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.
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.
