About the Project
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 http://nercgw4plus.ac.uk/
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.
Candidate
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.
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.
Funding Notes
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.
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, 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. (2 014): A comparison of ensemble post-processing methods for extreme events, Quarterly Journal of the Royal Meteorological Society, 140, 1112-1120.
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