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Assessing the Performance of the Ensemble Kalman Filter for Soil Moisture Profile Retrieval

GOU Haofeng1,2,LIU Yanhua1,ZHANG Shuwen1,LI Deqin1(1.Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province,College of Atmospheric Sciences,Lanzhou University,Lanzhou 730000,China;2.Lanzhou Meteorological Bureau,Lanzhou 730020,China)  
The ensemble Kalman filter is an easy to use,flexible,and efficient data assimilation algorithm widely used in Land Surface Data Assimilation System.It bases on the normality approximation of model error and observational error as well as the linearity assumption of the model.However,the soil moisture equation is highly nonlinear and also soil moisture can be highly skewed toward the wet or dry ends.To evaluate the effects of these approximations and the performance of the ensemble Kalman filter (EnKF) in estimating soil moisture profile based on the near-surface soil moisture measurements,the results from the EnKF are compared with those obtained from a Sequential Importance Resampling (SIR) particle filter that is one of nonlinear filters.The comparative results show:The EnKF can quickly and accurately obtain the exact soil moisture profile regardless of a small ensemble size or a large ensemble size;however,the SIR needs very large ensemble members.The near-surface soil moisture marginal forecast probability densities,the skewness and kurtosis obtained from the EnKF are completely different from those from the SIR filter;the densities from the EnKF is only one peak mode during the total assimilation time window while those from the SIR experience processes from one peak mode to two peak modes and again to one peak mode.
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