How can we efficiently simulate extreme weather risks? – PhD (Funded) Ref: 5511

Supervisors

Primary Supervisor - Dr William Seviour

Secondary Supervisors - Dr Stephen Thomson, Prof Adam Scaife, Dr Nick Dunstone (Met Office) 

Weather and climate modellers often have two main aims: (i) to accurately simulate the physics of impactful extreme events, and (ii) to accurately determine the probability of these events. Unfortunately, these goals are at odds; the more realistic a model is, the more difficult it is to run a large enough number of simulations to sample the tails of the probability distribution where extreme events lie. As a result, we have very large uncertainty in predictions of the likelihood of extreme events, despite their often devastating impacts. 

The aim of this project is to develop and test a new method for determining extreme weather risks. This method relies on successively branching simulations to focus resources on the extremes; a class of methods known as ‘rare event algorithms’. This approach is motivated by recent work in our group (Kent et al. 2023, https://doi.org/10.1029/2023GL106288) which has shown the ability to significantly alter the forecast probability of rare events with very small perturbations. The project will begin by studying an archetypal extreme event called a Sudden Stratospheric Warming, which often precedes extreme winter weather, such as the 2018 ‘Beast from the East’. Initial investigations will make use of the Isca idealised modelling framework developed in our group (https://execlim.github.io/Isca/), which allows for simulations to be run quickly and much of the complexity to be removed.

As the project progresses there will be an opportunity to analyse and potentially run new simulations with the Met Office’s operational seasonal forecast system, GloSea6. This work will be done in collaboration with the project’s Met Office co-supervisors (the Met Office is also based in Exeter). This part of the analysis would focus on a few case studies, in which we aim to apply methods developed from the idealised modelling, to see whether we can more efficiently determine the risk of an SSW.

This is a fully-funded PhD studentship. The student will be encouraged to shape the direction of the project, for example, to focus on other types of extreme event (e.g. heatwaves, storms) later in the project, as suits their interests. They will also be encouraged to write results of the project for publication and to present the work at international conferences, for which funding is available.

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate subject such as Mathematics, Physics, Meteorology, Oceanography, or Computer Science. Knowledge of scientific programming languages (e.g. Python, R, Matlab) would be advantageous, but is not essential.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information.

How to apply

Apply now

To apply, please click the ‘Apply Now’ button above. In the application process you will be asked to upload several documents

•             CV

•             Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).

•             Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)

•             Two academic references. 

•             If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.

The closing date for applications is midnight on April 21st  2025. 

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

Please quote reference 5511  on your application and in any correspondence about this studentship.

Summary