Everyone talks about the weather, but how is AI transforming the way we analyze weather data, evaluate forecasts, and push the boundaries of what is scientifically possible?
Are you planning to submit a paper that uses the Weather Generator Tool?
Join us for inspiring talks, engaging discussions, and meaningful networking on gender and diversity in research
The WeatherGenerator Service Call 2026 invites European research groups and industry partners outside the WeatherGenerator consortium to propose applications or integrations of the WeatherGenerator. Selected projects will receive up to 3 person-months of expert engineering support and will run through 2027. Deadline: 16 October 2026, 14:00 CET.
The WeatherGenerator Community Hackathon 📍 Location: Max Planck Institute for Biogeochemistry, Jena, Germany 📅 Date: 26–28 January 2027 📝 Registration: 1 September – 1 November 2026, via REGISTRATION Join our first Community Hackathon and become part of the WeatherGenerator community! The WeatherGenerator project will host its first Community hackathon on 26–28 January 2027 in Jena, Germany. This in-person, hands-on event will bring together researchers and professionals working across Earth System sciences to explore the WeatherGenerator, exchange ideas and become part of its growing community. The goal is that each participating team can run and fine-tune the WeatherGenerator for its own application while learning from and contributing to the community around the open-source model. The WeatherGenerator is an open-source European foundation model of the Earth system aiming to provide new machine-learned Digital Twin for Destination Earth. It is developed by 16 partners in a Horizon Europe project coordinated by ECMWF. The WeatherGenerator learns an unified latent representation from a wide variety of Earth system datasets - reanalyses, model output, satellite products and in-situ observations - using a self-supervised, masked-token approach. Rather than training a separate model for their task, users can use this general WeatherGenerator foundation model and fine-tune it towards the outputs that matter for their application.
What is representation learning and how does it support smarter weather forecasting? Sophie Xhonneux from ECMWF explores exactly that in the second WeatherGenerator Science Explainer. The atmosphere is measured continuously by satellites, weather stations, and balloons, producing vast, overlapping datasets. Rather than training separate models on all of this data, WeatherGenerator learns a compressed, unified representation that captures the underlying physics of the atmosphere, enabling more efficient and powerful forecasting across applications.
Join Martin Schultz and expert guests Peter Dueben and Christian Lessig from ECMWF for the first podcast episode on the role of AI in Weather Forecasting and how the WeatherGenerator plays a role in this.
Christian Lessig,
Martin Schultz,
Peter Dueben
