Collections#
eodag maintains a catalog of EO collections including some of their metadata. Each collection is
given an identifier (e.g. S2_MSI_L2A) that should then be used by users to search for this kind
of product.
This catalog is saved as a YAML file that can be viewed here. The example below shows the catalog entry for the collection Sentinel 2 Level-2A.
S2_MSI_L2A:
description: |
The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C
products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection).
instruments: ["MSI"]
constellation: SENTINEL2
platform: S2A,S2B
processing:level: L2
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL2 Level-2A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-03-26T00:00:00Z", null]]}}
This collection catalog can be obtained from the API:
from eodag import EODataAccessGateway
dag = EODataAccessGateway()
dag.list_collections()
Or from the CLI:
eodag list
The catalog is used in different ways by eodag:
Collections made available for a given provider (search/download) are listed in its configuration. This allows to unify the collection identifier among the providers.
eodag search --provider cop_dataspace --collection S2_MSI_L2A eodag search --provider planetary_computer --collection S2_MSI_L2A
Some of the metadata mapped can be used to search for products without specifying any identifier. In other terms, this catalog can be queried. When a search is made, the search criteria provided by the user are first used to search for the collection that best matches the criteria. The actual search is then performed with this collection.
eodag search --sensor-type OPTICAL --processing-level L2from eodag import EODataAccessGateway search_criteria = {"eodag:sensor_type": "OPTICAL", "processing:level": "L2"} dag = EODataAccessGateway() dag.search(**search_criteria)
The metadata stored in this file are sometimes added to the
propertiesattribute to anEOProduct. It depends on whether the metadata are already mapped or not for the provider used to search for products.
The catalog is saved as a YAML file and distributed alongside eodag.
Click on the link below to display its full content.
collections.yml
CBERS4_MUX_L2:
description: |
China-Brazil Earth Resources Satellite, CBERS-4 MUX camera Level-2 product. System corrected images, expect some
translation error.
instruments: ["MUX"]
constellation: CBERS
platform: CBERS-4
processing:level: L2
keywords: ["MUX", "CBERS", "CBERS-4", "L2"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-07T00:00:00Z", null]]}}
title: CBERS-4 MUX Level-2
CBERS4_AWFI_L2:
description: |
China-Brazil Earth Resources Satellite, CBERS-4 AWFI camera Level-2 product. System corrected images, expect some
translation error.
instruments: ["AWFI"]
constellation: CBERS
platform: CBERS-4
processing:level: L2
keywords: ["AWFI", "CBERS", "CBERS-4", "L2"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-07T00:00:00Z", null]]}}
title: CBERS-4 AWFI Level-2
CBERS4_PAN5M_L2:
description: |
China-Brazil Earth Resources Satellite, CBERS-4 PAN5M camera Level-2 product. System corrected images, expect some
translation error.
instruments: ["PAN5M"]
constellation: CBERS
platform: CBERS-4
processing:level: L2
keywords: ["PAN5M", "CBERS", "CBERS-4", "L2"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-07T00:00:00Z", null]]}}
title: CBERS-4 PAN5M Level-2
CBERS4_PAN10M_L2:
description: |
China-Brazil Earth Resources Satellite, CBERS-4 PAN10M camera Level-2 product. System corrected images, expect some
translation error.
instruments: ["PAN10M"]
constellation: CBERS
platform: CBERS-4
processing:level: L2
keywords: ["PAN10M", "CBERS", "CBERS-4", "L2"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-07T00:00:00Z", null]]}}
title: CBERS-4 PAN10M Level-2
CBERS4_MUX_L4:
description: |
China-Brazil Earth Resources Satellite, CBERS-4 MUX camera Level-4 product. Orthorectified with ground control
points.
instruments: ["MUX"]
constellation: CBERS
platform: CBERS-4
processing:level: L4
keywords: ["MUX", "CBERS", "CBERS-4", "L4"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-07T00:00:00Z", null]]}}
title: CBERS-4 MUX Level-4
CBERS4_AWFI_L4:
description: |
China-Brazil Earth Resources Satellite, CBERS-4 AWFI camera Level-4 product. Orthorectified with ground control
points.
instruments: ["AWFI"]
constellation: CBERS
platform: CBERS-4
processing:level: L4
keywords: ["AWFI", "CBERS", "CBERS-4", "L4"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-07T00:00:00Z", null]]}}
title: CBERS-4 AWFI Level-4
CBERS4_PAN5M_L4:
description: |
China-Brazil Earth Resources Satellite, CBERS-4 PAN5M camera Level-4 product. Orthorectified with ground control
points.
instruments: ["PAN5M"]
constellation: CBERS
platform: CBERS-4
processing:level: L4
keywords: ["PAN5M", "CBERS", "CBERS-4", "L4"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-07T00:00:00Z", null]]}}
title: CBERS-4 PAN5M Level-4
CBERS4_PAN10M_L4:
description: |
China-Brazil Earth Resources Satellite, CBERS-4 PAN10M camera Level-4 product. Orthorectified with ground control
points.
instruments: ["PAN10M"]
constellation: CBERS
platform: CBERS-4
processing:level: L4
keywords: ["PAN10M", "CBERS", "CBERS-4", "L4"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-12-07T00:00:00Z", null]]}}
title: CBERS-4 PAN10M Level-4
# MARK: Landsat -----------------------------------------------------------------------
# https://www.usgs.gov/faqs/what-naming-convention-landsat-collections-level-1-scenes
L8_OLI_TIRS_C1L1:
description: |
Landsat 8 Operational Land Imager and Thermal Infrared Sensor Collection 1 Level-1 products. Details at
https://landsat.usgs.gov/sites/default/files/documents/LSDS-1656_Landsat_Level-1_Product_Collection_Definition.pdf
instruments: ["OLI", "TIRS"]
constellation: LANDSAT8
platform: L8
processing:level: L1
keywords: ["OLI", "TIRS", "LANDSAT", "LANDSAT8", "L8", "L1", "C1", "COLLECTION1"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-02-11T00:00:00Z", null]]}}
title: Landsat 8 Level-1
LANDSAT_C2L1:
description: |
The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.
instruments: ["OLI", "TIRS"]
constellation: LANDSAT
platform: L1,L2,L3,L4,L5,L6,L7,L8
processing:level: L1
keywords: ["OLI", "TIRS", "LANDSAT", "L1", "L2", "L3", "L4", "L5", "L6", "L7", "L8", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat Collection 2 Level-1 Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1972-07-25T00:00:00Z", null]]}}
LANDSAT_C2L2:
description: |
Collection 2 Landsat OLI/TIRS Level-2 Science Products (L2SP) include
Surface Reflectance and Surface Temperature scene-based products.
instruments: ["OLI", "TIRS"]
constellation: LANDSAT
platform: L8,L9
processing:level: L1
keywords: ["OLI", "TIRS", "LANDSAT", "L8", "L9", "L2", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat OLI and TIRS Collection 2 Level-2 Science Products 30-meter multispectral data.
