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Multifidelity computer model emulation with high‐dimensional output: An application to storm surge

Ma, P.; Karagiannis, G.; Konomi, B.A.; Asher, T.G.; Toro, G.R.; Cox, A.T.

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Authors

P. Ma

B.A. Konomi

T.G. Asher

G.R. Toro

A.T. Cox



Abstract

Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm surges. Such computer models can be implemented with a wide range of fidelity levels, with computational burdens varying by several orders of magnitude due to the nature of the system. The threat posed by surge makes greater fidelity highly desirable, however, such models and their high-volume output tend to come at great computational cost, which can make detailed study of coastal flood hazards prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different fidelity levels important. We propose a parallel partial autoregressive cokriging model to predict highly accurate storm surges in a computationally efficient way over a large spatial domain. This emulator has the capability of predicting storm surges as accurately as a high-fidelity computer model given any storm characteristics over a large spatial domain.

Citation

Ma, P., Karagiannis, G., Konomi, B., Asher, T., Toro, G., & Cox, A. (2022). Multifidelity computer model emulation with high‐dimensional output: An application to storm surge. Journal of the Royal Statistical Society: Series C, 71(4), 861-883. https://doi.org/10.1111/rssc.12558

Journal Article Type Article
Acceptance Date Feb 19, 2022
Online Publication Date Apr 9, 2022
Publication Date 2022-08
Deposit Date Apr 11, 2022
Publicly Available Date Jan 31, 2023
Journal Journal of the Royal Statistical Society: Series C (Applied Statistics)
Print ISSN 0035-9254
Electronic ISSN 1467-9876
Publisher Royal Statistical Society
Peer Reviewed Peer Reviewed
Volume 71
Issue 4
Pages 861-883
DOI https://doi.org/10.1111/rssc.12558
Related Public URLs https://arxiv.org/abs/1909.01836

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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
© 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.




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