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Interpretable Deep Learning for Hurricane Intensity Prediction

Richard Loft, Computational and Information Systems Laboratory, National Center for Atmospheric Research

GTC 2020

Hurricanes can experience rapid increases in intensity, in which they can strengthen from a tropical storm to a major hurricane in only a couple of days. These rapid intensification periods are currently difficult to predict, but deep learning may be able to detect spatial patterns in the storms that are precursors to rapid intensification. We'll show how a convolutional neural network trained on output from the Hurricane Weather Research and Forecasting model can produce probabilistic estimates of rapid intensification. We'll also show how deep-learning interpretation techniques can reveal what storm structures are associated with rapid intensification versus rapid weakening.

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