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PhD Studentship: Combining Explainable AI and Ensemble Forecasts for Advance Warning of Extreme Winter Weather Risks

University of Exeter - Department of Mathematics and Statistics

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students, International Students
Funding amount: £18,622 per year
Hours: Full Time
Placed On: 8th December 2023
Closes: 23rd January 2024
Reference: 5036

Supervisors:

Dr. William Seviour, U. of Exeter (w.seviour@exeter.ac.uk)

Dr. Stephen Thomson, U. of Exeter

Prof Adam Scaife, Met Office and U. of Exeter

Sudden stratospheric warmings (SSWs), consisting of the rapid breakdown of the wintertime stratospheric polar vortex, are among the most dramatic dynamical phenomena in Earth’s atmosphere. They are also strongly associated with extreme weather events in Europe and North America; for example, the 2018 “Beast from the East”. However, the predictability of SSWs is usually limited to 1-2 weeks, even in state-of-the-art prediction systems.

One potential option for improving the prediction timescales of SSWs, and thereby providing further advance warning of their impacts, is the use of artificial intelligence (AI). However, it is often unclear why an AI system has made a particular prediction, limiting our ability to gain physical understanding from it. This problem can be overcome with explainable AI (XAI) methods, in which the drivers of predictions can be identified. The goal of this project is therefore to use XAI, alongside numerical forecasts, to investigate sources of extended-range prediction of SSWs and their impacts on surface weather.

We expect this PhD project to progress in three main stages:

  1. Investigate whether regional circulation patterns can be identified by XAI as SSW drivers in a highly idealised general circulation model. This will make use of our group’s Isca modelling framework (execlim.github.io/Isca), which can be run relatively cheaply to generate a large training data set. We are motivated by recent work that has shown XAI methods can successfully identify global-scale SSW drivers (Wu et al. 2022, doi.org/10.1017/eds.2022.19).
  2. Determine regional ‘drivers’ of SSWs in Met Office subseasonal forecasts. This builds on recent work in our group that has identified a high sensitivity of forecasts to perturbations in a given region prior to a given SSW. However, it is not clear how common such a sensitivity is across all events.
  3. Apply XAI to Met Office forecasts. Here we aim to compare XAI-derived and forecast SSW probabilities, and also determine whether XAI predictions are sensitive to the same regions identified by the previous work.

The student will join an established, vibrant research group, and will be encouraged to contribute ideas and steer the direction of the project.

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.

How to apply

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)
  • Names of two referees familiar with your academic work. You are not required to obtain references yourself. We will request references directly from your referees if you are shortlisted.
  • If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English as per our guidance at www.exeter.ac.uk/pg-research/apply/english

The closing date for applications is Tuesday 23rd January 2024.

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