Datasets
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
United States Climate Reference Network (USCRN) stations.
There are over 130 USCRN stations over the conterminous United States (U.S.), Alaska, and Hawaii.
The USCRN stations are managed and maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA).
The USCRN observations include air temperature, humidity, wind speed, precipitation, solar radiation,
Interval/period: Sun, 01/01/2006 - Sat, 05/09/2026
ECMWF is now running a series of data-driven forecasts as part of its experimental suite. These machine-learning based models are very fast, and they produce a 10-day forecast with 6-hourly time steps in approximately one minute. The outputs are available in graphical form.
Currently, three of these models are available:
Interval/period: N/A
Interval/period: Mon, 01/01/1979 - Wed, 04/01/2026
The data are bias adjusted using the Distribution Based Scaling (DBS) method versus the global reference dataset HydroGFD2.0, both bias adjustment method and global reference dataset developed by the Swedish Meteorological and Hydrological Institute (SMHI).
The DBS method is a parametric quantile-mapping variant.
Interval/period: Thu, 10/12/2000 - Thu, 10/18/2018
Interval/period: Wed, 01/01/1986 - Sun, 12/31/2023
Interval/period: Wed, 01/01/1986 - Sun, 12/31/2023
Interval/period: Mon, 01/01/1979 - Mon, 09/30/2024
Interval/period: Sat, 01/01/2000 - Sun, 12/31/2017
Interval/period: Thu, 02/01/1940 - Fri, 01/23/2026
Interval/period: Sat, 03/01/1986 - Wed, 11/30/2011
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
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
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
ECMWF is now running its own Artificial Intelligence Forecasting System (AIFS). The AIFS consists of a deterministic model and an ensemble model. The deterministic model has been running operationally since 25 February 2025; further details can be found on the dedicated Implementation of AIFS Single v1 page.
Interval/period: N/A
LAPrec1871 starts in 1871 and is based on data from 85 input series;
LAPrec1901 starts in 1901 and is based on data from 165 input series.
Interval/period: Sun, 01/01/1871 - Sat, 05/09/2026
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: Sat, 09/01/1990 - Sat, 02/28/2026
This dataset provides annual air quality reanalyses for Europe based on both unvalidated (interim) and validated observations.
Interval/period: N/A
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: N/A
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: N/A
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: N/A
This dataset provides geographical distributions of the radiative forcing (RF) by key atmospheric constituents. The radiative forcing estimates are based on the CAMS reanalysis and additional model simulations and are provided separately for...
- carbon dioxide
- methane
- tropospheric ozone
- stratospheric ozone
- interactions between anthropogenic aerosols and radiation
Interval/period: N/A
EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.
Interval/period: N/A
EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.
Interval/period: N/A
Interval/period: Sat, 09/01/1984 - Sat, 01/31/2026