Presented by Peter Dueben (ECMWF, UK)

(1) Predicting weather and climate require modelling the Earth System – a huge system that consists of many individual components that show chaotic behaviour and for which conventional tools often struggle to provide satisfying results. (2) A huge amount of data of the Earth System is available from both observations and modelling. (3) Machine learning methods allow learning complex non-linear behaviour from data if enough data is available and to apply the learned tools efficiently on modern supercomputers. If you combine (1), (2) and (3), it is easy to see that there are a large number of potential application areas for machine learning in weather and climate science that are currently explored. However, whether these approaches will succeed is still unclear as there are also a number of challenges for the application of machine learning tools in weather predictions. This talk will provide an introduction to machine learning, outline how to apply machine learning in Earth System modelling, show examples for the application of machine learning throughout the weather and climate modelling workflow, and discuss the challenges that will need to be tackled.

Learning Objectives

Describe the relevance of Machine Learning and its application to judge why there is such a hype around the topic at the moment
Explore how machine learning can be used in weather and climate modelling
List a number of specific examples for the use of machine learning at ECMWF
Discuss challenges for machine learning in weather and climate science