Datasets

500 hPa geopotential height and 850 hPa temperature

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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500-1000 hPa thickness and mean sea level pressure

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...

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Lead time of anomaly correlation coefficient (ACC) reaching multiple thresholds ( High resolution (HRES) 500 hPa height forecasts)

This diagram gives a measure of the effectiveness of the model in forecasting 500 hPa heights at ...

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Scores of forecasts of upper-air parameters by AIFS - experimental machine learning model

These plots compare recent IFS and experimental AIFS verification scores for 500 hPa ...

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Scores of forecasts of upper-air parameters by experimental machine learning models

These plots compare recent IFS and experimental AIFS verification scores for 500 hPa ...

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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: 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: 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: 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: 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: 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: Temperature and geopotential at various pressure levels

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: Wind and geopotential heights at various pressure levels

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: Pangu-Weather ML model: 500 hPa geopotential height and 850 hPa temperature

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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Experimental: Pangu-Weather ML model: Temperature and geopotential at various pressure levels

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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Experimental: Pangu-Weather ML model: Wind and geopotential heights at various pressure levels

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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AIFS ENS Control: 500 hPa geopotential height and 850 hPa temperature

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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AIFS ENS Control: Temperature and geopotential at various pressure levels

In the northern hemisphere the air rotates anti-clockwise around low contour centres and clockwise around the high contour centres. In the southern hemisphere the air rotates clockwise around low contour centres and ...

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AIFS ENS Control: Wind and geopotential heights at various pressure levels

The air flow is not the same at all levels and can be very different in strength and direction when associated with vigorous weather systems...

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AIFS ENS Ensemble mean and spread: 300 hPa geopotential height

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

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AIFS ENS Ensemble mean and spread: 500 hPa geopotential height

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

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