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Statistical post-processing of ensemble forecasts of compound weather risk

  • Full or part time
  • Application Deadline
    Monday, May 13, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The University of Exeter EPSRC DTP (Engineering and Physical Sciences Research Council Doctoral Training Partnership) is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Students will be given sector-leading training and development with outstanding facilities and resources. Studentships will be awarded to outstanding applicants, the distribution will be overseen by the University’s EPSRC Strategy Group in partnership with the Doctoral College.

Supervisors:
Dr Frank Kwasniok, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences
Dr Chris Ferro, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences

Project description:
* Motivation *
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.

Probabilistic weather forecasts are typically derived from ensemble forecasts generated by numerical weather prediction models. An ensemble is a collection of deterministic forecasts, where the forecasts differ in the initial conditions supplied to the model and/or the numerical weather prediction model used. 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,
wind speed or precipitation.

* Project strategy *
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.

* Objectives *
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.

Candidates should have a keen interest in the application of mathematics and statistics in weather and climate science. Prior knowledge in statistical post-processing and numerical weather prediction is not necessary.

Funding Notes

For successful eligible applicants the studentship comprises:

An index-linked stipend for up to 3.5 years full time (currently £14,777 per annum for 2018/19), pro-rata for part-time students.
Payment of University tuition fees (UK/EU)
Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students

Related Subjects

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