0
Collection("AG_ERA5")
| id: |
'AG_ERA5', |
| title: |
'Agrometeorological indicators from 1979 to present derived from reanalysis', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1979-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'ERA5', |
| platform: |
'ERA5', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'Reanalysis'
, 'ERA5'
, 'CDS'
, 'Atmospheric'
, 'climate'
, 'land'
, 'agriculture'
, 'AgERA5'
, 'surface'
], |
| license: |
'other', |
| description: |
'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. To this end, the variables provided in this dataset match the input needs of most agriculture and
agro-ecological models.
Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1°
spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression
equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational
high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography,
finer land use pattern and finer land-sea delineation of the ECMWF HRES model.
The data was produced on behalf of the Copernicus Climate Change Service.
', |
|
|
1
Collection("CAMS_EAC4")
| id: |
'CAMS_EAC4', |
| title: |
'CAMS global reanalysis (EAC4)', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2003-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmosphere'
, 'Atmospheric'
, 'EWMCF'
, 'EAC4'
], |
| license: |
'other', |
| description: |
'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.
This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued.
Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade.
Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.
The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data.
The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available.
The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.
The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates.
For this reason, EAC4 is only available from 2003 onwards.
Although the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide. This is made possible by the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.
', |
|
|
2
Collection("CAMS_EAC4_MONTHLY")
| id: |
'CAMS_EAC4_MONTHLY', |
| title: |
'CAMS global reanalysis (EAC4) monthly averaged fields', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2003-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmosphere'
, 'Atmospheric'
, 'EWMCF'
, 'EAC4'
], |
| license: |
'other', |
| description: |
'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.
This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued.
Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade.
Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.
The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data.
The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available.
The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.
The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates.
For this reason, EAC4 is only available from 2003 onwards.
Although the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide.
This is made possible by the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.
', |
|
|
3
Collection("CAMS_EU_AIR_QUALITY_FORECAST")
| id: |
'CAMS_EU_AIR_QUALITY_FORECAST', |
| title: |
'CAMS European air quality forecasts', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2022-01-03 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmosphere'
, 'Atmospheric'
, 'Air'
, 'Forecast'
, 'EEA'
], |
| license: |
'other', |
| description: |
'This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts.
The production is based on an ensemble of eleven air quality forecasting systems across Europe.
A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products.
The spread between the eleven models are used to provide an estimate of the forecast uncertainty.
The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used.
In parallel, air quality forecasts are produced once a day for the next four days.
Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
', |
|
|
4
Collection("CAMS_EU_AIR_QUALITY_RE")
| id: |
'CAMS_EU_AIR_QUALITY_RE', |
| title: |
'CAMS European air quality reanalyses', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2013-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmosphere'
, 'Atmospheric'
, 'Air'
, 'EEA'
], |
| license: |
'other', |
| description: |
'This dataset provides annual air quality reanalyses for Europe based on both unvalidated (interim) and validated observations.
CAMS produces annual air quality (interim) reanalyses for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global reanalyses.
The production is currently based on an ensemble of nine air quality data assimilation systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products.
The spread between the nine models can be used to provide an estimate of the analysis uncertainty.
The reanalysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used.
Additional sources of observations can complement the in-situ data assimilation, like satellite data.
An interim reanalysis is provided each year for the year before based on the unvalidated near-real-time observation data stream that has not undergone full quality control by the data providers yet.
Once the fully quality-controlled observations are available from the data provider, typically with an additional delay of about 1 year, a final validated annual reanalysis is provided.
Both reanalyses are available at hourly time steps at height levels.
', |
|
|
5
Collection("CAMS_GAC_FORECAST")
| id: |
'CAMS_GAC_FORECAST', |
| title: |
'CAMS global atmospheric composition forecasts', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2015-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmosphere'
, 'Atmospheric'
, 'Forecast'
, 'GAC'
], |
| license: |
'other', |
| description: |
'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.
The initial conditions of each forecast are obtained by combining a previous forecast with current satellite observations through a process called data assimilation.
This best estimate of the state of the atmosphere at the initial forecast time step, called the analysis, provides a globally complete and consistent dataset allowing for estimates at locations where observation data coverage is low or for atmospheric pollutants for which no direct observations are available.
The forecast itself uses a model of the atmosphere based on the laws of physics and chemistry to determine the evolution of the concentrations of all species over time for the next five days.
Apart from the required initial state, it also uses inventory-based or observation-based emission estimates as a boundary condition at the surface.
The CAMS global forecasting system is upgraded about once a year resulting in technical and scientific changes.
The horizontal or vertical resolution can change, new species can be added, and more generally the accuracy of the forecasts can be improved.
Details of these system changes can be found in the documentation.
Users looking for a more consistent long-term data set should consider using the CAMS Global Reanalysis instead, which is available through the ADS and spans the period from 2003 onwards.
Finally, because some meteorological fields in the forecast do not fall within the general CAMS data licence, they are only available with a delay of 5 days.
', |
|
|
6
Collection("CAMS_GFE_GFAS")
| id: |
'CAMS_GFE_GFAS', |
| title: |
'CAMS global biomass burning emissions based on fire radiative power (GFAS)', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2003-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmosphere'
, 'Atmospheric'
, 'Fire'
, 'FRP'
, 'DM'
, 'MODIS'
, 'NASA'
, 'EOS'
, 'ECMWF'
, 'GFAS'
], |
| license: |
'other', |
| description: |
'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.
The CAMS Global Fire Assimilation System (GFAS) utilises satellite observations of fire radiative power (FRP) to provide near-real-time information on the location, relative intensity and estimated emissions from biomass burning and vegetation fires.
Emissions are estimated by (i) conversion of FRP observations to the dry matter (DM) consumed by the fire, and (ii) application of emission factors to DM for different biomes, based on field and laboratory studies in the scientific literature, to estimate the emissions.
Emissions estimates for 40 pyrogenic species are available from GFAS, including aerosols, reactive gases and greenhouse gases, on a regular grid with a spatial resolution of 0.1 degrees longitude by 0.1 degrees latitude.
This version of GFAS (v1.2) provides daily averaged data based on a combination of FRP observations from two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, one on the NASA EOS-Terra satellite and the other on the NASA EOS-Aqua satellite from 1 January 2003 to present. GFAS also provides daily estimates of smoke plume injection heights derived from FRP observations and meteorological information from the operational weather forecasts from ECMWF.
GFAS data have been used to provide surface boundary conditions for the CAMS global atmospheric composition and European regional air quality forecasts, and the wider atmospheric chemistry modelling community.
', |
|
|
7
Collection("CAMS_GLOBAL_EMISSIONS")
| id: |
'CAMS_GLOBAL_EMISSIONS', |
| title: |
'CAMS global emission inventories', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2000-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmosphere'
, 'Atmospheric'
, 'Emissions'
, 'Pollutants'
, 'GHG'
], |
| license: |
'other', |
| description: |
'This data set contains gridded distributions of global anthropogenic and natural emissions.
Natural and anthropogenic emissions of atmospheric pollutants and greenhouse gases are key drivers of the evolution of the composition of the atmosphere, so an accurate representation of them in forecast models of atmospheric composition is essential.
CAMS compiles inventories of emission data that serve as input to its own forecast models, but which can also be used by other atmospheric chemical transport models.
These inventories are based on a combination of existing data sets and new information, describing anthropogenic emissions from fossil fuel use on land, shipping, and aviation, and natural emissions from vegetation, soil, the ocean and termites.
The anthropogenic emissions on land are further separated in specific activity sectors (e.g., power generation, road traffic, industry).
The CAMS emission data sets provide good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.
Because most inventory-based data sets are only available with a delay of several years, the CAMS emission inventories also extend these existing data sets forward in time by using the trends from the most recent available years, producing timely input data for real-time forecast models.
Most of the data sets are updated once or twice per year adding the most recent year to the data record, while re-processing the original data record for consistency, when needed. This is reflected by the different version numbers.
', |
|
|
8
Collection("CAMS_GREENHOUSE_EGG4")
| id: |
'CAMS_GREENHOUSE_EGG4', |
| title: |
'CAMS global greenhouse gas reanalysis (EGG4)', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2003-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmospheric'
, 'Atmosphere'
, 'CO2'
, 'CH4'
, 'GHG'
, 'ECMWF'
, 'EGG4'
], |
| license: |
'other', |
| description: |
'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.
