Snow-to-Liquid Ratio Variability and Prediction at a High-Elevation Site in Utah’s Wasatch Mountains

Abstract

Abstract Contemporary snowfall forecasting is a three-step process involving a quantitative precipitation forecast (QPF), determination of precipitation type, and application of a snow-to-liquid ratio (SLR). The final step is often performed using climatology or algorithms based primarily on temperature. Based on a record of consistent and professional daily snowfall measurements, this study 1) presents general characteristics of SLR at Alta, Utah, a high-elevation site in interior North America with frequent winter storms; 2) diagnoses relationships between SLR and atmospheric conditions using reanalysis data; and 3) develops a statistical method for predicting SLR at the study location. The mean SLR at Alta is similar to that observed at lower elevations in the surrounding region, with substantial variability throughout the winter season. Using data from the North American Regional Reanalysis, temperature, wind speed, and midlevel relative humidity are shown to be related to SLR, with the strongest correlation occurring between SLR and near-crest-level (650 hPa) temperature. A stepwise multiple linear regression (SMLR) equation is constructed that explains 68% of the SLR variance for all events, and 88% for a high snow-water equivalent (>25 mm) subset. To test predictive ability, the straightforward SMLR approach is applied to archived 12–36-h forecasts from the National Centers for Environmental Prediction Eta/North American Mesoscale (Eta/NAM) model, yielding an improvement over existing operational SLR prediction techniques. Errors in QPF over complex terrain, however, ultimately limit skill in forecasting snowfall amount.