【摘要】 为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。更多还原

【Abstract】 To improve the accuracy of estimation of soil moisture under model parameters with uncertainties,this paper develops a soil moisture assimilation scheme based on state-parameter estimation method,in which the dual ensemble Kalman filter(DEnKF)is integrated into Simple Biosphere model(version 2)(SiB2)to assimilate surface soil moisture observations to simultaneously optimize model parameters(soil texture and organic matter)and update model states(soil moisture in three soil layers).A series of numerical experiments are conducted to assess the performance of the proposed scheme based on obtained observations from Arou station in the upper reaches of Heihe river basin in 2008.Results showed that DEnKF can optimize parameters and state variables simultaneously.Compared to EnKF,DEnKF can obtain more accurate estimation of soil moisture in surface and root zone than EnKF,especially when observation data are scarce.The proposed soil moisture scheme is easy to realize and can correct model bias,so it is suitable for operational data assimilation systems on regional and global scales.更多还原