Accurate forecasting of snowfall accumulation has widespread economic and safety consequences. Owing to the complex characteristics and dynamics inherent in winter weather systems, snowfall accumulation forecasts tend to have a large degree of uncertainty associated with them. Numerical Weather Prediction (NWP) ensemble prediction systems were developed to address the uncertainty in weather forecasts. A deterministic forecast is of utmost importance to the public; therefore, several post-processing methods for combining ensemble members have been developed. This study examines the use of several statistical guidance methods for post-processing the Global Ensemble Forecast System (GEFS) output in order to predict 24-hour snowfall accumulation. Out of the seven methods—an artificial neural network, linear regression, least median squares regression, support vector regression, radial basis function network, conjunctive rule, and k-nearest neighbor—the k-nearest neighbor method produces significantly more accurate forecasts, ones with lower mean absolute error, and the best calibrated ensemble spread as measured by rank histograms, spread-skill plots, and quantile-quantile plots.
