PhD Studentship: Novel statistical AI approaches for modelling and evaluating extreme windstorm risk. UNRISK NERC Centre for Doctoral Training PhD studentship 2025/26 Entry

Understanding Uncertainty to Reduce Climate Risks (UNRISK) is a Centre for Doctoral Training – Recruiting now!

UNRISK is a Centre for Doctoral Training with fully funded PhD research opportunities at the University of Leeds, University College London, and the University of Exeter collaborating with over 40 external partners. UNRISK will train students with the multidisciplinary knowledge and skills across climate science, data science and decision science to tackle the pressing challenge of reducing the risks associated with rapid climate change. UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing them with a stipend, university fees and residential training for 3 years and 9 months. Find out more at https://unrisk-cdt.ac.uk/ and browse the projects at https://unrisk-cdt.ac.uk/projects/.

Project Information

Extra-tropical cyclones are fascinating storms that have devastating impacts on society. For example, in winter 2013/14, more than 10 extreme storms passed over Europe, leading to total insured losses of more than $3.3 billion. Losses due to such events are expected to rise rapidly due to climate change. Insurers make decisions about such losses by using stochastic event simulations from catastrophe models. Such models have many simplifying assumptions and so it is useful to have independent ways to evaluate such simulations. This project will build on existing work by advancing current approaches using modern statistical AI methods (e.g. Bayesian hierarchical spatial extreme models) for characterising extreme wind speeds from windstorm spatial footprint datasets for Europe, Japan, and other regions. As well as providing new insight into storm behaviour, this project will help insurers develop a more reliable view of storm risk, resulting in better protection for society.

This project will address one or more of the following questions:

1. What is the distribution of extreme wind gust speeds at any given location?
2. How do these distributions depend on local climate modes and climate change?
3. How does the maximum upper wind speed vary over space and in time as the climate evolves?

This will involve extending current statistical methods for analysing windstorm footprint datasets and will widen their impact to a global scale. The approaches will enable better validation and adjustment of catastrophe model estimates of extreme wind speeds and how maximal wind speeds might change due to climate change.

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