Learn about sources of error in NWP, how they are quantified, and how ensembles are evaluated.
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.
Six modules introducing the main topics in machine learning in the context of weather and climate.
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.
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.
