A Snow Water Equivalent Retrieval Framework Coupling Microwave Remote Sensing and Hydrology Model

Retrieving snow water equivalent (SWE) from X-, Ku-, and Ka-band microwave observations suffers from low sensitivity and non-uniqueness due to a lack of understanding of snow microphysics and complex terrain covered by snowpack. This paper demonstrates a general framework to combine the power of hydrology modeling and microwave remote sensing. A column-based multi-layer snow hydrology model which includes the grain size evolution is coupled with the dense media radiative transfer (DMRT-QMS). The snow variables are then updated by an ensemble Kalman Filter (EnKF) algorithm to incorporate information from microwave observations. The proposed approach has the potential to improve the accuracy of SWE remote sensing taking advantage of multi-source data fusion in a data assimilation framework. An SWE retrieval (per 4 hours) RMSE of 25.81mm is achieved, which meets the international standard such as NASA SCLP and ESA CoReH2O and is 62.25% lower than that of open-loop hydrology model prediction. This paper also makes a sensitivity analysis of the microwave observation interval to the retrieval results, offering insights into the choice of temporal resolution in microwave remote sensing.