Displaying 1 - 25 of 113

10hPa temperature: Weekly mean anomalies

This chart shows 7-day mean anomalies of temperature at 10hPa from the ECMWF Sub-seasonal range ...

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2  m Temperature Area Averages - Seasonal Forecast - SEAS5

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

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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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Accumulated Cyclone Energy (Verification) - Seasonal Forecast - SEAS5

This chart provides information on the verification of forecasts of Accumulated Cyclone Energy ...

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Accumulated Cyclone Energy - Seasonal Forecast - SEAS5

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

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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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(EXPERIMENTAL) Thermal comfort parameters

Various thermall comfort parameters showing thermal comfort

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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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Madden-Julian Oscillation (MJO) index - Sub-seasonal range forecast

This diagram shows time evolution of a Madden-Julian Oscillation (MJO) index. ...

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Mean sea level pressure and 200 hPa wind

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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Mean sea level pressure and 850 hPa wind speed

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 zonal wind at 10 hPa - Sub-seasonal range forecast

This shows the daily distribution and evolution of mean zonal wind at 10hPa at 60N or 60S. ...

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Sea Surface Temperature Area Averages - Seasonal Forecast - SEAS5

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

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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: 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: 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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