Accurate knowledge and understanding of the sea state and its variability is crucial to numerous oceanic and coastal engineering applications, but also to climate change and related impacts including coastal erosion and inundation, and changing sea-ice interaction. The largest impacts and highest risks are associated with the most energetic conditions.
Various studies have examined the extremes of wave height both locally and globally, on a range of temporal scales and using a range of data sources (Izaguirre et al., 2011; Timmermans et al., 2017; Kumar et al., 2018; Stopa et al., 2019; Young & Ribal, 2019). Simulated hindcasts, re-analysis and satellite observations have been used as source data but more recently, independent groups (Young and Ribal, 2019; European Space Agency, Climate Change Initiative) have produced updated quality controlled and calibrated data sets based on the most currently available satellite observations. These offer potentially the most accurate and up-to-date insight into the spatial and temporal variability of recent (extreme) wave climate. Furthermore, the larger samples of observation arising from the use of more recent observations may provide the opportunity to examine extremes at fairly small spatial scales, such as coastal regions. However, recent comparative analysis of several global gridded products has revealed considerable disagreement in temporal and geographic characteristics of (mean) wave climate with indication that similar, if not more dramatic, discrepancies exist for more extreme conditions. The development of statistical models is crucial since the underlying causes behind discrepancies remain unclear, as do the potential implications for projected impacts in relevant regions.
The primary application area of this proposal relates to characterising extreme conditions in oceans world wide but the proposed statistical methodology is generic. Different oceans exhibit very different behaviour and statistical descriptions need to be sensitive and flexible in this respect. The proposed statistical approach will be based on graphical models for multivariate extremes and will facilitate spatio-temporal of variables such as significant wave height, wind speed and current speed, but also combinations of the different variables within one model, with quite general extremal dependence structure. This is particularly useful when computer simulators for extremes of an environment are compared with actual observations but also because the most important characteristics of environmental extremes are not contemporaneous. For example, the extremum of significant wave height within a storm may not coincide with extrema of wind speed or wave peak frequency and the proposed statistical models will aim at capturing and understanding such temporal incoherences better. In this project the student will exploit multiple sea state data sets in order to identify geographic regions of interest characterised by variability and disagreement across data sets, taking particular account of regions potentially vulnerable to coastal impact. The effects of interannual and decadal variability may be relevant, as well as the influence of severe weather systems such as tropical cyclones.
The output from this research will help to identify more broadly and consistently (geographically) where and why uncertainty affects temporal characteristics of the sea state representation across the climate-quality data record, and how this might influence longer term projections and planning in regions where impacts could become important.
The student is anticipated to be located at the School of Mathematics at the University of Edinburgh, with good opportunity to interact with researchers at the NOC sites. The project would suit a student with strengths primarily in statistics and the physical sciences (Physics, Maths, Engineering), familiarity with programming (typically R, Python or similar), the handling and analysis of large data sets and an interest in oceanographic and coastal processes, and their interaction and impacts on coastal communities and industry.
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
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