## Stochastic surrogate model for meteotsunami early warning system in the eastern Adriatic Sea

### Abstract

The meteotsunami early warning system prototype using stochastic surrogate approach and running operationally in the eastern Adriatic Sea is presented. First, the atmospheric Internal Gravity Waves (IGWs) driving the meteotsunamis are either forecasted with state-of-the-art deterministic models at least a day in advance or detected through measurements at least 2-h before the meteotsunami reaches sensitive locations. The extreme sea-level hazard forecast at endangered locations is then derived with an innovative stochastic surrogate model - implemented with generalized Polynomial Chaos Expansion (gPCE) method and synthetic IGWs forcing a barotropic ocean model - used with the input parameters extracted from deterministic model results and/or measurements. The evaluation of the system, both against five historical events and for all the detected potential meteotsunamis since late 2018 when the early warning system prototype became operational, reveals that the meteotsunami hazard is conservatively assessed but often overestimated at some locations. Despite some needed improvements and developments, this study demonstrates that gPCE-based methods can be used for atmospherically-driven extreme sea-level hazard assessment, and in geosciences in wide.

Type
Publication
Journal of Geophysical Research: Oceans
Date
Citation
C. Denamiel, J. Šepić, X. Huan, C. Bolzer, and I. Vilibić. Stochastic surrogate model for meteotsunami early warning system in the eastern Adriatic Sea. Journal of Geophysical Research: Oceans. https://dx.doi.org/10.1029/2019JC015574

### BibTeX

@article{Denamiel2019a,
author = {Denamiel, Cl\'{e}a and \v{S}epi\'{c}, Jadranka and Huan, Xun and Bolzer, C\'{e}lia and Vilibi\'{c}, Ivica},
doi = {10.1029/2019JC015574},
journal = {Journal of Geophysical Research: Oceans},
number = {},
pages = {},
title = {{Stochastic surrogate model for meteotsunami early warning system in the eastern Adriatic Sea}},
volume = {},
year = {2019}
}