Learn about sources of predictability, seasonal forecast skill and the ECMWF sub-seasonal forecasting system.
Learn how EFI, SOT and Model Climate are built and provide forecast guidance for extreme, or severe weather events.
Learn about the ways in which forecast jumpiness can appear and how it can be mitigated.
Six modules introducing the main topics in machine learning in the context of weather and climate.
Learn about sources of error in NWP, how they are quantified, and how ensembles are evaluated.
Five modules covering decision trees, deep learning, uncertainty and generative models, and physics-guided approaches.
Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.
Learn about the main sources of uncertainty in weather forecasting and how they are addressed in early warning systems.
Learn about seasonal predictability, how numerical seasonal forecast models work and their outputs.
