Overview
Discover how machine learning is transforming extreme weather and climate simulations. Under the Destination Earth (DestinE) initiative, ECMWF is developing a new series of three online courses on Machine Learning for Earth Systems Modelling.
The courses offer a structured learning pathway, from foundations and context to advanced architectures and real-world applications, showing how machine learning (ML) complements physical modelling and supports next-generation digital twins of the Earth.
This first training course of the series introduces the foundations of machine learning in Earth system modelling, connecting ML concepts with physical modelling and real applications. Participants explore how AI is used within the DestinE initiative, including the digital twins and next-generation AI simulation systems. The course also addresses ethics, regulation, and future skills, bringing together expert perspectives to prepare learners for advanced ML applications.
Registration End Date: March 2026
Course Start Date: March 2026
Course End Date: May 2026
Objectives of the training
After this course, you will be able to:
Target audience
This course is the first in a three-part series of Destination Earth online training courses on machine learning in Earth Systems modelling, targeting researchers, practitioners, or technical specialists from meteorological services, climate centres, academic institutes, or industries.
This first course, Foundations and New Frontiers, specifically targets a broad audience of high-level information users and policy/decision-makers from both European public and private sectors, academia, and industry. This course will also be of interest to a technical audience of non-ML meteorologists who want to be onboarded into the world of ML.
Prerequisites
- A background in meteorology, climate systems, or Earth systems sciences.
- Familiarity with statistics and data interpretation.
- Some prior knowledge of machine learning concepts and some coding experience is helpful, but not essential.
Course structure and content
The course consists of a series of modules that combine a range of learning activities and applied examples to support a progressive understanding of ML in Earth System Sciences (ESS). The modules guide learners from core concepts to practical applications, illustrating how ML methods are developed, evaluated, and integrated into weather and climate workflows.
Learning materials include video lectures with visual explanations, interactive webinars, panel discussions featuring experts, and hands-on Python notebooks providing practical demonstrations using open climate datasets and models. Additional resources include curated readings and DestinE documentation, podcasts, auto-graded quizzes to check understanding, and forum discussions that encourage reflection and exchange after selected modules.
Course lectures and contributors
This course was created by ECMWF under the DestinE initiative and contracted to Wageningen University (WU) in collaboration with the Karlsruhe Institute of Technology (KIT) and Wageningen Research (WR). Scientific content is provided by European experts from ECMWF, academia, national meteorological services, and private industry (contributor details will be published in the following update).
Lectures
- Peer, Nowack, KIT
- Christian Lessig, ECMWF
- Matthew Chantry, ECMWF
- Stephan Siemens, ECMWF
- Peter Dueben, ECMWF
- Sara Hahner, ECMWF
- Tom Beucler, University of Lausanne
- Fernando Iglesias Suárez, Predictia
- Martin Schultz, Forschungszentrum Jülich
- Phillip Stier, University of Oxford
- Sophie Buurman, Royal Netherlands Meteorological Institute
- Rachel Furner, ECMWF
- Tobias Selz, KIT
- Stefanie Holborn, Deutscher Wetterdienst
Contributors
- Samuel Sutanto, Wageningen University
- Wouter Smolenaars, Wageningen University
- Natalia Gomez Solano, Wageningen University
- Imme Benedict, Wageningen University
- Martin Janssens, Wageningen University
- Bouke Hefting, Wageningen University
- Dwaipayan Chatterjee, KIT
- Janine Quist, Wageningen Research
Course Content
- Announcements