Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning
arXiv:2010.05783
Abstract
Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestible by TC experts if it is to be adopted at operational centers like the National Hurricane Center. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does: a powerful tool for monitoring high-dimensional time series of key physically relevant predictors and the means to understand how the predictors relate to one another and to short-term intensity changes.
arXiv commentTo appear in the Tackling Climate Change with Machine Learning workshop at NeurIPS 2020 (Proposals Track) 3 pages, 1 figure
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