Satellite Detection of Snowmelt Events for Improving Spring Flood Forecasts in the Red River of the North Basin

The University of New Hampshire (UNH) and National Oceanic and Atmospheric Administration’s (NOAA) North Central River Forecast Center (NCRFC) have partnered to improve spring flood predictions for the Red River of the North Basin (RRB). The NCRFC integrates daily near-real time satellite passive microwave snow water equivalent (SWE) observations into NOAA’s operational forecasting environment to support decision-making and improve river snowmelt flood forecasts. Uncertainty associated with wet snow signal contamination has hampered the utility of near-real time passive microwave SWE observations in operational flood forecasts. This presentation describes a new quality control protocol that detects wet snow signals in the RRB. We demonstrate the signal detection scheme’s identification of wet snow events using 6 years (2012-2018) of daily, satellite Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) SWE time series in the RRB. The method applies a dynamic thresholding algorithm to identify abrupt decreases in the SWE time series associated with wet snow. The method accounts for sudden increases as well as for less pronounced variations in SWE and is less likely to identify such events as snow melt signals in the SWE time series. We evaluate the method’s performance by comparing the time series of snow melt events with independent observations of air temperature and melt signals from coincident satellite SMAP L-band radiometer data. We examine temporal consistency between the two satellite methods and contrast results to daily SWE and snow melt events in NOAA’s SNOw Data Assimilation System (SNODAS) and JAXA’s Level-3 Advanced Microwave Scanning Radiometer 2 (AMSR2).