A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles

作     者：张述文 李得勤 邱崇践 

作者机构：Key Laboratory for Semi-Arid Climate Change of the Ministry of EducationCollege of Atmospheric Sciences Lanzhou University Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2011年第28卷第1期

页      面：195-206页

核心收录：

学科分类：09[农学] 0903[农学-农业资源与环境] 090301[农学-土壤学] 

基　　金：supported by the National Natural Science Foundation of China (Grant Nos 40775065 and 41075074) the National Special Fund for Meteorology (Grant No GYHY200806029) 

主　　题：multimodel EnKF soil moisture land data assimilation land surface model 

摘      要：With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms： the Simple Model Average （SMA） and the Weighted Average Method （WAM）. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.