Improved Regional Analysis of Snowmelt Detection Using Calibrated, Enhanced-Resolution Brightness Temperatures (CETB) across Conterminous US (CONUS) (Invited Presentation)

Snowmelt plays a substantial role in the hydrologic cycle, and it is one of the most significant contributing factors for flood events in snow-dominated watersheds. Passive microwave remote sensing has been used in the past to detect melt-freeze events with the Special Sensor Microwave Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMIS), and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). This study investigates the newly available (2018) passive microwave Calibrated, Enhanced-Resolution Brightness Temperature dataset (CETB) distributed by the National Snow and Ice Data Center to estimate melt timing at higher spatial resolution (~3-6 km vs. 25 km). SSM/I and AMSR-E CETB data are used to characterize frequency of melt events and seasonal melt onset date across the conterminous United States (CONUS). We employ existing algorithms using the diurnal amplitude variation (DAV) and cross-polarized gradient ratio (XPGR) methods at 36 GHz and 18 GHz to detect snowmelt. We compare algorithm results with ground-based measurements compiled from multiple sources by the National Weather Service (NWS) National Operational Hydrologic Remote Sensing Center at over 1000 stations across the US from 2003 to present. Therefore, we examine the performance of passive microwave snowmelt algorithms in different geographic regions and snow classifications. We show that the higher-resolution datasets yield an improvement in snowmelt detection in landscapes with heterogeneous topography and land cover. This work provides insight into the performance of higher-resolution reprocessed CETB data for snowmelt analysis and will enable hydrologists to analyze the timing and trends in snowmelt in various regions and snow classes.