Enhanced Identification of Snow Melt and Refreeze Events From Passive Microwave Brightness Temperature Using Air Temperature

Snow melt and refreeze events are important determinants of spring runoff timing, and snowpack stratigraphy and metamorphism. Previous studies have established the utility of differences between twice-daily passive microwave brightness temperature (T_b) observations, called the diurnal amplitude variation (DAV), for identifying snow melt and refreeze. Liquid water in snow leads to a large increase in microwave emissivity compared to a completely frozen snowpack, so phase changes from nighttime freezing and daytime melting result in high DAV values. However, the physical temperature of the land surface also contributes to brightness temperature, independent of the phase of water. Thus, it is important to account for physical temperature change when using T_b differences to detect snow melt and refreeze. Here, we use near-surface air temperature (T_a) to approximate the physical temperature of the land surface and compare diurnal T_b changes (ΔT_b) from the Advanced Microwave Scanning Radiometer for the Earth Observing System satellite instrument to coincident T_a changes. We find that an approximately linear relationship exists between ΔT_b and ΔT_a for frozen snow and fit this relationship using modal linear regression. Melt and refreeze events are identified as large positive and negative excursions from the regression line, respectively. We demonstrate the method in the Northern Great Plains, USA, and evaluate it using ground-based data from Senator Beck Basin Study Area, Colorado, USA. Melt and refreeze events identified from satellite observations mostly occur after the annual peak snow accumulation and are consistent with snow temperature and snowpack energy balance observations at Senator Beck Basin.