Datasets
This chart shows 7-day mean anomalies of temperature at 10hPa from the ECMWF Sub-seasonal range ...
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The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at ...
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The 850 hPa level is usually just above the boundary layer and at this level the day-night variation in temperature is generally negligible...
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This chart shows 7-day mean anomalies of 500hPa geopotential height from the ECMWF Sub-seasonal ...
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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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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:
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This diagram shows time evolution of a Madden-Julian Oscillation (MJO) index. ...
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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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Mean wave period is the spectrally averaged period of the waves. Wave periods are shown in seconds using colour shading – click on the middle icon to the bottom right for the scale...
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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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The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at ...
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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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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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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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