The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets.
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.
This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued.
Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.
The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data.
The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available.
The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.
The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates.
For this reason, EAC4 is only available from 2003 onwards.
The analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.
', |
|
|
9
Collection("CAMS_GREENHOUSE_EGG4_MONTHLY")
| id: |
'CAMS_GREENHOUSE_EGG4_MONTHLY', |
| title: |
'CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2003-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmospheric'
, 'Atmosphere'
, 'CO2'
, 'CH4'
, 'Greenhouse'
, 'ECMWF'
, 'EGG4'
], |
| license: |
'other', |
| description: |
'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.
The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets.
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.
This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued.
Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.
The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data.
The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available.
The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.
The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates.
For this reason, EAC4 is only available from 2003 onwards.
The analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.
', |
|
|
10
Collection("CAMS_GREENHOUSE_INVERSION")
| id: |
'CAMS_GREENHOUSE_INVERSION', |
| title: |
'CAMS global inversion-optimised greenhouse gas fluxes and concentrations', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1979-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmosphere'
, 'Atmospheric'
, 'IPCC'
, 'CO2'
, 'CH4'
, 'N2O'
], |
| license: |
'other', |
| description: |
'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).
Natural and anthropogenic surface fluxes of greenhouse gases are key drivers of the evolution of Earth's climate, so their monitoring is essential.
Such information has been used in particular as part of the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC).
Ground-based and satellite remote-sensing observations provide a means to quantifying the net fluxes between the land and ocean on the one hand and the atmosphere on the other hand.
This is done through a process called atmospheric inversion, which uses transport models of the atmosphere to link the observed concentrations of CO2, CH4 and N2O to the net fluxes at the Earth's surface.
By correctly modelling the winds, vertical diffusion, and convection in the global atmosphere, the observed concentrations of the greenhouse gases are used to infer the surface fluxes for the last few decades.
For CH4 and N2O, the flux inversions account also for the chemical loss of these greenhouse gases. The net fluxes include contributions from the natural biosphere (e.g., vegetation, wetlands) as well anthropogenic contributions (e.g., fossil fuel emissions, rice fields).
The data sets for the three species are updated once or twice per year adding the most recent year to the data record, while re-processing the original data record for consistency.
This is reflected by the different version numbers. In addition, fluxes for methane are available based on surface air samples only or based on a combination of surface air samples and satellite observations (reflected by an 's' in the version number).
', |
|
|
11
Collection("CAMS_GRF")
| id: |
'CAMS_GRF', |
| title: |
'CAMS global radiative forcings', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2003-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmospheric'
, 'Atmosphere'
, 'RF'
, 'CO2'
, 'CH4'
, 'O3'
, 'Aerosol'
], |
| license: |
'other', |
| description: |
'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 CO2 CH4, O3 (tropospheric and stratospheric), interactions between anthropogenic aerosols and radiation and interactions between anthropogenic aerosols and clouds.
Radiative forcing measures the imbalance in the Earth's energy budget caused by a perturbation of the climate system, such as changes in atmospheric composition caused by human activities.
RF is a useful predictor of globally-averaged temperature change, especially when rapid adjustments of atmospheric temperature and moisture profiles are taken into account.
RF has therefore become a quantitative metric to compare the potential climate response to different perturbations.
Increases in greenhouse gas concentrations over the industrial era exerted a positive RF, causing a gain of energy in the climate system.
In contrast, concurrent changes in atmospheric aerosol concentrations are thought to exert a negative RF leading to a loss of energy.
Products are quantified both in "all-sky" conditions, meaning that the radiative effects of clouds are included in the radiative transfer calculations, and in "clear-sky" conditions, which are computed by excluding clouds in the radiative transfer calculations.
The upgrade from version 1.5 to 2 consists of an extension of the period by 2017-2018, the addition of an "effective radiative forcing" product and new ways to calculate the pre-industrial reference state for aerosols and cloud condensation nuclei.
More details are given in the documentation section.
New versions may be released in future as scientific methods develop, and existing versions may be extended with later years if data for the period is available from the CAMS reanalysis.
Newer versions supercede old versions so it is always recommended to use the latest one.
CAMS also produces distributions of aerosol optical depths, distinguishing natural from anthropogenic aerosols, which are a separate dataset. See "Related Data".
', |
|
|
12
Collection("CAMS_GRF_AUX")
| id: |
'CAMS_GRF_AUX', |
| title: |
'CAMS global radiative forcing - auxilliary variables', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2003-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Atmospheric'
, 'Atmosphere'
, 'RF'
, 'CO2'
, 'CH4'
, 'O3'
, 'Aerosol'
], |
| license: |
'other', |
| description: |
'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.
The data are a necessary complement to the "CAMS global radiative forcings" dataset (see "Related Data").
The calculation of aerosol radiative forcing requires a discrimination between aerosol of anthropogenic and natural origin.
However, the CAMS reanalysis, which is used to provide the aerosol concentrations, does not make this distinction.
The anthropogenic fraction was therefore derived by a method which uses aerosol size as a proxy for aerosol origin.
', |
|
|
13
Collection("CAMS_SOLAR_RADIATION")
| id: |
'CAMS_SOLAR_RADIATION', |
| title: |
'CAMS solar radiation time-series', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2004-01-02 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CAMS', |
| platform: |
'CAMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Copernicus'
, 'ADS'
, 'CAMS'
, 'Solar'
, 'Radiation'
], |
| license: |
'other', |
| description: |
'The CAMS solar radiation services provide historical values (2004 to present) of global (GHI), direct (BHI) and diffuse (DHI) solar irradiation, as well as direct normal irradiation (BNI).
The aim is to fulfil the needs of European and national policy development and the requirements of both commercial and public downstream services, e.g. for planning, monitoring, efficiency improvements and the integration of solar energy systems into energy supply grids.
For clear-sky conditions, an irradiation time series is provided for any location in the world using information on aerosol, ozone and water vapour from the CAMS global forecasting system.
Other properties, such as ground albedo and ground elevation, are also taken into account.
Similar time series are available for cloudy (or "all sky") conditions but, since the high-resolution cloud information is directly inferred from satellite observations, these are currently only available inside the field-of-view of the Meteosat Second Generation (MSG) satellite, which is roughly Europe, Africa, the Atlantic Ocean and the Middle East.
Data is offered in both ASCII and netCDF format.
Additionally, an ASCII "expert mode" format can be selected which contains in addition to the irradiation, all the input data used in their calculation (aerosol optical properties, water vapour concentration, etc).
This additional information is only meaningful in the time frame at which the calculation is performed and so is only available at 1-minute time steps in universal time (UT).
', |
|
|
14
Collection("ERA5_LAND")
| id: |
'ERA5_LAND', |
| title: |
'ERA5-Land hourly data from 1950 to present', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1950-01-02 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'ERA5', |
| platform: |
'ERA5', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'Reanalysis'
, 'ERA5'
, 'CDS'
, 'Atmospheric'
, 'land'
, 'hourly'
, 'evolution'
], |
| license: |
'other', |
| description: |
'ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several
decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of
the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a
globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several
decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to
control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is
called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can
rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land,
they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input
air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference
between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate
correction'. The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of
uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes
governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go
back in time, because the number of observations available to create a good quality atmospheric forcing is lower.
ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields.
