Sensitivity analysis on land data assimilation scheme of soil moisture

We develop a one-dimensional land data assimilation scheme based on the ensemble Kalman filter (EnKF) and simple biosphere model (SiB2).In order to evaluate the performance of the assimilation system, we do some assimilation experiments, using GAME-Tibet observation data from July 6 to August 9 in 1998, at the MS3608 site on the Tibetan plateau.When the current observations, in situ observations of soil moisture at the depth of 4,20 and 100 cm are assimilated into land surface model (SiB2), the best estimations of soil moisture at the surface layer, the root zone and the deep layer are calculated.We also analyze the influence of the ensemble size, the assimilation cycle, the model error, the background error and the observation error on the assimilation system.The results indicate: (1) The error in assimilation system can be reduced by increasing the ensemble members, but it will make the operation efficiency lower; (2) As for EnKF, it is unimportant for the performance of the system whether the estimation of the background is accurate; (3) The accurate estimation of the model error and the observation error can decrease the soil moisture error in the surface layer, the root zone and the deep layer; And (4) the estimation of soil moisture can be improved by using the data assimilation method.