The Efficacy of Ten Snow‐to‐Liquid Ratio Methods to Predict Snowfall for Impactful Winter Storms in the Northeast United States

Abstract Forecasting snowfall accumulation is a challenging process that utilizes predicted variables usually from the microphysics parameterization of a numerical weather model, and evaluating these forecasts can be difficult with scarce quality observations. However, snowfall serves as a measure of the impact of a winter storm as larger accumulations are often tied to more significant socioeconomic impacts. Here, 10 different snow‐to‐liquid ratio (SLR) methods are applied to consistent precipitation output from the Weather Research and Forecasting model for 14 impactful Nor'easters in the Northeast United States (NE US). The applied SLR methods are evaluated against the event total snowfall accumulations from the gridded National Snowfall Analysis. SLR methods were evaluated with and without the inclusion of forecasted precipitation biases and by median 2‐m temperature. The results showed that methods that utilized temperature, or atmospheric thickness, above the surface, such as KAKQ and Kuchera, produced the most consistent snowfall accumulations, with correlations ranging from 0.77 to 0.94. The evaluation of precipitation forecast biases indicated that precipitation biases contributed around 50% of the error in snowfall forecasts; the performance of SLR methods varied substantially depending on if these biases were included. When exploring the impact of surface temperature on the SLR methods, snowstorms with colder temperatures (<−5°C) had more variability in the forecasted SLR compared to warmer temperatures (>−2.5°C), where the range in possible SLR values was narrower. These results create a baseline evaluation of SLRs in the NE US and lay the groundwork for future studies across different regions and snowstorm types.