Innovations in Winter Storm Forecasting and Decision Support Services

Abstract

Winter storms are disruptive to society and the economy, and they often cause significant injuries and deaths. Innovations in winter storm forecasting have occurred across the value chain over the past two decades, from physical understanding, to observations, to model forecasts, to post-processing, to forecaster knowledge and interpretation, to products and services, and ultimately to decision support. These innovations enable more accurate and consistent forecasts, which are increasingly being translated into actionable information for decision makers. This paper reviews the current state of winter storm forecasting in the context of the U.S. National Weather Service operations and describes a potential future state. Given predictability limitations, a key challenge of winter storm forecasting has been characterizing uncertainty and communicating the forecast in ways that are understandable and useful to decision makers. To address this challenge, particular focus is placed on establishing a probabilistic framework, with probabilistic hazard information serving as a foundation for winter storm decision support services. The framework is guided by social science research to ensure effective communication of risk to meet users’ needs. Solutions to gaps impeding progress in winter storm forecasting are highlighted, including better understanding of mesoscale phenomenon, the need for better ensemble calibration, a rigorous and consistent database of observed impacts, and linking multi-parameter probabilities (e.g., probability of intense snowfall rates at rush hour) with users’ information needs and decisions.

Improving prediction of mountain snowfall in the southwestern United States using machine learning methods

“…The weakness in this type of approach is the assumption that SLR during all winter storm events is constant, plus the 10:1 ratio differs from the local climatology as computed in Baxter et al (2005). In the past decade, attempts at introducing more dynamic means of SLR prediction have been introduced, but these methods have not been without their shortcomings (Novak et al, 2023). An alternative dynamic approach to SLR prediction, as presented in this work, could inform better snowfall forecasting, for example, through statistical post‐processing of output from the National Blend of Models (NBM) within the NWS.…”