Statistical Post-Processing of Ensemble Forecasts of Compound Weather Risk - Mathematics - NERC GW4+ DTP PhD Studentship Ref: 2786

About the award

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 six Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Met Office, 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. For further details about the programme please see

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018, research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.


Lead supervisor: Dr Frank Kwasniok
Co-Supervisor: Dr Chris Ferro
Co-Supervisor: Prof Jonathan Rougier
Co-Supervisor: Dr Gavin Evans
Co-Supervisor: Dr Piers Buchannan

Location:  Streatham Campus, Exeter

Project description:

Probabilistic weather forecasts present users with likelihoods for the occurrence of different weather events. Demand for such forecasts is increasing as they provide users with a basis for risk-based decisions. For example, a council may decide to deploy a road gritting service if the probability of widespread ice formation exceeds 50%. It is crucial that probabilistic forecasts are well calibrated. For example, events predicted to occur with probability 70% should subsequently occur 70% of the time. Decisions based on poorly calibrated forecasts, forecasts in which the probability of an event is systematically under- or overestimated, could lead to inappropriate actions and significant losses. This is particularly true for extreme weather events which impact most heavily on society.

While an extreme event at a single location can be damaging to the local area, the consequences may be even more serious if there is a compounding effect due to (i) the event occurring simultaneously at several locations, (ii) several meteorological variables taking extreme values at the same time (e.g., wind speed and precipitation) or (iii) temporal persistence of the event or serial clustering of several events of the same type.

Project Aims and Methods

The project will develop novel multivariate statistical techniques for recalibrating forecast ensembles that capture spatial, temporal and cross-variable dependence. These will improve probabilistic prediction of compound weather risk. A particular emphasis will lie on high-impact extreme weather events.

The research will be conducted in close collaboration with the Met Office as CASE partner. We will use historical data from the Met Office's ensemble prediction system MOGREPS together with the corresponding verifications. Meteorological variables of interest are temperature, surface pressure, wind speed and precipitation.

The main objectives of the project are:
(i) to develop and explore novel methods for multivariate statistical post-processing of forecast ensembles with a particular view to extreme weather events;
(ii) to improve probabilistic prediction of UK compound weather risk due to temperature, wind speed and precipitation;
(iii) to help implement better techniques in the Met Office's operational post-processing suite in order to improve prediction of UK compound weather risk.


We will require at least an upper second class honours degree in a relevant subject such as mathematics, statistics or meteorology. Pre-existing knowledge in statistics and/or numerical weather prediction as
evidenced by appropriate module choices will be an advantage. Additional criteria are a high level of self-motivation and a keen interest of the candidate in the application of mathematics and statistics in weather and climate science.


Case Award Description

The Met Office as CASE partner will contribute £1,000 per year over the duration of the studentship. The student will spend at least three months (probably six to eight weeks per year) working at the Met Office. The Met Office will provide suitable data sets for the project as well as appropriate guidance.


The student will receive high-quality research training in various aspects of weather and climate science through interaction with expert staff and other postgraduate researchers as well as an extensive external and internal seminar programme. Training in general meteorology, physics of climate and statistics will be provided through lecture series on the programme MSc Mathematics (Climate Science) offered by the College. The student will benefit from attending courses at the Academy for PhD Training in Statistics (APTS) where Exeter is a member. The Mathematics Research Institute at Exeter is also a member of the
EPSRC-funded MAGIC Taught Course Centre for PhD Training in Mathematics. Training may be complemented by external sources, e.g., a summer school on statistical methods in weather and climate science, numerical weather prediction, data assimilation or general meteorology. Moreover, the student will acquire transferable skills such as presentation techniques and writing skills.


* Gneiting T., Raftery A. E., Westveld A. H., Goldman T. (2005): Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133, 1098-1118.

* Raftery A. E., Gneiting T., Balabdaoui F., Polakowski M. (2005): Using Bayesian model averaging to calibrate forecast ensembles, Monthly Weather Review, 133, 1155-1174.

* Williams R. M., Ferro C. A. T., Kwasniok F. (2014): A comparison of ensemble post-processing methods for extreme events, Quarterly Journal of the Royal Meteorological Society, 140, 1112-1120.

Entry 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.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

All applicants would need to meet our English language requirements by the start of the  project

Applicants who are classed as International for tuition fee purposes are not eligible for funding.


Application deadline:7th January 2018
Value:£14,553 per annum for 2017-18
Duration of award:per year
Contact: PGR Recruitment

How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

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

You will be asked to name 2 referees as part of the application process however we will not contact these people until the shortlisting stage. Your referees should not be from the prospective supervisory team.

The closing date for applications is midnight on 7 January 2018.  Interviews will be held at the University of Exeter between 5 - 16 February 2018.

If you have any general enquiries about the application process please email  Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;

• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.