Near Real-Time Monitoring of Significant Sea Wave Height through Microseism Recordings: Analysis of an Exceptional Sea Storm Event

• Published in: Journal of marine science and engineering Vol. 9; no. 3; p. 319
• Main Authors: Cutroneo, Laura, Ferretti, Gabriele, Barani, Simone, Scafidi, Davide, De Leo, Francesco, Besio, Giovanni, Capello, Marco
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2021
• Subjects: Adrian storm; Atmospheric models; automatic near real-time monitoring; Beaches; Coasts; Earthquake damage; Empirical analysis; extreme sea storm events; Hurricanes; Infrastructure; Mathematical models; Microseisms; Monitoring; Parameters; Prediction models; Pressure gradients; Rain; significant sea wave height; Storm damage; Storms; Typhoons; Wave height; Weather; Wind; Ligurian Sea; Gulf of Lion; Mediterranean Sea; Europe
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse9030319

Microseisms are used to estimate significant sea wave heights (Hs) in different parts of the world and also during extreme events (e.g., typhoons and hurricanes), as they are generated by the effect of sea waves on the sea bottom and are strictly related to the wave height. On 29 October 2018, an exceptional sea storm event (the Adrian storm) occurred in the Ligurian Sea (NW Mediterranean Sea), producing severe damage to coastal constructions and infrastructures. However, the microseism measured at seismic stations located near the coast did not show equivalent high energy, thus resulting in a severe underestimation of the Hs predicted. In the present study, the Adrian storm was compared to other sea storms that have occurred in the Ligurian Sea in recent decades. The aim of this paper is to statistically examine the distinctive peculiarities of the Adrian storm in order to find new parameters to insert in the empirical models used in the procedure recently implemented for monitoring of Hs through microseism recordings in the Ligurian Sea, improving the effectiveness in Hs estimates in cases of extreme events that do not produce high-energy microseisms. The results show that the additional parameters to be taken into account into the predictive model are the atmospheric pressure gradient and the wind intensity. A correction term is finally proposed and applied to the predictive model to significantly reduce the Hs underestimation.