Datasets
Bulk Wind Shearcharts show the vector value (in wind arrow form) of the shear between the low level, near surface level (10 m) and a mid-tropospheric level (about 6 km)...
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The surface wind is influenced by the roughness of the earth's surface and is likely to be less strong, and a little backed (in the northern hemisphere) or veered (in the southern hemisphere)...
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This chart shows the anomaly in the wind speed at 10 m above the earth's surface (in m/s) ...
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These charts show surface pressure patterns. Areas of high pressure (anticyclones) are usually associated with settled weather...
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Air temperatures at 2 m above the earth's surface approximate most closely to the conditions a person would most likely experience...
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This chart provides information on the verification of forecasts of Accumulated Cyclone Energy ...
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The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at a ...
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Various thermall comfort parameters showing thermal comfort
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Maximum Wind gusts at 10 m above the earth's surface during the 6 hour period previous to the selected validity time are shown using colour shading. 10 m wind gusts are a post-processed product...
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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...
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Wind speeds near the surface are roughly proportional to the distance between isobars so closely packed isobars mean strong surface winds...
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This shows the daily distribution and evolution of mean zonal wind at 10hPa at 60N or 60S. ...
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Scores of forecasts of surface parameters by experimental machine learning models
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
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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.
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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.
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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.
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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.
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GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.
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GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.
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GraphCast (Google DeepMind): a deep learning-based system developed by Google DeepMind.It is initialised with ECMWF analysis. GraphCast operates at 0.25° resolution.
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Pangu-Weather: a deep learning-based system developed by Huawei. It is initialised with ECMWF analysis. Pangu-Weather operates at 0.25° resolution.
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Pangu-Weather: a deep learning-based system developed by Huawei. It is initialised with ECMWF analysis. Pangu-Weather operates at 0.25° resolution.
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