Learn about sources of error in NWP, how they are quantified, and how ensembles are evaluated.
Learn how EFI, SOT and Model Climate are built and provide forecast guidance for extreme, or severe weather events.
Six modules introducing the main topics in machine learning in the context of weather and climate.
Five modules covering decision trees, deep learning, uncertainty and generative models, and physics-guided approaches.
Learn about the unique role of snow in forecasting, from short-range to seasonal time scales.
Learn about the ways in which forecast jumpiness can appear and how it can be mitigated.
Learn about sources of predictability, seasonal forecast skill and the ECMWF sub-seasonal forecasting system.
Learn about the role of satellite observations and measurements, and how these are assimilated and monitored for NWP.
Learn about seasonal predictability, how numerical seasonal forecast models work and their outputs.
Learn about uncertainties and chaotic behaviour in NWP, why ensembles are needed and how they are used at ECMWF.
Learn about data assimilation and how it is used to define ‘optimal' initial conditions for NWP at ECMWF.
Learn how sub-grid-scale processes (not explicitly simulated in NWP), are parameterised and how challenges are overcome.
