Mountain Rain or Snow: Validating Probabilistic Snow Level Forecasts with Real-Time Precipitation Phase Data

Accurately forecasting whether precipitation will fall as rain or snow is critical for understanding and managing the impacts on water resources, infrastructure, and ecosystems in mountainous regions. The phase of precipitation—whether rain or snow—can significantly influence snowpack development, river flow, and flood potential. However, forecasting precipitation phase in mountainous terrain is complex and cannot simply be determined with near surface weather observations. The atmospheric temperature profile, humidity, surface pressure, warm air advection, cold air damming, and other microclimate phenomena are some of the reasons that precipitation phase forecasting remains a challenge. These challenges are amplified in mountainous regions where terrain shapes local and regional weather patterns. To address these challenges, we developed the Mountain Rain or Snow participatory science project and recruited over 1,300 observers across the US to build a precipitation phase validation dataset focused on eight regions. Across the eight areas of the continental US, we have collected over 78,000 observations to date. These observations enable forecasters to validate and refine predictions of where snow and rain will occur, enhancing the accuracy of snow level forecasts in mountains where the outcome during a high precipitation event may lead to record breaking snowfall or widespread flooding. By improving our understanding of the conditions that lead to rain versus snow, this dataset supports better predictions of snowpack development, water resource management, and the potential for rain-induced flooding. This pilot study explores the application of Mountain Rain or Snow data for real-time verification of snow level forecasting, ultimately contributing to more reliable weather predictions and better-informed decision-making in regions where accurate snow level forecasts are vital.