Statistical post-processing of ensemble forecasts of compound weather risk, NERC GW4+ DTP PhD studentship for 2022 Entry, PhD in Mathematics Ref: 4259
About the award
Dr Frank Kwasniok, University of Exeter, Department of Mathematics
Dr Chris Ferro, University of Exeter, Department of Mathematics
Dr Gavin Evans, Met Office
Dr Piers Buchanan, Met Office
Location: Streatham Campus, University of Exeter, Exeter, Devon
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. For further details about the programme please see http://nercgw4plus.ac.uk/
For eligible successful applicants, the studentships comprises:
- An stipend for 3.5 years (currently £15,609 p.a. for 2021/22) 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
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 structure. 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, 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.
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. 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.
Background reading and references
* 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,
* 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, 145, 3535-3552.
* Allen S., Ferro C. A. T., Kwasniok F. (2020): Recalibrating wind-speed forecasts using regime-dependent ensemble model output statistics, Quarterly Journal of the Royal Meteorological Society, 146, 2576-2596.
* Allen S., Evans G. R., Buchanan P., Kwasniok F. (2021): Incorporating the North Atlantic Oscillation into the post-processing of MOGREPS-G wind speed forecasts, Quarterly Journal of the Royal Meteorological Society, 147, 1403-1418.
* Allen S., Evans G. R., Buchanan P., Kwasniok F. (2021): Accounting for skew when post-processing MOGREPS-UK temperature forecast fields, Monthly Weather Review, 149, 2835-2852.
NERC GW4+ DTP studentships are open to UK and Irish nationals who, if successful in their applications, will receive a full studentship including payment of university tuition fees at the home fees rate.
A limited number of full studentships are also available to international students which are defined as EU (excluding Irish nationals), EEA, Swiss and all other non-UK nationals. For further details please see the NERC GW4+ website.
Those not meeting the nationality and residency requirements to be treated as a ‘home’ student may apply for a limited number of full studentships for international students. Although international students are usually charged a higher tuition fee rate than ‘home’ students, those international students offered a NERC GW4+ Doctoral Training Partnership full studentship starting in 2022 will only be charged the ‘home’ tuition fee rate (which will be covered by the studentship).
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD. More information on this is available from the universities you are applying to (contact details are provided in the project description that you are interested in.
The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.
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”.
- 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, please see the entry requirements for details.
- Two references
You will be asked to submit two references as part of the application process. If you are not able to upload your reference documents with your application please ensure you provide details of your referees. If you provide contact details of referees only, 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 email@example.com, 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 Friday 10 January 2022. Interviews will be held between 28 February and 4 March 2022. 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 firstname.lastname@example.org. Project-specific queries should be directed to the lead 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.
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|Application deadline:||10th January 2022|
|Value:||£15,609 per annum for 2021-2022|
|Duration of award:||per year|
|Contact: PGR Enquiriesemail@example.com|