NERC Industrial CASE PhD studentship: Statistical post-processing of ensemble forecasts of extreme weather events

University of Exeter - College of Engineering, Mathematics and Physical Sciences

Main supervisor: Dr Frank Kwasniok (University of Exeter) Co-supervisors: Dr Chris Ferro (University of Exeter), Prof Jonathan Rougier (University of Bristol), Nina Schuhen (Met Office)

Project Description:


Despite impressive improvements in the forecast skill of numerical weather prediction in the past decades there are still limitations due to model error and problems in generating ensembles. Model error may be addressed using multi-model ensembles or by stochastic parametrization.

Nevertheless, it is observed in the Met Office's practice that forecast ensembles are still biased both in location and dispersion. They tend to be underdispersive, leading to overconfident uncertainty estimates and an underestimation of extreme weather events. Systematic biases are significant in subgrid-scale weather phenomena such as UK temperature, precipitation or wind speed at particular locations and state-of-the-art systems occasionally miss extreme weather events within the ensemble distribution.

Statistical post-processing

This leads to the idea of combining dynamical and statistical information to improve prediction by statistical post-processing of the dynamical ensemble. Proposed methods range from simple model output statistics schemes known since the 1970s to more advanced approaches such as ensemble dressing, Bayesian model averaging and non-homogeneous Gaussian regression. Until now, research on statistical post-processing has focussed on the average case, there has been little mention of rare or extreme weather events.

Project strategy

The project will tailor existing and develop new methods for statistical post-processing of forecast ensembles with a particular view on extreme weather events. We will develop the promising novel approach of state-dependent post-processing. The post-processing will be conditional on the large-scale circulation regime the forecast model is in. We will use the Met Office's existing catalogue of weather regimes for this purpose. We will use historical data from the Met Office's ensemble prediction system MOGREPS together with the corresponding verifications.

We are interested in short- to medium-range weather forecasting where there is considerable variability but still some skill in the ensemble.

The research will be conducted in close collaboration with the Met Office as CASE partner.


The main objectives of the project are:

(i) To develop and explore novel methods for statistical post-processing of forecast ensembles for extreme events.

(ii) To improve probabilistic prediction of extreme UK temperature, surface pressure, precipitation and wind speed.

(iii) To help implement better techniques in the Met Office's operational post-processing suite in order to improve prediction of extreme UK weather events.

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK. Applicants with a Lower Second Class degree will be considered if they also have Master’s degree. Applicants with a minimum Upper Second Class degree and significant relevant non-academic experience are encouraged to apply. All applicants would need to meet our English language requirements by the start of the project

This studentship will be funded by NERC. The studentship will provide funding for a stipend (currently £14,296 per annum for 2016-2017), research costs and UK/EU tuition fees at Research Council UK rates for 48 months (4 years) for full-time students (part-time students pro-rata).

This studentship is available to applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes. Applicants who are classed as International for tuition fee purposes are not eligible for funding.

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South West England