June 2023 Detecting distributional differences in labeled sequence data with application to tropical cyclone satellite imagery
Trey McNeely, Galen Vincent, Kimberly M. Wood, Rafael Izbicki, Ann B. Lee
Author Affiliations +
Ann. Appl. Stat. 17(2): 1260-1284 (June 2023). DOI: 10.1214/22-AOAS1668

Abstract

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 labels. 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 convection that are representative of the lead-up to rapid intensity change events. Dependencies between nearby sequences are handled by a bootstrap procedure 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 binary TC event labels. We show empirical evidence that our proposed method identifies archetypes of infrared imagery associated with elevated rapid intensification risk, typically marked by deep or deepening core convection over time. Such results provide a foundation for improved forecasts of rapid intensification.

Funding Statement

This work is supported in part by NSF Grant DMS-2053804 and NSF PHY-2020295.
RI is grateful for the financial support of CNPq (309607/2020-5) and FAPESP (2019/11321-9).

Citation

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Trey McNeely. Galen Vincent. Kimberly M. Wood. Rafael Izbicki. Ann B. Lee. "Detecting distributional differences in labeled sequence data with application to tropical cyclone satellite imagery." Ann. Appl. Stat. 17 (2) 1260 - 1284, June 2023. https://doi.org/10.1214/22-AOAS1668

Information

Received: 1 February 2022; Revised: 1 July 2022; Published: June 2023
First available in Project Euclid: 1 May 2023

MathSciNet: MR4582712
zbMATH: 07692382
Digital Object Identifier: 10.1214/22-AOAS1668

Keywords: association studies , functional data , high-dimensional time series , remote sensing , two-sample testing , weather forecasting

Rights: Copyright © 2023 Institute of Mathematical Statistics

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Vol.17 • No. 2 • June 2023
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