文章基本信息

标题：An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation
其他标题：An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation
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作者：Tian Xiang-Jun ; Xie Zheng-Hui
期刊名称：Atmospheric and Oceanic Science Letters
印刷版ISSN：1674-2834
电子版ISSN：2376-6123
出版年度：2009
卷号：2
期号：3
页码：125-129
DOI：10.1080/16742834.2009.11446792
出版社：Taylor and Francis Ltd
摘要：Abstract 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.
关键词：land data assimilation ; En3-DVar ; POD ; EnKF