Description
In this first tier of our MOOC on Machine Learning (ML) in Weather and Climate, we give a broad overview of the key concepts ML and its applications in recent years to topics from forecasting and data assimilation to post-processing, observations and computing. This course is aimed as an introduction, which is accessible to those with an interest in ML, weather and climate, but without necessarily requiring a very technical background.
This course features a mixture of interactive modules, webinar recordings, quizzes, podcasts and code examples.
Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.
Course Content
- Experts' opinions on Machine Learning (🕓25 min)
- What is Machine Learning and what types of Machine Learning are there? (🕓 10 min)
- Why should we consider machine learning for weather and climate modelling? (🕓 15 min)
- Challenges for Machine Learning in weather and climate modelling (🕓 10 min)
- State-of-the-art and challenges of using observations in NWP (🕓 15 min)
- How can Machine Learning help for observation processing in NWP? (🕓 10 min)
- Machine learning for the processing of spatial data (🕓 15 min)
- Concept and state-of-the-art (🕓 15 min)
- Forecasting with ML (🕓 10 min)
- WeatherBench (🕓 30 min)
- Quiz
- A 10 minute Introduction to Data Assimilation (🕓 12 min)
- Similarities between Data Assimilation and Machine Learning (🕓 18 min)
- Concept and state-of-the-art (🕓 15 min)
- Post-processing with ML (🕓 12 min)
- The WMO S2S challenge (🕓 9 min)
- The synergies between machine learning and high-performance computing (🕓 15 min)
- Interactive map of supercomputers (🕓 10 min)
- Machine Learning with HPC (🕓 15 min)
- Cloud Computing and European Weather Cloud (🕓 18 min)