Datasets

ECMWF is now running a series of data-driven forecasts as part of its experimental suite. These machine-learning based models are very fast, and they produce a 10-day forecast with 6-hourly time steps in approximately one minute. The outputs are available in graphical form.

Currently, three of these models are available:

ECMWF is now running its own Artificial Intelligence Forecasting System (AIFS). The AIFS consists of a deterministic model and an ensemble model. The deterministic model has been running operationally since 25 February 2025; further details can be found on the dedicated Implementation of AIFS Single v1 page.

Emissions of atmospheric pollutants from biomass burning and vegetation fires are key drivers of the evolution of atmospheric composition, with a high degree of spatial and temporal variability, and an accurate representation of them in models is essential.

This data set contains gridded distributions of global anthropogenic and natural emissions.

This data set contains net fluxes at the surface, atmospheric mixing ratios at model levels, and column-mean atmospheric mixing ratios for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20).