DETECTING DISTRIBUTIONAL DIFFERENCES IN LABELED SEQUENCE DATA WITH APPLICATION TO TROPICAL CYCLONE SATELLITE IMAGERY

• 成果类型: Article
• 署名作者: Mcneely, Trey; Vincent, Galen; Wood, Kimberly M.; Izbicki, Rafael; Lee, Ann B.
• 署名单位: Carnegie Mellon University; Mississippi State University; Universidade Federal de Sao Carlos
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1668
• 发表日期: 2023
• 页码: 1260-1284
• 关键词: bootstrap methods; north-atlantic; intensity

摘要

Our goal is to quantify whether, and if so how, spatiotemporal patterns in tropical cyclone (TC) satellite imagery signal an upcoming rapid intensity change event. To address this question, we propose a new nonparametric test of association between a time series of images and a series of binary event la-bels. We ask whether there is a difference in distribution between (dependent but identically distributed) 24-hour sequences of images preceding an event vs. a nonevent. By rewriting the statistical test as a regression problem, we leverage neural networks to infer modes of structural evolution of TC con-vection that are representative of the lead-up to rapid intensity change events. Dependencies between nearby sequences are handled by a bootstrap proce-dure that estimates the marginal distribution of the label series. We prove that type I error control is guaranteed as long as the distribution of the label series is well estimated which is made easier by the extensive historical data for bi-nary TC event labels. We show empirical evidence that our proposed method identifies archetypes of infrared imagery associated with elevated rapid inten-sification risk, typically marked by deep or deepening core convection over time. Such results provide a foundation for improved forecasts of rapid inten-sification.