ң�м�����Ӧ�� 2004, 19(5) 424-430 DOI:     ISSN: 1004-0323 CN: 62-1099/TP

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A Review of Land Data Assimilation System
HUANG Chun-lin, LI Xin
(Cold and Aird Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences,Lanzhou730000,China)
Abstract:

The improvement and development of atmospheric and oceanic data assimilation system promotesthe study of land data assimilation system (LDAS). In the beginning of 21st century, with the formationof the North American Land Data Assimilation System(NLDAS) and the Global Land Data AssimilationSystem(GLDAS), the study that utilizes satellite and radar data to assimilate soil moisture, surfacetemperature and energy flux has being carried on. At the same time, the land data assimilation has beenthe hotspot of the study in land surface process and hydrology process. In this paper, the main frameworkof land data assimilation system is summarized in detail. The North American Land Data AssimilationSystem (NLDAS) and Global Land Data Assimilation System (GLDAS), European Land DataAssimilation System (ELDAS) and West China Land Data Assimilation System (WCLDAS) areintroduced. At last, some problems that need to be resolved in the study of land data assimilation systemare pointed out.

Keywords: Data Assimilation   Land Surface Model,   Kalman Filter   Simulated Annealing  
�ո����� 2004-05-07 �޻����� 2004-08-02 ����淢������ 2004-10-20 
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��1����Daley R. Atmospheric Data Analysis��M��.New York: Cambridge University Press, 1991.��2����Talagrand O. Assimilation of Observations, An Introduction��J��. Journal of Meteorological Society of Japan, 1997, 75(1):191��209.��3����McLaughlin D. Recent Development in Hydrologic Data Assimilation��J��. Reviews of Geophysics. 1995:977��984.��4������Ծɽ.����ͬ��——����Ե�𡢺������Ҫ������J��.����Ԥ��,1999,16(1):11��20.��5����McLaughlin D. An Integrated Approach to Hydrologic DataAssimilation: Interpolation, Smoothing, and Filtering��J��.Advances in Water Resources, 2002 25:1275��1286.��6��������,С�ؿ���,�̹���.һ������ģ���˻𷨵�½������ͬ���㷨��J��.�����ѧ��չ,2003 18(4):632��636.��7����Li Xin, Koike T, Pathmathevan M. A Very Fast SimulatedRe-Annealing (VFSA) Approach for Land Data Assimilation��J��. Computer&Geosciences,2004, 30:239��248.��8����Pathmathevan M, Koike T, Li X. A New Satellite-BasedData Assimilatoin Algorithm to Determine Spatial andTemporal Variations of Soil Moisture and TemperatureProfiles��J��. Journal of the Meteorological Society of Japan,2003,81(5):1111��1135.��9����Pathmathevan M, Koike T, Li X,et al. A Simplified LandData Assimilation Scheme and Its Application to SoilMoisture Experiments in 2002 ( SMEX02 )��J��. WaterResources Research, 2003, 39(12), 1341, doi: 10. 1029/2003WR002124.��10����Galantowicz J F, Entekhabi D, Njoku E G. Tests of Sequential Data Assimilation for Retrieving Profile Soil Moistureand Temperature from Observed L-band Radiobrightness��J��.IEEE Transactions on Geoscience and Remote Sensing,1999, 37(4):1860��1870.��11����Hoeben R, Troch P A. Assimilation of Active MicrowaveObservation Data for Soil Moisture Profile Estimation��J��.Water Resources Research, 2000, 36(10):2805��2819.��12����Kaleita AL, Kumar P. AVHRR Estimates of Surface Temperature During the Southern Great Plains 1997 Experiment��J��. Journal of Geophysical Research 2000,105(D16):20,791��801.��13����Kumar P. Assimilation of Near-Surface Temperature UsingExtended Kalman filter��J��. Advances in Water Resources,2003,26:7��93.��14����Lakshmi V. A Simple Surface Temperature Assimilation Scheme for Use in Land Surface Models��J��. Water ResourceResearch, 2000;36(12):3687��3700.��15����Houser P R, Shuttleworth W J, Gupta H V. Integration ofSoil Moisture Remote Sensing and Hydrologic ModelingUsing Data Assimilation��J��. Water Resource Research,1998,34(12):3405��3420.��16����Martin Verlaan. Efficient Kalman Filtering Algorithms forHydrodynamic Models��D��.Delft University of Technology,1998.��17����Reichle R H, Entekhabi D. Downscaling of Radio BrightnessMeasurements for Soil Moisture Estimation: A Four-Dimensional Variational Data Assimilation Approach��J��.Water Resources Research, 2001, 37(9):2353��2364.��18����Schuurmans J M, Troch P A. Assimilation of Remotely Sensed Latent Heat Flux in Distributed Hydrological Model��J��.Advances in Water Resources, 2003, 26:151��159.