期刊
ADVANCES IN WATER RESOURCES
卷 26, 期 2, 页码 137-149出版社
ELSEVIER SCI LTD
DOI: 10.1016/S0309-1708(02)00088-X
关键词
data assimilation; land surface modeling; microwave remote sensing; soil moisture
An Ensemble Kalman filter (EnKF) is used to assimilate airborne measurements of 1.4 GHz surface brightness temperature (TB) acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97) into the TOPMODEL-based Land-Atmosphere Transfer Scheme (TOPLATS). In this way, the potential of using EnKF-assimilated remote measurements of T-B to compensate land surface model predictions for errors arising from a climatological description of rainfall is assessed. The use of a real remotely sensed data source allows for a more complete examination of the challenges faced in implementing assimilation strategies than previous studies where observations were synthetically generated. Results demonstrate that the EnKF is an effective and computationally competitive strategy for the assimilation of remotely sensed T-B measurements into land surface models. The EnKF is capable of extracting spatial and temporal trends in root-zone (40 cm) soil water content from T-B measurements based solely on surface (5 cm.) conditions. The accuracy of surface state and flux predictions made with the EnKF, ESTAR T-B measurements, and climatological rainfall data within the Central Facility site during SGP97 are shown to be superior to predictions derived from open loop modeling driven by sparse temporal sampling of rainfall at frequencies consistent with expectations of future missions designed to measure rainfall from space (6-10 observations per day). Specific assimilation challenges posed by inadequacies in land surface model physics and spatial support contrasts between model predictions and sensor retrievals are discussed. (C) 2002 Elsevier Science Ltd. All rights reserved.
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