Research & Innovation Services

Adaptive methods for problems in meteorology

KT Fellowship Award

Department of Mathematical Sciences, University of Bath, and the Met Office
Prof Chris Budd
“A new method of adapting computational grids to the expected solution is now being exploited in the high resolution analyses used to drive the short-range forecasts for the UK. Particular benefit is found in predicting low-level temperatures, which is very important for maintaining the road network in a safe condition and for predicting fog.”
Professor Mike Cullen, Head of Data Assimilation at the Met Office
 
“It was really satisfying seeing our work applied to help councils deal with icy road conditions, and to have our work validated in the extreme weather conditions experienced around Christmas [2010].”
Dr Emily Walsh, Knowledge Transfer Fellow, Department of Mathematical Sciences, University of Bath

The challenge

To forecast localised storms and fog, spatial data for environmental variables such as wind speed, atmospheric pressure, air temperature and moisture content are needed at high resolution.  The computational grids (meshes) used by the Met Office to compute weather forecasts lack sufficient resolution for local forecasting. Such fine resolution is necessary for local and regional organisations to more effectively take action in cases of extreme weather events.  Storms, fog and ice can have significant social impact – fatalities, damage to property, and paralysis of transport networks. It is thus highly beneficial that such events are forecasted accurately and efficiently. 

The response

The University of Bath’s Department of Mathematical Sciences is at the forefront of research in adaptivity. This is a sophisticated mathematical approach to automatically reducing computational mesh sizes at specific points of interest, increasing resolution while minimising any escalation in computing power.

Dr Emily Walsh, the KT Fellow for the project, spent three months working with the Met Office, with project lead Professor Budd spending a day a month at the Met Office over five months. The resulting two-way knowledge transfer between the University and the Met Office was highly beneficial for both parties.

Benefits and outcomes

The project benefited the Met Office’s numerical weather prediction and data assimilation. In the latter case the work was incorporated into the Met Office’s Operational Code used to predict surface temperatures. This had an immediate impact on analyses to better advise County Councils on when to grit roads. The project has strengthened the University’s relationships with the Met Office, leading to a joint project with the University of the West of England (UWE), plus three new PhD studentships.

Project team

Professor Chris Budd, Principal Investigator, Department of Mathematical Sciences
Dr Emily Walsh, KT Fellow, Department of Mathematical Sciences
Dr Tom Melvin and Dr Chiara Piccolo, the Met Office

Funded by the University of Bath’s EPSRC Knowledge Transfer Account.