Datasets
Displaying 1 - 25 of 84
The Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) is an international reference observing network, established in 2006, of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN measurements are providing high-quality climate data records from the surface, through the troposphere, and into the stratosphere.
Interval/period: Sun, 01/01/1978 - Wed, 10/17/2018
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.
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 - Sun, 05/10/2026
This dataset provides estimates of water vapour derived from atmospheric delays in Global Navigation Satellite System
(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
(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 - Sun, 05/10/2026
This dataset provides daily mid-morning measurements for Lake Surface Water Temperature (LSWT) derived from satellite observations. LSWT, together with five other variables (lake ice cover and thickness, water leaving reflectance, water level and extent) is recognised as the Essential Climate Variable (ECV) "Lakes" by the Global Climate Observing System (GCOS). LSWT is a key parameter in determining lake ecological conditions as it influences physical, chemical and biological processes.
Interval/period: Thu, 06/01/1995 - Sun, 05/10/2026
This dataset provides lake water levels for 311 selected lakes on four continents derived from satellite radar altimetry.
Interval/period: Wed, 01/01/1992 - Sun, 05/10/2026
This catalogue entry provides access to vertical profiles of standard meteorological variables. It includes two archives.
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
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 - Sun, 05/10/2026
This dataset provides monthly means of mass-consistent, vertically integrated, atmospheric energy and moisture budget quantities derived from 1-hourly ERA5 reanalysis data.
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.
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 - Sun, 05/10/2026
The Essential Climate Variables for assessment of climate variability from 1979 to present dataset contains a selection of climatologies, monthly anomalies and monthly mean fields of Essential Climate Variables (ECVs) suitable for monitoring and assessment of climate variability and change. Selection criteria are based on accuracy and temporal consistency on monthly to decadal time scales.
Interval/period: Mon, 01/01/1979 - Wed, 04/01/2026
The dataset presents climate impact indicators related to extreme precipitation in Europe under current climate conditions. The suite of indicators include recent historic records, recurrence intervals, and other relevant statistical measures to evaluate the magnitude and frequency of extreme precipitation events. These are provided as gridded products, with one product covering the whole of Europe, and the other higher resolution product focused on 20 European cities that were identified as vulnerable to urban pluvial flooding based on stakeholder surveys.
Interval/period: Sun, 01/01/1950 - Tue, 12/31/2019
The C3S Arctic Regional Reanalysis second generation (CARRA2) dataset contains daily and monthly meteorological variables at 2.5 km resolution. These variables are specified at single levels (including surface) and also at soil, height, pressure and model levels. These daily and monthly data are pre-calculated and have the following types depending on the variables: daily and monthly averages, extremes and totals.
Interval/period: Wed, 01/01/1986 - Sun, 12/31/2023
The C3S Arctic Regional Reanalysis second generation (CARRA2) dataset contains 3-hourly analyses at 2.5 km resolution. These variables are specified at single levels (including surface) and also at soil, height, pressure and model levels. Additionally, hourly forecasts are available between the analysis times and particularly forecasts up to 18 hours initialised from the analyses at 00 and 12 UTC.
Interval/period: Wed, 01/01/1986 - Sun, 12/31/2023
Within the hydrological cycle, precipitation is the main component of water transport from the atmosphere to the Earth’s surface. Precipitation varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply of freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation or the lack of precipitation.
Interval/period: Mon, 01/01/1979 - Mon, 09/30/2024
This dataset provides global estimates of daily accumulated and monthly means of precipitation. The precipitation estimates are based on a merge of passive microwave observations from two different radiometer classes operating on multiple Low Earth Orbit (LEO) satellites.
Interval/period: Sat, 01/01/2000 - Sun, 12/31/2017
This dataset provides monthly and zonally averaged tropospheric humidity profiles derived from globally distributed GPS radio occultation (RO) measurements from EUMETSAT's Metop polar-orbiting satellites. Humidity plays an important role in the Earth's climate system, due to the strong greenhouse effect of water vapour but also for its role in the global energy transport, vertically in the atmosphere and horizontally between different geographical regions.
Interval/period: Fri, 12/01/2006 - Mon, 12/01/2025
The present UERRA dataset contains analyses of atmospheric variables on height levels, from 1961 to 2019.
Interval/period: Thu, 10/18/2018 - Wed, 07/31/2019
The present UERRA dataset contains analyses of atmospheric variables on pressure levels, from 1961 to 2019.
It has been generated using the UERRA-HARMONIE system by combining model data with observations into a complete and consistent dataset using the laws of physics.
It has been generated using the UERRA-HARMONIE system by combining model data with observations into a complete and consistent dataset using the laws of physics.
Interval/period: Thu, 10/18/2018 - Wed, 07/31/2019
This UERRA dataset contains analyses of surface and near-surface essential climate variables from
UERRA-HARMONIE and MESCAN-SURFEX systems. Forecasts up to 30 hours initialised
from the analyses at 00 and 12 UTC are available only through the CDS-API (see Documentation).