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-02-11T00:00:00Z", null]]}}
LANDSAT_C2L2_SR:
description: |
The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected
from Earth's surface to the Landsat sensor.
instruments: ["OLI", "TIRS"]
constellation: LANDSAT
platform: L4,L5,L7,L8
processing:level: L2
keywords: ["OLI", "TIRS", "LANDSAT", "L4", "L5", "L7", "L8", "L2", "SR", "surface", "reflectance", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", null]]}}
LANDSAT_C2L2_ST:
description: |
The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).
instruments: ["OLI", "TIRS"]
constellation: LANDSAT
platform: L4,L5,L7,L8
processing:level: L2
keywords: ["OLI", "TIRS", "LANDSAT", "L4", "L5", "L7", "L8", "L2", "ST", "surface", "temperature", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", null]]}}
LANDSAT_C2L2ALB_BT:
description: |
The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance
calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.
instruments: ["OLI", "TIRS"]
constellation: LANDSAT
platform: L4,L5,L7,L8
processing:level: L2
keywords: ["OLI", "TIRS", "LANDSAT", "L4", "L5", "L7", "L8", "L2", "BT", "Brightness", "Temperature", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", null]]}}
LANDSAT_C2L2ALB_SR:
description: |
The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected
from Earth's surface to the Landsat sensor.
instruments: ["OLI", "TIRS"]
constellation: LANDSAT
platform: L4,L5,L7,L8
processing:level: L2
keywords: ["OLI", "TIRS", "LANDSAT", "L4", "L5", "L7", "L8", "L2", "L2ALB", "SR", "Surface", "Reflectance", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", null]]}}
LANDSAT_C2L2ALB_ST:
description: |
The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).
instruments: ["OLI", "TIRS"]
constellation: LANDSAT
platform: L4,L5,L7,L8
processing:level: L2
keywords: ["OLI", "TIRS", "LANDSAT", "L4", "L5", "L7", "L8", "L2", "L2ALB", "Surface", "Temperature", "ST", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", null]]}}
LANDSAT_C2L2ALB_TA:
description: |
The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1
radiance product.
instruments: ["OLI", "TIRS"]
constellation: LANDSAT
platform: L4,L5,L7,L8
processing:level: L2
keywords: ["OLI", "TIRS", "LANDSAT", "L4", "L5", "L7", "L8", "L2", "L2ALB", "TA", "Top", "Atmosphere", "Reflectance", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", null]]}}
LANDSAT_TM_C1:
description: |
Landsat 4-5 TM image data files consist of seven spectral bands (See band designations).
The resolution is 30 meters for bands 1 to 7.
(Thermal infrared band 6 was collected at 120 meters, but was resampled to 30 meters.)
The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).
instruments: ["TM"]
constellation: LANDSAT
platform: L4,L5
processing:level: L1
keywords: ["TM", "LANDSAT", "L4", "L5", "L1", "C1", "COLLECTION1"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat 4-5 Thematic Mapper (TM) Collection-1 Level-1 Data Products
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", "2013-06-06T00:00:00Z"]]}}
LANDSAT_TM_C2L1:
description: |
Landsat 4-5 TM image data files consist of seven spectral bands (See band designations).
The resolution is 30 meters for bands 1 to 7.
(Thermal infrared band 6 was collected at 120 meters, but was resampled to 30 meters.)
The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).
instruments: ["TM"]
constellation: LANDSAT
platform: L4,L5
processing:level: L1
keywords: ["TM", "LANDSAT", "L4", "L5", "L1", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat 4-5 Thematic Mapper (TM) Collection-2 Level-1 Data Products
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", "2013-06-06T00:00:00Z"]]}}
LANDSAT_TM_C2L2:
description: |
Collection 2 Landsat 4-5 Thematic Mapper (TM) Level-2 Science Products (L2SP) include
Surface Reflectance and Surface Temperature scene-based products.
instruments: ["TM"]
constellation: LANDSAT
platform: L4,L5
processing:level: L2
keywords: ["TM", "LANDSAT", "L4", "L5", "L2", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Landsat 4-5 Thematic Mapper (TM) Collection-2 Level-2 Data Products
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-08-22T00:00:00Z", "2013-06-06T00:00:00Z"]]}}
LANDSAT_ETM_C1:
description: |
Landsat 7 ETM+ images consist of eight spectral bands with a spatial resolution of 30 meters for bands 1 to 7.
The panchromatic band 8 has a resolution of 15 meters. All bands can collect one of two gain settings (high or low)
for increased radiometric sensitivity and dynamic range, while Band 6 collects both high and low gain for all
scenes. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).
instruments: ["ETM+"]
constellation: LANDSAT
platform: L7
processing:level: L1
keywords: ["ETM", "ETM+", "LANDSAT", "L7", "L1", "C1", "COLLECTION1"]
eodag:sensor_type: OPTICAL
license: other
title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-1 Level-1 data from Landsat 7
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-04-15T00:00:00Z", null]]}}
LANDSAT_ETM_C2L1:
description: |
Landsat 7 ETM+ images consist of eight spectral bands with a spatial resolution of 30 meters for bands 1 to 7.
The panchromatic band 8 has a resolution of 15 meters. All bands can collect one of two gain settings (high or low)
for increased radiometric sensitivity and dynamic range, while Band 6 collects both high and low gain for all
scenes. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).
instruments: ["ETM+"]
constellation: LANDSAT
platform: L7
processing:level: L1
keywords: ["ETM", "ETM+", "LANDSAT", "L7", "L1", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-2 Level-1 data from Landsat 7
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-04-15T00:00:00Z", null]]}}
LANDSAT_ETM_C2L2:
description: |
Collection 2 Landsat 7 ETM+ Level-2 Science Products (L2SP) include Surface Reflectance and Surface Temperature
scene-based products.
instruments: ["ETM+"]
constellation: LANDSAT
platform: L7
processing:level: L2
keywords: ["ETM", "ETM+", "LANDSAT", "L7", "L2", "C2", "COLLECTION2"]
eodag:sensor_type: OPTICAL
license: other
title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-2 Level-2 data from Landsat 7
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-04-15T00:00:00Z", null]]}}
# MARK: MODIS --------------------------------------------------------------------------
MODIS_MCD43A4:
description: |
The MODerate-resolution Imaging Spectroradiometer (MODIS) Reflectance product MCD43A4 provides 500 meter
reflectance data adjusted using a bidirectional reflectance distribution function (BRDF) to model the values as if
they were taken from nadir view. The MCD43A4 product contains 16 days of data provided in a level-3 gridded data
set in Sinusoidal projection. Both Terra and Aqua data are used in the generation of this product, providing the
highest probability for quality assurance input data. It is designated with a shortname beginning with MCD, which
is used to refer to 'combined' products, those comprised of data using both Terra and Aqua.
instruments: ["MODIS"]
constellation: Terra+Aqua
platform: EOS AM-1+PM-1
processing:level: L3
keywords: ["MODIS", "Terra", "Aqua", "EOS", "AM-1+PM-1", "L3", "MCD43A4"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2000-03-05T00:00:00Z", null]]}}
title: MODIS MCD43A4
# MARK: NAIP ---------------------------------------------------------------------------
NAIP:
description: |
The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in
the continental U.S. This "leaf-on" imagery and typically ranges from 60 centimeters to 100 centimeters in
resolution and is available from the naip-analytic Amazon S3 bucket as 4-band (RGB + NIR) imagery in MRF format.
