Details of Award
NERC Reference : NE/R008590/1
Extracting likely scenarios from high-resolution ensemble forecasts in real-time
Training Grant Award
- Lead Supervisor:
- Professor J Methven, University of Reading, Meteorology
- Grant held at:
- University of Reading, Meteorology
- Science Area:
- Atmospheric
- Overall Classification:
- Atmospheric
- ENRIs:
- Environmental Risks and Hazards
- Science Topics:
- Boundary Layer Meteorology
- Large Scale Dynamics/Transport
- Water In The Atmosphere
- Regional & Extreme Weather
- Abstract:
- National weather forecast centres are moving to a new generation of ensemble forecast systems that run multiple convection-permitting model forecasts (grid spacing ~2km). They are needed because they can partially resolve the dynamics of small-scale high impact weather phenomena such as intense precipitation. This approach provides a large number of detailed forecasts of local weather for any given forecast time and location, and hence a wealth of new information about the predictability of high-impact events that affect society such as destructive winds, flash flooding, snow and fog. Since the Met Office MOGREPS-UK forecast was one of the first such systems to go operational there is now a unique 5-year forecast dataset. One barrier to the full and effective use of these new tools is that it is considerably more difficult for a human to fully process such a large amount of information in time to communicate early warnings. Therefore, there is a pressing need to develop a capability to synthesise these data into a manageable number of plausible scenarios or "storylines" that capture the phenomena of concern and provide emergency responders with a clear understanding of the possible outcomes they may face, together with an estimate of risk. Three approaches will be explored in generating scenarios from ensembles: a top-down approach from clustering global and regional forecast ensembles together, a bottom-up approach from statistical clustering of weather variables at high resolution, and an approach using physical insight to partition an ensemble before statistical matching. Case studies will be performed that include situations in which the ensemble is perceived to have produced insufficient variability in outcomes (such as a case in winter 2017 in which all MOGREPS-UK ensemble members produced too much snow over southern England) and try to answer the question of whether this sort of failing was related to too much uniformity in the larger-scale dynamics or to insufficient variability in physical processes. A major question is whether the high resolution ensemble tends to be over-confident in predicting particular weather outcomes, even if the latest global ensemble has a reliable spread-skill relationship when examining smoothly-varying fields in a conventional way. The outcome of this PhD will be greater insight into the predictability of local weather using high-resolution ensemble forecasts, and development of improved techniques to rapidly condense such a vast quantity of information into a few possible outcomes/storylines that can be more readily used for fast and reliable decision making. The project is timely since the MOGREPS-UK ensemble forecasts are planned to be extended to 5 days, further ahead than any other high resolution ensemble worldwide. This offers the student a unique opportunity to examine clustering in high impact weather forecasts which emerges at multi-day lead times. The project fits well with the remit of the new World Meteorological Organisation's High Impact Weather project which aims to conduct research addressing the United Nation's Sendai Framework for Disaster Risk Reduction. A key component is to improve risk prediction, warning services, and their utilisation by business, governments and the public. This offers excellent opportunities for the student to engage with this emerging field on an international level through workshops and collaborative research. At Reading the student would be part of both the mesoscale and dynamical processes research groups and be immersed in a large research activity relating to atmospheric dynamics and predictability. Through collaboration with the Met Office, the student will have the opportunity to work with researchers in high resolution modelling and forecast evaluation, operational forecasters and the multi-disciplinary team with expertise in hazards and communication with emergency responders.
- NERC Reference:
- NE/R008590/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Industrial CASE
This training grant award has a total value of £89,114
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - Student Stipend | Total - RTSG |
|---|---|---|
| £17,480 | £60,635 | £11,000 |
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