The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface
applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period
covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers,
businesses and individuals to access and use more accurate information on land states. Variables in the
dataset/application are: 10m u-component of wind, 10m v-component of wind, 2m dewpoint temperature, 2m temperature,
Evaporation from bare soil, Evaporation from open water surfaces excluding oceans, Evaporation from the top of
canopy, Evaporation from vegetation transpiration, Forecast albedo, Lake bottom temperature, Lake ice depth, Lake
ice temperature, Lake mix-layer depth, Lake mix-layer temperature, Lake shape factor, Lake total layer temperature,
Leaf area index, high vegetation, Leaf area index, low vegetation, Potential evaporation, Runoff, Skin reservoir
content, Skin temperature, Snow albedo, Snow cover, Snow density, Snow depth, Snow depth water equivalent, Snow
evaporation, Snowfall, Snowmelt, Soil temperature level 1, Soil temperature level 2, Soil temperature level 3,
Soil temperature level 4, Sub-surface runoff, Surface latent heat flux, Surface net solar radiation, Surface net
thermal radiation, Surface pressure, Surface runoff, Surface sensible heat flux, Surface solar radiation downwards,
Surface thermal radiation downwards, Temperature of snow layer, Total evaporation, Total precipitation, Volumetric
soil water layer 1, Volumetric soil water layer 2, Volumetric soil water layer 3, Volumetric soil water layer 4
', |
|
|
15
Collection("ERA5_LAND_MONTHLY")
| id: |
'ERA5_LAND_MONTHLY', |
| title: |
'ERA5-Land monthly averaged data from 1950 to present', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1950-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'ERA5', |
| platform: |
'ERA5', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'Reanalysis'
, 'ERA5'
, 'CDS'
, 'Atmospheric'
, 'land'
, 'monthly'
, 'evolution'
], |
| license: |
'other', |
| description: |
'ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several
decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of
the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a
globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several
decades back in time, providing an accurate description of the climate of the past. ERA5-Land provides a consistent
view of the water and energy cycles at surface level during several decades. It contains a detailed record from 1950
onwards, with a temporal resolution of 1 hour. The native spatial resolution of the ERA5-Land reanalysis dataset is
9km on a reduced Gaussian grid (TCo1279). The data in the CDS has been regridded to a regular lat-lon grid of
0.1x0.1 degrees. The data presented here is a post-processed subset of the full ERA5-Land dataset. Monthly-mean
averages have been pre-calculated to facilitate many applications requiring easy and fast access to the data, when
sub-monthly fields are not required. Hourly fields can be found in the ERA5-Land hourly fields CDS page.
Documentation can be found in the online ERA5-Land documentation. Variables in the dataset/application are: |
10m u-component of wind, 10m v-component of wind, 2m dewpoint temperature, 2m temperature, Evaporation from bare
soil, Evaporation from open water surfaces excluding oceans, Evaporation from the top of canopy, Evaporation from
vegetation transpiration, Forecast albedo, Lake bottom temperature, Lake ice depth, Lake ice temperature, Lake
mix-layer depth, Lake mix-layer temperature, Lake shape factor, Lake total layer temperature, Leaf area index, high
vegetation, Leaf area index, low vegetation, Potential evaporation, Runoff, Skin reservoir content, Skin temperature,
Snow albedo, Snow cover, Snow density, Snow depth, Snow depth water equivalent, Snow evaporation, Snowfall, Snowmelt,
Soil temperature level 1, Soil temperature level 2, Soil temperature level 3, Soil temperature level 4, Sub-surface
runoff, Surface latent heat flux, Surface net solar radiation, Surface net thermal radiation, Surface pressure,
Surface runoff, Surface sensible heat flux, Surface solar radiation downwards, Surface thermal radiation downwards,
Temperature of snow layer, Total evaporation, Total precipitation, Volumetric soil water layer 1, Volumetric soil
water layer 2, Volumetric soil water layer 3, Volumetric soil water layer 4
', |
|
|
16
Collection("ERA5_PL")
| id: |
'ERA5_PL', |
| title: |
'ERA5 hourly data on pressure levels from 1940 to present', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1940-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'ERA5', |
| platform: |
'ERA5', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'Reanalysis'
, 'ERA5'
, 'CDS'
, 'Atmospheric'
, 'land'
, 'sea'
, 'hourly'
, 'pressure'
, 'levels'
], |
| license: |
'other', |
| description: |
'ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades.
Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back
extension) and from 1979 onwards (final release plus timely updates, this page). ERA5 replaces the ERA-Interim
reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and
consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used
by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is
combined with newly available observations in an optimal way to produce a new best estimate of the state of the
atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way,
but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does
not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going
further back in time, to allow for the ingestion of improved versions of the original observations, which all
benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric,
ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at
three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates
are closely related to the information content of the available observing system which has evolved considerably over
time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean
averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread.
ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release
(called ERA5T), this data could be different from the final release 2 to 3 months later. So far this has not been
the case and when this does occur users will be notified. The data set presented here is a regridded subset of the
full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access.
It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in
this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been
regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate
(0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on
pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The
present entry is "ERA5 hourly data on pressure levels from 1979 to present". Variables in the dataset/application
are: Divergence, Fraction of cloud cover, Geopotential, Ozone mass mixing ratio, Potential vorticity, Relative
humidity, Specific cloud ice water content, Specific cloud liquid water content, Specific humidity, Specific rain
water content, Specific snow water content, Temperature, U-component of wind, V-component of wind, Vertical
velocity, Vorticity (relative)
', |
|
|
17
Collection("ERA5_PL_MONTHLY")
| id: |
'ERA5_PL_MONTHLY', |
| title: |
'ERA5 monthly averaged data on pressure levels from 1940 to present', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1940-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'ERA5', |
| platform: |
'ERA5', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Climate'
, 'ECMWF'
, 'Reanalysis'
, 'ERA5'
, 'CDS'
, 'Atmospheric'
, 'land'
, 'sea'
, 'monthly'
, 'pressure'
, 'levels'
], |
| license: |
'other', |
| description: |
'ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model
data with observations from across the world into a globally complete and consistent dataset using the
laws of physics. This principle, called data assimilation, is based on the method used by numerical
weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined
with newly available observations in an optimal way to produce a new best estimate of the state of the
atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the
same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades.
Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect
observations, and when going further back in time, to allow for the ingestion of improved versions of
the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly
estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty
estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and
spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the
information content of the available observing system which has evolved considerably over time. They
also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean
averages have been pre-calculated too, though monthly means are not available for the ensemble mean and
spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th
of each month). In case that serious flaws are detected in this early release (called ERA5T), this data
could be different from the final release 2 to 3 months later. So far this has only been the case for
the month September 2021, while it will also be the case for October, November and December 2021. For
months prior to September 2021 the final release has always been equal to ERA5T, and the goal is to
align the two again after December 2021. ERA5 is updated daily with a latency of about 5 days (monthly
means are available around the 6th of each month). In case that serious flaws are detected in this early
release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case
that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5
data set on native resolution. It is online on spinning disk, which should ensure fast and easy access.
It should satisfy the requirements for most common applications. Data has been regridded to a regular
lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1
degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on
pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).
', |
|
|
18
Collection("ERA5_SL")
| id: |
'ERA5_SL', |
| title: |
'ERA5 hourly data on single levels from 1940 to present', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1940-01-01 09:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'ERA5', |
| platform: |
'ERA5', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'Reanalysis'
, 'ERA5'
, 'CDS'
, 'Atmospheric'
, 'land'
, 'sea'
, 'hourly'
, 'single'
, 'levels'
], |
| license: |
'other', |
| description: |
'ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model
data with observations from across the world into a globally complete and consistent dataset using the
laws of physics. This principle, called data assimilation, is based on the method used by numerical
weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined
with newly available observations in an optimal way to produce a new best estimate of the state of the
atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the
same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades.
Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect
observations, and when going further back in time, to allow for the ingestion of improved versions of
the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly
estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty
estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and
spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the
information content of the available observing system which has evolved considerably over time. They
also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean
averages have been pre-calculated too, though monthly means are not available for the ensemble mean
and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are
detected in this early release (called ERA5T), this data could be different from the final release
2 to 3 months later. In case that this occurs users are notified. The data set presented here is a
regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which
should ensure fast and easy access. It should satisfy the requirements for most common applications.
Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees
for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub
sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels
(atmospheric,ocean-wave and land surface quantities).
', |
|
|
19
Collection("ERA5_SL_MONTHLY")
| id: |
'ERA5_SL_MONTHLY', |
| title: |
'ERA5 monthly averaged data on single levels from 1940 to present', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1940-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'ERA5', |
| platform: |
'ERA5', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Climate'
, 'ECMWF'
, 'Reanalysis'
, 'ERA5'
, 'CDS'
, 'Atmospheric'
, 'land'
, 'sea'
, 'monthly'
, 'single'
, 'levels'
], |
| license: |
'other', |
| description: |
'ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.
Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model
data with observations from across the world into a globally complete and consistent dataset using the
laws of physics. This principle, called data assimilation, is based on the method used by numerical
weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined
with newly available observations in an optimal way to produce a new best estimate of the state of the
atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the
same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades.
Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect
observations, and when going further back in time, to allow for the ingestion of improved versions of the
original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly
estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty
estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and
spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the
information content of the available observing system which has evolved considerably over time. They
also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean
averages have been pre-calculated too, though monthly means are not available for the ensemble mean
and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around
the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T),
this data could be different from the final release 2 to 3 months later. In case that this occurs users
are notified. The data set presented here is a regridded subset of the full ERA5 data set on native
resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy
the requirements for most common applications. Data has been regridded to a regular lat-lon grid of 0.25
degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for
ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper
air fields) and single levels (atmospheric, ocean-wave and land surface quantities).
', |
|
|
20
Collection("FIRE_HISTORICAL")
| id: |
'FIRE_HISTORICAL', |
| title: |
'Fire danger indices historical data from the Copernicus Emergency Management Service', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1940-03-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'CEMS', |
| platform: |
'CEMS', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'EFFIS'
, 'fire'
, 'historical'
, 'ERA5'
, 'european'
, 'sustainability'
, 'CEMS'
, 'system'
], |
| license: |
'other', |
| description: |
'This data set provides complete historical reconstruction of meteorological conditions favourable to the start,
spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the
Copernicus Emergency Management Service for the European Forest Fire Information System (EFFIS). The European
Forest Fire Information System incorporates the fire danger indices for three different models developed in Canada,
United States and Australia. In this dataset the fire danger indices are calculated using weather forecast from
historical simulations provided by ECMWF ERA5 reanalysis. ERA5 by combining model data and a vast set of quality
controlled observations provides a globally complete and consistent data-set and is regarded as a good proxy for
observed atmospheric conditions. The selected data records in this data set are regularly extended with time as
ERA5 forcing data become available. This dataset is produced by ECMWF in its role of the computational centre for
fire danger forecast of the CEMS, on behalf of the Joint Research Centre which is the managing entity of the service.
Variables in the dataset/application are: Build-up index, Burning index, Danger rating, Drought code, Duff moisture
code, Energy release component, Fine fuel moisture code, Fire daily severity index, Fire danger index, Fire weather
index, Ignition component, Initial spread index, Keetch-Byram drought index, Spread component
', |
|
|
21
Collection("GLACIERS_DIST_RANDOLPH")
| id: |
'GLACIERS_DIST_RANDOLPH', |
| title: |
'Glaciers distribution data from the Randolph Glacier Inventory for year 2000', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2000-01-01 00:00:00+00:00
, 2000-12-31 23:59:00+00:00
]
]
}
}, |
| instruments: |
[], |
| platform: |
'INSITU', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'WGMS'
, 'INSITU'
, 'CDS'
, 'C3S'
, 'glacier'
, 'randolph'
, 'distribution'
, 'inventory'
], |
| license: |
'other', |
| description: |
'A glacier is defined as a perennial mass of ice, and possibly firn and snow, originating on the land surface
from the recrystallization of snow or other forms of solid precipitation and showing evidence of past or
present flow. There are several types of glaciers such as glacierets, mountain glaciers, valley glaciers and
ice fields, as well as ice caps. Some glacier tongues reach into lakes or the sea, and can develop floating
ice tongues or ice shelves. Glacier changes are recognized as independent and high-confidence natural
indicators of climate change. Past, current and future glacier changes affect global sea level, the regional
water cycle and local hazards.\nThis dataset is a snapshot of global glacier outlines compiled from\nmaps,
aerial photographs and satellite images mostly acquired in the period 2000-2010.
', |
|
|
22
Collection("GRIDDED_GLACIERS_MASS_CHANGE")
| id: |
'GRIDDED_GLACIERS_MASS_CHANGE', |
| title: |
'Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1975-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'WGMS'
, 'INSITU'
, 'CDS'
, 'C3S'
, 'glacier'
, 'randolph'
, 'mass'
, 'gridded'
], |
| license: |
'other', |
| description: |
'The dataset provides annual glacier mass changes distributed on a global regular grid at 0.5° resolution (latitude,
longitude). Glaciers play a fundamental role in the Earth's water cycles. They are one of the most important
freshwater resources for societies and ecosystems and the recent increase in ice melt contributes directly to the
rise of ocean levels. Due to this they have been declared as an Essential Climate Variable (ECV) by GCOS, the Global
Climate Observing System. Within the Copernicus Services, the global gridded annual glacier mass change dataset
provides information on changing glacier resources by combining glacier change observations from the Fluctuations
of Glaciers (FoG) database that is brokered from World Glacier Monitoring Service (WGMS). Previous glacier products
were provided to the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) as a homogenized
state-of-the-art glacier dataset with separated elevation and mass change time series collected by scientists and
the national correspondents of each country as provided to the WGMS (see Related data). The new approach combines
glacier mass balances from in-situ observations with glacier elevation changes from remote sensing to generate a new
gridded product of annual glacier mass changes and related uncertainties for every hydrological year since 1975/76
provided in a 0.5°x0.5° global regular grid. The dataset bridges the gap on spatio-temporal coverage of glacier
change observations, providing for the first time in the CDS an annually resolved glacier mass change product using
the glacier elevation change sample as calibration. This goal has become feasible at the global scale thanks to a
new globally near-complete (96 percent of the world glaciers) dataset of glacier elevation change observations recently
ingested by the FoG database. To develop the distributed glacier change product the glacier outlines were used from
the Randolph Glacier Inventory 6.0 (see Related data). A glacier is considered to belong to a grid-point when its
geometric centroid lies within the grid point. The centroid is obtained from the glacier outlines from the Randolph
Glacier Inventory 6.0. The glacier mass changes in the unit Gigatonnes (1 Gt = 1x10^9 tonnes) correspond to the
total mass of water lost/gained over the glacier surface during a given year. Note that to propagate to mm/cm/m of
water column on the grid cell, the grid cell area needs to be considered. Also note that the data is provided for
hydrological years, which vary between the Northern Hemisphere (01 October to 30 September next year) and the
Southern Hemisphere (01 April to 31 March next year). This dataset has been produced by researchers at the WGMS on
behalf of Copernicus Climate Change Service. Variables in the dataset/application are: Glacier mass change Variables
in the dataset/application are: Uncertainty
', |
|
|
23
Collection("SATELLITE_CARBON_DIOXIDE")
| id: |
'SATELLITE_CARBON_DIOXIDE', |
| title: |
'Carbon dioxide data from 2002 to present derived from satellite observations', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2002-10-01 00:00:00+00:00
, 2022-12-31 23:59:59+00:00
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'carbon-dioxide'
], |
| license: |
'other', |
| description: |
'This dataset provides observations of atmospheric carbon dioxide (CO2)\namounts obtained from observations
collected by several current and historical \nsatellite instruments. Carbon dioxide is a naturally occurring
Greenhouse Gas (GHG), but one whose abundance has been increased substantially above its pre-industrial value
of some 280 ppm by human activities, primarily because of emissions from combustion of fossil fuels,
deforestation and other land-use change. The annual cycle (especially in the northern hemisphere) is primarily
due to seasonal uptake and release of atmospheric CO2 by terrestrial vegetation.\nAtmospheric carbon dioxide
abundance is indirectly observed by various satellite instruments. These instruments measure spectrally
resolved near-infrared and/or infrared radiation reflected or emitted by the Earth and its atmosphere. In the
measured signal, molecular absorption signatures from carbon dioxide and other constituent gasses can be
identified. It is through analysis of those absorption lines in these radiance observations that the averaged
carbon dioxide abundance in the sampled atmospheric column can be determined.\nThe software used to analyse
the absorption lines and determine the carbon dioxide concentration in the sampled atmospheric column is
referred to as the retrieval algorithm. For this dataset, carbon dioxide abundances have been determined by
applying several algorithms to different satellite \ninstruments. Typically, different algorithms have
different strengths and weaknesses and therefore, which product to use for a given application typically
depends on the application.\nThe data set consists of 2 types of products: (i) column-averaged mixing ratios
of CO2, denoted XCO2 and (ii) mid-tropospheric CO2 columns. The XCO2 products have been retrieved from
SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT and OCO-2. The mid-tropospheric CO2 product has been retrieved from the
IASI instruments on-board the Metop satellite series and from AIRS. \nThe XCO2 products are available as Level
2 (L2) products (satellite orbit tracks) and as Level 3 (L3) product (gridded). The L2 products are available
as individual sensor products (SCIAMACHY: BESD and WFMD algorithms; GOSAT: OCFP and SRFP algorithms) and as a
multi-sensor merged product (EMMA algorithm). The L3 XCO2 product is provided in OBS4MIPS format. \nThe IASI
and AIRS products are available as L2 products generated with the NLIS algorithm.\nThis data set is updated on
a yearly basis, with each update cycle adding (if required) a new data version for the entire period, up to
one year behind real time.\nThis dataset is produced on behalf of C3S with the exception of the SCIAMACHY and
AIRS L2 products that were generated in the framework of the GHG-CCI project of the European Space Agency (ESA)
Climate Change Initiative (CCI).\n\nVariables in the dataset/application are:\nColumn-average dry-air mole
fraction of atmospheric carbon dioxide (XCO2), Mid-tropospheric columns of atmospheric carbon dioxide (CO2)
', |
|
|
24
Collection("SATELLITE_FIRE_BURNED_AREA")
| id: |
'SATELLITE_FIRE_BURNED_AREA', |
| title: |
'Fire burned area from 2001 to present derived from satellite observations', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2001-01-01 00:00:00+00:00
, 2022-04-01 23:59:59+00:00
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'burned'
], |
| license: |
'other', |
| description: |
'The Burned Area products provide global information of total burned area (BA) at pixel and grid scale. The BA is
identified with the date of first detection of the burned signal in the case of the pixel product, and with the
total BA per grid cell in the case of the grid product. The products were obtained through the analysis of
reflectance changes from medium resolution sensors (Terra MODIS, Sentinel-3 OLCI), supported by the use of MODIS
thermal information. The burned area products also include information related to the land cover that has been
burned, which has been extracted from the Copernicus Climate Change Service (C3S) land cover dataset, thus
assuring consistency between the datasets.