NAIP data is delivered at the state level; every year, a number of states receive updates, with an overall update
cycle of two or three years. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with
a 300 meter buffer on all four sides. NAIP imagery is formatted to the UTM coordinate system using NAD83. NAIP
imagery may contain as much as 10% cloud cover per tile.
instruments: ["film", "digital-cameras"]
constellation: National Agriculture Imagery Program
platform: NAIP
processing:level: N/A
keywords: ["film", "digital", "cameras", "Agriculture", "NAIP"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", null]]}}
title: National Agriculture Imagery Program
# MARK: Sentinel 1 ---------------------------------------------------------------------
S1_AUX_GNSSRD:
description: |
The Auxiliary (AUX) product contains the precise orbit ephemerides and the GNSS-Raw Data (GNSSRD) data.
The precise orbit ephemerides are provided as a text file in the format of the International GNSS Service (IGS).
The GNSS-Raw Data (GNSSRD) data is provided as a binary file in a specific format.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: AUX
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "AUX", "GNSSRD", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Auxiliary Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-06T00:00:00Z", null]]}}
S1_AUX_MOEORB:
description: |
The Auxiliary (AUX) product contains the precise orbit ephemerides and the GNSS-Raw Data (GNSSRD) data.
The precise orbit ephemerides are provided as a text file in the format of the European Space Operations Centre
(ESOC).
The GNSS-Raw Data (GNSSRD) data is provided as a binary file in a specific format.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: AUX
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "AUX", "MOEORB", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Auxiliary Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-06T00:00:00Z", null]]}}
S1_AUX_POEORB:
description: |
The Auxiliary (AUX) product contains the precise orbit ephemerides and the GNSS-Raw Data (GNSSRD) data.
The precise orbit ephemerides are provided as a text file in the format of the European Space Operations Centre
(ESOC).
The GNSS-Raw Data (GNSSRD) data is provided as a binary file in a specific format.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: AUX
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "AUX", "POEORB", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Auxiliary Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-06T00:00:00Z", null]]}}
S1_AUX_PREORB:
description: |
The Auxiliary (AUX) product contains the precise orbit ephemerides and the GNSS-Raw Data (GNSSRD) data.
The precise orbit ephemerides are provided as a text file in the format of the European Space Operations Centre
(ESOC).
The GNSS-Raw Data (GNSSRD) data is provided as a binary file in a specific format.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: AUX
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "AUX", "PREORB", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Auxiliary Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-06T00:00:00Z", null]]}}
S1_AUX_PROQUA:
description: |
The Auxiliary (AUX) product contains the precise orbit ephemerides and the GNSS-Raw Data (GNSSRD) data.
The precise orbit ephemerides are provided as a text file in the format of the European Space Operations Centre
(ESOC).
The GNSS-Raw Data (GNSSRD) data is provided as a binary file in a specific format.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: AUX
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "AUX", "PROQUA", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Auxiliary Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-06T00:00:00Z", null]]}}
S1_AUX_RESORB:
description: |
The Auxiliary (AUX) product contains the precise orbit ephemerides and the GNSS-Raw Data (GNSSRD) data.
The precise orbit ephemerides are provided as a text file in the format of the European Space Operations Centre
(ESOC).
The GNSS-Raw Data (GNSSRD) data is provided as a binary file in a specific format.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: AUX
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "AUX", "RESORB", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Auxiliary Product
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-06T00:00:00Z", null]]}}
S1_SAR_L3_IW_MCM:
description: |
Level-3 IW (Interferometric Wide Swath) products are terrain corrected (geocoded) products in map geometry,
projected using the Range-Doppler model and resampled using nearest neighbor method. The pixel spacing is
approximately 10 m x 10 m. The product is available in two polarizations: Single (HH or VV) and Dual (HH+HV or
VV+VH). The product is provided in GeoTIFF format.
Product containing Cloud Optimized GeoTIFF images, without SAFE formatting.
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: L3
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "L3", "IW", "MCM", "COG"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Level-3 IW MCM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2023-01-01T00:00:00Z", null]]}}
S1_SAR_L3_DH_MCM:
description: |
Level-3 DH (Dual polarization High Resolution) products are terrain corrected (geocoded) products in map geometry,
projected using the Range-Doppler model and resampled using nearest neighbor method. The pixel spacing is
approximately 10 m x 10 m. The product is available in dual polarization (HH+HV or VV+VH). The product is provided
in GeoTIFF format.
Product containing Cloud Optimized GeoTIFF images, without SAFE formatting.
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: L3
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "L3", "DH", "MCM", "COG"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Level-3 DH MCM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2023-01-01T00:00:00Z", null]]}}
S1_SAR_OCN:
description: |
Level-2 OCN products include components for Ocean Swell spectra (OSW) providing continuity with ERS and ASAR WV
and two new components: Ocean Wind Fields (OWI) and Surface Radial Velocities (RVL).
The OSW is a two-dimensional ocean surface swell spectrum and includes an estimate of the wind speed and direction
per swell spectrum. The OSW is generated from Stripmap and Wave modes only. For Stripmap mode, there are multiple
spectra derived from internally generated Level-1 SLC images. For Wave mode, there is one spectrum per vignette.
The OWI is a ground range gridded estimate of the surface wind speed and direction at 10 m above the surface
derived from internally generated Level-1 GRD images of SM, IW or EW modes.
The RVL is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated
geometrical Doppler.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: L2
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "L2", "OCN", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Level-2 OCN
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-03T00:00:00Z", null]]}}
S1_SAR_GRD:
description: |
Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and
projected to ground range using an Earth ellipsoid model. Phase information is lost. The resulting product has
approximately square spatial resolution pixels and square pixel spacing with reduced speckle at the cost of worse
spatial resolution.
GRD products can be in one of three resolutions: |
Full Resolution (FR),
High Resolution (HR),
Medium Resolution (MR).
