DEnKF-based Assimilation of MODIS-Derived Snow Cover Products into Common Land Model Considering the Model Sub-grid Heterogeneity

The use of perturbed observations in the traditional ensemble Kalman filter (EnKF) introduces uncertainties and results in sub-optimal model state estimates. A modified EnKF method, the deterministic ensemble Kalman filter (DEnKF), can approach the analysis error covariance matrix without perturbing observations. As a forecast operator, the common land model (CoLM) is advantageous for sub-grid heterogeneity analysis. To reduce some errors stemming from the uncertainty in snow data assimilation, a new DEnKF-based snow data assimilation method is proposed for considering model sub-grid heterogeneity. The proposed method was used to assimilate the MODIS-derived snow cover products into CoLM for improving simulated snow depth. The daily snow depth of five meteorological stations from November 2007 to April 2008 in Altay is used for validation. The experimental results show that the DEnKF-based assimilation method can improve the simulated snow depth effectively. The improved snow depth does not only show the consistent time trends with in-situ snow depth but also reflects time-varying characteristics for different seasons.