The algorithms for BA retrieval were developed by the University of Alcala (Spain), and processed by Brockmann
Consult GmbH (Germany). Different product versions are available. FireCCI v5.0cds and FireCCI v5.1cds were
developed as part of the Fire ECV Climate Change Initiative Project (Fire CCI) and brokered to C3S, offering the
first global burned area time series at 250m spatial resolution. FireCCI v5.1cds used a more mature algorithm than
the previous version. This algorithm was adapted to Sentinel-3 OLCI data to create the C3S v1.0 burned area
product, extending the BA database to the present.
During July 2020, an error in some files in the version v5.1cds were identified, affecting the files of the grid
product of January 2018, and the pixel and grid products of October, November and December 2019. These errors were
fixed, and a new version, v5.1.1cds, was created for the whole time series, to replace version v5.1cds. The latter
product has been deprecated, but it is temporally kept in the database for transparency and traceability reasons.
Only version v5.1.1cds should be used.
The BA products are useful for researchers studying climate change, as they provide crucial information on burned
biomass, which can be translated to greenhouse gases emissions amongst other contaminants. Burned area is also
useful for land cover change studies, fire management and risk analysis.
', |
|
|
25
Collection("SATELLITE_METHANE")
| id: |
'SATELLITE_METHANE', |
| title: |
'Methane data from 2003 to present derived from satellite observations', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2002-10-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'methane'
], |
| license: |
'other', |
| description: |
'This dataset provides observations of atmospheric methane (CH4)\namounts obtained from observations collected
by several current and historical \nsatellite instruments. Methane is a naturally occurring Greenhouse Gas
(GHG), but one whose abundance has been increased substantially above its pre-industrial value of some 720 ppb
by human activities, primarily because of agricultural emissions (e.g., rice production, ruminants) and fossil
fuel production and use. A clear annual cycle is largely due to seasonal wetland emissions.\nAtmospheric
methane abundance is indirectly observed by various satellite instruments. These instruments measure spectrally
resolved near-infrared and infrared radiation reflected or emitted by the Earth and its atmosphere. In the
measured signal, molecular absorption signatures from methane and constituent gasses can be identified. It is
through analysis of those absorption lines in these radiance observations that the averaged methane abundance
in the sampled atmospheric column can be determined.\nThe software used to analyse the absorption lines and
determine the methane concentration in the sampled atmospheric column is referred to as the retrieval algorithm.
For this dataset, methane abundances have been determined by applying several algorithms to different satellite
instruments.\nThe data set consists of 2 types of products: (i) column-averaged mixing ratios of CH4, denoted
XCH4 and (ii) mid-tropospheric CH4 columns. \nThe XCH4 products have been retrieved from SCIAMACHY/ENVISAT and
TANSO-FTS/GOSAT. The mid-tropospheric CH4 product has been retrieved from the IASI instruments onboard the
Metop satellite series. The XCH4 products are available as Level 2 (L2) products (satellite orbit tracks) and
as Level 3 (L3) product (gridded). The L2 products are available as individual sensor products (SCIAMACHY: WFMD
and IMAP algorithms; GOSAT: OCFP, OCPR, SRFP and SRPR algorithms) and as a multi-sensor merged product (EMMA
algorithm). The L3 XCH4 product is provided in OBS4MIPS format. The IASI products are available as L2 products
generated with the NLIS algorithm.\nThis data set is updated on a yearly basis, with each update cycle adding
(if required) a new data version for the entire period, up to one year behind real time.\nThis dataset is
produced on behalf of C3S with the exception of the SCIAMACHY L2 products that were generated in the framework
of the GHG-CCI project of the European Space Agency (ESA) Climate Change Initiative (CCI).\n\nVariables in the
dataset/application are:\nColumn-average dry-air mole fraction of atmospheric methane (XCH4), Mid-tropospheric
columns of atmospheric methane (CH4)
', |
|
|
26
Collection("SATELLITE_SEA_ICE_CONCENTRATION")
| id: |
'SATELLITE_SEA_ICE_CONCENTRATION', |
| title: |
'Sea ice concentration daily gridded data from 1978 to present derived from satellite observations', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1978-10-25 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'sea'
, 'ice'
], |
| license: |
'other', |
| description: |
'This dataset provides daily gridded data of sea ice concentration for both hemispheres derived from satellite passive microwave
brightness temperatures. Sea ice is an important component of our climate system and a sensitive indicator of climate change.
Its presence or its retreat has a strong impact on air-sea interactions, the Earth's energy budget as well as marine ecosystems.
It is listed as an Essential Climate Variable by the Global Climate Observing System. Sea ice concentration is defined as the
fraction of the ocean surface in a pixel or grid cell that is covered with sea ice. It is one of the parameters commonly used to
characterise the sea-ice cover. Other sea ice parameters include sea ice thickness, sea ice edge, and sea ice type, also
available in the Climate Data Store.
The dataset consists of two products produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) with research & development from European Space Agency Climate Change Initiative projects (ESA CCI):
The Global Sea Ice Concentration Climate Data Record based on measurements from the following sensors: Scanning Multichannel Microwave Radiometer (SMMR; 1978-1987), Special Sensor Microwave/Imager (SSM/I; 1987-2006), and Special Sensor Microwave Imager/Sounder (SSMIS; 2005 onward). This product spans the period from October 1978 to present and is updated daily by an Interim Climate Data Record. In the following, it is referred to as the SSMIS product.
The Global Sea Ice Concentration Climate Data Record based on measurements from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor (2002-2011) and its successor, AMSR2 (2012-2020). This product spans the 2002-2020 period and is not updated. In the following, it is referred to as the AMSR product. Note, that this product was first produced by the European Space Agency Climate Change Initiative Phase 2 project (ESA CCI) and has been transferred to EUMETSAT OSI SAF since version 3.0.
Both products are provided on the same polar projection with a grid resolution of 25 km. However, the AMSR product has a true spatial resolution (as resolved by the sensor) of about 15-25 km versus 30-60 km for the SSMIS product. Therefore, the AMSR product provides a much more detailed view of the sea ice cover than the SSMIS product, especially in the marginal ice zone, the transitional zone between open water and the dense sea ice pack. On the other hand, the clear strength of the SSMIS product is its more than 40-year long and consistent record with daily updates.
