Datasets

(EXPERIMENTAL) Probabilities: point rainfall during last 12 hours

This chart shows probability information regarding point precipitation, as derived from the ...

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(EXPERIMENTAL) Probabilities: point rainfall during last 24 hours

This chart shows probability information regarding point precipitation, as derived from the ...

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(EXPERIMENTAL) Probabilities: point rainfall during last 6 hours

This chart shows probability information regarding point precipitation, as derived from the ...

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(EXPERIMENTAL) Surface temperature: Quantile-based weekly guidance maps

Detailed information on these EXPERIMENTAL products can be found

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(EXPERIMENTAL) Thermal comfort parameters

Various thermall comfort parameters showing thermal comfort

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(EXPERIMENTAL) Wave spectral partitions (wind waves and up to 3 swell partitions)

The idea of the plot is to be able to display each spectral partition as an arrow, with the direction being the mean direction, the length of the arrow the significant wave height, the colour of the arrow the mean wave period with the different arrow head for each partitions.

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Experimental: Aurora ML model: 2 m temperature and 10 m wind

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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Experimental: Aurora ML model: 500 hPa geopotential height and 850 hPa temperature

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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Experimental: Aurora ML model: Mean sea level pressure and 200 hPa wind

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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Experimental: Aurora ML model: Mean sea level pressure and 850 hPa wind speed

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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Experimental: Aurora ML model: Temperature and geopotential at various pressure levels

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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Experimental: Aurora ML model: Wind and geopotential heights at various pressure levels

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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Experimental: FourCastNet ML model: 2 m temperature and 10 m wind

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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Experimental: FourCastNet ML model: 500 hPa geopotential height and 850 hPa temperature

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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Experimental: FourCastNet ML model: Mean sea level pressure and 200 hPa wind

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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Experimental: FourCastNet ML model: Mean sea level pressure and 850 hPa wind speed

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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Experimental: FourCastNet ML model: Temperature and geopotential at various pressure levels

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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Experimental: FourCastNet ML model: Total column water

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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Experimental: FourCastNet ML model: Wind and geopotential heights at various pressure levels

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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Experimental: GraphCast ML model: 2 m temperature and 10 m wind

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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Experimental: GraphCast ML model: 500 hPa geopotential height and 850 hPa temperature

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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Experimental: GraphCast ML model: Mean sea level pressure and 200 hPa wind

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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Experimental: GraphCast ML model: Mean sea level pressure and 850 hPa wind speed

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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(EXPERIMENTAL) 2 m temperature: Quantile-based weekly guidance maps

Detailed information on these EXPERIMENTAL products can be found

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(EXPERIMENTAL) Mean sea level pressure: Quantile-based weekly guidance maps

Detailed information on these EXPERIMENTAL products can be found

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