Yes, I think ML could be useful at places where the current physics modelling falls short. The nowcasting rain example from DeepMind is in some respects pretty comparable with the Next Ocean wave prediction.

In the wave prediction case, wave propagation and dispersion is pretty well understood. But one could add ML-based nonlinear terms to the equations to capture everything we don't know. That has the possibility of giving better predictions.

In contrast with the rogue wave example, there is a lot of relevant input data (the radar backscatter) and the model output can be verified after the fact (the ship's 6-dof acceleration). What I was objecting to was the idea of: slap a ML model on the whole problem and call it a day.