The two products share the same algorithm baseline, which is both a continuation of the EUMETSAT OSI SAF approach and a series of innovations contributed by ESA CCI activities. For both products, the underlying algorithm makes use of a combination of the same three temperature channels near 19 GHz and 37 GHz. The data also share a common data format, that allows expert users to revert some of the filtering steps and access the raw output of the SIC algorithms. Both are level-4 products in the sense that gaps are filled by temporal and spatial interpolation. However, gap filling is not applied to fill in days when no input satellite data are available.
Further details about each product can be found below as well as in the Documentation section.
', |
|
|
27
Collection("SATELLITE_SEA_ICE_EDGE_TYPE")
| id: |
'SATELLITE_SEA_ICE_EDGE_TYPE', |
| title: |
'Sea ice edge and type daily gridded data from 1978 to present derived from satellite observations', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1978-10-25 00:00:00+00:00
, 2023-05-02 23:59:59+00:00
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'sea'
, 'ice'
], |
| license: |
'other', |
| description: |
'This dataset provides daily gridded data of sea ice edge and sea ice type derived from brightness temperatures
measured by satellite passive microwave radiometers. Sea ice is an important component of our climate system and
a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions,
the Earth's energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as
an Essential Climate Variable. Sea ice edge and type are some of the parameters used to characterise sea ice. Other
parameters include sea ice concentration and sea ice thickness, also available in the Climate Data Store. Sea ice
edge and type are defined as follows: Sea ice edge classifies the sea surface into open water, open ice, and closed
ice depending on the amount of sea ice present in each grid cell. This variable is provided for both the Northern
and Southern Hemispheres. Note that a sea ice concentration threshold of 30% is used to distinguish between open
water and open ice, which differs from the 15% threshold commonly used for other sea ice products such as sea ice
extent. Sea ice type classifies ice-covered areas into two categories based on the age of the sea ice: multiyear
ice versus seasonal first-year ice. This variable is currently only available for the Northern Hemisphere and
limited to the extended boreal winter months (mid-October through April). Sea ice type classification during summer
is difficult due to the effect of melting at the ice surface which disturbs the passive microwave signature. Both
sea ice products are based on measurements from the series of Scanning Multichannel Microwave Radiometer (SMMR),
Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) sensors and share the
same algorithm baseline. However, sea ice edge makes use of two lower frequencies near 19 GHz and 37 GHz and a
higher frequency near 90 GHz whereas sea ice type only uses the two lower frequencies. This dataset combines
Climate Data Records (CDRs), which are intended to have sufficient length, consistency, and continuity to assess
climate variability and change, and Interim Climate Data Records (ICDRs), which provide regular temporal extensions
to the CDRs and where consistency with the CDRs is expected but not extensively checked. For this dataset, both the
CDR and ICDR parts of each product were generated using the same software and algorithms. The CDRs of sea ice edge
and type currently extend from 25 October 1978 to 31 December 2020 whereas the corresponding ICDRs extend from
January 2021 to present (with a 16-day latency behind real time). All data from the current release of the datasets
(version 2.0) are Level-4 products, in which data gaps are filled by temporal and spatial interpolation. For product
limitations and known issues, please consult the Product User Guide. This dataset is produced on behalf of
Copernicus Climate Change Service (C3S), with heritage from the operational products generated by EUMETSAT Ocean and
Sea Ice Satellite Application Facility (OSI SAF). Variables in the dataset/application are: Sea ice edge, Sea ice
type Variables in the dataset/application are: Status flag, Uncertainty
constellation:
', |
|
|
28
Collection("SATELLITE_SEA_ICE_THICKNESS")
| id: |
'SATELLITE_SEA_ICE_THICKNESS', |
| title: |
'Sea ice thickness monthly gridded data for the Arctic from 2002 to present derived from satellite observations', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2002-10-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'sea'
, 'ice'
], |
| license: |
'other', |
| description: |
'This dataset provides monthly gridded data of sea ice thickness for the Arctic region based on satellite radar altimetry
observations. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its
presence or its retreat has a strong impact on air-sea interactions, the Earth's energy budget as well as marine ecosystems.
It is recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice thickness is one of the
parameters commonly used to characterise sea ice, alongside sea ice concentration, sea ice edge, and sea ice type, also
available in the Climate Data Store.
Satellite radar altimeters provide measurements of the sea ice freeboard, which is the difference between the height of the
surface of sea ice and the surface of water in open leads (areas of open water within the sea ice). Because of the buoyancy
of ice in water, typically about 90% of the ice thickness remains under water and thus the total ice thickness is about 10
times the freeboard. However, snow on top of sea ice changes this ratio and complicates the estimation of the ice thickness,
requiring the use of auxiliary information about snow depth and density. The retrieval of ice thickness uses the narrow
radar swath at the nadir of the satellite at full resolution of approximately 1-10 km and a point spacing of 300 meters.
This Level-2 sea-ice thickness products (not provided here) is then gridded for a period of a month to obtain full coverage
of a north polar grid at a resolution of 25 km. The algorithm used was developed as part of the European Space Agency
Climate Change Initiative (ESA CCI) on Sea Ice.
The data provided here are Level-3 Collated (L3C) products: they contain monthly gridded values from orbit data from a single
platform (Envisat or CryoSat-2) without interpolation or any other form of gap filling. The files also contain estimates of
the algorithm uncertainty as well as a quality status flag indicating potential issues with the retrieval not captured in the
algorithm uncertainty. Sources of uncertainty in the algorithm are related to the auxiliary data and to the use of different
radar altimeter concepts in Envisat (pulse-limited) and CryoSat-2 (synthetic aperture radar).
This dataset combines a Climate Data Record (CDR), which has sufficient length, consistency, and continuity to be used to
assess climate variability and change, and an Interim Climate Data Record (ICDR), which provides regular temporal extensions
to the CDR and where consistency with the CDR is expected but not extensively checked. Here, the CDR is based on measurements
from the RA-2 altimeter on Envisat (October 2002 to October 2010) and the SIRAL altimeter on CryoSat-2 (November 2010 to April
2020). The ICDR is based on observations from CryoSat-2 only (from April 2015 onward) and is updated monthly with a one-month
delay behind real time. Users should note that the quality and accuracy of the data record are higher during the CryoSat-2
period than during the Envisat period. As a result, care should be taken when combining the two missions to assess long-term
changes and trends. More information can be found in the Product User Guide and Product Quality Assessment Report.
This dataset is currently limited spatially to the Arctic region and temporally to the winter months of October through April
due to unresolved bias originating from melting snow or open melt ponds in the remaining five months. For a similar reason,
no sea-ice thickness data with sufficient quality exist for the Southern Hemisphere. The extension of the CDR/ICDR to other
periods, regions, and radar altimeter missions is under development in the extension of the ESA CCI Sea Ice project (ESA CCI+).
This dataset is produced on behalf of the Copernicus Climate Change Service (C3S).
