March 2021 High-fidelity hurricane surge forecasting using emulation and sequential experiments
Matthew Plumlee, Taylor G. Asher, Won Chang, Matthew V. Bilskie
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Ann. Appl. Stat. 15(1): 460-480 (March 2021). DOI: 10.1214/20-AOAS1398


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.


This material was based upon work partially supported by the National Science Foundation under Grant Award Number DMS-1638521 (to the Statistical and Applied Mathematical Sciences Institute), Grant Award Number DMS-1953111, Grant Award Number ACI-1339723 and by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-ND0001-01.

The authors are partially supported by the Statistical and Applied Mathematical Sciences Institute.


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Matthew Plumlee. Taylor G. Asher. Won Chang. Matthew V. Bilskie. "High-fidelity hurricane surge forecasting using emulation and sequential experiments." Ann. Appl. Stat. 15 (1) 460 - 480, March 2021.


Received: 1 July 2019; Revised: 1 September 2020; Published: March 2021
First available in Project Euclid: 18 March 2021

Digital Object Identifier: 10.1214/20-AOAS1398

Keywords: computer experiments , Gaussian process , sequential experiments , surrogate modeling

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.15 • No. 1 • March 2021
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