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.
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.
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.
Explore the key microphysical and warm-phase processes of cloud and precipitation parametrisation and their use in NWP.
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.
How SCM is used to investigate the physical processes of a global model in isolation, its applications and limitations.
