EMS Technology Achievement Award 2025 for ANEMOI

The EMS Technology Achievement Award for significant technology achievements and innovations in the field of meteorology and earth observation –

The European Meteorological Society is awarding the EMS Technology Achievement Award 2025 to the Anemoi Framework:

The Anemoi Framework is an excellent example of a European collaborative effort, which offers enhanced forecast accuracy through the use of advanced machine-learning methodologies. The flexible open-source approach enables various European stakeholders to further integrate AI in operational forecasting.

The Award will be presented to the partners of Anemoi at the Awards Session during the EMS Annual Meeting 2025 in Ljubljana.

The Anemoi Framework represents a pioneering effort in the integration of machine learning (ML) with meteorological forecasting, all developed through a collaborative European initiative. 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.
Artificial intelligence is a formidable challenge in meteorology and Anemoi is the answer that rises to this challenge. The crucial point of this development is to allow everyone, from large meteorological services to masters students, to be able to use advanced machine learning techniques very quickly and efficiently through European cooperation, training and open source.

Key Objectives and Innovations

  • Enhanced Forecast Accuracy: Anemoi leverages ML-driven innovations, including Graph Neural Networks and Transformer architectures, to provide high-resolution, probabilistic weather predictions that are comparable in skill to those produced by traditional numerical weather prediction (NWP) models.
  • Scalability and Flexibility: The framework supports applications ranging from global forecasts to highly localized predictions, adapting to diverse meteorological conditions across Europe.
  • Efficiency and Accessibility: Data-driven models reduce computational costs and facilitate rapid forecast generation, making state-of-the-art forecasting tools accessible to both national meteorological services and academic researchers.
  • Community-Centric Development: Anemoi fosters collaboration through open-source contributions, regular workshops, and active engagement with the meteorological community, promoting shared knowledge and innovation.

Collaborative Impact

Anemoi is a cornerstone of the EUMETNET Artificial Intelligence (E-AI) Programme, which aims to integrate AI into operational meteorology. It has been instrumental in the development of ML-powered weather models such as AIFS (ECMWF), Bris (MET Norway), and AICON (DWD). The framework has facilitated inter-agency cooperation, with contributions from ECMWF, KNMI, FMI, MeteoSwiss, RMI, Meteo France, DWD, Met Norway, and others, driving innovation across European forecasting systems.

Real-World Applications

  • Regional Adaptation: Many of the above organisations are deploying Anemoi-powered regional models that provide high-resolution forecasts tailored to specific geographic regions.
  • Operational Readiness: ECMWF’s AIFS model has demonstrated improvements in medium-range forecasts, with some metrics surpassing traditional NWP models, and is now the first data-driven operational weather forecasting model with more models built with Anemoi soon to follow.
  • Democratizing Forecasting: The framework has engaged over 400 participants from 55 countries through training sessions, thus expanding the reach of ML-based forecasting.

Future Prospects

Anemoi continues to evolve, integrating probabilistic forecasting methodologies and expanding capabilities for nowcasting and long-term climate applications. By fostering collaboration and maintaining an open-source ethos, the framework is poised to shape the future of European meteorology, bridging the gap between research and operational forecasting.

Through its innovative technology and community-driven approach, Anemoi sets a new benchmark for ML-powered meteorological forecasting, ensuring that Europe remains at the forefront of weather prediction advancements.

Participating Organizations and key individuals

  • European Centre for Medium-Range Weather Forecasts (ECMWF): Mariana Clare, Jesper Dramsch, Simon Lang, Mihai Alexe, Baudouin Raoult, Gert Mertes, Mario Santa Cruz, Helen Theissen, Harrison Cook, Ana Prieto Nemesio, Sara Hahner, Gabriel Moldovan, Rilwan Adewoyin, Cathal O’Brien, Jan Polster, Vera Gahlen, Jakob Schlör, Gareth Jones, Ewan Pinnington, Lorenzo Zampieri, Florian Pinault, Nina Raoult, Rachel Furner, Aaron Hopkinson, Matthew Chantry, Florian Pappenberger
  • Royal Netherlands Meteorological Institute (KNMI): Jasper Wijnands, Sophie Buurman, Bastien François
  • Finnish Meteorological Institute (FMI): Leila Hieta, Mikko Partio, Marko Laine
  • Federal Office of Meteorology and Climatology (MeteoSwiss): Ophélia Miralles, Daniele Nerini, Carlos Osuna, Andreas Pauling, Alberto, Pennino, Francesco Zanetta
  • Royal Meteorological Institute of Belgium (KMI/IRM/RMI): Dieter Van den Bleeken, Michiel Van Ginderachter, Piet Termonia
  • Swedish Meteorological and Hydrological Institute (SMHI): Tomas Landelius, Daniel Yazgi, Swapan Mallick
  • GeoSphere Austria: Çağlar Küçük, Pascal Gfäller, Irene Schicker, Alexander Kann
  • German Weather Service (DWD): Tobias Göcke, Marek Jacob, Florian Prill, Roland Potthast, Jan Keller, Sabrina Wahl, Hendrik Reich
  • Norwegian Meteorological Institute (MET Norway): Olav Ersland, Lars Falk-Petersen, Håvard Homleid Haugen, Magnus Sikora Ingstad, Jørn Kristiansen, Ina Kullmann, Mateusz Matuszak, Máté Mile, Thomas Nipen, Even Nordhagen, Aram Salihi, Ivar Seierstad, Roel Stappers, Paulina Tedesco
  • State Meteorological Agency (AEMET): María Teresa García Galvez, Jose Luis Casado Rubio
  • Italian Air Force Meteorological Service (ITAF MET): Antonio Vocino
  • Météo-France (MF): Sara Akodad
  • Danish Meteorological Institute (DMI): Leif Denby, Kasper Stener Hintz, Simon Kamuk Christiansen
  • European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT): Michael Schick, Sina Montazeri, Miruna Stoicescu

More information and related links

About

EMS Technology Achievement Award. The European Meteorological Society (EMS) seeks to recognize achievements that are influential on developments of technologies and technical solutions in meteorology and related areas (e.g. oceanography, atmospheric chemistry and hydrology). The EMS Technology Achievement Award is granted to individuals or corporations in recognition of technological contributions associated with instrumentation and methodologies used in these areas, have advanced the methods and technologies of environmental observing and forecasting systems and demonstrated the potential to impact on the field at the European scale.
https://www.emetsoc.org/awards/award-category/ems-technology-achievement-award/

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