Structured extreme value models for environmental science.

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Environmental processes such as precipitation, temperature or wind can have devastating consequences, i.e., floods, drought, or wildfires. These extreme events can have severe impact on society, but they are quite rare, which means that making inference about them is complex. My work uses a particular type of statistical methodology based on extreme value theory. The focus is on statistical modelling of both multivariate and spatial-temporal temporal extreme value problems. I will look to improve on existing methods, which typically require a large number of parameters, by making pragmatic and physically-motivated simplifying assumptions about the underlying structure of the extremal dependence. The type of assumptions I will look to make includes graphical model structure, vines, clustering and mixtures.In the multivariate setting, the aim is to develop generic methods and to explore their implementation to air pollutant data. For example, when multiple different chemicals are of interest, some chemicals are more directly related than others given they have common pollutant sources. In the spatial-temporal analysis, I will investigate adapting existing methods to very large spatial-scale studies, e.g., rainfall or temperature over the whole of Europe, which can have multiple separate extreme events occurring simultaneously; a feature that is not possible to describe in currently used models. The outcome of the research will be improved modelling and understanding of the extremal characteristics of a range of natural hazards.In partnership with Oslo University.