This four-day course focuses on machine learning for numerical weather prediction (NWP). This will include:
- An overview on the use of machine learning in Earth Sciences
- Introduction into the most important machine learning methods that are relevant for Earth Sciences.
- Introduction into software and hardware frameworks at ECMWF to facilitate the use of machine learning.
- Examples for the use of specific machine learning tools across the weather and climate prediction workflow and how they can be prepared for use in operational predictions.
As there are many general courses on machine learning available – including free online courses – this course will have a particular focus on the use of machine learning in the domain of Earth Sciences.
As well as lectures there will be discussions and hands-on sessions.
Main topics
- An overview on the use of machine learning in Earth Sciences
- Available datasets and how to retrieve them
- Software and hardware for machine learning in NWP
- Machine learning models (in the context of Earth sciences), including deep learning approaches
- Data-driven forecasting, based around AIFS and the Anemoi framework
- Further examples of machine learning applications
Requirements
Participants should have a good meteorological or a good machine learning background, or both. Participants should also have some limited experience with Python code and Jupyter notebooks. Basic experience with machine learning applications in Earth system sciences and the handling of Earth system data would be advantageous. Some practical experience in numerical weather prediction is an advantage.
All lectures are in English.