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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Land cover classification gridded maps from 1992 to present derived from satellite observations

This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover (LC) maps, four quality flags are produced to document the reliability of the classification and change detection.

calendar_today Interval/period: Wed, 01/01/1992 - Sat, 01/01/2022

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Land surface temperature monthly gridded data from 1995 to present derived from satellite observations

This dataset contains monthly daytime and nighttime clear-sky land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on polar orbiting satellites. Daytime and nighttime temperatures correspond to 10:00 and 22:00 local solar time (local solar time is defined as the time in a 24 hour day with 12 noon occurring when the sun is at its highest point at that longitude).

calendar_today Interval/period: Thu, 06/01/1995 - Tue, 12/31/2024

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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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Leaf area index and fraction absorbed of photosynthetically active radiation 10-daily gridded data from 1981 to present

This catalogue entry provides satellite-derived estimates of two related variables: Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR). LAI and fAPAR are both Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS), meaning they have been designated as essential for contributing to a comprehensive view of Earth’s climate, its variability and trends.

calendar_today Interval/period: Tue, 09/01/1981 - Sun, 12/01/2024

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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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