EUMETNET's E-AI Programme: Advancing Weather, Climate, and Environmental Applications through Artificial Intelligence (AI) and Machine Learning (ML)

EUMETNET's E-AI programme aims to leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance weather, climate, and environmental applications. It is a strategic initiative and was setup by the EUMETNET Assembly as a five-year Optional Programme, which started January 2024. The programme combines the forces of European National Meteorological and Hydrological Services (NMHSs) and external partners, including ECMWF and EUMETSAT, to advance in these areas. To achieve its objectives, a strategic reallocation of development resources towards AI/ML-based techniques and capacity building are required.

E-AI is structured around three primary pillars: (a) Data Curation, (b) Analysis, Modelling, and Post-processing, and (c) Products and Services. These pillars are accompanied by Communication and Training activities, which support the general transition towards AI-based technologies. The programme is guided by its Strategic Expert Group, which has conducted comprehensive assessments of the AI/ML landscape to inform the strategic direction of the NMHSs. By promoting collaborative development under a permissive open licence, E-AI fosters widespread adoption, a culture of openness, and synergistic innovation. In line with its guiding principles, the programme welcomes further collaboration with international partners, academia, and industry.

To pursue its targets, the E-AI programme has organised a series of workshops, online tutorials, and established working groups. The workshops were structured around the three primary pillars, featuring both in-person and online events. These included joint workshops with EUMETSAT on data curation in pillar (a), ECMWF Machine Learning Pilot Project workshops in pillar (b), and workshops on Products and Services in pillar (c). The workshops have engaged approximately 200 scientists, while the online tutorials reached an audience of over 400 individuals. The workshops have also identified interest in establishing about a dozen working groups, focusing on specific aspects of AI and ML, particularly in the areas of products and services. We will present updates on the various activities, including the development of E-AI ML-ready datasets, the exploration of multimodal applications combining large language models with meteorological fields, and approaches to Machine Learning Operations (MLOps).