Improving the forecasting of storms

The uniform computational grids used by the UK’s Met Office to compile weather forecasts can lack sufficient resolution for local forecasting. To forecast local storms and fog, data for environmental variables such as wind speed, atmospheric pressure, air temperature and moisture content are needed at high resolution.

However, a frequent problem in forecasting fog or icy roads is the misinterpretation of local structures in the procedures for assimilating observational data into the forecasts. Storms, fog and ice can have a significant social impact, and therefore accurate forecasting is essential for local organisations to take action effectively.

Adaptive mesh generation

One of our researchers, Professor Chris Budd from the Department of Mathematical Sciences, is at the forefront of a sophisticated mathematical approach called ‘adaptive mesh generation’ that automatically reduces computational mesh sizes at specific points of interest, increasing resolution while minimising any escalation in computing power.

By working closely with the Met Office, our research has led to improved procedures for numerical weather prediction and data assimilation. The Met Office has incorporated adaptive meshes into its Operational Code for Data Assimilation, and when this is used to predict ground surface temperatures it has had an immediate impact on analyses used to advise councils on road gritting requirements.

The project has led to a further, highly successful project with the Met Office to develop a network in climate modelling.