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Assessing the Performance of Separate Bias Kalman Filter in Correcting the Model Bias for Estimation of Soil Moisture Profiles

查看全文 作　　者：Bangjun [1,2]CAO;Fuping [2,3]MAO;Shuwen [2]ZHANG;Shaoying [2]LI;Tian [2,4]WANG 高影响力作者 机构地区：[1]School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225;[2]Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000;[3]No. 2 Qishan Road, Mount Wuyi 354301;[4]Moji Co. Ltd., Beijing 100016高影响力机构 出　　处：《Journal of Meteorological Research》索引2019年第33卷第3期,共9页高影响力期刊 基　　金：Supported by the National Natural Science Foundation of China(41575098);National Key Research and Development Program of China(2018YFC1505702) 摘　　要：The performance of separate bias Kalman filter (SepKF) in correcting the model bias for the improvement of soil moisture profiles is evaluated by assimilating the near-surface soil moisture observations into a land surface model (LSM). First, an observing system simulation experiment (OSSE) is carried out, where the true soil moisture is known, two types of model bias (i.e., constant and sinusoidal) are specified, and the bias error covariance matrix is assumed to be proportional to the model forecast error covariance matrix with a ratio λ. Second, a real assimilation experiment is carried out with measurements at a site over Northwest China. In the OSSE, the soil moisture estimation with the SepKF is improved compared with ensemble Kalman filter (EnKF) without the bias filter, because SepKF can properly correct the model bias, especially in the situation with a large model bias. However, the performance of SepKF becomes slightly worse if the constant model bias increases or temporal variability of the sinusoidal model bias becomes large. It is suggested that the ratio λ should be increased (decreased) in order to improve the soil moisture estimation if temporal variability of the sinusoidal model bias becomes high (low). Finally, the assimilation experiment with real observations also shows that SepKF can further improve the estimation of soil moisture profiles compared with EnKF without the bias correction. 关 键 词：soil moisture BIAS correction ENSEMBLE KALMAN filter (EnKF) Noah-MP
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