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.
Investigating CAMS regional air-quality forecasts to examine high-resolution pollutant fields and time series.
Investigating and visualising CAMS NOx anthropogenic and natural emissions inventories.
Exploring bias in CAMS forecasts using observations and introducing how MOS helps address systematic errors.
Computing and visualising the global AQI from CAMS pollutants to assess air-quality conditions worldwide.
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.
Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.
Calculating and mapping the European AQI from CAMS pollutant data and analysing pollutants with heat maps.
