Participants are expected to have: 

• A background in meteorology, climate systems, Earth Systems Sciences, or related fields  

• Familiarity with statistics and data interpretation  

• Basic programming experience, preferably in Python  

• Introductory knowledge of machine learning concepts  

• Experience interpreting geophysical datasets or working with Earth system data workflows  

Learning materials include video lectures with visual explanations, hands-on Python notebooks, webinar tutorials, live and recorded panels, podcasts, curated readings, quizzes, and optional reflection activities. Practical components use real-world datasets and workflows relevant to modern AI-based weather forecasting and Destination Earth.

Sam Allen, Postdoctoral Researcher at the Karlsruhe Institute of Technology (KIT)

Sam Allen is a postdoc in statistics at the Karlsruhe Institute of Technology (KIT). His research focuses on probabilistic forecasting and forecast evaluation, including both theoretical foundations and applications in weather and climate prediction. Sam is the co-author of widely-used forecast evaluation software packages, and his current research concerns the development of methods to assess the quality of AI-based weather forecasts.

William Becker, Data scientist and former training Specialist at the European Centre for Medium-Range Weather Forecasts (ECMWF)

William is a data scientist who developed training courses in machine learning at ECMWF under the EU’s Destination Earth initiative up until May 2026. Previously he has worked as a researcher and policy analyst at the European Commission’s Joint Research Centre and for a number of UN and European agencies. William holds a PhD in Mechanical Engineering, but has worked in fields from meteorology and climate science to international relations and migration.

Eulalie Boucher, Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Eulalie Boucher is a Scientist for Machine Learning at ECMWF, working on AI-DOP, focusing on end-to-end forecasting from observations only. Eulalie completed a PhD at the Observatoire de Paris | PSL, where the research explored the use of deep learning for satellite infrared spectrometer observations. The work combines machine learning and Earth observation data for weather and climate applications.

Corentin Carton de Wiart, Team Lead Data Processing Services at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Corentin Carton de Wiart leads the Data Processing Services Team at the European Centre for Medium-Range Weather Forecasts (ECMWF), focusing on the development of post-processing frameworks for operational weather forecasting. With more than 15 years of experience, his expertise spans scientific and operational software architecture, high-performance computing, computational fluid dynamics, and Earth sciences. Before joining ECMWF in 2018, he worked as a Postdoctoral Researcher at NASA Ames on turbulence modelling and as a Senior Research Engineer at Cenaero, where he earned his PhD in Computational Fluid Mechanics in collaboration with UCLouvain.

Harrison Cook, Research Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Harrison Cook is a Research Software Engineer at ECMWF, focusing on machine learning for weather forecasting, software development, and scientific evaluation. His work involves developing and maintaining large-scale software systems that process vast amounts of meteorological data to support forecasting and research activities. Harrison is particularly interested in enabling scientists through scalable and collaborative software solutions for machine learning and artificial intelligence applications in weather and climate science. Before joining ECMWF, he worked as a Data Scientist at the Australian Bureau of Meteorology.

Jesper Dramsch, Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Jesper Dramsch is a Scientist for Machine Learning at ECMWF, working on AI-driven weather forecasting systems. They are a core developer of the Artificial Intelligence Forecasting System (AIFS) and contribute to Anemoi, the open-source machine learning framework for weather forecasting developed with European national meteorological services. Their work focuses on graph neural networks, scalable AI infrastructure, and operational machine learning workflows for numerical weather prediction. They're a Software Sustainability Institute Fellow focused on reproducible research and co-organised ECMWF's first MOOC on Machine Learning in Weather and Climate, as well as, contributes to training activities for ECMWF Member States, write the newsletter "Late to the Party", and have a knack for cutting through hype. Jesper also serves as co-chair of the Working Group Modelling within the UN ITU Global Initiative on AI for Natural Disaster Management.

Azadeh Gholoubi, Lead Machine Learning Engineer at the National Oceanic and Atmospheric Administration (NOAA)

Azadeh Gholoubi is a Lead Machine Learning Engineer at NOAA’s National Weather Service Environmental Modeling Center (NOAA/NWS/EMC), where she works on machine learning, data assimilation, and Earth-system prediction. Since joining NOAA in 2020, she has contributed to projects spanning land and satellite data assimilation, environmental data science, and machine learning applications. Before joining NOAA, she worked as a research scientist at the Bureau of Economic Geology and Utah State University, focusing on geoscience, hydrologic modeling, and data-driven environmental applications.

Masilin Gudoshava, Climate Modeller at the IGAD Climate Prediction and Applications Centre

Håvard Homleid Haugen, Researcher at the Norwegian Meteorological Institute (MET Norway)

Håvard Homleid Haugen is a researcher at the Norwegian Meteorological Institute (MET Norway), working on data-driven weather forecasting and machine learning for numerical weather prediction. His work contributes to the development of regional AI-based weather models within the Anemoi framework, including stretched-grid forecasting approaches for high-resolution prediction. Håvard collaborates on research related to graph neural networks, probabilistic forecasting, and operational AI weather prediction systems.

Matthias Karlbauer, Machine Learning Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Matthias Karlbauer is a Machine Learning Scientist at ECMWF, working on deep learning approaches for weather and climate prediction. His work focuses on probabilistic forecasting and AI-based forecasting systems for Earth-system modelling. Before joining ECMWF, Matthias worked on learning turbulent heat fluxes with machine learning as a PostDoc at the University of Tübingen, Germany. Prior to that, during his doctoral studies, he contributed to the development and evaluation of deep learning weather prediction models as an Applied Scientist Intern at Amazon Web Services (AWS).

