A group of scientists from the Universities of Lincoln, Sheffield, and Reading have collaborated on an innovative research project that could significantly improve seasonal weather predictions in the UK and Northwest Europe. They have developed a new method called the NARMAX model that uses artificial intelligence (AI) and machine learning to understand atmospheric changes and provide more accurate forecasts.

The NARMAX model has implications for various industries, including agri-food, energy, leisure, and tourism. Traditionally, weather forecasting centers have relied on costly supercomputer models that often fall short of accurately capturing the variations of atmospheric conditions during the summer months. However, the NARMAX model uses AI and machine learning algorithms to forecast the state of the North Atlantic jet stream and atmospheric circulation, which are crucially connected to surface air temperature and precipitation anomalies.

Dr. Ian Simpson, a Postdoctoral Research Associate at the University of Lincoln, explained that the NARMAX models can translate the links between circulation and jet stream patterns and seasonal surface weather conditions in northwest Europe into predictions of seasonal weather patterns, such as temperature and precipitation anomalies.

The study's outcomes hold tremendous potential for enhancing seasonal forecasting. Farmers, for example, will benefit from more accurate predictions, allowing them to plan their crops and optimize their farming systems accordingly. Additionally, the study provides insights into the causes behind atmospheric circulation changes, enabling scientists to improve the outputs of supercomputer models.

The three-year research initiative, titled 'Northwest European Seasonal Weather Prediction from Complex Systems Modelling,' received £650,000 in grant funding from the UK Government's Natural Environment Research Council. The study's findings have been published in esteemed scientific journals, Meteorological Applications and the International Journal of Climatology.

This study demonstrates the potential of AI and machine learning for improving seasonal weather predictions. With the help of these cutting-edge technologies, the researchers have taken a significant step toward providing more accurate and reliable seasonal forecasts.