PhD Studentship: Extreme value modelling of rainfall from high resolution radar data in a changing climate. 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

Extreme rainfall events vary in duration and magnitude, from multiple days of fairly high rainfall to extreme rainfall over a few hours. Flooding results from either event type. Short-lived events, e.g. thunderstorms, can be limited to just a few kilometers, and if weather stations aren’t nearby, may lack measurements. Radar data bridge this gap with their high resolution, wide spatial coverage and high frequency of sampling. But statistical modelling of these data, and using them understand future rainfall events, is a challenge that involves modelling many different-sized grid cells of data at many time points, downscaling model output of too-low resolution to resolve local events, and developing software to fit models. Once developed, these models can improve our quantitative understanding of extreme rainfall events, offering better insight into the spatial dependence structure of extreme rainfall, how it changes between resolution and how local events may change into the future.

To capture radar data, this project will develop extreme value models for the variation in extreme rainfall from one grid cell to another over time, for many grid cells over large areas. Models will need to accommodate varying grid cell size, as cells increase in size with distance from radar sites. This project will also build downscaling models, via geostatistics, that link radar data to climate model output, the latter being of insufficient resolution to resolve localised rainfall events, but may still hold valuable information in their projections quantifying how such events may change into the future. With Met Office support we can employ best practice converting radar reflectivities to rain rates, which is a complex and involved process, due, e.g., to beam attenuation through intense rain.

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