High impact weather events from fronts – weather forecasts and future projections, NERC GW4+ DTP PhD studentship for 2023 Entry, PhD in Mathematics and Statistics.

About the Partnership

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science.

Project Background

Extreme rainfall events that cause flooding are often caused by atmospheric fronts (Catto and Pfahl 2013). For example, the flooding associated with Storm Desmond in December 2015, which, along with the subsequent storm Eva, caused economic losses estimated between £1.3 and £5.8 billion, was from the rain that fell along the storm’s front (Matthews et al 2018). Predicting the location of fronts and their intense rainfall can present a challenge in weather forecasting, resulting in difficulties giving accurate weather warnings. Understanding the characteristics of high impact fronts and their dynamical precursors in observations can help to improve these predictions and future projections. In this project, the goal will be to develop greater understanding of high impact fronts, to then apply this understanding to evaluate both ensemble weather forecasts and climate projections of extreme precipitation events associated with fronts.  

Project Aims and Methods

The aims of this project will be to understand the lifecycle and dynamical and thermodynamic characteristics of fronts that contribute to extreme precipitation events in observations. Further, the methods developed will be applied to the Unified Model (UM) Climate Model, and the Numerical Weather Prediction model (specifically the Met Office Global and Regional Ensemble Prediction System – MOGREPS) in order to understand how well the models can represent these features. 

Errors in frontal structure identified in the climate model may be able to provide insight into the causes of errors in the Met Office’s weather forecasts. We will investigate whether the systems that are simulated poorly in the climate model responsible for causing the largest uncertainty in the ensemble forecasts. Answering this may directly contribute to improving the representation of fronts in the models, and therefore the Met Office’s weather and climate forecasts, as well as improving their weather warnings. With the guidance of the supervisors, the candidate will be given the opportunity to modify the research focus and weighting of the different aspects of the project to reflect their interests and strengths. This studentship comes with a generous budget for travel and training (£17k).

Candidate requirements

Applicants should 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 Mathematics, Physics, Meteorology, Statistics, Environmental Science or another related field. Knowledge of scientific programming languages (e.g., Matlab, Python, IDL, R) would be advantageous.

Project partners 

This project is supported by a CASE partnership with the Met Office with Dr Duncan Ackerley as the main Met Office supervisor. Links with the Met Office will ensure access to data and expertise from their seamless modelling capability across prediction time scales. Visits to the Met Office will allow the candidate to learn their computer systems, undertake training on key software tools such as Python, and communicate results to the key stakeholders such as model developers and operational forecasters. 

Training

The candidate will be based within the internationally recognised Exeter Climate Systems Research Centre. They will receive training on data analysis of large datasets (Big Data), weather and climate modelling, scientific writing and presenting in accordance with the postgraduate programme at the University of Exeter, GW4 initiatives such as the Water Security Alliance, and through participation in Met Office training. The candidate will be expected to take part in relevant national and international conferences and workshops.

For further information and to submit an application please visit - https://www.exeter.ac.uk/study/funding/award/?id=4605