The resolution is dependent upon the amount of multi-looking performed. Level-1 GRD products are available in MR
and HR for IW and EW modes, MR for WV mode and MR, HR and FR for SM mode.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: L1
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "L1", "GRD", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Level-1 Ground Range Detected
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-03T00:00:00Z", null]]}}
S1_SAR_GRD_JP2:
description: |
Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and
projected to ground range using an Earth ellipsoid model. Phase information is lost. The resulting product has
approximately square spatial resolution pixels and square pixel spacing with reduced speckle at the cost of worse
spatial resolution.
GRD products can be in one of three resolutions: |
Full Resolution (FR),
High Resolution (HR),
Medium Resolution (MR).
The resolution is dependent upon the amount of multi-looking performed. Level-1 GRD products are available in MR
and HR for IW and EW modes, MR for WV mode and MR, HR and FR for SM mode.
Product without SAFE formatting, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: L1
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "L1", "GRD", "JP2"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Level-1 Ground Range Detected
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-03T00:00:00Z", null]]}}
S1_SAR_GRD_COG:
description: |
Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and
projected to ground range using an Earth ellipsoid model. Phase information is lost. The resulting product has
approximately square spatial resolution pixels and square pixel spacing with reduced speckle at the cost of worse
spatial resolution.
GRD products can be in one of three resolutions: |
Full Resolution (FR),
High Resolution (HR),
Medium Resolution (MR).
The resolution is dependent upon the amount of multi-looking performed. Level-1 GRD products are available in MR
and HR for IW and EW modes, MR for WV mode and MR, HR and FR for SM mode.
Product containing Cloud Optimized GeoTIFF images, without SAFE formatting.
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: L1
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "L1", "GRD", "COG"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Level-1 Ground Range Detected
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-03T00:00:00Z", null]]}}
S1_SAR_SLC:
description: |
Level-1 Single Look Complex (SLC) products consist of focused SAR data geo-referenced using orbit and attitude
data from the satellite and provided in zero-Doppler slant-range geometry. The products include a single look in
each dimension using the full transmit signal bandwidth and consist of complex samples preserving the phase
information.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: L1
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "L1", "SLC", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 Level-1 Single Look Complex
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-03T00:00:00Z", null]]}}
S1_SAR_RAW:
description: |
The SAR Level-0 products consist of the sequence of Flexible Dynamic Block Adaptive Quantization (FDBAQ) compressed
unfocused SAR raw data. For the data to be usable, it will need to be decompressed and processed using a SAR
processor.
SAFE formatted product, see
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification
instruments: ["SAR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL1
platform: S1A,S1B,S1C
processing:level: RAW
keywords: ["SAR", "SENTINEL", "SENTINEL1", "S1", "S1A", "S1B", "S1C", "L0", "RAW", "SAFE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL1 SAR Level-0
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-04-03T00:00:00Z", null]]}}
# MARK: Sentinel 2 ---------------------------------------------------------------------
S2_MSI_L1C:
description: |
The Level-1C product is composed of 100x100 km2 tiles (ortho-images in UTM/WGS84 projection). It results from
using a Digital Elevation Model (DEM) to project the image in cartographic geometry. Per-pixel radiometric
measurements are provided in Top Of Atmosphere (TOA) reflectances along with the parameters to transform them
into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60
meters depending on the native resolution of the different spectral bands. In Level-1C products, pixel
coordinates refer to the upper left corner of the pixel. Level-1C products will additionally include Cloud Masks
and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure).
SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats
instruments: ["MSI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL2
platform: S2A,S2B,S2C
processing:level: L1
keywords: ["MSI", "SENTINEL", "SENTINEL2", "S2", "S2A", "S2B", "S2C", "L1", "L1C", "SAFE"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-06-23T00:00:00Z", null]]}}
title: SENTINEL2 Level-1C
S2_MSI_L1C_JP2:
description: |
The Level-1C product is composed of 100x100 km2 tiles (ortho-images in UTM/WGS84 projection). It results from
using a Digital Elevation Model (DEM) to project the image in cartographic geometry. Per-pixel radiometric
measurements are provided in Top Of Atmosphere (TOA) reflectances along with the parameters to transform them
into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60
meters depending on the native resolution of the different spectral bands. In Level-1C products, pixel
coordinates refer to the upper left corner of the pixel. Level-1C products will additionally include Cloud Masks
and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure).
Product without SAFE formatting, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats
instruments: ["MSI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL2
platform: S2A,S2B,S2C
processing:level: L1
keywords: ["MSI", "SENTINEL", "SENTINEL2", "S2", "S2A", "S2B", "S2C", "L1C", "JP2"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-06-23T00:00:00Z", null]]}}
title: SENTINEL2 Level-1C
S2_MSI_L2A:
description: |
The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C
products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection).
SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats
instruments: ["MSI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL2
platform: S2A,S2B,S2C
processing:level: L2
keywords: ["MSI", "SENTINEL", "SENTINEL2", "S2", "S2A", "S2B", "S2C", "L2", "L2A", "SAFE"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL2 Level-2A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-03-26T00:00:00Z", null]]}}
S2_MSI_L2A_JP2:
description: |
The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C
products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection).
Product without SAFE formatting, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats
instruments: ["MSI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL2
platform: S2A,S2B,S2C
processing:level: L2
keywords: ["MSI", "SENTINEL", "SENTINEL2", "S2", "S2A", "S2B", "S2C", "L2", "L2A", "JP2"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL2 Level-2A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-06-23T00:00:00Z", null]]}}
S2_MSI_L2A_COG:
description: |
The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C
products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection).
Product containing Cloud Optimized GeoTIFF images, without SAFE formatting.
instruments: ["MSI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL2
platform: S2A,S2B,S2C
processing:level: L2
keywords: ["MSI", "SENTINEL", "SENTINEL2", "S2", "S2A", "S2B", "S2C", "L2", "L2A", "COG"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL2 Level-2A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-06-23T00:00:00Z", null]]}}
S2_MSI_L2A_MAJA:
description: |
The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows using MAJA.
