Improvements in Freezing Rain Prediction by Incorporating Droplet Temperature Characteristics
The Freezing Rain Accumulation Model (FRAM) is a successful statistical method for predicting ice-to-liquid ratios (ILR) and resultant ice accumulation due to freezing rain. FRAM utilizes predictions of precipitation rate, surface wind speed, and surface wet-bulb temperature as predictors of ILR. Ice accretion efficiency is also dictated by the droplet temperature, but unfortunately these values are not well-sampled in time and space by current observation platforms. Previous studies have documented that the elevated melting layer can be very deep and very warm (as warm as +10°C). In this warm layer aloft the raindrops acquire a substantial amount of heat, requiring more heat to be removed from the drop in order to freeze upon contact with the sub-freezing surface. If this heat is not removed efficiently, it could lead to a disproportionately great amount of runoff and relatively low ILR. In an effort to enhance the FRAM predictive ability, RAP proximity-sounding characteristics of both the warm layer and near-surface cold layer were evaluated during hundreds of freezing rain events, including maximum and minimum temperature and wet-bulb temperature, warm and cold layer depth, and warm and cold layer heat energy. These upper air thermal characteristics were directly related to ILR, leading to improved predictability and understanding of icing efficiency.