$ whoami

I like to do science with computers
main author of the first (?) high-performance, pure Python GCM
not a wave expert
(also a person)

The Physics

[McAllister, 2019]

The Physics

Causes of rogue waves:

Non-linear instabilities (e.g. BFI)
Crossing sea states
Wind-wave interactions
Wave-current interactions
Non-linear focusing
Linear statistics

[Adcock, 2014]

Wave Statistics

A Gaussian sea?

Wave Statistics

A Gaussian sea?

Wave Statistics

Nonlinear seas

[Gemmrich, 2011]

Hypotheses

There are no freaks, our statistics are off

There are freaks, normal waves should break before reaching extreme heights

There are freaks, special mechanisms are required to transfer that much energy into one wave

[Fedele, 2016]

Correlations

Sea state

[Cattrell, 2018], [Janssen, 2009]

Weather

[Cavaleri, 2016]

2D wave field

[Cousins, 2016]

Local features

... but what has the most predictive power?

A model

ML
Neural network
Gradient boosting
Random forest

Prediction
Rogue wave likelihood

Data

Raw time series

Sea state

Weather

Physics

(Wave model)

Spectra

Directional spread

Possible pitfalls

Ask a silly question, get a silly answer

Rogues are more likely during a storm

Possible pitfalls

Too little data

P=0

Possible pitfalls

Does not generalize [Cattrell, 2018]

We find that frequency of occurrence of rogue waves and their generating mechanism is not spatially uniform, and each location is likely to have its own unique sensitivities [...] We conclude that forecastable predictors of rogue wave occurrence will need to be location specific and reflective of their generation mechanism.

So, why ML?

Shed light on relative importance of effects
Quantitative predictions
Weights should yield some physical insight
Might generalize (fingers crossed)

References

McAllister, M. "How scientists recreated a monster wave that looks like Hokusai’s famous image." https://theconversation.com/how-scientists-recreated-a-monster-wave-that-looks-like-hokusais-famous-image-110304
Gemmrich, J., and C. Garrett. "Dynamical and statistical explanations of observed occurrence rates of rogue waves." Natural Hazards and Earth System Sciences 11.5 (2011): 1437-1446.
Cavaleri, Luigi, et al. "The Draupner wave: A fresh look and the emerging view." Journal of Geophysical Research: Oceans 121.8 (2016): 6061-6075.
Cattrell, A. D., et al. "Can rogue waves be predicted using characteristic wave parameters?." Journal of Geophysical Research: Oceans 123.8 (2018): 5624-5636.
Adcock, Thomas AA, and Paul H. Taylor. "The physics of anomalous (‘rogue’) ocean waves." Reports on Progress in Physics 77.10 (2014): 105901.

References

Cousins, Will, and Themistoklis P. Sapsis. "Reduced-order precursors of rare events in unidirectional nonlinear water waves." Journal of Fluid Mechanics 790 (2016): 368-388.
Fedele, Francesco, et al. "Real world ocean rogue waves explained without the modulational instability." Scientific reports 6 (2016): 27715.
Janssen, P. A. E. M., and Jean Bidlot. On the extension of the freak wave warning system and its verification. European Centre for Medium-Range Weather Forecasts, 2009.

An ocean of data