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Could the Unreliable British Summer Soon Be a Thing of the Past?

Accurate seasonal weather forecasts are a vital tool not just for holidaymakers, they also have great impact on a range of sectors, from shaping public safety policies such as flood prevention strategies, to helping the agricultural industry mitigate low crop yields which would increase prices for consumers.

Research led by Edward Hanna, Professor of Climate Science and Meteorology at the °µÍø³Ô¹Ï, has developed a new method using AI and machine learning which could improve predicting seasonal weather conditions in the UK and Northwest Europe.

The ‘NARMAX’ model offers a powerful tool in the quest to better understand changes in atmospheric circulation as well as making more accurate seasonal weather predictions. It could also benefit many sectors, including agri-food, energy, leisure, and tourism industries.

Our published papers demonstrate great potential for NARMAX modelling to play a significant role in helping to refine the next generation of supercomputer forecasting models, which have been historically computationally hungry, and in enhancing seasonal forecasts.

Predicting the Weather

To predict seasonal weather over Northwest Europe, major weather forecasting centres currently rely on expensive supercomputer models. To supplement these conventional methods, the group used an AI and machine learning method known as NARMAX (Nonlinear AutoRegressive Moving Average models with eXogenous inputs) to predict the state of the North Atlantic jet stream and atmospheric circulation, both strongly linked to surface air temperature and precipitation anomalies.

NARMAX has been used successfully in many other fields of research and in this case, early predictions were made for both summer and winter, for several different air circulation patterns which commonly affect the North Atlantic region and subsequent Northwest European seasonal weather.

The study results showed high accuracy for both seasons, and all three circulation patterns examined. This is important because the conventional and more expensive supercomputer models struggle to accurately predict seasonal atmospheric conditions over this area in summer, tending to underestimate year-to-year variations for both seasons.

In addition, the NARMAX method has been used to analyse possible causes of atmospheric circulation changes. This information could be used for interpretation and to help improve the supercomputer model outputs.

Geography at °µÍø³Ô¹Ï

From climate change and natural disasters to health inequalities and food security, geography is at the heart of many of humanity’s greatest challenges. At °µÍø³Ô¹Ï, we believe that meeting these challenges requires us to understand the links between humans and the environments we inhabit.

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Sharing Expertise

"This breakthrough has significant implications that could play a crucial role in improving seasonal forecasting, as well as informing the development of future weather forecasting models, particularly during the summer months," explains Edward Hanna, Professor of Climate Science and Meteorology.

“This is an exciting project that has brought together diverse disciplines and experts in meteorological science and machine learning with the aims of improving seasonal weather prediction and applying the results to end users."

The three-year research project ‘Northwest European Seasonal Weather Prediction from Complex Systems Modelling’ was allocated £650,000 UK Government grant funding from the Natural Environment Research Council, part of UK Research and Innovation.

The research is published in the Royal Meteorological Society journals, and . 

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