  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.
