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集合Kalman滤波在土壤湿度同化中的应用
引用本文:张生雷,谢正辉,师春香,等.集合Kalman滤波在土壤湿度同化中的应用[J].大气科学,2008,32(6):1419-1430.
作者姓名:张生雷  谢正辉  师春香  
作者单位:1.中国科学院大气物理研究所国际气候与环境中心, 北京 100029; 中国科学院研究生院, 北京 100049
基金项目:国家自然科学基金资助项目90411007, 国家重点基础研究发展规划项目2005CB321703, 中国科学院知识创新工程重要方向项目KZCX2\|YW\|126\|2、 KZCX2\|YW\|217
摘    要:基于非饱和土壤水模型和集合卡尔曼滤波 (Ensemble Kalman Filter, 简称EnKF) 并结合陆面水文模型——可变下渗能力模型 (Variable Infiltration Capacity, 简称VIC模型) 发展了一个土壤湿度同化方案。利用1998年6~8月淮河流域能量和水循环试验 (HUBEX) 项目外场观测试验区——史灌河流域梅山站土壤湿度逐日观测资料及1986~1993年合肥和南阳两站点的土壤湿度旬观测资料进行同化试验, 结果表明该同化方案能完整估计土壤湿度廓线, 同化的土壤湿度与观测资料基本吻合, 反映了土壤湿度的日、 旬、 月、 季变化, 同化方案是合理的。与基于扩展卡尔曼滤波 (Extended Kalman Filter, 简称EKF) 的土壤湿度同化方案的结果比较, 基于EnKF的土壤湿度同化方案易于实现, 且通过选择恰当的集合样本数其同化效果总体上略优于EKF同化方案, 但前者同化时需要花费较多的计算时间。

关 键 词:陆面数据同化  土壤湿度  集合卡尔曼滤波  扩展卡尔曼滤波  VIC模型

Applications of ensemble Kalman filter in soil moisture assimilation
Zhang Shenglei,Xie Zhenghui,Shi Chunxiang and et al.Applications of ensemble Kalman filter in soil moisture assimilation[J].Chinese Journal of Atmospheric Sciences,2008,32(6):1419-1430.
Authors:Zhang Shenglei  Xie Zhenghui  Shi Chunxiang and
Institution:1.Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Graduate University of Chinese Academy of Sciences, Beijing 1000492.Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
Abstract:A soil moisture assimilation scheme is developed,which is based on the ensemble Kalman filter(EnKF),an unsaturated soil water flow model and the Variable Infiltration Capacity(VIC) land surface hydrologic model.To validate and verify the scheme,several assimilation experiments with in-situ observations and infiltration derived from the VIC model driven by observed meteorologic forcings are presented.The results demonstrate that the assimilation scheme can retrieve the true soil moisture profile,improve the estimation of soil moisture,and capture the daily,dekad,monthly and seasonal variation of soil moisture,which shows that the scheme is reasonable.Comparisons of the assimilation experiments with the algorithms EnKF and Extended Kalman Filter(EKF) show that the EnKF scheme with the suitable ensemble size is easy to implement and is a little superior to the EKF scheme for soil moisture assimilation,but the computational cost for the EnKF scheme is higher than that for the EKF scheme.
Keywords:land data assimilation  soil moisture  ensemble Kalman filter  extended Kalman filter  VIC model
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