Description du poste
One of the major challenges for the current and future climate diagnostics is the occurrence of very rare, but extreme intense precipitation events. Those record-shattering events lead to the Valencia-Flood in October 2024 in Spain or the Ahr-floods in July 2021 in Germany with billions of Euro of financial losses and unfortunately significant numbers of fatalities (cf. e.g., Leckebusch & McCarthy (2026) and references therein). While the principal underlying physical mechanism of precipitation formation and the principal mechanisms of necessary precursor conditions are well understood, the exact amount of extreme precipitation and when and where those events may occur are less well understood. The same holds true for the principal rate of occurrence, which is difficult to estimate as those events may occur only once or twice per century in the current climate. Even more difficult is an assessment of those events for future climate conditions, altered by human activities. We can already diagnose that those events are more likely to occur under the influence of a warming climate, but the exact changes in those statistics of their occurrence remains a challenge. One of the reasons for this is the inability of current climate models to fully simulate those rare and extreme precipitation events in their full characteristics, from very local scale processes to the wider statistical occurrences for e.g., 30-years climatic time spans. This project will develop and test a novel approach to estimate those rare events. It will investigate whether a combination of AI- and Extreme Value Theory (EVT)-approaches can produce reliable estimates for future climate states. Thus, can AI–EVT models trained on reanalyses and large hindcast ensembles estimate regional extreme-rainfall distributions that remain reliable when transferred between global warming levels and circulation regimes? This project would best suit a student with a Computer Science background, or otherwise a numerical background for example Meteorology, Physics, or Maths, with experience coding in Python or similar (e.g. R, Matlab), and preferably with some experience in AI approaches. For further information on this project and details of how to apply to it please click the 'Apply' button above. Further information on how to apply for a CENTA studentship can be found on the CENTA website: https://centa.ac.uk/apply/ Funding notes: This project is offered through the CENTA3 DLA, funded by the Natural Environment Research Council (NERC). Funding covers: annual stipend, tuition fees (at home-fee level), Research Training Support Grant. Academic requirements: at least a 2:1 at UK BSc level or a pass at UK MSc level or equivalent. Further information: https://centa.ac.uk/apply/ International students are eligible for studentships to a maximum of 30% of the cohort. Funding does not cover any additional costs relating to moving or residing in the UK. International applicants must fulfil the University of Birmingham’s international student entry requirements, including English language. Further information: https://www.birmingham.ac.uk/postgraduate/pgt/requirements-pgt/international/index.aspx . References: Angus, M., and G.C. Leckebusch, 2020: On the dependency of Atlantic Hurricane and European Windstorm Hazard. Geophysical Research Letters, 47(23), https://doi.org/10.1029/2020GL090446 Angus, M., M. Widmann, A. Orr, R. Ashrit, G.C. Leckebusch, and A. Mitra, 2024: A comparison of two statistical postprocessing methods for heavy precipitation forecasts over India during the summer monsoon. QJR Meteorol. Soc. 2024; 1–19, http://doi.org/10.1002/qj.4677 Beale, R. et al, 2025: AI4Climate Policy brief: AI will not replace climate science — but it can make it fit for an age of extremes. https://www.birmingham.ac.uk/news/2025/ai-will-not-replace-climate-science-but-it-can-make-it-fit-for-an-age-of-extremes .