Low adhesion forecasting: how collaboration is supporting advancements to tackle a £355 million annual problem

Here she explains how the team is advancing forecasting for low adhesion and leaf fall impacts, supported by a collaborative approach involving academia and industry. 

Contents

• How adhesion forecasts make rail operators better prepared 
• Advancing forecasting science 
• Delivering more through collaboration  
• Looking to a future of better decision making 

Adhesion is the ‘grip’ between the rail and train wheels that's essential for safe braking. However, certain weather and environmental conditions can reduce adhesion, including ‘leaves on the line’ and low moisture phenomena such as dew, drizzle, fog and frost.  

While other contaminants such as rust and grease can also reduce adhesion, it's leaf fall and low moisture that are mainly responsible. Passing trains trap and crush leaves onto the rail surface forming a hard, black, slippery layer that's estimated by the Rail Safety and Standards Board (RSSB) to cost the industry and wider society £355 million annually.  

The notorious autumn increase in delay minutes caused by leaf fall – and service cancellations due to wheel 'flats' and other damage – is a media favourite. However, poor adhesion can bring costly delays and impacts the safe operation of the railway including trains passing through red lights and level crossings, and over-running stops.  

How adhesion forecasts make rail operators better prepared 

Building on more than 25 years of leaf fall records and forecasting experience, our teams have developed models to predict low adhesion conditions. Data on vegetation type and location, records of previous environmental and weather conditions as well as detailed weather forecasts are used to calculate leaf fall. Detailed models forecast moisture and dew on the rails. We also use a blend of other weather models to estimate the timing of drizzle and light intermittent rain or fog, given that transitioning between dry and wet conditions is critical for forecasting low adhesion. 

Our forecasts range from very detailed information showing specific rainfall amounts or leaf fall quantities for particular locations through to simple, colour-based risk forecasts for fast interpretations of the conditions, to trigger particular processes within an organisation. 

Adhesion and leaf fall forecasts are critical because they allow train and network operators to plan their autumn mitigation in good time. This includes organising trackside resources to inspect and clean the rail, avoiding stopping at certain stations, and ensuring materials for mitigation treatments are available. Low adhesion forecasts are made available to drivers before joining their train so they can adjust driving styles on route. They're also used by decision support systems to alter brake rates and switch from Automatic Train Operation (ATO) to manual driving when required.  

Advancing forecasting science 

Forecasting leaf fall and low adhesion has changed significantly since the first leaf fall model was developed 25 years ago. The first crude, large area forecasts provided only an indication of day-to-day risk variation over a season. 

Advancements in computing power, weather forecasting and data observations, as well as a better understanding of the fundamental causes of low adhesion and leaf fall, have led to the improved forecasts granularity both in space and time. These make for much more targeted decision making and capture the transient nature of the low adhesion problem. Sharing data and knowledge with clients has helped to develop targeted risk models, such as wind throw models which forecast the risk of fallen trees and branches. 

Delivering more through collaboration  

As well as providing operational services, our Met Office team works closely with industry, academia and SMEs to improve the understanding of the low adhesion problem.  

Working with the University of Sheffield, an RSSB-funded project under the ADHERE programme focused on developing standards for low adhesion forecasting, a tough challenge as adhesion is hard to measure across the rail network. Designed to build confidence in using forecasts for operational decision making, the study explored what statistics and impacts could be used to measure forecast performance.  

An additional project with the University of Huddersfield looked at understanding the response of the train to varying adhesion levels caused by low levels of moisture and leaf fall.  

Improving how we communicate our forecasts remains a top priority. The in-cab RailSmartADS (Adhesion Digital Solution) developed in collaboration with 3Squared enables operators to model, capture and disseminate accurate adhesion forecasts to help train drivers reduce risk and minimise incidents.  
We're also working closely with organisations to ensure continuous developments in our weather and leaf fall forecasting capabilities are incorporated into automated adhesion management systems, such as those used by Transport for London.  

Looking to a future of better decision making 

As forecasting skills develop, and more data becomes available, our teams will continue working with industry to improve forecasts further.  

Longer term outlooks could help resource managers to better understand how improved adhesion forecasting supports more effective planning and mitigation days in advance.  

We are always keen to work with others in the industry to advance forecasting further and support safe and efficient operations.