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《大气和海洋科学快报(英文版)》 2009-03
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An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation

TIAN Xiang-Jun and XIE Zheng-Hui ICCES/LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China  
Land surface models are often highly nonlinear with model physics that contain parameterized discontinuities. These model attributes severely limit the application of advanced variational data assimilation methods into land data assimilation. The ensemble Kalman filter (EnKF) has been widely employed for land data assimilation because of its simple conceptual formulation and relative ease of implementation. An updated ensemble-based three-dimensional variational assimilation (En3-DVar) method is proposed for land data assimilation.This new method incorporates Monte Carlo sampling strategies into the 3-D variational data assimilation framework. The proper orthogonal decomposition (POD) technique is used to efficiently approximate a forecast ensemble produced by the Monte Carlo method in a 3-D space that uses a set of base vectors that span the ensemble. The data assimilation process is thus significantly simplified. Our assimilation experiments indicate that this new En3-DVar method considerably outperforms the EnKF method by increasing assimilation precision. Furthermore, computational costs for the new En3-DVar method are much lower than for the EnKF method.
【Fund】: supported by the National Natural Science Foundation of China (Grant No. 40705035);; the National High Technology Research and Development Program of China (863 Program) (Grant Nos. 2009AA12Z129 and 2007AA12Z144)
【CateGory Index】: P456.7
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