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