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Statistical post-processing of ensemble forecasts of compound weather risk. Mathematics PhD studentship (NERC GW4+ DTP funded)


College of Engineering, Mathematics and Physical Sciences

Dr F Kwasniok , Dr C A T Ferro Friday, January 08, 2021 Competition Funded PhD Project (Students Worldwide)

About the Project

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.

Candidate requirements:

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.

CASE Partner:

The Met Office will provide suitable data sets for the project as well as appropriate guidance. The Met Office supervisors will contribute to the project from an operational and user-oriented point of view. The student will interact with Met Office staff and spend time working at the Met Office (at least three months in total).

Training:

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. The Mathematics Research Institute at Exeter is also a member of the EPSRC-funded MAGIC Taught Course Centre for PhD Training in Mathematics. 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. Moreover, the student will acquire transferable skills such as presentation techniques and writing skills.

Useful Links:

For information relating to the research project please contact the lead Supervisor via
http://empslocal.ex.ac.uk/people/staff/fk206/

Prospective applicants:

For information about the application process please contact the Admissions team via .

Each research studentship project advertisement has an ‘Apply Now’ button linking to an application portal. Please note that applications received via other routes including a standard programme application route will not be considered for the studentship funding.

The application deadline is Friday 8 January 2021 at 2359 GMT. Interviews will take place from 8th to 19th February 2021.

For more information about the NERC GW4+ Doctoral Training Partnership please visit https://www.nercgw4plus.ac.uk



Funding Notes

NERC GW4+ funded studentship available for September 2021 entry. For eligible students, the studentship will provide funding of fees and a stipend which is currently £15,285 per annum for 2020-21.

References

* Gneiting T., Raftery A. E., Westveld A. H., Goldman T. (2005): Calibrated probabilistic forecastingusing 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 averagingto 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 methodsfor 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 ensembleforecasts, Quarterly Journal of the Royal Meteorological Society, 145, 3535-3552.
* Allen S., Ferro C. A. T., Kwasniok F. (2020): Recalibrating wind-speed forecasts using regime-dependent ensemble model output statistics, Quarterly Journal of the Royal Meteorological Society, 146, 2576-2596.


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