6/23/2023 0 Comments The Class of 86 by R.A. Williams![]() ![]() Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. ![]()
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