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Tropical cyclone (TC) intensity forecasts are ultimately issued by humanforecasters. The human in-the-loop pipeline requires that any forecastingguidance must be easily digestible by TC experts if it is to be adopted atoperational centers like the National Hurricane Center. Our proposed frameworkleverages deep learning to provide forecasters with something neitherend-to-end prediction models nor traditional intensity guidance does: apowerful tool for monitoring high-dimensional time series of key physicallyrelevant predictors and the means to understand how the predictors relate toone another and to short-term intensity changes.

Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning