DEnKF-Variational Hybrid Snow Cover Fraction Data Assimilation for Improving Snow Simulations with the Common Land Model

This study assesses the analysis performance of a hybrid DEnKF-variational data assimilation (DA) method (DEnVar) for assimilating the MODIS snow cover fraction (SCF) into the Common Land Model (CoLM). Coupling a deterministic ensemble Kalman filter (DEnKF) with a one-dimensional variational DA method (1DVar), DEnVar without observation perturbations is a two-step DA method. That is, the analysis ensemble mean and analysis error covariance of DEnKF are introduced into the 1DVar hybrid cost function, and the analysis mean of DEnKF is replaced by the 1DVar analysis. The analysis performance of DEnVar was experimentally compared with DEnKF, 1DVar, and EnVar (hybrid ensemble-variational DA) at five sites in the Altay region of China from November 2008 to March 2009. From our results, it is shown that the four DA experiments can improve snow simulations at most sites when the available MODIS SCF is assimilated. The DEnVar experiment using the hybrid error covariance shows the best analysis performance among the four DA experiments at most sites. Furthermore, sensitivity tests show that DEnVar is slightly sensitive to the weighting coefficient, which controls the respective weights of ensembleand (National Meteorological Center) NMC-based error covariances, but is highly sensitive to the observation error. DEnVar obtains better analysis performance when using the ensemble-based analysis error covariance rather than the hybrid error covariance coupling ensemble-based analysis and static NMC-based OPEN ACCESS Remote Sens. 2014, 6 10613 error covariances. The inaccurate distribution of observation error may invalidate the DEnVar method.