  Tropical cyclone (TC) intensity forecasts are issued by human forecasters who
evaluate spatio-temporal observations (e.g., satellite imagery) and model
output (e.g., numerical weather prediction, statistical models) to produce
forecasts every 6 hours. Within these time constraints, it can be challenging
to draw insight from such data. While high-capacity machine learning methods
are well suited for prediction problems with complex sequence data, extracting
interpretable scientific information with such methods is difficult. Here we
leverage powerful AI prediction algorithms and classical statistical inference
to identify patterns in the evolution of TC convective structure leading up to
the rapid intensification of a storm, hence providing forecasters and
scientists with key insight into TC behavior.
