Datasets
This chart shows 7-day mean anomalies of temperature at 10hPa from the ECMWF Sub-seasonal range ...
Interval/period: N/A
The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at ...
Interval/period: N/A
The 850 hPa level is usually just above the boundary layer and at this level the day-night variation in temperature is generally negligible...
Interval/period: N/A
This chart shows 7-day mean anomalies of 500hPa geopotential height from the ECMWF Sub-seasonal ...
Interval/period: N/A
This chart provides information on the verification of forecasts of Accumulated Cyclone Energy ...
Interval/period: N/A
The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at a ...
Interval/period: N/A
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 - Tue, 05/12/2026
(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 - Tue, 05/12/2026
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 - Tue, 05/12/2026
Various thermall comfort parameters showing thermal comfort
Interval/period: N/A
This diagram shows time evolution of a Madden-Julian Oscillation (MJO) index. ...
Interval/period: N/A
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 - Tue, 05/12/2026
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...
Interval/period: N/A
Wind speeds near the surface are roughly proportional to the distance between isobars so closely packed isobars mean strong surface winds...
Interval/period: N/A
This shows the daily distribution and evolution of mean zonal wind at 10hPa at 60N or 60S. ...
Interval/period: N/A
The ECMWF seasonal forecasts (SEAS5) are produced every month with a 51-member ensemble at ...
Interval/period: N/A
Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
Interval/period: N/A
Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
Interval/period: N/A
Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
Interval/period: N/A
Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
Interval/period: N/A
Aurora: a deep learning-based system developed by Microsoft. It is initialised with ECMWF analysis. Aurora operates at 0.1° resolution.
Interval/period: N/A
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.
Interval/period: N/A
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.
Interval/period: N/A
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.
Interval/period: N/A