A New Probabilistic Wave Breaking Model for Dominant Wind-sea Waves Based on the Gaussian Field Theory
Caio Eadi Stringari, Marc Prevosto, Jean Fran\c{c}ois Filipot, Fabien, Leckler, Pedro Veras Guimar\~aes

TL;DR
This paper introduces a probabilistic wave breaking model for dominant wind-sea waves based on Gaussian wave field theory, enabling better prediction of wave breaking probability using spectral data.
Contribution
It develops a novel probabilistic model for wave breaking probability derived from Gaussian wave field theory, incorporating a non-linear kinematic criterion and enabling spectral model integration.
Findings
Model has errors comparable to existing models on field data.
Optimized parameters can improve model performance for specific datasets.
Additional field data needed for comprehensive validation.
Abstract
This paper presents a novel method for obtaining the probability wave of breaking () of deep water, dominant wind-sea waves (that is, waves made of the energy within 30\% of the peak wave frequency) derived from Gaussian wave field theory. For a given input wave spectrum we demonstrate how it is possible to derive a joint probability density function between wave phase speed () and horizontal orbital velocity at wave crest () from which a model for can be obtained. A non-linear kinematic wave breaking criterion consistent with the Gaussian framework is further proposed. Our model would allow, therefore, for application of the classical wave breaking criterion (that is, wave breaking occurs if ) in spectral wave models which, to the authors' knowledge, has not been done to date. Our results show that the proposed theoretical model has errors in the same…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOcean Waves and Remote Sensing · Oceanographic and Atmospheric Processes · Tropical and Extratropical Cyclones Research
