Five modules covering decision trees, deep learning, uncertainty and generative models, and physics-guided approaches.
Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.
Investigating and visualising CAMS NOx anthropogenic and natural emissions inventories.
Evaluating CAMS global aerosol forecasts by comparing model AOD with AERONET observations at selected sites.
Exploring CAMS global reanalysis fields to study atmospheric composition, trends, and long-term variability.
Exploring bias in CAMS forecasts using observations and introducing how MOS helps address systematic errors.
Investigating CAMS regional air-quality forecasts to examine high-resolution pollutant fields and time series.
Access CAMS global atmospheric composition forecasts and view time series as animations.
Create maps of CAMS global atmospheric composition forecast data for multiple parameters.
Visualising vertical profiles of species from CAMS data to examine atmospheric layering and profile behaviour.
Downloading, calculating and plotting global CO2 time series using CAMS reanalysis data.
Introducing the theory of comparing satellite retrievals with model fields using averaging kernels and operators.
