# Statistical Post-Processing of Ensemble Forecasts of Compound Weather Risk - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2882

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide 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.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Supervisors
Dr Frank Kwasniok
Dr Chris Ferro

Location
Streatham Campus, Exeter

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.

Entry Requirements
You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in statistics, mathematics, physics or meteorology. Experience in programming is desirable.

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.

## Summary

Application deadline: 10th January 2018 3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year. per year pgrenquiries@exeter.ac.uk

## How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
proficiency in English.  For further details of the University’s English language requirements please see