', |
|
|
29
Collection("SATELLITE_SEA_LEVEL_GLOBAL")
| id: |
'SATELLITE_SEA_LEVEL_GLOBAL', |
| title: |
'Sea level gridded data from satellite observations for the global ocean', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1993-01-01 00:00:00+00:00
, 2022-08-04 23:59:59+00:00
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'HYDROLOGICAL', |
| keywords: |
['Climate'
, 'ECMWF'
, 'CDS'
, 'C3S'
, 'sea'
, 'level'
, 'global'
], |
| license: |
'other', |
| description: |
'This data set provides gridded daily global estimates of sea level anomaly based on satellite altimetry
measurements. The rise in global mean sea level in recent decades has been one of the most important and well-known
consequences of climate warming, putting a large fraction of the world population and economic infrastructure at
greater risk of flooding. However, changes in the global average sea level mask regional variations that can be one
order of magnitude larger. Therefore, it is essential to measure changes in sea level over the world's oceans as
accurately as possible. Sea level anomaly is defined as the height of water over the mean sea surface in a given
time and region. In this dataset sea level anomalies are computed with respect to a twenty-year mean reference
period (1993-2012) using up-to-date altimeter standards. In the past, the altimeter sea level datasets were
distributed on the CNES AVISO altimetry portal until their production was taken over by the Copernicus Marine
Environment Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S) in 2015 and 2016
respectively. The sea level data set provided here by C3S is climate-oriented, that is, dedicated to the monitoring
of the long-term evolution of sea level and the analysis of the ocean/climate indicators, both requiring a
homogeneous and stable sea level record. To achieve this, a steady two-satellite merged constellation is used at all
time steps in the production system: one satellite serves as reference and ensures the long-term stability of the
data record; the other satellite (which varies across the record) is used to improve accuracy, sample mesoscale
processes and provide coverage at high latitudes. The C3S sea level data set is used to produce Ocean Monitoring
Indicators (e.g. global and regional mean sea level evolution), available in the CMEMS catalogue. The CMEMS sea
level dataset has a more operational focus as it is dedicated to the retrieval of mesoscale signals in the context
of ocean modeling and analysis of the ocean circulation on a global or regional scale. Such applications require the
most accurate sea level estimates at each time step with the best spatial sampling of the ocean with all satellites
available, with less emphasis on long-term stability and homogeneity. This data set is updated three times a year
with a delay of about 6 months relative to present time. This delay is mainly due to the timeliness of the input
data, the centred processing temporal window and the validation process. However, these processing and validation
steps are essential to enhance the stability and accuracy of the sea level products and make them suitable for
climate applications. This dataset includes estimates of sea level anomaly and absolute dynamic topography together
with the corresponding geostrophic velocities. More details about the sea level retrieval algorithms, additional
filters, optimisation procedures, and the error estimation are given in the Documentation tab. Variables in the
dataset/application are: Absolute dynamic topography, Absolute geostrophic velocity meridian component, Absolute
geostrophic velocity zonal component, Geostrophic velocity anomalies meridian component, Geostrophic velocity
anomalies zonal component, Sea level anomaly
', |
|
|
30
Collection("SEASONAL_MONTHLY_PL")
| id: |
'SEASONAL_MONTHLY_PL', |
| title: |
'Seasonal forecast monthly statistics on pressure levels', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1981-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'seasonal'
, 'forecast'
, 'monthly'
, 'pressure'
, 'levels'
], |
| license: |
'other', |
| description: |
'This entry covers pressure-level data aggregated on a monthly time resolution. \nSeasonal forecasts provide
a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of
predictable changes in some of the slow-varying components of the system. For example, ocean temperatures
typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying
atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric
conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range
(e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail
- both in time and space - of the evolution of the state of the atmosphere over a few days into the future.
Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes
at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large
uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast
products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the
individual components of the Earth system, the best tools for long-range forecasting are climate models
which include as many of the key components of the system and possible; typically, such models include
representations of the atmosphere, ocean and land surface. These models are initialised with data describing
the state of the system at the starting point of the forecast, and used to predict the evolution of this
state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components
of the Earth system can be described with the use of ensembles, uncertainty arising from approximations
made in the models are very much dependent on the choice of model. A convenient way to quantify the effect
of these approximations is to combine outputs from several models, independently developed, initialised and
operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by
state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several
European countries is collected, processed and combined to enable user-relevant applications. The composition
of the C3S seasonal multi-system and the full content of the database underpinning the service are described
in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by
the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing
applied (data at original time resolution, processing on temporal aggregation and post-processing related to
bias adjustment).\nThe variables available in this data set are listed in the table below. The data includes
forecasts created in real-time (since 2017) and retrospective forecasts (hindcasts) initialised at equivalent
intervals during the period 1993-2016.\n\nVariables in the dataset/application are:\nGeopotential, Specific
humidity, Temperature, U-component of wind, V-component of wind
', |
|
|
31
Collection("SEASONAL_MONTHLY_SL")
| id: |
'SEASONAL_MONTHLY_SL', |
| title: |
'Seasonal forecast monthly statistics on single levels', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1981-01-01 00:00:00+00:00
, 2023-05-01 00:00:00+00:00
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'seasonal'
, 'forecast'
, 'monthly'
, 'single'
, 'levels'
], |
| license: |
'other', |
| description: |
'This entry covers single-level data aggregated on a monthly time resolution. \nSeasonal forecasts provide
a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of
predictable changes in some of the slow-varying components of the system. For example, ocean temperatures
typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying
atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric
conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range
(e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail
- both in time and space - of the evolution of the state of the atmosphere over a few days into the future.
Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at
local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large
uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast
products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the
individual components of the Earth system, the best tools for long-range forecasting are climate models
which include as many of the key components of the system and possible; typically, such models include
representations of the atmosphere, ocean and land surface. These models are initialised with data describing
the state of the system at the starting point of the forecast, and used to predict the evolution of this
state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the
components of the Earth system can be described with the use of ensembles, uncertainty arising from
approximations made in the models are very much dependent on the choice of model. A convenient way to
quantify the effect of these approximations is to combine outputs from several models, independently
developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast
service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and
operated at forecast centres in several European countries is collected, processed and combined to enable
user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the
database underpinning the service are described in the documentation. The data is grouped in several catalogue
entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure
surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal
aggregation and post-processing related to bias adjustment).\nThe variables available in this data set are
listed in the table below. The data includes forecasts created in real-time (since 2017) and retrospective
forecasts (hindcasts) initialised at equivalent intervals during the period 1993-2016.\n\nVariables in the
dataset/application are:\n10m u-component of wind, 10m v-component of wind, 10m wind gust since previous
post-processing, 10m wind speed, 2m dewpoint temperature, 2m temperature, East-west surface stress rate of
accumulation, Evaporation, Maximum 2m temperature in the last 24 hours, Mean sea level pressure, Mean
sub-surface runoff rate, Mean surface runoff rate, Minimum 2m temperature in the last 24 hours, North-south
surface stress rate of accumulation, Runoff, Sea surface temperature, Sea-ice cover, Snow density, Snow
depth, Snowfall, Soil temperature level 1, Solar insolation rate of accumulation, Surface latent heat flux,
Surface sensible heat flux, Surface solar radiation, Surface solar radiation downwards, Surface thermal
radiation, Surface thermal radiation downwards, Top solar radiation, Top thermal radiation, Total cloud cover,
Total precipitation
', |
|
|
32
Collection("SEASONAL_ORIGINAL_PL")
| id: |
'SEASONAL_ORIGINAL_PL', |
| title: |
'Seasonal forecast subdaily data on pressure levels', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1981-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'seasonal'
, 'forecast'
, 'subdaily'
, 'pressure'
, 'levels'
], |
| license: |
'other', |
| description: |
'his entry covers pressure-level data at the original time resolution (once every 12 hours). \nSeasonal
forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months,
as a result of predictable changes in some of the slow-varying components of the system. For example, ocean
temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the
overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote
atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all
long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more
precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days
into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict
precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions
have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful
forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between
the individual components of the Earth system, the best tools for long-range forecasting are climate models
which include as many of the key components of the system and possible; typically, such models include
representations of the atmosphere, ocean and land surface. These models are initialised with data describing
the state of the system at the starting point of the forecast, and used to predict the evolution of this state
in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of
the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in
the models are very much dependent on the choice of model. A convenient way to quantify the effect of these
approximations is to combine outputs from several models, independently developed, initialised and
operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced
by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in
several European countries is collected, processed and combined to enable user-relevant applications. The
composition of the C3S seasonal multi-system and the full content of the database underpinning the service
are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently
defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of
post-processing applied (data at original time resolution, processing on temporal aggregation and
post-processing related to bias adjustment).\nThe variables available in this data set are listed in the
table below. The data includes forecasts created in real-time (since 2017) and retrospective forecasts
(hindcasts) initialised at equivalent intervals during the period 1993-2016.\n\nVariables in the
dataset/application are:\nGeopotential, Specific humidity, Temperature, U-component of wind, V-component
of wind
', |
|
|
33
Collection("SEASONAL_ORIGINAL_SL")
| id: |
'SEASONAL_ORIGINAL_SL', |
| title: |
'Seasonal forecast daily and subdaily data on single levels', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1981-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'seasonal'
, 'forecast'
, 'daily'
, 'single'
, 'levels'
], |
| license: |
'other', |
| description: |
'This entry covers single-level data at the original time resolution (once a day, or once every 6 hours,
depending on the variable). \nSeasonal forecasts provide a long-range outlook of changes in the Earth system
over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying
components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or
months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g.
temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual'
atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from
a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the
state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the
atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons
long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties,
long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven
the complex, non-linear interactions between the individual components of the Earth system, the best tools for
long-range forecasting are climate models which include as many of the key components of the system and
possible; typically, such models include representations of the atmosphere, ocean and land surface. These
models are initialised with data describing the state of the system at the starting point of the forecast,
and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge
of the initial conditions of the components of the Earth system can be described with the use of ensembles,
uncertainty arising from approximations made in the models are very much dependent on the choice of model.
