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  High-resolution Rainstorm Forecasting using Machine Learning

   Centre for Accountable, Responsible and Transparent AI

This project is no longer listed on and may not be available.

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  Dr Andy Barnes, Dr Thomas Kjeldsen  No more applications being accepted  Self-Funded PhD Students Only

About the Project

The aim of this project is to develop new high-resolution machine learning based forecasting methods for intense rainstorms in the UK. The consequences of intense rainstorms include flooding which brings with it substantial risk to human life as well as economic consequences (e.g. in 2015/16 the cost of flooding was estimated at £1.6 billion) and landslides. Our ability to forecast these intense rainstorms currently falls to traditional rainfall forecasting techniques which require access to a high-performance computer to process even the sparsest forecast. However, recently a new array of machine learning based methods have been shown to utilise meteorological data in new ways to provide efficient regional forecasts of monthly rainfall (Barnes et al., 2022; 2023). This project will develop a novel approach to forecasting intense rainstorms by combining meteorological datasets (e.g. radar and satellite imagery) with new breakthroughs in machine learning and artificial intelligence. While clear guidance will be provided in the early stages of the PhD on the best approach to take, it is intended and expected that the project will evolve with the interests of the student, both in terms of core scientific focus and methodology.

Successful applicants will have, or expect to receive, a master's degree or first or upper-second bachelor's degree in a relevant subject.

Formal applications should be accompanied by a research proposal and made via the University of Bath’s online application form for the PhD in Computer Science programme. Further information about the application process can be found here.

Start date: Between 8 January and 30 September 2024.

Computer Science (8) Environmental Sciences (13)

Funding Notes

We welcome applications from candidates who can source their own funding. Tuition fees for the 2023/4 academic year are £4,700 (full-time) for Home students and £26,600 (full-time) for International students. For information about eligibility for Home fee status:


Barnes, A.P., Kjeldsen, T.R. Video-based Convolutional Neural Network Forecasting for Rainfall Forecasting. IEEE Geoscience and Remote Sensing Letters. 19, 1-5 (2022).
Barnes, A.P., McCullen, N. & Kjeldsen, T.R. Forecasting seasonal to sub-seasonal rainfall in Great Britain using convolutional-neural networks. Theor Appl Climatol 151, 421–432 (2023).

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