HIGH-FIDELITY HURRICANE SURGE FORECASTING USING EMULATION AND SEQUENTIAL EXPERIMENTS

• 成果类型: Article
• 署名作者: Plumlee, Matthew; Asher, Taylor G.; Chang, Won; Bilskie, Matthew, V
• 署名单位: Northwestern University; University of North Carolina; University of North Carolina Chapel Hill; University System of Ohio; University of Cincinnati; University System of Georgia; University of Georgia
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1398
• 发表日期: 2021
• 页码: 460-480
• 关键词: computer-model calibration; gaussian process emulation; storm-surge; DESIGN; prediction; RISK; INFORMATION; strategy; scale; waves

摘要

Probabilistic hurricane storm surge forecasting using a high-fidelity model has been considered impractical due to the overwhelming computational expense to run thousands of simulations. This article demonstrates that modern statistical tools enable good forecasting performance using a small number of carefully chosen simulations. This article offers algorithms that quickly handle the massive output of a surge model while addressing the missing data at unsubmerged locations. Also included is a new optimal design criterion for selecting simulations that accounts for the log transform required to statistically model surge data. Hurricane Michael (2018) is used as a testbed for this investigation and provides evidence for the approach's efficacy in comparison to the existing probabilistic surge forecast method.