occurred in the China Sea[J]. Marine Environmental Science,2003, 22(3):7-11.[赵冬至,赵玲,张丰收.我国海域赤潮灾害的类型、分布与变化趋势[J]. 海洋环境科学, 2003,22(3):7-11.]
[52]Li Guosheng, Wang Fang, Liang Qiang,et al. Estimation of ocean primary productivity by remote sensing and Introduction to spatio-temporal variation mechanism for the east China sea[J].Acta Geographica Sinica,2003, 58(4):483-493.[李国胜,王芳,梁强,等.东海初级生产力遥感反演及其时空演化机制[J].地理学报, 2003,58(4):483-493.]

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