Learn how sub-grid-scale processes (not explicitly simulated in NWP), are parameterised and how challenges are overcome.
Learn about data assimilation and how it is used to define ‘optimal' initial conditions for NWP at ECMWF.
Learn about uncertainties and chaotic behaviour in NWP, why ensembles are needed and how they are used at ECMWF.
Learn about seasonal predictability, how numerical seasonal forecast models work and their outputs.
Learn about the role of satellite observations and measurements, and how these are assimilated and monitored for NWP.
Explore sources of uncertainty in NWP and how this is represented in the IFS using stochastic physics.
Learn about sources of predictability, seasonal forecast skill and the ECMWF sub-seasonal forecasting system.
Learn about the ways in which forecast jumpiness can appear and how it can be mitigated.
This lesson focuses on ECGATE - ECMWF's server allocated for users' tasks, from submitting jobs to correcting errors.
Learn about the unique role of snow in forecasting, from short-range to seasonal time scales.
Four case studies exploring the conditions that cause deep convection, considering predictability and forecast errors.
The Meteorological Archival and Retrieval System (MARS) enables users to access and retrieve ECMWF’s historical data.
