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

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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
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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.
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