Datasets
Displaying 26 - 50 of 108
This dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling.
Interval/period: Mon, 01/01/1979 - Thu, 04/30/2026
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
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 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 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
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: Tue, 03/01/2011 - Tue, 06/09/2026
This dataset provides a gridded modelled time series of river discharge, forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).
Interval/period: Sun, 01/03/1999 - Sat, 11/25/2023
This dataset provides atmospheric CH₄ observations as derived from satellite radiances. It consists of two types of products each covering a different part of the atmosphere: (i) The average CH₄ mixing ratio of the entire atmosphere from the Earth’s surface to the top of the atmosphere. These column-averaged mixing ratio products are referred to as “XCH4” products. (ii) Mid-tropospheric (MT) CH₄ mixing ratio products. These products are referred to as "MTCH4" products. These products can be used to monitor the spatial distribution and temporal evolution of CH₄.
Interval/period: Tue, 10/01/2002 - Tue, 12/31/2024
ERA5–Drought is a global reconstruction of drought indices from 1940 to present.
The dataset comprises two standardised drought indices:
- the Standardised Precipitation Index (SPI)
- the Standardised Precipitation-Evapotranspiration Index (SPEI).
The dataset comprises two standardised drought indices:
- the Standardised Precipitation Index (SPI)
- the Standardised Precipitation-Evapotranspiration Index (SPEI).
Interval/period: Mon, 01/01/1940 - Sun, 03/01/2026
This dataset provides bias-corrected reconstruction of near-surface meteorological variables derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). It is intended to be used as a meteorological forcing dataset for land surface and hydrological models.
Interval/period: Mon, 01/01/1979 - Mon, 12/31/2018
This dataset provides a record of fuel characteristics at high spatiotemporal resolution: ~9km, daily.
The two main variable groups are fuel load and fuel moisture, both of which are further divided by live/dead and wood/foliage fractions.
The dataset combines state-of-the-art model data (ERA5-Land) with observations from multiple satellites and in-situ variables into a globally complete and consistent dataset.
The two main variable groups are fuel load and fuel moisture, both of which are further divided by live/dead and wood/foliage fractions.
The dataset combines state-of-the-art model data (ERA5-Land) with observations from multiple satellites and in-situ variables into a globally complete and consistent dataset.
Interval/period: Wed, 01/01/2003 - Fri, 12/31/2021
This dataset provides global daily estimates of ocean surface chlorophyll-a concentration and remote sensing reflectance derived from multiple satellite sensors. These two products are part of the broader discipline of ocean colour remote sensing, which analyses ocean surface radiances measured from space to derive information about the optical properties and constituents of the upper ocean.
Interval/period: Thu, 09/04/1997 - Tue, 12/31/2024
This dataset provides global ocean and sea-ice reanalysis (ORAS5: Ocean Reanalysis System 5) monthly mean data prepared by the European Centre for Medium-Range Weather Forecasts (ECMWF) OCEAN5 ocean analysis-reanalysis system. This system comprises 5 ensemble members from which one member is published in this catalogue entry.
Interval/period: Wed, 01/01/1958 - Sun, 03/01/2026
This catalogue entry provides monthly mean time series from the global ocean and sea-ice reanalysis ORAS5 (Ocean Reanalysis System 5), with data organised to prioritise temporal access at grid point locations rather than full spatial fields. The dataset enables users to efficiently retrieve long, continuous time series for individual ocean points, supporting analyses of ocean variability and change without the need to download and process large global gridded datasets.
Interval/period: Wed, 01/01/1958 - Wed, 01/01/2025
This dataset provides monthly averaged ozone values derived from a large set of satellite sensors with global coverage. Ozone is naturally formed in the atmosphere through the interaction between molecular oxygen and solar radiation. In the stratosphere, it acts as a shield protecting us from the UV radiation emitted by the Sun. At ground level however, it is a human health irritant and a component of smog. Ozone and climate change are strongly related.
Interval/period: Wed, 04/01/1970 - Tue, 07/01/2025
CAMS produces global forecasts for atmospheric composition twice a day. The forecasts consist of more than 50 chemical species (e.g. ozone, nitrogen dioxide, carbon monoxide) and seven different types of aerosol (desert dust, sea salt, organic matter, black carbon, sulphate, nitrate and ammonium aerosol). In addition, several meteorological variables are available as well.
Interval/period: Thu, 01/01/2015 - Thu, 05/07/2026
Emissions of atmospheric pollutants from biomass burning and vegetation fires are key drivers of the evolution of atmospheric composition, with a high degree of spatial and temporal variability, and an accurate representation of them in models is essential.
Interval/period: Wed, 01/01/2003 - Wed, 12/03/2025
This data set contains gridded distributions of global anthropogenic and natural emissions.
Interval/period: Sat, 01/01/2000 - Thu, 12/31/2020
This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual.
Interval/period: Wed, 01/01/2003 - Thu, 12/31/2020
This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual.
Interval/period: Wed, 01/01/2003 - Thu, 12/31/2020
This data set contains net fluxes at the surface, atmospheric mixing ratios at model levels, and column-mean atmospheric mixing ratios for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20).
Interval/period: Mon, 01/01/1979 - Tue, 12/31/2024
This dataset provides aerosol optical depths and aerosol-radiation radiative effects for four different aerosol origins: anthropogenic, mineral dust, marine, and land-based fine-mode natural aerosol. The latter mostly consists of biogenic aerosols.
Interval/period: Wed, 01/01/2003 - Sun, 12/31/2017