MAJA uses MUSCATE processing center at CNES, in the framework of THEIA land data center. Sentinel-2 level 1C data
are downloaded from PEPS. The full description of the product format is available at
https://theia.cnes.fr/atdistrib/documents/PSC-NT-411-0362-CNES_01_00_SENTINEL-2A_L2A_Products_Description.pdf
instruments: ["MSI"]
providers: [{"url":"https://www.cnes.fr","name":"Centre National d'Etudes Spatiales (CNES)","roles":["processor"]}]
constellation: SENTINEL2
platform: S2A,S2B,S2C
processing:level: L2
keywords: ["MSI", "SENTINEL", "SENTINEL2", "S2", "S2A", "S2B", "S2C", "L2", "L2A", "MAJA"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL2 Level-2A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-06-23T00:00:00Z", null]]}}
S2_MSI_L2B_MAJA_SNOW:
description: |
The Theia snow product is derived from Sentinel-2 L2A images generated by Theia. It indicates the snow presence or
absence on the land surface every fifth day if there is no cloud. The product is distributed by Theia as a raster
file (8 bits GeoTIFF) of 20 m resolution and a vector file (Shapefile polygons). More details about the snow
products description are available at http://www.cesbio.ups-tlse.fr/multitemp/?page_id=10748#en
instruments: ["MSI"]
providers: [{"url":"https://www.cnes.fr","name":"Centre National d'Etudes Spatiales (CNES)","roles":["processor"]}]
constellation: SENTINEL2
platform: S2A,S2B,S2C
processing:level: L2
keywords: ["MSI", "MAJA", "SENTINEL", "sentinel2", "S2", "S2A", "S2B", "S2C", "L2", "L2B", "SNOW"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-06-23T00:00:00Z", null]]}}
title: SENTINEL2 snow product
S2_MSI_L2B_MAJA_WATER:
description: |
A description of the Land Water Quality data distributed by Theia is available at
https://theia.cnes.fr/atdistrib/documents/THEIA-ST-411-0477-CNES_01-03_Format_Specification_of_OBS2CO_WaterColor_Products.pdf
instruments: ["MSI"]
providers: [{"url":"https://www.cnes.fr","name":"Centre National d'Etudes Spatiales (CNES)","roles":["processor"]}]
constellation: SENTINEL2
platform: S2A,S2B,S2C
processing:level: L2
keywords: ["MSI", "MAJA", "SENTINEL", "sentinel2", "S2", "S2A", "S2B", "S2C", "L2", "L2B", "WATER"]
eodag:sensor_type: OPTICAL
license: other
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-06-23T00:00:00Z", null]]}}
title: SENTINEL2 L2B-WATER
# MARK: Sentinel 3 ---------------------------------------------------------------------
# MARK: S3 OLCI L1
S3_EFR:
description: |
OLCI (Ocean and Land Colour Instrument) Full resolution: 300m at nadir. Level 1 products are calibrated
Top Of Atmosphere radiance values at OLCI 21 spectral bands. Radiances are computed from the instrument digital
counts by applying geo-referencing, radiometric processing (non-linearity correction, smear correction, dark offset
correction, absolute gain calibration adjusted for gain evolution with time), and stray-light correction for
straylight effects in OLCI camera's spectrometer and ground imager. Additionally, spatial resampling of OLCI pixels
to the 'ideal' instrument grid, initial pixel classification, and annotation at tie points with auxiliary
meteorological data and acquisition geometry are provided. The radiance products are accompanied by error estimate
products, however the error values are currently not available. - All Sentinel-3 NRT products are available at
pick-up point in less than 3h. - All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in
less than 30 days. Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the
EU Copernicus programme.
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "EFR"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 EFR
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_EFR_BC002:
description: |
OLCI (Ocean and Land Colour Instrument) Full resolution: 300m at nadir. Level 1 products are calibrated
Top Of Atmosphere radiance values at OLCI 21 spectral bands. Radiances are computed from the instrument digital
counts by applying geo-referencing, radiometric processing (non-linearity correction, smear correction, dark offset
correction, absolute gain calibration adjusted for gain evolution with time), and stray-light correction for
straylight effects in OLCI camera's spectrometer and ground imager. Additionally, spatial resampling of OLCI pixels
to the 'ideal' instrument grid, initial pixel classification, and annotation at tie points with auxiliary
meteorological data and acquisition geometry are provided. The radiance products are accompanied by error estimate
products, however the error values are currently not available. - All Sentinel-3 NRT products are available at
pick-up point in less than 3h. - All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in
less than 30 days. Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the
EU Copernicus programme.
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "EFR", "BC002"]
eodag:sensor_type: OPTICAL
license: other
title: OLCI Level 1B Full Resolution (version BC002) - Sentinel-3 - Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-04-25T00:00:00Z", "2019-10-29T00:00:00Z"]]}}
S3_ERR:
description: |
OLCI (Ocean and Land Colour Instrument) Reduced resolution: 1200m at nadir. All Sentinel-3 NRT products are
available at pick-up point in less than 3h. Level 1 products are calibrated Top Of Atmosphere radiance values
at OLCI 21 spectral bands. Radiances are computed from the instrument digital counts by applying geo-referencing,
radiometric processing (non-linearity correction, smear correction, dark offset correction, absolute gain
calibration adjusted for gain evolution with time), and stray-light correction for straylight effects in OLCI
camera's spectrometer and ground imager. Additionally, spatial resampling of OLCI pixels to the 'ideal' instrument
grid, initial pixel classification, and annotation at tie points with auxiliary meteorological data and acquisition
geometry are provided. The radiance products are accompanied by error estimate products, however the error values
are currently not available. - All Sentinel-3 NRT products are available at pick-up point in less than 3h
- All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days Sentinel-3 is
part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "ERR"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 ERR
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_ERR_BC002:
description: |
OLCI (Ocean and Land Colour Instrument) Reduced resolution: 1200m at nadir. Level 1 products are calibrated
Top Of Atmosphere radiance values at OLCI 21 spectral bands. Radiances are computed from the instrument digital
counts by applying geo-referencing, radiometric processing (non-linearity correction, smear correction, dark
offset correction, absolute gain calibration adjusted for gain evolution with time), and stray-light correction
for straylight effects in OLCI camera's spectrometer and ground imager. Additionally, spatial resampling of
OLCI pixels to the 'ideal' instrument grid, initial pixel classification, and annotation at tie points with
auxiliary meteorological data and acquisition geometry are provided. The radiance products are accompanied
by error estimate products, however the error values are currently not available. Sentinel-3 is part of a
series of Sentinel satellites, under the umbrella of the EU Copernicus programme. This collection contains
reprocessed data from baseline collection 002. Operational data can be found in the corresponding collection.
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "ERR", "BC002"]
eodag:sensor_type: OPTICAL
license: other
title: OLCI Level 1B Reduced Resolution (version BC002) - Sentinel-3 - Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-04-25T00:00:00Z", "2019-10-29T00:00:00Z"]]}}
S3_RAC:
description: |
Sentinel 3 OLCI products output during Radiometric Calibration mode
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L2", "RAC"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 RAC
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
# MARK: S3 OLCI L2
S3_OLCI_L2LRR:
description: |
The OLCI Level-2 Land Reduced Resolution (OL_2_LRR) products contain land and atmospheric geophysical products
at Reduced resolution with a spatial sampling of approximately 1.2 km. The products are assumed to be computed
in Near Real Time (NRT) (i.e. delivered to users less than 3 hours after acquisition), in Non-Time Critical
(NTC) (i.e. within 1 month after acquisition) or in re-processed NTC.
Details at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2LRR", "LRR"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 OLCI Level-2 Land Reduced Resolution
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_OLCI_L2LFR:
description: |
The OLCI Level-2 Land Full Resolution (OL_2_LFR) products contain land and atmospheric geophysical products at Full
resolution with a spatial sampling of approximately 300 m. The products are assumed to be computed in Near Real
Time (NRT) (i.e. delivered to users less than 3 hours after acquisition), in Non-Time Critical (NTC) (i.e.
within 1 month after acquisition) or in re-processed NTC.
