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SCENARIO: Exploiting the benefits of convective-scale ensemble forecasts


Department of Meteorology

Reading United Kingdom Applied Mathematics Astrophysics Data Analysis Environmental Biology Fluid Mechanics Meteorology Other Other

About the Project

Accurate weather forecasts save lives and livelihoods and are important to a host of industries from energy to agriculture and retail. They also, theoretically at least, help us to get our washing dry and plan our visit to the beach. However, while large-scale weather systems such as winter storms can be forecast several days in advance, it is unlikely that the exact timing and location of individual convective clouds will ever be predictable more than a few hours ahead. Consequently, most operational weather forecasting centres, including the Met Office, produce so-called “ensemble forecasts”: multiple equally-likely forecasts that allow predictions of the probability of events such as heavy rainfall to be made (see Figure) e.g. “the chance of precipitation at 3 pm in Reading is 60%”. This project provides an opportunity for a student to collaborate with leading operational forecast centres and work at the forefront of convective-scale ensemble design.

Convective-scale ensemble forecasts are run with fine model resolution (grid boxes ~2 km across) in regional domains (e.g. covering the UK) and use much coarser resolution parent forecasts to provide lateral boundary conditions. The individual ensemble members are perturbed so that their initial conditions differ, i.e. they start from initial conditions that, while different, are all consistent with current observations, previous forecasts, and their errors. These forecasts diverge from each other (known as “spread”) as the forecast length increases, indicating increasing forecast uncertainty.

The project aim is to investigate the factors controlling the spread of convective-scale ensembles at multiple spatial scales and under different flow regimes, and so improve the skill of short-range weather forecasts.

Suzanne Gray talks about this project on YouTube: https://youtu.be/1li1_GOywog

Training opportunities:

The student will be able to work closely with Met Office supervisors. He or she will spend time at the Met Office headquarters in Exeter working with research groups, including the research-to-operations and data assimilation groups depending on research direction, as well as meeting with the supervisor working within the MetOffice@Reading (and so based in the Meteorology Department). As the use of ensembles is rapidly developing across the Met Office partnership, regular meetings (through videoconference or based at Exeter) take place to coordinate the latest research and developments. The student will join a research team facing new challenges in the way that the Met Office seeks to exploit convective-scale ensembles. The candidate may thus be asked to disseminate their research to operational and research centres across the world.

Student profile:

Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree, Masters Degree with Merit, or equivalent in physics, mathematics or a closely related environmental or physical science and with an interest in atmospheric processes and forecasting. Knowledge of statistical methods and computer programing as well as experience of working with large gridded datasets is desirable, but not essential.

To apply, please follow the instructions at https://research.reading.ac.uk/scenario/apply/


Funding Notes

This project is potentially funded by the Scenario NERC Doctoral Training Partnership, subject to a competition to identify the strongest applicants.

The project has CASE funding from the Met Office.

Due to UKRI rules, the DTP can only fund a very limited number of international students. We will only consider applications from international students with an outstanding academic background placing them in the top 10% of their cohort.

References

Bauer, P, A. Thorpe and G Brunet (2015), The quiet revolution of numerical weather prediction, Nature, 525, 47–55. doi:10.1038/nature14956

Hagelin, S., J. Son, R. Swinbank, A. McCabe, N. Roberts and W. Tennant (2017), The Met Office convective‐scale ensemble, MOGREPS‐UK. Q.J.R. Meteorol. Soc., 143, 2846-2861. doi:10.1002/qj.3135

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