Verification of High-Resolution Banded Snowfall Forecasts

Narrow regions of intense snowfall, also known as snowbands, present hazardous travel conditions due to rapid onset, high precipitation rates, and limited visibility. A promising approach to operational prediction of snowbands is the application of convection-allowing models such as the High-Resolution Rapid Refresh (HRRR). However, to the best of our knowledge, no attempt has been made to verify the skill of such high-resolution forecasts. To this end, we have adapted and modified an objective snowband definition to allow for algorithmic detection of bands in both model-forecasted and observed base radar reflectivity fields. This automated method, based upon a thresholding procedure combined with Real-Time Mesoscale Analysis (RTMA) temperature data, identified 102 hourly reflectivity images containing bands for the 2016-2017 winter season. Out of these 102 bands, the HRRR only positively identified 33 at a 12-hour lead while producing 49 false alarms, suggesting that the HRRR is generally not adequately representing these mesoscale features. Traditional skill scores paint a bleak portrait of snowband forecast capabilities, but an object-oriented verification procedure directly comparing the salient features of forecasted and observed bands such as location, duration, shape, and intensity will help to further elucidate model shortcomings.