Complex Systems Modelling: Using AI and Machine Learning to Improve Seasonal Weather Predictions

A team of researchers here at the University of Sheffield have developed a new method which is hoped will improve predicting seasonal weather conditions in the UK and Northwest Europe.

Image of the weather

A team of researchers here at the University of Sheffield, alongside colleagues at the Universities of Lincoln and Reading, have developed a new method which is hoped will improve the prediction of 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.

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. 

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. 

Dr Yiming Sun, Research Associate at the University of Sheffield, said: “We have developed and applied a NARMAX machine learning method to predict the seasonal state of the North Atlantic atmospheric circulation and jet stream. 

“The model has demonstrated a high degree of predictive accuracy compared to the dynamical models. Therefore, NARMAX can be used to help improve seasonal forecast skill and inform the development of dynamical supercomputer models.” 

NARMAX methods were originally developed by the Complex Systems and Signal Processing (CSSP) Research Group, Department of Automatic Control and Systems Engineering (ACSE), The University of Sheffield. Dr. Hua-Liang Wei, Senior Lecturer (Associate Professor) with CSSP and Co-I of the three-year NERC project, is a well-known expert in nonlinear system identification and interpretable machine learning (including NARMAX), and has pioneered the application of NARMAX, machine learning and AI techniques in seasonal weather prediction. 

He added, “It is exciting to see NARMAX methods, together with other machine learning techniques, have significantly contributed to achieving the excellent results to date. We believe the methods will help make more contributions to future research to the field of seasonal weather prediction.”   

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, Meteorological Applications and International Journal of Climatology. Both papers are available to read online:

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