Articles | Volume 20, issue 8
https://doi.org/10.5194/nhess-20-2335-2020
https://doi.org/10.5194/nhess-20-2335-2020
Research article
 | 
26 Aug 2020
Research article |  | 26 Aug 2020

Lagrangian modelling of a person lost at sea during the Adriatic scirocco storm of 29 October 2018

Matjaž Ličer, Solène Estival, Catalina Reyes-Suarez, Davide Deponte, and Anja Fettich

Related authors

CRITER 1.0: A coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data
Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-208,https://doi.org/10.5194/gmd-2024-208, 2025
Preprint under review for GMD
Short summary
HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025,https://doi.org/10.5194/gmd-18-605-2025, 2025
Short summary
Application of HIDRA2 Deep Learning Model for Sea Level Forecasting Along the Estonian Coast of the Baltic Sea
Amirhossein Barzandeh, Marko Rus, Matjaž Ličer, Ilja Maljutenko, Jüri Elken, Priidik Lagemaa, and Rivo Uiboupin
EGUsphere, https://doi.org/10.5194/egusphere-2024-3691,https://doi.org/10.5194/egusphere-2024-3691, 2024
Short summary
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024,https://doi.org/10.5194/gmd-17-4705-2024, 2024
Short summary
HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023,https://doi.org/10.5194/gmd-16-271-2023, 2023
Short summary

Related subject area

Sea, Ocean and Coastal Hazards
Untangling the waves: decomposing extreme sea levels in a non-tidal basin, the Baltic Sea
Marvin Lorenz, Katri Viigand, and Ulf Gräwe
Nat. Hazards Earth Syst. Sci., 25, 1439–1458, https://doi.org/10.5194/nhess-25-1439-2025,https://doi.org/10.5194/nhess-25-1439-2025, 2025
Short summary
Accelerating compound flood risk assessments through active learning: A case study of Charleston County (USA)
Lucas Terlinden-Ruhl, Anaïs Couasnon, Dirk Eilander, Gijs G. Hendrickx, Patricia Mares-Nasarre, and José A. Á. Antolínez
Nat. Hazards Earth Syst. Sci., 25, 1353–1375, https://doi.org/10.5194/nhess-25-1353-2025,https://doi.org/10.5194/nhess-25-1353-2025, 2025
Short summary
Tsunami detection methods for ocean-bottom pressure gauges
Cesare Angeli, Alberto Armigliato, Martina Zanetti, Filippo Zaniboni, Fabrizio Romano, Hafize Başak Bayraktar, and Stefano Lorito
Nat. Hazards Earth Syst. Sci., 25, 1169–1185, https://doi.org/10.5194/nhess-25-1169-2025,https://doi.org/10.5194/nhess-25-1169-2025, 2025
Short summary
Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast
Kai Bellinghausen, Birgit Hünicke, and Eduardo Zorita
Nat. Hazards Earth Syst. Sci., 25, 1139–1162, https://doi.org/10.5194/nhess-25-1139-2025,https://doi.org/10.5194/nhess-25-1139-2025, 2025
Short summary
Probabilistic tsunami hazard analysis of Batukaras, a tourism village in Indonesia
Wiwin Windupranata, Muhammad Wahyu Al Ghifari, Candida Aulia De Silva Nusantara, Marsyanisa Shafa, Intan Hayatiningsih, Iyan Eka Mulia, and Alqinthara Nuraghnia
Nat. Hazards Earth Syst. Sci., 25, 1057–1069, https://doi.org/10.5194/nhess-25-1057-2025,https://doi.org/10.5194/nhess-25-1057-2025, 2025
Short summary

Cited articles

Allen, A. A. and Plourde, J. V.: Review of Leeway: Field Experiments and Implementation, Tech. rep., U. S. Coast Guard, Connecticut, USA, 1999. a, b, c, d, e, f, g
Breivik, Ø. and Allen, A. A.: An operational search and rescue model for the Norwegian Sea and the North Sea, J. Marine Syst., 69, 99–113, https://doi.org/10.1016/j.jmarsys.2007.02.010, 2008. a, b, c, d, e, f
Cavaleri, L., Bajo, M., Barbariol, F., Bastianini, M., Benetazzo, A., Bertotti, L., Chiggiato, J., Davolio, S., Ferrarin, C., Magnusson, L., Papa, A., Pezzutto, P., Pomaro, A., and Umgiesser, G.: The October 29, 2018 storm in Northern Italy – an exceptional event and its modeling, Prog. Oceanogr., 178, p. 102178, https://doi.org/10.1016/j.pocean.2019.102178, 2019. a, b
Craig, P. D. and Banner, M. L.: Modeling Wave-Enhanced Turbulence in the Ocean Surface Layer, J. Phys. Oceanogr., 24, 2546–2559, https://doi.org/10.1175/1520-0485(1994)024<2546:MWETIT>2.0.CO;2, 1994. a
Dagestad, K.-F., Röhrs, J., Breivik, Ø., and Ådlandsvik, B.: OpenDrift v1.0: a generic framework for trajectory modelling, Geosci. Model Dev., 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018, 2018. a, b
Download
Short summary
In 2018 windsurfer’s mast broke about 1 km offshore during a scirocco storm in the northern Adriatic. He was drifting in severe conditions until he eventually beached alive and well in Sistiana (Italy) 24 h later. We conducted an interview with the survivor to reconstruct his trajectory. We simulate his trajectory in several ways and estimate the optimal search-and-rescue area for a civil rescue response. Properly calibrated virtual drifter properties are key to reliable rescue area forecasting.
Share
Altmetrics
Final-revised paper
Preprint