This lesson looks at the three classes of parametrization schemes and the main characteristics of the IFS scheme.
Six modules introducing the main topics in machine learning in the context of weather and climate.
Explore the key microphysical and warm-phase processes of cloud and precipitation parametrisation and their use in NWP.
Learn about sources of error in NWP, how they are quantified, and how ensembles are evaluated.
This lesson covers key processes in ice and mixed-phase clouds and precipitation, and parametrization uncertainties.
Learn how sub-grid-scale processes (not explicitly simulated in NWP), are parameterised and how challenges are overcome.
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 data assimilation and how it is used to define ‘optimal' initial conditions for NWP at ECMWF.
Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.
An introduction to the basic concepts for the design of a cloud and precipitation microphysics parametrisation.
Learn about the main sources of uncertainty in weather forecasting and how they are addressed in early warning systems.