Details at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2LFR", "LFR"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 OLCI Level-2 Land Full Resolution
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_OLCI_L2WRR:
description: |
The OLCI Level-2 Water Reduced Resolution (OL_2_WRR) products contain water and atmospheric geophysical products
at Reduced resolution with a spatial sampling of approximately 1.2 km. The products are assumed to be computed
in Near Real Time (NRT) (i.e. delivered to users less than 3 hours after acquisition), in Non-Time Critical
(NTC) (i.e. within 1 month after acquisition) or in re-processed NTC.
Details at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2WRR", "WRR"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 OLCI Level-2 Water Reduced Resolution
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_OLCI_L2WRR_BC003:
description: |
OLCI Level 2 Marine products provide spectral information on the colour of the oceans (water reflectances). These
radiometric products are used to estimate geophysical parameters e.g. estimates of phytoplankton biomass through
determining the Chlorophyll-a (Chl) concentration. In coastal areas, they also allow monitoring of the sediment
load via the Total Suspended Matter (TSM) product. Reduced resolution products are at a nominal 1km resolution.
This collection contains reprocessed data from baseline collection 003. Operational data can be found in the
corresponding collection.
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2WRR", "WRR", "REPROCESSED", "BC003"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 OLCI Level-2 Water Reduced Resolution Reprocessed from BC003
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_OLCI_L2WFR:
description: |
The OLCI Level-2 Water Full Resolution (OL_2_WFR) products contain water and atmospheric geophysical products at Full
resolution with a spatial sampling of approximately 300 m. The products are assumed to be computed in Near Real
Time (NRT) (i.e. delivered to users less than 3 hours after acquisition), in Non-Time Critical (NTC) (i.e.
within 1 month after acquisition) or in re-processed NTC.
Details at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2WFR", "WFR"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 OLCI Level-2 Water Full Resolution
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_OLCI_L2WFR_BC003:
description: |
OLCI Level 2 Marine products provide spectral information on the colour of the oceans (water reflectances). These
radiometric products are used to estimate geophysical parameters e.g. estimates of phytoplankton biomass through
determining the Chlorophyll-a (Chl) concentration. In coastal areas, they also allow monitoring of the sediment
load via the Total Suspended Matter (TSM) product. Full resolution products are at a nominal 300m resolution. This
collection contains reprocessed data from baseline collection 003. Operational data can be found in the
corresponding collection.
Details at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2WFR", "WFR", "REPROCESSED", "BC003"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 OLCI Level-2 Water Full Resolution Reprocessed from BC003
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_OLCI_L4BALTIC:
description: |
Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations
For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR - Rome, Italy), is
providing Bio-Geo_Chemical (BGC) regional datasets: + ''plankton'' with the phytoplankton chlorophyll
concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) Upstreams: OLCI-S3A & S3B
Temporal resolution: monthly Spatial resolution: 300 meters To find this product in the catalogue, use the search
keyword ""OCEANCOLOUR_BAL_BGC_L4_NRT"". DOI (product) : https://doi.org/10.48670/moi-00295
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L4
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L4", "BGC", "CHL", "BALTIC"]
eodag:sensor_type: OPTICAL
license: other
title: SENTINEL3 OLCI Baltic Sea Surface Ocean Colour Plankton
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2023-04-10T00:00:00Z", null]]}}
S3_OL_2_WFRBC003:
description: |
OLCI Level 2 Marine products provide spectral information on the colour of the oceans (water reflectances).
These radiometric products are used to estimate geophysical parameters e.g. estimates of phytoplankton biomass
through determining the Chlorophyll-a (Chl) concentration. In coastal areas, they also allow monitoring of
the sediment load via the Total Suspended Matter (TSM) product. Full resolution products are at a nominal
300m resolution. This collection contains reprocessed data from baseline collection 003. Operational data
can be found in the corresponding collection.
instruments: ["OLCI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["OLCI", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "Radiation", "Ocean", "Ocean Colour"]
eodag:sensor_type: OPTICAL
license: proprietary
title: OLCI Level 2 Ocean Colour Full Resolution (version BC003) - Sentinel-3 - Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-04-25T00:00:00Z", "2021-04-28T00:00:00Z"]]}}
# MARK: S3 SLSTR
S3_SLSTR_L1RBT:
description: |
SLSTR Level-1 observation mode products consisting of full resolution,
geolocated, co-located nadir and along track view, Top of Atmosphere
(TOA) brightness temperatures (in the case of thermal IR channels) or
radiances (in the case of visible, NIR and SWIR channels) from all
SLSTR channels, and quality flags, pixel classification information
and meteorological annotations
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1RBT", "RBT"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SENTINEL3 SLSTR Level-1
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SLSTR_L1RBT_BC003:
description: |
The SLSTR level 1 products contain: the radiances of the 6 visible (VIS), Near Infra-Red (NIR) and
Short Wave Infra-Red (SWIR) bands (on the A and B stripe grids); the Brightness Temperature (BT) for
the 3 Thermal Infra-Red (TIR) bands; the BT for the 2 Fire (FIR) bands. Resolution: 1km at nadir (TIR),
500m (VIS). All are provided for both the oblique and nadir view. These measurements are accompanied
with grid and time information, quality flags, error estimates and meteorological auxiliary data.
Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.
This collection contains reprocessed data from baseline collection 003. Operational data can be found
in the corresponding collection.
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1RBT", "RBT", "VIS", "NIR", "SWIR", "BT", "TIR", "FIR", "Reprocessed", "BC003"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SLSTR Level 1B Radiances and Brightness Temperatures (version BC003) - Sentinel-3 - Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-04-19T00:00:00Z", "2018-04-04T00:00:00Z"]]}}
S3_SLSTR_L1RBT_BC004:
description: |
SLSTR Level 1B Radiances and Brightness Temperatures (version BC004) - Sentinel 3 - Reprocessed
The SLSTR level 1 products contain: the radiances of the 6 visible (VIS), Near Infra-Red (NIR) and Short Wave
Infra-Red (SWIR) bands (on the A and B stripe grids); the Brightness Temperature (BT) for the 3 Thermal Infra-Red
(TIR) bands; the BT for the 2 Fire (FIR) bands. Resolution: 1km at nadir (TIR), 500m (VIS). All are provided for
both the oblique and nadir view. These measurements are accompanied with grid and time information, quality flags,
error estimates and meteorological auxiliary data. Sentinel-3 is part of a series of Sentinel satellites, under the
umbrella of the EU Copernicus programme. This collection contains reprocessed data from baseline collection 004.
Operational data can be found in the corresponding collection.
