Enhancing the estimation of continental-scale snow water equivalent by assimilating MODIS snow cover with the ensemble Kalman filter

High-quality continental-scale snow water equivalent (SWE) data sets are generally not available, although they are important for climate research and water resources management. This study investigates the feasibility of a framework for developing such needed data sets over North America, through the ensemble Kalman filter (EnKF) approach, which assimilates the snow cover fraction observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) into the Community Land Model (CLM). We use meteorological forcing from the Global Land Data Assimilation System (GLDAS) to drive the CLM and apply a snow density-based observation operator. This new operator is able to fit the observed seasonally varying relationship between the snow cover fraction and the snow depth. Surface measurements from Canada and the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) estimates (in particular regions) are used to evaluate the assimilation results. The filter performance, including its ensemble statistics in different landscapes and climatic zones, is interpreted. Compared to the open loop, the EnKF method more accurately simulates the seasonal variability of SWE and reduces the uncertainties in the ensemble spread. Different simulations are also compared with spatially distributed climatological statistics from a re-gridded data set, which shows that the SWE estimates from the EnKF are most improved in the mountainous west, the northern Great Plains, and the west and east coast regions. Limitations of the assimilation system are analyzed, and the domain-wide innovation mean and normalized innovation variance are assessed, yielding valuable insights (e. g., about the misrepresentation of filter parameters) as to implementing the EnKF method for large-scale snow properties estimation.