Datasets

500-1000 hPa thickness is a measure of the mean temperature of a column of the atmosphere between these pressure levels and can be used to distinguish between warm and cold air masses and...

Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.

Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.

FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.

FourCastNet v2-small:a deep learning-based system developed by NVIDIA in collaboration with researchers at several US universities.It is initialised with ECMWF analysis. FourCastNet operates at 0.25° resolution.

GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.

GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.

Detailed information on these EXPERIMENTAL products can be found

This dataset provides geographical distributions of the radiative forcing (RF) by key atmospheric constituents. The radiative forcing estimates are based on the CAMS reanalysis and additional model simulations and are provided separately for...
    - carbon dioxide
    - methane
    - tropospheric ozone
    - stratospheric ozone
    - interactions between anthropogenic aerosols and radiation

EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.

EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.

This dataset provides annual air quality reanalyses for Europe based on both unvalidated (interim) and validated observations.

This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual.

This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual.

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).

Wind speed at 200 hPa highlights the jet stream (areas of strong winds in the upper troposphere) which can help identify movement and development of depressions...

Wind speeds near the surface are roughly proportional to the distance between isobars so closely packed isobars mean strong surface winds...

Precipitation totals include all precipitation types (rain, snow etc.) (in mm of rainfall or rainfall equivalent) falling in 6 hour or 12 hour periods using colour shading...

This display helps with the recognition of clouds of different layers, even when they overlap. Brighter colouring represents greater cloud cover. Cloud-free areas appear white while areas of full cloud cover at all levels appear dark grey (e.g. active fronts)...

Precipitation totals include all precipitation types (rain, snow etc.) (in mm of rainfall or rainfall equivalent) falling in 6 hour or 12 hour periods using colour shading...

Wind speed at 200 hPa highlights the jet stream (areas of strong winds in the upper troposphere) which can help identify movement and development of depressions...

Wind speeds near the surface are roughly proportional to the distance between isobars so closely packed isobars mean strong surface winds...

Precipitation totals include all precipitation types (rain, snow etc.) (in mm of rainfall or rainfall equivalent) falling in 6 hour or 12 hour periods using colour shading...

This display helps with the recognition of clouds of different layers, even when they overlap. Brighter colouring represents greater cloud cover. Cloud-free areas appear white while areas of full cloud cover at all levels appear dark grey (e.g. active fronts)...

AIFS ENS: a deep learning-based system developed by ECMWF. It is initialised with ECMWF perturbed forecasts and operates at N320 (~0.25Deg) resolution