Large-Scale High-Resolution Modeling of Microwave Radiance of a Deep Maritime Alpine Snowpack

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Applying passive microwave (PM) remote sensing to estimate mountain snow water equivalent (SWE) is challenging due in part to the large PM footprints and the high subgrid spatial variability of snow properties. In this paper, we linked the land surface model Simplified Simple Biosphere version 3.0 (SSiB3) with the radiative transfer model Microwave Emission Model of Layered Snowpacks, and we forced the coupled model with the disaggregated North American Data Assimilation System phase 2 (NLDAS-2) meteorological data to simulate the snow properties and the 36.5-GHz microwave brightness temperature $(T_{b})$ at a spatial resolution of 90 m. The modeled SWE and $T_{b}$ were used to interpret the radiance observed by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and to explore the impact of snow spatial variability on the microwave radiance in a mountain environment. The modeling was carried out over the Upper Kern Basin, Sierra Nevada. We developed new methods for modeling the effect of large snowfall events on the snow grain size. We aggregated the modeled radiance to the satellite scale using the AMSR-E 36.5-GHz antenna sampling pattern. The methods were calibrated for water years (WYs) 2004–2006 and validated for WYs 2003, 2007, and 2008. The coefficient governing the grain growth rate was also calibrated. The modeling results showed that the new snow grain estimation scheme reduced the error in the modeled radiance by 55.2% during the calibration period. The $T_{b}$ root-mean-square error was 3.1 K during the snow accumulation season for the validation period. The modeling results showed that, in the study area, the microwave signal saturated for SWE values between 0.3 and 0.5 m. It was found that the subfootprint-scale SWE variability has a significant impact on the saturation of spaceborne PM observations. The experiments demonstrate that this modeling system improves the accuracy of the radiance modeling, which is critical for estimating the mountain SWE via PM remote sensing either for informing direct retrieval algorithms or for data assimilation. We plan to use the modeling framework in future radiance assimilation studies.