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...

The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).

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

Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections

The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).

The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).

The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).

The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).

The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).

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