Uncertainty Propagation Using Polynomial Chaos Expansions for Extreme Sea-level Hazard Assessment: The Case of the Eastern Adriatic Meteotsunamis

Abstract

This study quantifies the hazard associated with extreme sea-levels due to eastern Adriatic meteotsunamis — long waves generated by traveling atmospheric disturbances, and assesses the sensitivity of the ocean response to the disturbances responsible for those events. In this spirit, a surrogate model of meteotsunami maximum elevation based on generalized Polynomial Chaos Expansion (gPCE) methods, is implemented. The approach relies on the definition of a synthetic pressure disturbance — depending on six different stochastic parameters known to be important for meteotsunami generation, which is used as forcing to produce series of meteotsunami simulations defined with sparse grid methods (up to 10689 used in this study). The surrogate model and the sensitivity study are then obtained with a Pseudo Spectral Approximation (PSA) method based on the chosen meteotsunami simulations. This study mainly presents the developed methodology and discusses the feasibility of implementing such gPCE-based surrogate models to assess the hazard and to study the sensitivity of meteorologically-driven extreme sea-levels.

Publication
Journal of Physical Oceanography
Date
Links
Citation
C. Denamiel, X. Huan, J. Šepić, and I. Vilibić. Uncertainty Propagation Using Polynomial Chaos Expansions for Exteme Sea-level Hazard Assessment: The Case of the Eastern Adriatic Meteotsunamis. Published online, Journal of Physical Oceanography, 2020. https://dx.doi.org/10.1175/JPO-D-19-0147.1

BibTeX

@article{Denamiel2020a,
author = {Denamiel, Cl\'{e}a and Huan, Xun and \v{S}epi\'{c}, Jadranka and Vilibi\'{c}, Ivica},
doi = {10.1175/JPO-D-19-0147.1},
journal = {Journal of Physical Oceanography},
number = {},
pages = {},
title = {{Uncertainty Propagation Using Polynomial Chaos Expansions for Extreme Sea-level Hazard Assessment: The Case of the Eastern Adriatic Meteotsunamis}},
volume = {},
year = {2020}
}