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Our goal is to quantify whether, and if so how, spatio-temporal patterns intropical cyclone (TC) satellite imagery signal an upcoming rapid intensitychange event. To address this question, we propose a new nonparametric test ofassociation between a time series of images and a series of binary eventlabels. We ask whether there is a difference in distribution between (dependentbut identically distributed) 24-h sequences of images preceding an event versusa non-event. By rewriting the statistical test as a regression problem, weleverage neural networks to infer modes of structural evolution of TCconvection that are representative of the lead-up to rapid intensity changeevents. Dependencies between nearby sequences are handled by a bootstrapprocedure that estimates the marginal distribution of the label series. Weprove that type I error control is guaranteed as long as the distribution ofthe label series is well-estimated, which is made easier by the extensivehistorical data for binary TC event labels. We show empirical evidence that ourproposed method identifies archetypes of infrared imagery associated withelevated rapid intensification risk, typically marked by deep or deepening coreconvection over time. Such results provide a foundation for improved forecastsof rapid intensification.

Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery