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/
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.
How to apply
In order to formally apply for the PhD Project you will need to go to the following web page.
The closing date for applications is 1600 hours GMT on Friday 10th January 2022.
Interviews will be held between 28th February and 4th March 2022.
If you have any general enquiries about the application process please email email@example.com or phone: 0300 555 60 60 (UK callers) or +44 (0) 1392 723044 (EU/International callers). Project-specific queries should be directed to the main supervisor
NERC GW4+ funded studentship available for September 2022 entry. For eligible students, the studentship will provide funding of fees and a stipend which is currently £15,609 per annum for 2021-22.
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