An updated grant agreement for the Machine Learning Project (MLP) at the European Centre for Medium-Range Weather (ECMWF) has been signed to enable three new national meteorological services to join the initiative.
The agreement, which was signed by ECMWF director-general Florian Pappenberger and director-general of MET Norway Roar Skålin on June 19, will see the Latvian Environment, Geology and Meteorology Centre (LEGMC) and Morocco’s General Directorate of Meteorology (DMN) join the MLP on January 1, 2026, and the Environmental Agency of Slovenia (ARSO) join on January 1, 2027.
Pappenberger commented, “I am delighted to welcome Latvia, Slovenia and Morocco to the Machine Learning Project. Through this initiative, ECMWF is actively investing in collaborative machine learning developments across our member and co-operating states – bringing together expertise and resources to accelerate innovation and ensure that advances in ML are translated rapidly into operational forecasting.”
Fostering collaboration
Launched in 2024, the MLP is a collaborative initiative between ECMWF and partners from across its member and co-operating states, which is led by Met Norway and MeteoSwiss. The project falls under the umbrella of the EUMETNET Artificial Intelligence and Machine Learning for Weather, Climate and Environmental Applications (E-AI) program and aims to advance the use of machine learning in numerical weather prediction.
The MLP fosters collaboration across key areas, namely data-driven forecasting, ensemble forecasting, data assimilation and machine learning operations (MLOps). It supports the development and evaluation of ML-based models, their integration into operational workflows, and improved readiness through robust infrastructure and evaluation.
Roar Skålin, president of the ECMWF Council and director-general of MET Norway, added, “It is very encouraging to see the Machine Learning Project continue to grow. Seventeen national meteorological services now participate in the project, and its strength lies in its collaborative approach. Rather than developing ML capabilities independently, we are joining up our expertise to advance collectively and accelerating development in the process.
“This is exactly why ECMWF was created in the first place – to enable us to bring resources together to drive the science behind our forecasts and make it operational in a swift and effective way.”
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