Mitigating risks to UK surface transport infrastructure through physical modelling

Since the 1980s the Met Office has produced Surface Transport Forecasts (STF) for the UK. These forecasts allow mitigation of the UK surface transport infrastructure’s vulnerability to weather-based impacts across the autumn and winter season, for example road ice (roads are gritted during ice events), and low rail adhesion (the speed of trains is adapted). Although historically sufficient, these forecasts have limitations, such as not accurately forecasting high summer maximum temperatures, which are becoming more common due to the changing climate. These high temperatures lead to melting road surfaces and buckling railway lines. The current STF system additionally struggles with the future needs of Connected Autonomous Vehicles, for example hazards which specifically impact the on-board sensors such as road spray, and flexible machine-machine communication.

In order to address the limitations of the current STF system, the Met Office is building a new system. This is both a refresh of the pipeline, with a goal to make it flexible, robust, and portable, as well as a revisit of the scientific code within. Updates to the scientific core centre around upgrading the physics model to the Joint UK Land Environment Simulator (JULES). This allows us to accurately capture summer maximum temperatures and carefully model the depth of water on the road (vital for a road spray forecast). Other scientific updates include using Machine Learning based approaches for bias correction and the spin up of new forecast locations (necessary for delivering the service via an API), and building probabilistic ensemble-based forecasts.

The physics model JULES is a community model used as the land-surface component of the Met Office’s Unified Model, but which can also be used – as we do here – as a stand-alone surface-exchange-scheme driven by forecast output from Numerical Weather Prediction models. JULES models a comprehensive list of physical land-surface energy processes, as well as modelling water and snow stores. We have extended JULES to better capture processes specific to a road. Externally to JULES we have implemented a shading scheme and heating due to longwave radiation emitted by traffic.