Simon Lang, Principal Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Simon Lang is a Principal Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF). His research focuses on developing data-driven forecasting models and advancing probabilistic weather prediction.

Christian Lessig, Senior Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Christian Lessig is a senior scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the team lead for machine learning modeling in the Earth system modeling section. His research tries to understand the limits of machine learning for weather and climate predictions and the role observations can play to improve predictions.

Bo Lu, Research Scientist at the China Meteorological Administration

Dr. Lu is a Research Scientist at the China Meteorological Administration (CMA) and Vice President of the Xiong'an Institute of Meteorological Artificial Intelligence. He serves as a Scientific Steering Committee member of the Global Climate Observing System (GCOS) and a member of the WIPPS Expert Team on Operational Climate Prediction Systems (ET-OCPS). His current research focuses on AI-based climate prediction. Dr. Lu leads the development of "Fengshun", a data-driven subseasonal-to-seasonal forecasting model that is now operational at CMA.

Gabriel Moldovan, Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Gabriel Moldovan is a scientist at ECMWF working on machine learning and data-driven approaches for weather forecasting and Earth-system modelling. His work focuses on AI-based forecasting systems, operational machine learning workflows, and the integration of modern data-driven methods into numerical weather prediction. Gabriel contributes to the development and evaluation of scalable forecasting approaches for weather and climate applications.

Lucy Mtilatila, Senior Scientist at the Malawi Department of Climate Change and Meteorological Services

Joel Oskarsson, Postdoctoral Fellow at ETH Zurich

Joel Oskarsson is a Postdoctoral Fellow at the ETH Zurich AI Center, working at the intersection of machine learning and Earth-system science. His research focuses on probabilistic machine learning methods for modelling data with spatial and temporal dependencies, with applications in weather forecasting and climate modelling. Joel completed his PhD in Computer Science at Linköping University, where his research explored graph-based machine learning approaches for complex spatial and temporal systems.

Rolland Potthast, Researcher at the German Weather Service (DWD)

Ana Prieto Nemesio, Machine Learning Engineer Team Lead at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Ana Prieto Nemesio is the Machine Learning Engineering Team Lead at ECMWF, where she leads ML engineering activities and supports the development of Anemoi, an open-source data-driven weather forecasting framework co-developed with European national meteorological services. She has a background in Aerospace Engineering and holds a master’s degree in advanced computational methods and flow management. Ana began her career working on research projects at the intersection of artificial intelligence and remote sensing as a Machine Learning Engineer. She later worked as a Computer Vision Data Scientist, focusing on climate risk modelling and leading the development of a high-resolution digital terrain model using deep learning techniques.

Mario Santa Cruz, Machine Learning Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Mario Santa Cruz López is a Machine Learning Scientist at ECMWF, where he works on the development of AI-driven weather and Earth-system forecasting models within the Anemoi framework. His research interests include the integration of observations into operation data-driven forecasting systems, multi-domain modelling, and training strategies with multiple datasets and high-resolution modelling.

Maria Luisa Taccari, Scientific Machine Learning Researcher at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Maria Luisa Taccari develops machine learning applications for hydrological forecasting within the Destination Earth initiative at ECMWF, with a focus on river discharge and water levels. She is currently also a Visiting Research Scientist at the European Commission’s Joint Research Centre, contributing to AI applications for flood prediction and early warning systems. Maria Luisa holds completed a PhD at the University of Leeds on deep learning surrogate models for groundwater forecasting.

Jonathan Weyn, Data Scientist and Meteorologist at Microsoft AI

Dr. Jonathan Weyn is a data scientist and meteorologist currently building operational weather forecasting solutions at Microsoft AI. He completed his PhD in Atmospheric Sciences at the University of Washington where he pioneered application of deep learning to weather prediction. At Microsoft, Jonathan has worked on high-impact projects like Aurora, a foundation model for the atmosphere, and partnered with the European Centre for Medium-range Weather Forecasts (ECMWF) to use AI for ensemble post-processing. His research helps extend forecast skill and support next-generation forecasting systems, helping democratize global weather prediction.

Sophie Xhonneux, Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF)

Lorenzo Zampieri is a scientist at ECMWF working on machine learning approaches for sea ice, ocean, and wave modelling for the AIFS. His research focuses on sea ice prediction and predictability, thermodynamic parameterisations, and the application of machine learning to Earth-system modelling. Before joining ECMWF, Lorenzo worked as a postdoctoral researcher at the National Center for Atmospheric Research (NCAR) and as a junior scientist at the Euro-Mediterranean Center on Climate Change (CMCC). He completed his PhD in Physics at the Alfred Wegener Institute and the University of Bremen.

Contributors

Samuel Sutanto, Assistant Professor Compound Hydrological Extremes and Climate Services at Wageningen University | Principal Investigator

Wouter Smolenaars, Assistant Professor - Water-Food Systems-Adaptation at Wageningen University | Course co-coordinator

Natalia Gomez-Solano, Researcher Climate Information Services at Wageningen University | Course co-coordination

Dwaipayan Chatterjee, Scientist at KIT | Course scientific coordinator

Imme Benedict, Assistant Professor in Meteorology at Wageningen University | Course developer

Bouke Hefting, Student Assistant at Wageningen University| Course developer

Janine Quist, Programme manager Continuing Education at Wageningen University| Educational and Communication Expert

Vassianna Alexopoulou, Online course moderator at Wageningen University| Forum Moderator

Martin Janssens, Assistant Professor in Meteorology at Wageningen University | Course developer