Anemoi: A New Collaborative Framework for Data-driven Weather Forecasting

The Anemoi Framework (EMS Technology Award 2025) represents a pioneering effort in the integration of machine learning (ML) with meteorological forecasting, all developed through a collaborative European initiative. Data-driven machine learning approaches are currently revolutionizing weather prediction with state-of-the-art models outperforming equation-based forecasting system across a wide range of scores. Through this, they have rapidly emerged as candidates for operational weather forecasting systems, delivering accurate forecasts for low computational cost. Designed to enhance the accuracy, efficiency, and accessibility of data-driven weather forecasting, Anemoi builds upon advanced ML techniques and a modular, open-source architecture to democratize access to cutting-edge forecasting tools.

The framework is organised into distinct Python packages covering the entire machine learning lifecycle—from the creation of customised datasets from diverse meteorological sources to the development and training of advanced deep learning graph models. Once a model is trained, Anemoi enables users to run it in operations, using the outputs of physics-based NWP analyses or ensembles as initial conditions, while maintaining comprehensive lineage tracking.

Anemoi has been instrumental in the development of ML-powered weather models including AIFS (ECMWF), Bris (MET Norway), and AICON (DWD), and supports both global and limited area domain models in both deterministic and ensemble settings. These applications demonstrate Anemoi’s potential to enhance forecasting accuracy by integrating ML techniques into existing systems.

More than just a technical framework, Anemoi represents a collaborative effort among meteorological services, researchers, and technologists, fostering knowledge exchange and innovation.