Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Remote Sensing Observations of Dominant Breaking Waves in Intermediate to Deep Water from a Lighthouse During Storm Conditions

Version 1 : Received: 19 March 2021 / Approved: 22 March 2021 / Online: 22 March 2021 (13:53:53 CET)

How to cite: Eadi Stringari, C.; Filipot, J.; Leckler, F.; Duarte, R. Remote Sensing Observations of Dominant Breaking Waves in Intermediate to Deep Water from a Lighthouse During Storm Conditions. Preprints 2021, 2021030538 (doi: 10.20944/preprints202103.0538.v1). Eadi Stringari, C.; Filipot, J.; Leckler, F.; Duarte, R. Remote Sensing Observations of Dominant Breaking Waves in Intermediate to Deep Water from a Lighthouse During Storm Conditions. Preprints 2021, 2021030538 (doi: 10.20944/preprints202103.0538.v1).

Abstract

Wave breaking is one of the most important yet poorly understood water wave phenomena. It is via wave breaking that waves dissipate most of their energy and the occurrence of wave breaking directly influences several environmental processes, from ocean-atmosphere gas exchanges to beach morphodynamics. Large breaking waves also represent a major threat for navigation and for the survivability of offshore structures. This paper provides a systematic search for intermediate to deep water breaking waves with particular focus on the elusive occurrence of plunging breakers. Using modern remote sensing and deep learning techniques, we identify and track the evolution of over four thousand unique wave breaking events using video data collected from La Jument lighthouse during ten North Atlantic winter storms. Out of all identified breaking waves (Nb=4683), ≈22% were dominant breaking waves, that is, waves that have speeds within [0.77cp, 1.43cp], where cp is the peak wave speed. Correlations between the occurrence rate of dominant breaking waves (that is, waves per area and time per peak wave period) and wave steepness and wave age were observed. As expected, the number of identified plunging waves was small and six waves of all detected breaking waves, or 0.13%, could undoubtedly be considered as plunging waves. Two waves were also identified as unusually large, or rogue waves. Although afflicted by several technical issues, the data presented here provides a good indication that the probability of occurrence of plunging waves should be better incorporated into the design of offshore structures, particularly the ones that aim to harvest energy in offshore environments.

Keywords

wave breaking; remote sensing; intermediate water waves, dominant waves; plunging waves; rogue waves; machine learning.

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