This lesson looks at the three classes of parametrization schemes and the main characteristics of the IFS scheme.
This lesson will take you through what convection is and the phenomena it causes.
This lesson covers key processes in ice and mixed-phase clouds and precipitation, and parametrization uncertainties.
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.
This lesson focuses on ECGATE - ECMWF's server allocated for users' tasks, from submitting jobs to correcting errors.
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.
Explore sources of uncertainty in NWP and how this is represented in the IFS using stochastic physics.
Learn about the role of satellite observations and measurements, and how these are assimilated and monitored for NWP.
