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- W2174733467 abstract "The National Severe Storms Laboratory has developed algorithms that compute a number of Doppler radar and environmental attributes known to be relevant for the detection/prediction of severe hail. Based on these attributes, two neural networks have been developed for the estimation of severe-hail size: one for predicting the severe-hail size in a physical dimension, and another for assigning a probability of belonging to one of three hail size classes. Performance is assessed in terms of multidimensional (i.e., nonscalar) measures. It is shown that the network designed to predict severe-hail size outperforms the existing method for predicting severe-hail size. Although the network designed for classifying severe-hail size produces highly reliable and discriminatory probabilities for two of the three hail-size classes (the smallest and the largest), forecasts of midsize hail, though highly reliable, are mostly nondiscriminatory." @default.
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- W2174733467 date "2001-10-01" @default.
- W2174733467 modified "2023-10-13" @default.
- W2174733467 title "A Bayesian Neural Network for Severe-Hail Size Prediction" @default.
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- W2174733467 doi "https://doi.org/10.1175/1520-0434(2001)016<0600:abnnfs>2.0.co;2" @default.
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