High-fidelity hurricane surge forecasting using emulation and sequential experiments

Abstract

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.