Discover how machine learning is being applied to real-world Earth system challenges and explore emerging approaches shaping the future of weather and climate modelling.
This is the third and final course in the Destination Earth (DestinE) Machine Learning for Earth Systems Modelling training series developed by ECMWF. Building on the foundations and technical skills introduced in Courses 1 and 2, Course 3 focuses on advanced applications of machine learning across weather, climate and Earth system science.
Participants will explore how ML methods can be applied to extreme events, downscaling, coupled Earth systems, data assimilation and long-range prediction, while also examining explainability and trust, foundation and hybrid models, operational workflows and data-driven scientific discovery.
The course has a strong practical emphasis, combining expert lectures and talks with Jupyter notebooks using real-world workflows, Python scripts, demonstrations, readings, quizzes and expert discussions. It can be followed as the final step in the
complete three-course learning pathway or as standalone advanced modules by experienced practitioners with the required technical background.
Main topics
The course covers:
- ML workflows for extreme events
- Explainability, trust and model failure
- Foundation models
- Hybrid modelling
- Sub-seasonal and long-range prediction
- Downscaling
- Coupled Earth systems
- ML-based data assimilation
- End-to-end modelling
- Forecast in a Box
- Data-driven scientific discovery
- Community reflection
Target audience
Course 3 is an advanced technical course aimed primarily at:
- Researchers and developers in weather, climate and Earth system science
- Operational numerical weather prediction (NWP) and climate model developers
- ML researchers working with environmental or geophysical data
- Advanced PhD students and postdoctoral researchers
Requirements
Participants are expected to have:
- Familiarity with Python and Python-based ML ecosystems
- Experience with NumPy/xarray and PyTorch or JAX
- An understanding of the fundamentals of numerical weather prediction and Earth system modelling
- Knowledge of core ML methods covered in Courses 1 and 2
Prior completion of Course 1 – Foundations and New Frontiers and Course 2 – Architectures, Data, and Prediction is recommended for learners who do not already have equivalent knowledge and experience.
Register now!
Register for this course through our eLearning portal.