Weather Prediction in the UK and Northwest Europe

Climate scientists and engineers, including academics from ACSE, aim to untangle jet stream's disruptive effects with new seasonal forecasting technique.

British soldiers and mountain rescue teams evacuated people in York 2015
British soldiers and mountain rescue teams evacuated people in York, UK, as the River Ouse flooded in 2015. Credit: Ian Forsyth/Getty Images

For meteorologists the British summer climate has for decades proved stubbornly difficult to make accurate seasonal forecasts about - in large part due to the disruptive effects of the North Atlantic Jet Stream.

Now a major new research project led by the University of Lincoln, in collaboration with the Universities of Oxford, Reading, and Sheffield, and the Met Office, aims to transform the way forecasters model the effects of the jet stream and other atmospheric circulations on the weather of Britain and its near neighbours.

Seasonal weather characteristics have major effects on our daily life, with impacts on agriculture, food security, energy supply, public health, severe weather planning, and decision making such as flood prevention. Accurate seasonal weather forecasts are therefore always highly important.  However, although the advancement of modern computation techniques (both in hardware and software) have significantly enhanced weather forecasting accuracy, medium-range seasonal forecasts of the summer climate in the UK and Northwest Europe (NE) have remained defiantly difficult to model consistently, this is mainly because weather is driven by a variety of factors, including North Atlantic Jet Stream whose behaviour is extremely difficult to model and predict. 

The North Atlantic Jet Stream is a giant current of air which strongly influences seasonal weather conditions in Northwest Europe. Its strength and location can drive extreme weather events in the UK and nearby countries, like flash floods, freezing winters, or scorching summer days.

Until recently it was thought the jet stream was too unpredictable to effectively model in seasonal weather forecasts. Its behaviour is believed to be driven by a variety of factors, ranging from sea ice changes to fluctuations in solar radiation. Other atmospheric phenomena - like the infamous El Niño effect and even volcanic eruptions - are thought to shape its characteristics from one year to the next.

Modern physics-based dynamical seasonal forecasting systems run on giant supercomputers have enhanced forecasting accuracy for winter months but seasonal forecasts of the summer climate in the UK and Northwest Europe have remained defiantly difficult to model consistently.

Recently, scientists in climate and weather, agriculture, and systems engineering and complex system modelling, coming from UK institutions (Lincoln, Reading, Sheffield, Oxford), the Met Office, and the European Centre for Medium-range Weather Forecasts, have been awarded UK Government research funding to develop innovative probabilistic statistical forecasts of the state of the North Atlantic jet stream.

The three-year research project, entitled “Northwest European Seasonal Weather Prediction from Complex Systems Modelling”, has been allocated £650,000 UK Government grant funding from the Natural Environment Research Council (NERC), part of UK Research and Innovation (UKRI). 

The researchers plan to combine a complex statistical method known as NARMAX and machine learning technologies to provide the UK and Northwest Europe with improved seasonal regional forecasts - including more accurate summer climate predictions. Pilot studies using the technique, published in a paper in the Quarterly Journal of the Royal Meteorological Society in 2019, have already shown promising results.

The ACSE team will contribute by building models from measured meteorological data, aiming to significantly improve prediction accuracy for the UK and Northwest Europe's weather, using nonlinear system identification techniques, including NARMAX, and interpretable machine learning methods.

Dr Hua-Liang Wei

Senior Lecturer in Dynamical Modelling (3DM) and Computational Neuroscience (DMCN) in ACSE

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