PhD: Mechanisms for and predictions of occurrence of ocean rogue waves

Lancaster University

Lancaster, UK 🇬🇧

Project institution: Lancaster University

Project supervisor(s): Dr Suzana Ilic (Lancaster University), Prof Aneta Stefanovska (Lancaster University), Michael Thomas (Reliable Insights Ltd), Dr Paul Bartholomew (University of Edinburgh) and Dr Bryan Michael Williams (Lancaster University)

Overview and Background

Rogue waves, exceptionally high ocean waves, whose wave height exceeds the significant wave height by at least twice, are rare, transient phenomena that pose serious risks to shipping, fishing and maritime infrastructure, including offshore platforms and wind turbines. Understanding how they form, and their accurate and timely prediction is vital for assessing risks to marine operations.

Despite advances in theoretical and experimental research of rogue waves, the physical conditions and mechanisms that lead to the formation of rogue waves in the real sea are less understood and predictions are still challenging. The proposed PhD project/s will address these gaps by using ever-growing databases of field data, developing and refining novel data science approaches and exploiting developments in high performance computing.

Teaser Project 1:

This data-intensive project aims to accelerate novel time-localised analysis methods to investigate physical mechanisms underlying rogue waves and predict their occurrence.

Objectives:

  1. Use GPU Accelerated Computing to parallelise algorithms for time-localised phase coherence and couplings between waves recorded in many points in space, enabling scaling to higher-resolution and near real time analysis.
  2. Isolate the mechanisms leading to the formation of rogue waves using algorithms developed in Objective 1.
  3. Develop in-situ feature detection for automated analyses exploiting GPU and assess the relationship between the occurrence of rogue waves and their characteristics from time-series measured under different physical conditions.
  4. Develop a time-series-based prediction model, using the relationships identified in Objectives 2 and 3 and assess its ability to predict the occurrence of rogue waves.

Methods:

The numerical modelling and algorithms for time-series analysis will exploit GPU Accelerated Computing; exascale will then allow near real-time practical applications. The Multiscale Oscillatory Dynamics Analysis (MODA) toolbox for non-linear and time-localised phenomena in time-series (e.g. phase coherence, coupling and wave energy exchange [3&4]) will be parallelised and used to identify rogue wave mechanisms. Pattern analysis and automated featurisation will be developed to detect “anomaly” in the measured sea surface elevations. The methods will be first applied to laboratory data (e.g. [1]) and then to publicly available field measurements (e.g. Free Ocean Wave Dataset with more than 1.4 billion wave measurements). The newly developed prediction model will be systematically validated with measured data.

Teaser Project 2:

This is a data-intensive project focused on the computational optimisation of time series analyses for dynamic systems and the relationship between rogue wave properties and environmental conditions.

Objectives:

  1. Assess the current performance of the numerical tools included in MODA in terms of their relevance for detecting the mechanisms of rogue waves and their computational efficiency.
  2. Optimise the algorithms of the tools identified in Objective 1 with multiple-Graphics Processing Units (GPU) to improve time to results and experimental throughput, enabling large scale ensemble time-series analyses.
  3. Develop and apply a GPU version of MODA to field measured data to isolate mechanisms that lead to the formation of rogue waves.
  4. Assess the relationship between the occurrence of rogue waves and concurrent ocean and atmospheric data.

Methods:

The Multiscale Oscillatory Dynamics Analysis (MODA) toolbox offers several high-order methods for time-series analysis, some based on wavelets. The high computational demands of uncertainty evaluation methods limits their use for operational purposes.  Optimised algorithms, GPU-acceleration and exascale facilities will open up higher resolution and practical applications. MODA will identify the mechanisms underlying rogue wave formation using field measured time-series of surface elevations (e.g. Free Ocean Wave Dataset). The concurrent environmental data (e.g. surface ocean currents, wind and atmospheric pressure) will be collated either from field measurements or from the operational forecast models provided by meteorological offices. The correlation between the occurrence of rogue waves and environmental parameters will be investigated as well as ‘casual’ relationships between the identified mechanisms and the environmental conditions, which can be incorporated into predictions in the future.

References and Further Reading

  1. Luxmoore, J.F., Ilic, S. and Mori, N., 2019. On kurtosis and extreme waves in crossing directional seas: a laboratory experiment. Journal of Fluid Mechanics876, pp.792-817
  2. Mori N., Waseda, T., Chabchoub A.(eds.) (2023) Science and Engineering of Freak Waves, Elsevier (click here)
  3. Newman, J., Pidde, A. and Stefanovska, A., 2021. Defining the wavelet bispectrum. Applied and Computational Harmonic Analysis51, pp.171-224
  4. Stankovski, T., Pereira, T., McClintock, P.V. and Stefanovska, A., 2017. Coupling functions: universal insights into dynamical interaction mechanisms. Reviews of Modern Physics89(4), p.045001
  5. Barnes SJK,  Bjerkan J, Clemson PT, Newman J, Stefanovska A, 2024, Phase coherence – A time-localised approach to study interactions, Chaos, 34: 073155
  6. Rowland Adams J, Newman J, Stefanovska A, 2023, Distinguishing between dererministic oscillations and noise, The European Physical Journal Special Topics 232: 3435-3457
  7. Yang X., Rahmani H., Black S., Williams B. M. Weakly supervised co-training with swapping assignments for semantic segmentation. In European Conference on Computer Vision 2025 (pp. 459-478). Springer, Cham
  8. Jiang Z., Rahmani H., Black S., Williams B. M. A probabilistic attention model with occlusion-aware texture regression for 3D hand reconstruction from a single RGB image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 758-767)
  9. Jiang Z., Rahmani H., Angelov P., Black S., Williams B. M. Graph-context attention networks for size-varied deep graph matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 (pp. 2343-2352)

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