Explaining Rogue Waves in the Ocean Through Observations and Modelling

巨浪是海洋上意想不到的巨大表面波，可以对近海建筑和船只造成灾难性的破坏。这些“怪异”或“怪物”的海浪被怀疑已经倾覆了数百艘远洋船只，并导致了不幸的生命损失。对于异常波事件的形成，人们提出了一系列的解释，包括局域电流的影响、深度突变、调制不稳定性、二阶界非线性增强的色散（和定向）聚焦，以及通过与大气对流风暴相互作用产生的格林斯潘共振。只要具备适当的理论条件，它们都能产生异常浪。然而，关于实际海洋中异常浪形成的主要机制的争论仍在继续。这个项目的目的是在我们对真实海洋环境的理解上取得重大进展，主要是通过生成和分析为评估区域（和时间尺度）而创建的数值模型，在那里进行了异常波观测。这将得到对未开发的海洋数据集的新分析的支持，这些数据集包含了异常波的测量（或间接暗示了它们的发生）。实现该项目目标的建议途径是开发谱波模型来预测观测到的异常波事件区域的波况。然后可以使用光谱条件，与局部测深相结合，在局部区域上驱动相位分辨模型（例如非线性势流）。结果条件的统计和频谱分析可用于确定a)模型是否捕获了异常波事件的增强可能性？b)如果是，主要原因是什么？如果该方法成功地确定了海洋中异常浪的主要原因，那么除了验证整个项目中开发的评估方法外，这将使我们对真实海洋中异常浪形成的理解取得重大进展。这些发现的含义具有潜在的根本意义，具有广泛的实际意义。随着了解的加深，可以为海上船舶和结构的降低风险定义适当的极端“设计”条件，如果可以确定潜在风险增加的位置和/或时间尺度，则可以避免这些风险。因此，其结果有可能减少资金损失和生命损失。此外，支撑该项目的工作有可能回答围绕异常浪的一系列进一步未解的额外研究问题。未来的关键研究问题可能包括：1。这些异常浪（或增加的可能性）是否已经被预测到？异常波事件增加的可能性在空间上有多局域化？使用开发的框架，是否有可能确定创建异常波事件的“理想”（最坏情况）输入条件？在未来的气候中，异常浪会变得更加普遍吗？为了解决这个问题，我们可以从由气候预测的风驱动的波浪模型集合中产生一个极端波浪的数据集。

Rogue waves are large, unexpected surface waves on the ocean that can cause catastrophic damage to offshore structures and vessels. These 'freak' or 'monster' waves are suspected to have capsized hundreds of ocean-going vessels and resulted in an unfortunate loss of life. A range of proposed explanations exist for the formation of rogue wave events including the effect of localised currents, abrupt depth transitions, modulation instability, dispersive (and directional) focusing enhanced by second-order bound nonlinearity, and Greenspan resonance through interaction with atmospheric convective storms. All are able to create rogue waves given the right set of theoretical conditions. However, debate is ongoing as to the dominant mechanism(s) for rogue wave formation in real seas.This project aims to make significant progress in our understanding for real ocean settings, primarily through the generation and analysis of numerical models created to assess regions (and time scales), where rogue wave observations have been made. This will be supported by a novel analysis of unexploited ocean datasets that contain rogue wave measurements (or indirectly hint at their occurrence).A suggested route to achieving the project aims is to develop spectral wave models to hindcast the wave conditions in the region of observed rogue wave events. The spectral conditions can then be used, in combination with local bathymetry, to drive a phase-resolved model (e.g. nonlinear potential flow) over a localised region. The statistical and spectral analysis of the resulting conditions can be used to identify a) did the model capture an enhanced likelihood of rogue wave events? and b) if so, what is the dominant cause?If the approach is successful in identifying the dominant cause(s) of rogue waves in oceans, then this will make significant advances in our understanding of rogue wave formation in real seas in addition to validating the methodology developed throughout the project for their assessment. The implications of these findings have the potential to be fundamentally significant with wide-ranging practical implications. With improved understanding, appropriate extreme 'design' conditions can be defined for the de-risking of offshore vessels and structures, and if locations and/or time scales can be identified which have an increased risk potential, these can potentially be avoided. Consequently, the results have the potential to reduce capital loss as well as loss of life.In addition, the work underpinning this project has the potential to answer a range of further unanswered additional research questions surrounding rogue waves. Key future research questions may include:1. Could these rogue waves (or the increased likelihood of) have been predicted?2. How spatially localised is the increased likelihood of rogue wave events?3. Using the developed framework, is it possible to identify 'ideal' (worst-case) input conditions for creating rogue wave events?4. Will rogue waves will become more common in the future climate? To address this question, we could produce a dataset of extreme waves from an ensemble of wave models forced by winds from climate projections.