基于集合卡尔曼滤波的土壤水分同化试验

集合卡尔曼滤波是由大气数据同化发展的新的顺序同化算法,它利用蒙特卡罗方法计算背景场的误差协方差矩阵,克服了卡尔曼滤波需要线性化的模型算子和观测算子的难点。我们发展了一个基于集合卡尔曼滤波和简单生物圈模型(SiB2,Simple Biosphere Model)的单点陆面数据同化方案。利用1998年7月6日至8月9日青藏高原GAME-Tibet实验区MS3608站点的观测数据进行了同化试验。结果表明,利用集合卡尔曼滤波的数据同化方法可以明显地提高表层、根区、深层土壤水分的估算精度。

Experiments of Soil Moisture Data Assimilation System Based on Ensemble Kalman Filter

Ensemble Kalman filter is a new sequential data assimilation algorithmwhich originally developed from the field of atmospheric data assimilation.It calculates background error covariance matrix using Monte-Carlo method and is able to resolve the nonlinearity and discontinuityexist withinmodel operator and observation operator.When observation data are assimilated at each time step,background error statistics estimated from the phase-space distribution of an ensemble of model states are used to calculate the Kalman gain matrix and the analysis increments.In this work,we develop A one-dimensionalland data assimilation scheme based on ensemble Kalman filter and simple biospheremodel(SiB2) to assimilate soil moisture observation.We also do some assimilation experiments using GAME-Tibet observation data from July 6 to August 9,1998,at the MS3608 stationonthe TibetanPlateau.Once every 6 hours,in situ observations of soil moisture at the depths of 4,20,100 cm are assimilated into land surface model(SiB2) and the best estimationsof soil moisture atthe surface layer,the root zone and the deep layerare calculated.The results indicatethat the data assimilationcan significantly improve the soil moisture estimationin the surface layer,the root zone andthedeep layer.And we think that the Ensemble Kalman filter is both practical and effective for assimilating in situobservation into land surface models.