|Funding for:||UK Students, EU Students|
|Funding amount:||Tuition fees and stipend for 3.5 years (currently £15,609 p.a. for 2021/22)|
|Placed On:||22nd October 2021|
|Closes:||10th January 2022|
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.
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
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/ .
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.
Type / Role: