Datasets
Bulk Wind Shearcharts show the vector value (in wind arrow form) of the shear between the low level, near surface level (10 m) and a mid-tropospheric level (about 6 km)...
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The surface wind is influenced by the roughness of the earth's surface and is likely to be less strong, and a little backed (in the northern hemisphere) or veered (in the southern hemisphere)...
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This chart shows the anomaly in the wind speed at 10 m above the earth's surface (in m/s) ...
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These charts show surface pressure patterns. Areas of high pressure (anticyclones) are usually associated with settled weather...
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Air temperatures at 2 m above the earth's surface approximate most closely to the conditions a person would most likely experience...
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This chart provides information on the verification of forecasts of Accumulated Cyclone Energy ...
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The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at a ...
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Interval/period: Sun, 01/01/1978 - Wed, 10/17/2018
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.
Interval/period: Tue, 01/01/1924 - Sat, 05/09/2026
(GNSS) radio signals.
The initial data is collected from two in situ ground-based network of GNSS receivers – the International GNSS Service
(IGS) and EUREF Permanent Network (EPN). The IGS collects, archives, and freely distributes GNSS data from a
cooperatively operated global network of more than 500 ground-based GNSS stations since 1994. The EPN is a European
Interval/period: Mon, 01/01/1996 - Sat, 05/09/2026
The first is version 2 of the Integrated Global Radiosounding Archive (IGRA) from 1978 which incorporates global
radiosounding profiles of temperature, humidity and wind from a large number of data sources,
which is 30% larger than the previous version 1. IGRA v2 is the result of quality assurance procedures applied to the
Interval/period: Sun, 01/01/1978 - Sat, 05/09/2026
Various thermall comfort parameters showing thermal comfort
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The vertically integrated budget diagnostics include the tendencies and lateral fluxes of total energy, water vapour, and latent heat (with the latent heat of vaporization varying with temperature). In addition, the divergences of the lateral fluxes are provided.
Interval/period: Mon, 01/01/1979 - Sat, 05/09/2026
Maximum Wind gusts at 10 m above the earth's surface during the 6 hour period previous to the selected validity time are shown using colour shading. 10 m wind gusts are a post-processed product...
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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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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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This shows the daily distribution and evolution of mean zonal wind at 10hPa at 60N or 60S. ...
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Scores of forecasts of surface parameters by experimental machine learning models
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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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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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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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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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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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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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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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