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Statistical post-processing of ensemble forecasts of compound weather risk, NERC GW4+ DTP PhD studentship for 2022 Entry, PhD in Mathematics

University of Exeter - Department of Mathematics

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students
Funding amount: Tuition fees and stipend for 3.5 years (currently £15,609 p.a. for 2021/22)
Hours: Full Time
Placed On: 22nd October 2021
Closes: 10th January 2022
Reference: 4259

Funding: Tuition fees and stipend for 3.5 years (currently £15,609 p.a. for 2021/22). Also covers research budget of £11,000 for an international conference, lab, field and research expenses and a training budget of £3,250 for specialist training courses and expenses.

Lead Supervisor

Dr Frank Kwasniok, University of Exeter, Department of Mathematics

Additional Supervisors

Dr Chris Ferro, University of Exeter, Department of Mathematics

Dr Gavin Evans, Met Office

Dr Piers Buchanan, Met Office

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.  It aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science http://nercgw4plus.ac.uk/ .

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 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.

Useful links

For information relating to the research project please contact the lead Supervisor via F.Kwasniok@exeter.ac.uk . http://empslocal.ex.ac.uk/people/staff/fk206/ .

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