Evaluation of Winter Weather Prediction during Extreme Snowfall Events

From January 5th to March 22nd of 2018, five of the top 100 most significant snowstorms, in terms of the total area and total population affected, enveloped the Northeastern United States according to the Northeast Snowfall Impact Scale (NESIS). Roughly 286 million people were directly impacted as a result of all five snowstorms which spanned a total area of 775 million square-miles. During the March 5th to March 8th snowstorm alone, a powerful Nor’easter caused whiteout conditions accompanied by wind gusts in excess of 50 miles-per-hour for multiple stations across the Northeastern United States. The combination of winter weather conditions led to hundreds-of-thousands of power outages, numerous road restrictions, the declaration of three states of emergencies, and several fatalities.

Accurate prediction of such extreme snowstorms is essential to protect the safety and well-being of the public and mitigate infrastructural and socioeconomic damages, specifically power outages. Numerical Weather Prediction (NWP) of snowstorms faces challenges related to microphysical, initial and boundary layer processes that hinder the accuracy of forecasts. In addition, observations of mixed phase precipitation are very sparse spatially and temporally which causes an added challenge to validate predictions. We present an evaluation of extreme snowfall prediction for 34 significant NE US snowstorms from 2006 to 2017 using three NWP models (RAMS/ICLAMS, WRF 3.7, and WRF 3.8). Snow storm prediction is evaluated using 109 Meteorological Terminal Aviation Routine Weather Reports (METAR) and available radiosonde stations. Snow events have been identified due to their impacts on the electrical power grid and the variables considered for this evaluation are associated with winter storm severity, precipitation type, snowfall ratios, and liquid to ice ratios.