University of Exeter funding: NERC GW4+ DTP PhD studentship

Statistical post-processing of ensemble forecasts of compound weather risk. PhD in Mathematics (NERC GW4+ DTP) Ref: 3702

About the award

Supervisors

Lead Supervisor

Dr Frank Kwasniok, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter

Additional Supervisors

Dr Chris Ferro, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter

Dr Gavin Evans, Met Office

Dr Piers Buchanan, Met Office

Location: University of Exeter, Streatham Campus, Exeter, EX4 4QJ

This project is one of a number that are in competition for funding from the NERC GW4+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the GW4 Alliance of research-intensive universities: the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five unique and prestigious Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology & Hydrology, the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in the Earth, Environmental and Life sciences, designed to train tomorrow’s leaders in scientific research, business, technology and policy-making. For further details about the programme please see http://nercgw4plus.ac.uk/

For eligible successful applicants, the studentships comprises:

  • A stipend for 3.5 years (currently £15,009 p.a. for 2019/20) in line with UK Research and Innovation rates
  • Payment of university tuition fees;
  • A research budget of £11,000 for an international conference, lab, field and research expenses;
  • A training budget of £3,250 for specialist training courses and expenses.
  • Travel and accommodation is covered for all compulsory DTP cohort events
  • No course fees for courses run by the DTP

We are currently advertising projects for a total of 10 studentships at the University of Exeter.

Eligibility

Students who are resident in EU countries are eligible for the full award on the same basis as UK residents.  Applicants resident outside of the EU (classed as International for tuition fee purposes) are not eligible for DTP funding. Residency rules are complex and if you have not been resident in the UK or EU for the 3 years prior to the start of the studentship, please apply and we will check eligibility upon shortlisting.

Project Background 

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/collaborative 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.

Candidate Requirements

We will require at least an upper second class honours degree in a relevant subject such as mathematics, statistics, physics or meteorology. Pre-existing knowledge in statistics and/or numerical weather prediction as evidenced by appropriate module choices will be an advantage but not essential. 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 or Collaborative Partner

The Met Office will provide suitable data sets for the project as well as appropriate guidance. The Met Office supervisors will contribute to the project from an operational and user-oriented point of view. The student will interact with Met Office staff and spend time at the Met Office.

Training

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.

References / Background reading list

  • 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.
  • Allen S., Ferro C. A. T., Kwasniok F. (2019): Regime-dependent statistical post-processing of ensemble forecasts, Quarterly Journal of the Royal Meteorological Society, accepted.

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 http://www.exeter.ac.uk/postgraduate/apply/english/.

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.

Reference information
You will be asked to name 2 referees as part of the application process, however we will not expect receipt of references until after the shortlisting stage. Your referees should not be from the prospective supervisory team.

If you are shortlisted for interview, please ensure that your two academic referees email their references to the pgr-recruitment@exeter.ac.uk, 7 days prior to the interview dates.  Please note that we will not be contacting referees to request references, you must arrange for them to be submitted to us by the deadline.

References should be submitted by your referees to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

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

The closing date for applications is 1600 hours GMT Monday 6 January 2020.  Interviews will be held between 10 and 21 February 2020.  For more information about the NERC GW4+ DPT please visit https://nercgw4plus.ac.uk

If you have any general enquiries about the application process please email pgrenquiries@exeter.ac.uk.  Project-specific queries should be directed to the lead supervisor.


Data Sharing
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.

Summary

Application deadline:6th January 2020
Value:£15,009 per annum for 2019-20
Duration of award:per year
Contact: PGR Enquiries pgrenquiries@exeter.ac.uk