UERRA-HARMONIE is a 3-dimensional variational data assimilation system,
while MESCAN-SURFEX is a complementary surface analysis system.
Using the Optimal Interpolation method, MESCAN provides the best estimate of daily accumulated precipitation
UERRA-HARMONIE and MESCAN-SURFEX systems. Forecasts up to 30 hours initialised
from the analyses at 00 and 12 UTC are available only through the CDS-API (see Documentation).
UERRA-HARMONIE is a 3-dimensional variational data assimilation system,
while MESCAN-SURFEX is a complementary surface analysis system.
Using the Optimal Interpolation method, MESCAN provides the best estimate of daily accumulated precipitation
Interval/period: Thu, 10/18/2018 - Wed, 07/31/2019
The UERRA dataset provides estimations of the climate in Europe based on model data
combined with observations using the UERRA-HARMONIE system and MESCAN-SURFEX system.
UERRA-HARMONIE is a 3-dimensional data assimilation system, whereas
MESCAN-SURFEX is a complementary surface analysis system. In general, the
assimilation systems are able to estimate biases between observations and to
sift good-quality data from poor data. The laws of physics allow for estimates
combined with observations using the UERRA-HARMONIE system and MESCAN-SURFEX system.
UERRA-HARMONIE is a 3-dimensional data assimilation system, whereas
MESCAN-SURFEX is a complementary surface analysis system. In general, the
assimilation systems are able to estimate biases between observations and to
sift good-quality data from poor data. The laws of physics allow for estimates
Interval/period: Thu, 10/18/2018 - Wed, 07/31/2019
Upper Tropospheric Humidity (UTH) is of key importance to the Earth’s greenhouse effect and understanding of climate change. It is considered an Essential Climate Variable (ECV) because it controls key atmospheric processes, including those involved in water vapour and cloud feedbacks, that can amplify the climate system’s response to increases in other greenhouse gases. The Upper Tropospheric Humidity is defined as the integrated amount of Water Vapour in the atmospheric layer between ~500 hPa and ~200 hPa.
Interval/period: Tue, 07/05/1994 - Sun, 02/28/2021
This dataset provides gridded modelled hydrological time series forced with medium-range meteorological forecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is sub-daily high-resolution and ensemble forecasts of:
River discharge
Volumetric soil moisture
Snow water equivalent
Soil wetness index (root zone)
Runoff water equivalent (surface plus subsurface)
River discharge
Volumetric soil moisture
Snow water equivalent
Soil wetness index (root zone)
Runoff water equivalent (surface plus subsurface)
Interval/period: Thu, 10/11/2018 - Mon, 04/06/2026
This dataset provides gridded modelled hydrological time series forced with medium-range meteorological forecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is sub-daily high-resolution and ensemble forecasts of:
River discharge
Soil moisture for three soil layers
Snow water equivalent
Interval/period: N/A
This dataset provides an ensemble of forecast time series of gridded hydrological data. The data set is a product of the Global Flood Awareness System (GloFAS) and offers a consistent representation of key hydrological variables across the global domain including:
River discharge
Soil wetness index (root zone)
Snow water equivalent
Runoff water equivalent (surface plus subsurface)
River discharge
Soil wetness index (root zone)
Snow water equivalent
Runoff water equivalent (surface plus subsurface)
Interval/period: Tue, 11/05/2019 - Thu, 05/07/2026
This dataset provides gridded modelled sub-daily and daily hydrological time series forced with meteorological observations. The data set is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is up to 30 years modelled time series of:
River discharge
Volumetric soil moisture
Snow water equivalent
Soil wetness index (root zone)
Runoff water equivalent (surface plus subsurface)
River discharge
Volumetric soil moisture
Snow water equivalent
Soil wetness index (root zone)
Runoff water equivalent (surface plus subsurface)
Interval/period: Tue, 01/01/1991 - Tue, 05/05/2026
This dataset provides gridded modelled daily hydrological time series forced with meteorological reanalysis data. The data set is a product of the Global Flood Awareness System (GloFAS) and offers a consistent representation of key hydrological variables across the global domain including:
River discharge
Soil wetness index (root zone)
Snow water equivalent
Runoff water equivalent (surface plus subsurface)
River discharge
Soil wetness index (root zone)
Snow water equivalent
Runoff water equivalent (surface plus subsurface)
Interval/period: Mon, 01/01/1979 - Tue, 05/05/2026
The Sub-seasonal To Seasonal dataset (S2S) consists of global ensemble real-time forecasts and reforecasts from thirteen numerical weather prediction (NWP) and research centres.
S2S project behind the dataset started in 2013 as a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP).
The goal of S2S project was to improve sub-seasonal forecast skill through combining multiple forecasting systems, enable multi-model evaluations and enhance knowledge sharing between operational centres.
S2S project behind the dataset started in 2013 as a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP).
The goal of S2S project was to improve sub-seasonal forecast skill through combining multiple forecasting systems, enable multi-model evaluations and enhance knowledge sharing between operational centres.
Interval/period: Thu, 01/01/2015 - Wed, 05/06/2026