Datasets

500 hPa Geopotential - Anomaly Correlation Coefficient - SEAS5

This chart shows the spatial variation in the Anomaly Correlation Coefficient (ACC) for the ...

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500 hPa Geopotential - Reliability Diagram - SEAS5

The reliability diagram shows the reliability of the ECMWF seasonal forecast system (SEAS5) with ...

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500 hPa Geopotential - ROC Diagram - SEAS5

This chart shows the Relative Operating Characteristics (ROC) diagram for the three-month ...

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500 hPa Geopotential - ROC Skill Scores - SEAS5

This chart shows the spatial variation in the Relative Operating Characteristics (ROC) skill ...

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500 hPa Geopotential Anomaly - SEAS5

The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at a ...

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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 hPa height: Weekly mean anomalies

This chart shows 7-day mean anomalies of 500hPa geopotential height from the ECMWF Sub-seasonal ...

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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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In situ total column ozone and ozone soundings from 1924 to present from the World Ozone and Ultraviolet Radiation Data Centre

This dataset provides access to two types of ozone observations: total column ozone estimates and vertical profiles of ozone concentration.
The total ozone estimates are based on solar UV radiation measurements made by ground-based spectrophotometers (Dobson or Brewer type spectrophotometers).
The vertical profiles of ozone concentration are estimated primarily using ozonesonde observations.
Data are available for 159 Dobson stations, 109 Brewer stations and 135 ozonesondes stations.

calendar_today Interval/period: Tue, 01/01/1924 - Sat, 05/09/2026

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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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Aerosol properties gridded data from 1995 to present derived from satellite observations

This data set provides observational records of aerosol properties obtained from observations collected by various satellite instruments. Aerosols are minor constituents of the atmosphere by mass, but critical components in terms of impact on climate. Aerosols influence the global radiation balance directly by scattering and absorbing radiation, and indirectly through influencing cloud reflectivity, cloud cover and cloud lifetime.

calendar_today Interval/period: Thu, 06/01/1995 - Thu, 07/31/2025

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