Building open weather nowcasting together.
MLCast is a community of researchers, meteorologists, software engineers, students, and public-data stewards working on practical machine-learning tools for short-term weather forecasting.
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Subscribe by emailEuropean National Meteorological Services collaborate in MLCast through EUMETNET.
Visit EUMETNETMLCast started as a handful of researchers comparing notes on nowcasting models and grew into an open, cross-institution community. Here's who we are, what we build together, and why it matters.
An open network of researchers, meteorologists, software engineers, students, and public-data stewards — 90+ members from 30+ national weather services, universities, and research institutes in 20+ countries across four continents. No membership fees, no gatekeeping — anyone working on open weather nowcasting is welcome.
Independent and official: MLCast started as a grassroots community and still runs that way — it has since also become the official Working Group 6 on Nowcasting in the EUMETNET E-AI Optional Programme.
We build shared datasets, validation tools, model code, and worked examples that let teams train and benchmark short-term weather forecasts on common ground. We meet at conferences, trade fixes on Slack, and keep the tooling open so results stay reproducible.
Accurate nowcasts give people more lead time before storms and floods. Open, verifiable tools let any group — not just those with big budgets — reproduce results, build on each other's work, and turn research into forecasts that protect communities.
Gabriele Franch
MLCast is the missing link between operational meteorology and machine learning research. As a machine learning researcher, I have found in MLCast a home where scientific knowledge can be turned into operational tools.
FBK
Trento, Italy
Leif Denby
MLCast to me is about the true scientific pursuit: building deep learning models together to further our collective ability to model weather in a changing world
University of Leeds
Leeds, UK
DMI
Copenhagen, Denmark
Irene Livia Kruse
Weather doesn't stop at borders, and neither should innovation. I help bring together weather services, researchers, and organizations across borders to build better weather nowcasting, because together we can achieve far more than any of us could alone.
DMI
Copenhagen, Denmark