Nonlinear time series analysis: towards an effective forecast of rogue waves

Günter Steinmeyer; Simon Birkholz; Carsten Brée; Ayhan Demircan

doi:10.1117/12.2208251

9 March 2016 Nonlinear time series analysis: towards an effective forecast of rogue waves

Günter Steinmeyer, Simon Birkholz, Carsten Brée, Ayhan Demircan

Proceedings Volume 9732, Real-time Measurements, Rogue Events, and Emerging Applications; 973205 (2016) https://doi.org/10.1117/12.2208251
Event: SPIE LASE, 2016, San Francisco, California, United States

Abstract

Rogue waves are extremely large waves that exceed any expectation based on long-term observation and Gaussian statistics. Ocean rogue waves exceed the significant wave height in the ocean by a factor 2. Similar phenomena have been observed in a multiplicity of optical systems. While the optical systems show a much higher frequency of rogue events than the ocean, it appears nevertheless questionable what conclusions can be drawn for the prediction of ocean rogue waves. Here we tackle the problem from a different perspective and analyze the predictability of rogue events in two optical systems as well as in the ocean using nonlinear time-series analysis. Our analysis is exclusively based on experimental data. The results appear rather surprising as the optical rogue wave scenario of fiber-based supercontinuum generation does not allow any prediction whereas real ocean rogue waves and a multifilament scenario do bear a considerable amount of determinism, which allows, at least in principle, the prediction of extreme events. It becomes further clear that there exist two fundamentally different types of rogue-wave supporting systems. One class of rogue waves is obviously seeded by quantum fluctuations whereas in the other class, linear random interference of waves seems to prevail.

Citation Download Citation

Günter Steinmeyer, Simon Birkholz, Carsten Brée, and Ayhan Demircan "Nonlinear time series analysis: towards an effective forecast of rogue waves", Proc. SPIE 9732, Real-time Measurements, Rogue Events, and Emerging Applications, 973205 (9 March 2016); https://doi.org/10.1117/12.2208251

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