AI and Physics-Based Weather Forecasting: A Comparative Study of ECMWF's Operative AIFS and IFS Ensemble Wind Speed Predictions

Weather forecasts are issued by numerical weather prediction models, which describe the dynamic behaviour of the atmosphere. Due to the chaotic nature of the atmospheric processes, assessing the uncertainty of forecasts is essential. The state-of-the-art method is to run the prediction models several times with different initialisation and/or parameterisation to obtain an ensemble of forecasts, better representing the possible scenarios.

In the last few years, AI-based models have become the centre of attention in weather forecasting due to their accuracy and efficiency. The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed its Artificial Intelligence/Integrated Forecasting System (AIFS) model, which was first to provide data-driven ensemble forecasts in June 2024. Since July 2025, the AIFS ensemble model has been operational and runs in parallel with the physics-based Integrated Forecasting System (IFS) model of ECMWF, which is considered the gold standard in weather prediction. The new AIFS model can generate forecasts ten times faster than the classical physics-based one, while consuming approximately a thousand times less energy.

We present the results of our systematic comparison of the performances of the IFS and AIFS models by investigating the accuracy of raw and post-processed 10-metre wind-speed forecasts generated by the two models between July 2025 and November 2025 across several thousand station locations. The post-processed case involves the application of the parametric Ensemble Model Output Statistics method as well as a nonparametric quantile regression approach to correct any systematic biases and dispersion inaccuracies in the raw forecasts, which are usually detectable in the case of ensemble predictions.