Hybrid Assimilation of Snow Cover Improves Land Surface Simulations over Northern China

Abstract

Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, the small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021-2022. Compared to the open loop experiment (without SCF assimilation), the root mean square error (RMSE) of SCF is reduced by 6% through the original EnSRF, and is even lower (by 14%) in the EnSRFDI (i.e., combined DI and EnSRF) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling-Gupta efficiency (KGE) increasing at 60% and 56%-70% stations respectively, particularly under conditions with near-freezing temperature, where reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products.