Operational weather forecasting and the AIFS machine-learning system.
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Machine-learning models can now produce highly skilful weather forecasts. For example, AIFS-CRPS (which now runs operational as AIFS ENS at ECMWF), GenCast, FourCastNet 3 and Aurora demonstrate that data-driven systems can generate skilful probabilistic predictions at medium-range timescales. At the same time, machine learning can increase the spatial and temporal resolution of existing forecasts. Spatial downscaling
End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeline, including data assimilation, at a fraction of its cost. These systems are deterministic and issue no uncertainty. Here we render the Aardvark Weather model probabilistic by attaching one stochastic mechanism to each component: learned,
Every year, floods affect more people worldwide than any other natural hazard. Accurate streamflow forecasts, predicting how much water will flow through a river in the coming days, are essential for issuing timely warnings, managing reservoirs, and protecting lives. For over 15 years, ECMWF has provided the computational backbone for the European and Global Flood Awareness Systems (EFAS and GloFAS), coupling weather
A forecast valid at noon and another at 18:00 can tell us where the weather is going, but not everything that happens in between. Storms develop, move and weaken continuously, yet many state-of-the-art artificial intelligence (AI) weather forecasting systems provide forecasts only every six hours. While this resolution is sufficient for many applications, finer temporal detail is essential for applications such as fl
The summer of 2026 has been defined so far by unprecedented levels of heat. Since late May, a rapid succession of heatwaves has moved across Europe. June was the hottest on record for western Europe, some three degrees above the 1991–2020 average. England and France both recorded their hottest June ever, while several countries broke all-time records, and more than 10,000 excess deaths were reported across the contin
Tropical cyclones (TCs) are among the most destructive weather systems. Improving forecasts of their track and intensity is critical for helping communities to prepare for severe winds, storm surge and flooding. Forecasting TCs has been one of the more demanding tests for machine-learning weather models. Artificial intelligence (AI) weather models have made remarkable progress in predicting the tracks of TCs. However
Machine learning (ML) is rapidly moving from the margins of research into the centre of weather forecasting. Only a few years ago, ML models were largely experimental tools, tested alongside traditional numerical weather prediction (NWP) systems. Today, ML is becoming an increasingly important component of operational forecasting systems, complementing established numerical weather prediction. The potential is signif
Aurora 1.5: Fine-Tuning a Foundation Model for Medium-Range Ensemble Weather Prediction - Jonathan Weyn , - Zekun Ni , - Amit Misra , - Will Fein , - Haiyu Dong , - Wessel P. Bruinsma , - Richard E. Turner , - Matt Corey , - Kit Thambiratnam , - Kevin White , - Kenji Takeda We present Aurora 1.5, a fine-tuned variant of the Aurora atmospheric foundation model [Bodnar et al., 2025] optimized for skillful medium-range
Machine learning (ML) models have become an increasingly important component of the global weather forecasting landscape. Over the past few years at ECMWF, several externally developed models have been run alongside the Integrated Forecasting System (IFS) in an experimental setting, allowing their performance to be assessed against established numerical methods. The upgrade of the IFS to Cycle 50r1 has provided a tes
Artificial intelligence (AI) is rapidly transforming weather and climate science across Europe. What is changing is not just how models are built, but how information is produced, delivered and used by national meteorological services, public authorities and downstream users. We are shaping this transformation with our Member and Co‑operating States, building on decades of shared investment in data, infrastructure an
Machine learning (ML) weather models are evolving rapidly, and a key question is: how well do these systems represent the fast-evolving dynamics that define severe mid-latitude storms? We explored this question using Storm Amy, the first named storm of the 2025–26 season for the western group of the European storm-naming initiative. Amy brought disruptive winds across northwestern Europe in early October 2025. We com