Modelling of ocean waves with the Alber equation: application to non-parametric spectra and generalization to crossing seas

TL;DR

This paper extends the Alber equation to crossing seas, derives a new instability condition, and uses it to analyze real ocean spectra, identifying stability characteristics and correlating a novel PTI metric with rogue wave likelihood.

Contribution

It introduces a generalized Alber system for crossing seas, derives a modulation instability condition for complex spectra, and develops a PTI metric correlated with rogue wave risk.

Findings

Most realistic spectra are stable; a few are unstable.

PTI correlates strongly with steepness and BFI.

PTI correlates with kurtosis and rogue wave probability.

Abstract

The Alber equation is a phase-averaged second-moment model for the statistics of a sea state, which recently has been attracting renewed attention. We extend it in two ways: firstly, we derive a generalized Alber system starting from a system of nonlinear Schr\"odinger equations, which contains the classical Alber equation as a special case but can also describe crossing seas, i.e. two wavesystems with different wavenumbers crossing. (These can be two completely independent wavenumbers, i.e. in general different directions and different moduli.) We also derive the associated 2-dimensional scalar instability condition. This is the first time that a modulation instability condition applicable to crossing seas has been systematically derived for general spectra. Secondly, we use the classical Alber equation and its associated instability condition to quantify how close a given non-parametric spectrum is to being modulationally unstable. We apply this to a dataset of 100 non-parametric spectra provided by the Norwegian Meteorological Institute, and find the vast majority of realistic spectra turn out to be stable, but three extreme sea states are found to be unstable (out of 20 sea states chosen for their severity). Moreover, we introduce a novel "proximity to instability" (PTI) metric, inspired by the stability analysis. This is seen to correlate strongly with the steepness and Benjamin-Feir Index (BFI) for the sea states in our dataset (>85% Spearman rank correlation). Furthermore, upon comparing with phase-resolved broadband Monte Carlo simulations, the kurtosis and probability of rogue waves for each sea state are also seen to correlate well with its PTI (>85% Spearman rank correlation).