Innovative data driven approaches to predicting long-term European wind storm risk

actionable information to insurers. In particular, climate models struggle to capture atmospheric circulation variability. This means estimated current and future wind storm risks based on climate models need to be carefully interpreted using a combination of physical science and decision theory. We will support you to develop an exciting project related to European wind storms that addresses cutting-edge societally-relevant research questions at the interface of climate and decision sciences.

PhD Project

This project is linked to a new partnership between the University of Leeds and Lloyd's Banking Group and directly addresses real world problems. Some example topics that could be explored are:

• What drives multi-decadal variability in European wind storms and can this be predicted?
  Understanding long timescale variability in European wind storm risk can help insurers to price policies. We can use long-term observation based datasets to characterise extreme wind storms on timescales of decades and link this with population data to estimate losses. This could focus on the statistics of wind storms or the risk of specific events analogous to observed cases. We can use uncertainty quantification methods for estimating the signal-to-noise ratio in forecasts and post-processing techniques such as calibration and ensemble matching to boost the predictable signal and make long-range forecasts more useable.
• Can machine learning help to predict localised wind storms?
  Climate models have coarse resolution while wind storms can exhibit small scale features that contain the most damaging winds (e.g. sting jets). We could explore whether generative machine learning methods can be used to downscale climate model output to produce higher resolution forecasts. We can focus on methods such as probabilistic diffusion models and conditional generative adversarial networks that allow uncertainty information in the downscaled predictions to be quantified and where possible minimised through model training.
• Can decadal climate forecasts of European wind storms support planning and decision making by insurers?
  All forecasts contain uncertainties which tend to grow for longer-term predictions due to weaker predictable signals. Understanding the associated uncertainties and the tolerance of users to incorporate uncertainties in their decision making is crucial to move towards climate services.

Applicant Profile

We are seeking students with a quantitative background (e.g. Atmospheric Science, Physics, Maths, Engineering, Computer Science) and an interest in decision science who want to apply their skills to a problem of strong societal consequence and with a direct industrial application.

Funding Notes

This project is part of the UNRISK CDT, which offers 15-18 fully-funded NERC studentships, covering full university tuition fees; a personal stipend at standard UKRI rates; £6000 individual research and training costs; £5000 (per student) of cohort-level training; and a 'Flexible Fund' for special projects.

International applicants will need to cover costs related to applying for a student visa and the international health surcharge (IHS)

Applications are open to UK and international applicants. The number of awards for international applicants is limited by UKRI rules.

More information is available on the UNRISK website: View Website

References

• https://environment.leeds.ac.uk/see/staff/1404/professor-amanda-maycock
• https://eps.leeds.ac.uk/maths/staff/9226/douglas-parker
• https://business.leeds.ac.uk/research-cdr/staff/357/professor-barbara-summers
• https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/ukcp/ukcp18-factsheet-storms.pdf
• https://nhess.copernicus.org/articles/23/2841/2023/
• https://www.nature.com/articles/s41467-023-40102-6