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.
This lesson focuses on ECGATE - ECMWF's server allocated for users' tasks, from submitting jobs to correcting errors.
ecFlow is a workflow package that enables users to run a large number of programmes behind a firewall.
Metview is a meteorological workstation application that enables you to access and visualise meteorological data.
Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.
The SAPP system is the ECMWF's operational acquisition and pre-processing system for observations and other input data.
The Meteorological Archival and Retrieval System (MARS) enables access to ECMWF data. Explore its computing capability
This lesson describes the web services used to visualise geographical data and outlines what OGC and INSPIRE are.
This lesson provides an overview of Metview's main features to analyse and edit input data for the single-column model.
The Meteorological Archival and Retrieval System (MARS) enables users to access and retrieve ECMWF’s historical data.
This lesson is focused on how to gain flexibility and control when handling GRIB data using advanced ecCodes tools.