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1RBT", "RBT", "VIS", "NIR", "SWIR", "BT", "TIR", "FIR", "Reprocessed", "BC004"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SENTINEL3 SLSTR Level-1 RBT - Reprocessed from BC004
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-05-09T00:00:00Z", null]]}}
S3_SLSTR_L2LST:
description: |
The SLSTR Level-2 LST product provides land surface parameters generated on the wide 1 km measurement grid.
It contains measurement file with Land Surface Temperature (LST) values with associated parameters (LST
parameters are computed and provided for each pixel (re-gridded or orphan) included in the 1 km measurement grid)
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2LST", "LST"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SENTINEL3 SLSTR Level-2 LST
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SLSTR_L2WST:
description: |
The SLSTR Level-2 WST product provides water surface parameters generated on the wide 1 km measurement grid.
It contains measurement file with Water Surface Temperature (WST) values with associated parameters (WST
parameters are computed and provided for each pixel (re-gridded or orphan) included in the 1 km measurement grid)
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2WST", "WST"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SENTINEL3 SLSTR Level-2 WST
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SLSTR_L2WST_BC003:
description: |
The SLSTR SST has a spatial resolution of 1km at nadir. Skin Sea Surface Temperature following the
GHRSST L2P GDS2 format specification, see https://www.ghrsst.org/ . Sentinel-3 is part of a series of Sentinel
satellites, under the umbrella of the EU Copernicus programme. This collection contains reprocessed data from
baseline collection 003. Operational data can be found in the corresponding collection.
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2WST", "WST", "REPROCESSED", "BC003"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SENTINEL3 SLSTR Level-2 WST Reprocessed from BC003
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-04-18T00:00:00Z", null]]}}
S3_SLSTR_L2AOD:
description: |
The Copernicus NRT S3 AOD processor quantifies the abundance of aerosol particles and monitors their global
distribution and long-range transport, at the scale of 9.5 x 9.5 km2. All observations are made available in
less than three hours from the SLSTR observation sensing time. It is only applicable during daytime.
NOTE: The SLSTR L2 AOD product is generated by EUMETSAT in NRT only. An offline (NTC) AOD product is generated
from SYN data by ESA, exploiting the synergy between the SLSTR and OLCI instruments.
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2AOD", "AOD"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SENTINEL3 SLSTR Level-2 AOD
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SLSTR_L2FRP:
description: |
The SLSTR Level-2 FRP product is providing one measurement data file, FRP_in.nc, with Fire Radiative Power (FRP)
values and associated parameters generated for each fire detected over land and projected on the SLSTR 1 km grid.
The fire detection is based on a mixed thermal band, combining S7 radiometric measurements and,
for pixels associated with a saturated value of S7 (i.e. above 311 K), F1 radiometric measurements.
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2FRP", "FRP"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SENTINEL3 SLSTR Level-2 FRP
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SLSTR_L2:
description: |
The SLSTR Level-2 products are generated in five different types: 1. SL_2_WCT, including the Sea Surface Temperature
for single and dual view, for 2 or 3 channels (internal product only), 2. SL_2_WST, including the Level-2P Sea
surface temperature (provided to the users), 3. SL_2_LST, including the Land Surface Temperature parameters
(provided to the users), 4. SL_2_FRP, including the Fire Radiative Power parameters (provided to the users),
5.SL_2_AOD, including the Aerosol Optical Depth parameters (provided to the users). The Level-2 product are
organized in packages composed of one manifest file and several measurement and annotation data files (between 2
and 21 files depending on the package). The manifest file is in XML format and gathers general information
concerning product and processing. The measurement and annotation data files are in netCDF 4 format, and include
dimensions, variables and associated attributes. Regarding the measurement files: one measurement file, providing
the land surface temperature, associated uncertainties and other supporting fields, is included in the SL_2_LST
packet. The annotation data files are generated from the annotation files included in the SL_1RBT package and their
format is identical to the files in the Level-1 packet.The SL_2_LST packet contains 10 annotation files, providing
the same parameters as in SL_2_WCT and, in addition, some vegetation parameters.
instruments: ["SLSTR"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SLSTR", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "L2FRP", "FRP", "L2WCT", "WCT", "L2WST", "WST", "L2AOD", "AOD"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: SENTINEL3 SLSTR Level-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-07-05T00:00:00Z", null]]}}
# MARK: S3 SRAL
S3_SRA:
description: |
SRAL Level 1B: Complex echoes (In-phase (I) and Quadrature (Q)) for the Low Resolution Mode (LRM) and/or
Synthetic Aperture Radar (SAR) mode both for C Band and Ku band. When the altimeter is in SAR mode, this product
also contains the so-called Pseudo LRM (PLRM) echoes. - All Sentinel-3 Near Real Time (NRT) products are available
at pick-up point in less than 3h. - All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point
in less than 30 days. - All Sentinel-3 Short Time Critical (STC) products are available at pick-up point in less
than 48 hours. Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-1
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SRA_A:
description: |
A Level 1A SRAL product contains one "measurement data file" containing the L1A measurements parameters:
ECHO_SAR_Ku: L1A Tracking measurements (sorted and calibrated) in SAR mode - Ku-band (80-Hz)
ECHO_PLRM: L1A Tracking measurements (sorted and calibrated) in pseudo-LRM mode - Ku and C bands (80-Hz)
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-1 SRA_A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SRA_BS:
description: |
A Level 1B-S SRAL product contains one "measurement data file" containing the L1b measurements parameters:
ECHO_SAR_Ku : L1b Tracking measurements in SAR mode - Ku band (20-Hz) as defined in the L1b MEAS product
completed with SAR expert information
ECHO_PLRM : L1b Tracking measurements in pseudo-LRM mode - Ku and C bands (20-Hz) as defined in the L1b
MEAS product
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-1 SRA_BS
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SRA_1A_BC004:
description: |
SRAL Level 1A Unpacked L0 Complex Echoes (version BC004) - Sentinel-3 - Reprocessed
Fundamental science and engineering product development supporting operational users. This product is most
relevant to SAR processing specialists allowing fundamental studies on SAR processing such as Doppler beam
formation and for calibration studies using ground-based Transponders. Sentinel-3 is part of a series of Sentinel
satellites, under the umbrella of the EU Copernicus programme. This collection contains reprocessed data from
baseline collection 004. Operational data can be found in the corresponding collection.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1A
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1A", "REPROCESSED", "BC004"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-1A Unpacked - Reprocessed from BC004
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-03-01T00:00:00Z", null]]}}
S3_SRA_1A_BC005:
description: |
Fundamental science and engineering product development supporting operational users. This product is most relevant
to SAR processing specialists allowing fundamental studies on SAR processing such as Doppler beam formation and for
calibration studies using ground-based Transponders. Sentinel-3 is part of a series of Sentinel satellites, under
the umbrella of the EU Copernicus programme. This dataset contains reprocessed data from Baseline Collection 005,
and is continued by the operational NTC data stream from 10/Mar/2023 onwards.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1A
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1A", "REPROCESSED", "BC005"]
eodag:sensor_type: RADAR
license: other
title: SRAL Level 1A Unpacked L0 Complex Echoes (version BC005) - Sentinel-3 - Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-05-05T00:00:00Z", "2023-03-09T00:00:00Z"]]}}
S3_SRA_1B_BC004:
description: |
SRAL Level 1B (version BC004) - Sentinel-3 - Reprocessed
SRAL Level 1B: Complex echoes (In-phase (I) and Quadrature (Q)) for the Low Resolution Mode (LRM) and/or
Synthetic Aperture Radar (SAR) mode both for C Band and Ku band. When the altimeter is in SAR mode, this product
also contains the so-called Pseudo LRM (PLRM) echoes. Sentinel-3 is part of a series of Sentinel satellites, under
the umbrella of the EU Copernicus programme. This collection contains reprocessed data from baseline collection 004.
