Mesoscale Snowfall Prediction and Verification in Mountainous Terrain

Abstract

Short-term forecasting of precipitation often relies on meteorological radar coverage to provide information on the intensity, extent, and motion of approaching mesoscale features. However, in significant portions of mountainous regions, radar coverage is lacking because of topographic blocking, and the absence of radar signatures in sections of the radar scan produces uncertain or even misleading information to the public and operational forecasters. In addition, echo characteristics within the radar volume scan are often influenced by the vertical extent and type of precipitation. Each of these conditions limits the opportunity for accurate snowfall prediction and studies of precipitation climatology. To improve both short-term forecasting and postevent verification studies, much greater use can be made of specifically sited surface observations, tailored graphical output from mesoscale models, satellite remote sensing, and case study knowledge of local topographic influences. In this paper, methods to support snowfall forecasts and verification in radar-limited mountainous terrain are demonstrated that include matching the output parameters and graphics from high-resolution mesoscale models to surface mesonets and snowfall observations, analysis of continuous and event-based measurements of snow density, application of multispectral satellite data for verification and trend analysis, and characterization of orographic influences in different winter storm scenarios. The advantages of improved wintertime quantitative precipitation forecasting (QPF) in mountain regions include public safety responsibilities that are critical to National Weather Service (NWS) operations, and are relevant to any mountainous region with radar scan limitations or during periods of radar data outages.