A convenient way to quantify the effect of these approximations is to combine outputs from several models,
independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal
forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and
operated at forecast centres in several European countries is collected, processed and combined to enable
user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the
database underpinning the service are described in the documentation. The data is grouped in several catalogue
entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure
surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal
aggregation and post-processing related to bias adjustment).\nThe variables available in this data set are
listed in the table below. The data includes forecasts created in real-time (since 2017) and retrospective
forecasts (hindcasts) initialised at equivalent intervals during the period 1993-2016.\n\nVariables in the
dataset/application are:\n10m u-component of wind, 10m v-component of wind, 10m wind gust since previous
post-processing, 2m dewpoint temperature, 2m temperature, Eastward turbulent surface stress, Evaporation,
Land-sea mask, Maximum 2m temperature in the last 24 hours, Mean sea level pressure, Minimum 2m temperature
in the last 24 hours, Northward turbulent surface stress, Orography, Runoff, Sea surface temperature, Sea-ice
cover, Snow density, Snow depth, Snowfall, Soil temperature level 1, Sub-surface runoff, Surface latent heat
flux, Surface net solar radiation, Surface net thermal radiation, Surface runoff, Surface sensible heat flux,
Surface solar radiation downwards, Surface thermal radiation downwards, TOA incident solar radiation, Top net
solar radiation, Top net thermal radiation, Total cloud cover, Total precipitation
', |
|
|
34
Collection("SEASONAL_POSTPROCESSED_PL")
| id: |
'SEASONAL_POSTPROCESSED_PL', |
| title: |
'Seasonal forecast anomalies on pressure levels', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2017-09-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'seasonal'
, 'forecast'
, 'anomalies'
, 'pressure'
, 'levels'
], |
| license: |
'other', |
| description: |
'This entry covers pressure-level data post-processed for bias adjustment on a monthly time resolution.
\nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks
or months, as a result of predictable changes in some of the slow-varying components of the system. For
example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact
on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and
remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all
long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise
detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the
future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise
changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large
uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast
products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual
components of the Earth system, the best tools for long-range forecasting are climate models which include as
many of the key components of the system and possible; typically, such models include representations of the
atmosphere, ocean and land surface. These models are initialised with data describing the state of the system
at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile
uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system
can be described with the use of ensembles, uncertainty arising from approximations made in the models are
very much dependent on the choice of model. A convenient way to quantify the effect of these approximations
is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect,
the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal
forecast systems developed, implemented and operated at forecast centres in several European countries is
collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal
multi-system and the full content of the database underpinning the service are described in the documentation.
The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable
(single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original
time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe
variables available in this data set are listed in the table below. The data includes forecasts created in
real-time since 2017.\n\nVariables in the dataset/application are:\nGeopotential anomaly, Specific humidity
anomaly, Temperature anomaly, U-component of wind anomaly, V-component of wind anomaly
', |
|
|
35
Collection("SEASONAL_POSTPROCESSED_SL")
| id: |
'SEASONAL_POSTPROCESSED_SL', |
| title: |
'Seasonal forecast anomalies on single levels', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[2017-09-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['ECMWF'
, 'CDS'
, 'C3S'
, 'seasonal'
, 'forecast'
, 'anomalies'
, 'single'
, 'levels'
], |
| license: |
'other', |
| description: |
'This entry covers single-level data post-processed for bias adjustment on a monthly time resolution.
\nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks
or months, as a result of predictable changes in some of the slow-varying components of the system. For
example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an
impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both
local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the
essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives
a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a
few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to
predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric
conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and
meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions
between the individual components of the Earth system, the best tools for long-range forecasting are climate
models which include as many of the key components of the system and possible; typically, such models include
representations of the atmosphere, ocean and land surface. These models are initialised with data describing
the state of the system at the starting point of the forecast, and used to predict the evolution of this state
in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of
the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in
the models are very much dependent on the choice of model. A convenient way to quantify the effect of these
approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo
this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art
seasonal forecast systems developed, implemented and operated at forecast centres in several European countries
is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal
multi-system and the full content of the database underpinning the service are described in the documentation.
The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable
(single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original
time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe
variables available in this data set are listed in the table below. The data includes forecasts created in
real-time since 2017.\n\nVariables in the dataset/application are:\n10m u-component of wind anomaly, 10m
v-component of wind anomaly, 10m wind gust anomaly, 10m wind speed anomaly, 2m dewpoint temperature anomaly,
2m temperature anomaly, East-west surface stress anomalous rate of accumulation, Evaporation anomalous rate
of accumulation, Maximum 2m temperature in the last 24 hours anomaly, Mean sea level pressure anomaly, Mean
sub-surface runoff rate anomaly, Mean surface runoff rate anomaly, Minimum 2m temperature in the last 24 hours
anomaly, North-south surface stress anomalous rate of accumulation, Runoff anomalous rate of accumulation,
Sea surface temperature anomaly, Sea-ice cover anomaly, Snow density anomaly, Snow depth anomaly, Snowfall
anomalous rate of accumulation, Soil temperature anomaly level 1, Solar insolation anomalous rate of
accumulation, Surface latent heat flux anomalous rate of accumulation, Surface sensible heat flux anomalous
rate of accumulation, Surface solar radiation anomalous rate of accumulation, Surface solar radiation
downwards anomalous rate of accumulation, Surface thermal radiation anomalous rate of accumulation, Surface
thermal radiation downwards anomalous rate of accumulation, Top solar radiation anomalous rate of accumulation,
Top thermal radiation anomalous rate of accumulation, Total cloud cover anomaly, Total precipitation anomalous
rate of accumulation
', |
|
|
36
Collection("UERRA_EUROPE_SL")
| id: |
'UERRA_EUROPE_SL', |
| title: |
'UERRA regional reanalysis for Europe on single levels from 1961 to 2019', |
| extent: |
{
'spatial': {
'bbox': [[-180.0
, -90.0
, 180.0
, 90.0
]
]
}
,
'temporal': {
'interval': [[1961-01-01 00:00:00+00:00
, None
]
]
}
}, |
| instruments: |
[], |
| constellation: |
'SURFEX', |
| platform: |
'SURFEX', |
| eodag:sensor_type: |
'ATMOSPHERIC', |
| keywords: |
['Climate'
, 'ECMWF'
, 'Reanalysis'
, 'Regional'
, 'Europe'
, 'UERRA'
, 'UERRA-HARMONIE'
, 'SURFEX'
, 'MESCAN-SURFEX'
, 'CDS'
, 'Atmospheric'
, 'single'
, 'levels'
], |
| license: |
'other', |
| description: |
'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 and six-hourly air temperature and relative humidit
at 2 meters above the model topography. The land surface platform SURFEX is forced with downscaled forecast fields
from UERRA-HARMONIE as well as MESCAN analyses. It is run offline, i.e. without feedback to the atmospheric analysis
performed in MESCAN or the UERRA-HARMONIE data assimilation cycles. Using SURFEX offline allows to take full benefit
of precipitation analysis and to use the more advanced physics options to better represent surface variables such as
surface temperature and surface fluxes, and soil processes related to water and heat transfer in the soil and snow.
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 at locations where data coverage is low. The provision of
estimates at each grid point in Europe for each regular output time, over a long period, always using the same format,
makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over
time, and although the assimilation system can resolve data holes, the much sparser observational networks, e.g. in
1960s, will have an impact on the quality of analyses leading to less accurate estimates. The improvement over global
reanalysis products comes with the higher horizontal resolution that allows incorporating more regional details
(e.g. topography). Moreover, it enables the system even to use more observations at places with dense observation
networks. Variables in the dataset/application are: 10m wind direction, 10m wind speed, 2m relative humidity, 2m
temperature, Albedo, High cloud cover, Land sea mask, Low cloud cover, Mean sea level pressure, Medium cloud cover,
Orography, Skin temperature, Snow density, Snow depth water equivalent, Surface pressure, Surface roughness, Total
cloud cover, Total column integrated water vapour, Total precipitation
', |
|
|
.