��19����Walker J P, Willgoose G R. One-Dimensional Soil MoistureProfile Retrieval by Assimilation of Near-SurfaceObservations: A Comparison of Retrieval Algorithms��J��.Advances in Water Resources, 2001, 24:631��650.��20����Weiss M, Troufleau D. Coupling Canopy Functioning and Radiative Transfer Models for Remote Sensing Data Assimilation��J��. Agricultural and Forest Meteorology,2001,108:113��128.��21����http://ldas.gsfc.nasa.gov/��Z��.��22����Development of a European Land Data Assimilation Systemto Predict Floods and Droughts. Description of Work��Z��.http://www.knmi.nl/samenw/eldas/��23����http://ldas.westgis.ac.cn��Z��.��24����ţ��Ծ,������,�����.½������о�����״�뷢չ���ơ�J��.�����ѧ��չ,1997,12(2):20��25.��25�������ķ�,�����.½�����ģʽ�о��еļ������⡲J��.Ӧ������ѧ��,1997,8(����):50��57.��26������ǿ.����½�����ģʽ��J��.�����ѧ,1998,18(3):295��304.��27����Kalman R E. A New Approach to Linear Filtering and Prediction Problems��J��. Trans ASME Series D J Basic Eng,1960, 82:35��45.��28����Evensen G. Sequential Data Assimilation with a NonlinearQuasi-Geostrophic Model Using Monte-Carlo Methods toForecast Error Statistics��J��. Journal of GeophysicalResearch,1994, 99(C5):10143��10162.��29����Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization bySimulated Annealing��J��. Science, 1983, 220( 4598): 671��680.��30����Bach H, Mauser W. Methods and Examples for Remote Sensing Data Assimilation in Land Surface Process Modeling��J��.IEEE Transactions on Geoscience and Remote Sensing,2003,41(7):1629��1637.��31����Cosgrove B A. Real-Time and Retrospective Forcing in theNorth American Land Data Assimilation System (NLDAS)Project��J��. Journal of Geophysics Research, 2003, 108(D22),8842, doi:10.1029/2002JD003118.��32����Lohmann D. Streamflow and Water Balance Intercomparis-������ons of Four Land Surface Models in the North AmericanLand Data Assimilation System Project��J��. Journal ofGeophysics Research, 2004, 109, D07S91, doi: 10. 1029/2003JD003517.��33����Luo L. Validation of the North American Land Data Assimilation System (NLDAS) Retrospective Forcing Over theSouthern Great Plains��J��. Journal of Geophysics Research,2003, 108(D22), 8843, doi:10.1029/2002JD003246.��34����Mitchell K E. The Multi-institution North American LandData Assimilation System (NLDAS): Utilizing MultipleGCIP Products and Partners in a Continental DistributedHydrological Modeling System��J��. Journal of GeophysicsResearch, 2004, 109, D07S90, doi: 10. 1029/2003JD003823.��35����Sheffield J. Snow Process Modeling in the North AmericanLand Data Assimilation System (NLDAS): 1. Evaluation ofModel-Simulated Snow Cover Extent��J��. Journal ofGeophysics Research, 2003,108(D22), 8849, doi:10.1029/2002JD003274.��36����Pan M. Snow Process Modeling in the North American LandData Assimilation System ( NLDAS): 2. Evaluation ofModel Simulated Snow Water Equivalent��J��. Journal ofGeophysics Research, 2003, 108(D22), 8850, doi:10.1029/2003JD003994.��37����Pinker R T. Surface Radiation Budgets in Support of theGEWEX Continental-Scale International Project (GCIP) andthe GEWEX Americas Prediction Project (GAPP), Includingthe North American Land Data Assimilation System(NLDAS) project��J��. Journal of Geophysics Research,2003, 108(D22), 8844, doi:10.1029/2002JD003301.��38����Robock A. Evaluation of the North American Land DataAssimilation System Over the Southern Great Plains Duringthe Warm Season��J��. Journal of Geophysics Research,2003, 108(D22), 8846, doi:10.1029/2002JD003245.��39����Schaake J C. An Intercomparison of Soil Moisture Fields inthe North American Land Data Assimilation System(NLDAS)��J��. Journal of Geophysics Research, 2004,109,D01S90, doi:10.1029/2002JD003309.��40����Seuffert G, Wilker H. Soil Moisture Analysis CombiningScreen-Level Parameters and Microwave BrightnessTemperature: A Test with Field Data��J��. GeophysicalResearch Letters, 2003, 30 (10): 1498, doi: 10. 1029/2003GL017128.��41����Software Engineering Plan for the Land Information System��Z��. Version 2.1.2002, Oct. http://lis.gsfc.nasa.gov/��42����Software Design Document for the Land Information System��Z��. Version 1. http://lis.gsfc.nasa.gov/��43����Software Engineering Plan for the Land Information System��Z��. Version 1,2002,Jun. http://lis.gsfc.nasa.gov/��44����http://www.gmes-geoland.info/��Z��/��45����Jean-Christophe Calvet1, Pedro Viterbo2, Philippe Ciais,etal. Assimilation of Remote Sensing Data to Monitor theTerrestrial Carbon Cycle: The Carbon Observatory ofGeoland��Z��. http://www. globalcarbonproject. org/CARBON%20PORTAL/AGENDAS.

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