Operational data can be found in the corresponding collection.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1B
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1B", "REPROCESSED", "BC004"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-1B - Reprocessed from BC004
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-03-01T00:00:00Z", "2019-12-31T00:00:00Z"]]}}
S3_SRA_1B_BC005:
description: |
SRAL Level 1B: Complex echoes (In-phase (I) and Quadrature (Q)) for the Low Resolution Mode (LRM) and/or Synthetic
Aperture Radar (SAR) mode both for C Band and Ku band. When the altimeter is in SAR mode, this product also contains
the so-called Pseudo LRM (PLRM) echoes. Sentinel-3 is part of a series of Sentinel satellites, under the umbrella
of the EU Copernicus programme. This dataset contains reprocessed data from Baseline Collection 005, and is continued
by the operational NTC data stream from 10/Mar/2023 onwards.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1B
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1B", "REPROCESSED", "BC005"]
eodag:sensor_type: RADAR
license: other
title: SRAL Level 1B (version BC005) - Sentinel-3 - Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-05-05T00:00:00Z", "2023-03-09T00:00:00Z"]]}}
S3_SRA_BS_BC004:
description: |
SRAL Level 1B Stack Echoes (version BC004) - Sentinel-3 - Reprocessed
SRAL Level 1B: Complex echoes (In-phase (I) and Quadrature (Q)) for the Low Resolution Mode (LRM) and/or Synthetic
Aperture Radar (SAR) mode both for C Band and Ku band. When the altimeter is in SAR mode, this product also
contains the so-called Pseudo LRM (PLRM) echoes. Complex (In-phase and Quadrature) echoes (I's and Q;s) after
slant/Doppler range correction. This product is most relevant to geophysical retrieval algorithm developers
(over ocean, land and ice surfaces), surface characterisations studies (e.g. impact of sea state bias, wave
directional effects etc) and Quality Control systems. Sentinel-3 is part of a series of Sentinel satellites, under
the umbrella of the EU Copernicus programme. This collection contains reprocessed data from baseline collection 004.
Operational data can be found in the corresponding collection.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1B
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1B", "REPROCESSED", "STACK", "ECHOES", "BC004"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-1B Stack Echoes - Reprocessed from BC004
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-03-01T00:00:00Z", "2019-12-31T00:00:00Z"]]}}
S3_SRA_BS_BC005:
description: |
SRAL Level 1B: Complex echoes (In-phase (I) and Quadrature (Q)) for the Low Resolution Mode (LRM) and/or Synthetic
Aperture Radar (SAR) mode both for C Band and Ku band. When the altimeter is in SAR mode, this product also
contains the so-called Pseudo LRM (PLRM) echoes. Complex (In-phase and Quadrature) echoes (I's and Q;s) after
slant/Doppler range correction. This product is most relevant to geophysical retrieval algorithm developers
(over ocean, land and ice surfaces), surface characterisations studies (e.g. impact of sea state bias, wave
directional effects etc) and Quality Control systems. Sentinel-3 is part of a series of Sentinel satellites, under
the umbrella of the EU Copernicus programme. This dataset contains reprocessed data from Baseline Collection 005,
and is continued by the operational NTC data stream from 10/Mar/2023 onwards.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L1B
keywords: ["SRA", "SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L1", "L1B", "REPROCESSED", "STACK", "ECHOES", "BC005"]
eodag:sensor_type: RADAR
license: other
title: SRAL Level 1B Stack Echoes (version BC005) - Sentinel-3 - Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-05-05T00:00:00Z", "2023-03-09T00:00:00Z"]]}}
S3_WAT:
description: |
The products contain the typical altimetry measurements, like the altimeter range, the sea surface height, the wind
speed, significant wave height and all required geophysical corrections and related flags. Also the sea Ice
freeboard measurement is included. The measurements in the standard data file provide the measurements in low
(1 Hz = approx. 7km) and high resolution (20 Hz = approx. 300 m), in LRM mode or in SAR mode, for both C-band and
Ku band. The SAR mode is the default mode. The reduced measurement data file contains 1 Hz measurements only. The
enhanced measurement data file contains also the waveforms and associated parameters and the pseudo LRM measurements
when in SAR mode. This product contains the following datasets: Sea Level Global(NRT) (PDS_MG3_CORE_14_GLONRT),
Sea Level Global Reduced(NRT)(PDS_MG3_CORE_14_GLONRT_RD), Sea Level Global Standard(NRT) (PDS_MG3_CORE_14_GLONRT_SD),
Sea Level Global Enhanced(NRT) (PDS_MG3_CORE_14_GLONRT_EN) - All Sentinel-3 NRT products are available at pick-up
point in less than 3h. - All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less
than 30 days - All Sentinel-3 Short Time Critical (STC) products are available at pick-up point in less than 48
hours Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "WAT"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-2 WAT
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_WAT_BC004:
description: |
The products contain the typical altimetry measurements, like the altimeter range, the sea surface height, the wind
speed, significant wave height and all required geophysical corrections and related flags. Also the sea Ice
freeboard measurement is included. The measurements in the standard data file provide the measurements in low
(1 Hz = approx. 7km) and high resolution (20 Hz = approx. 300 m), in LRM mode or in SAR mode, for both C-band and
Ku band. The SAR mode is the default mode. The reduced measurement data file contains 1 Hz measurements only. The
enhanced measurement data file contains also the waveforms and associated parameters and the pseudo LRM
measurements when in SAR mode. Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU
Copernicus programme. This collection contains reprocessed data from baseline collection 004. Operational data can
be found in the corresponding collection.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "WAT", "REPROCESSED", "BC004"]
eodag:sensor_type: RADAR
license: other
title: SRAL Level 2 Altimetry Global - Reprocessed from BC004
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-03-01T00:00:00Z", "2019-12-31T00:00:00Z"]]}}
S3_WAT_BC005:
description: |
The products contain the typical altimetry measurements, like the altimeter range, the sea surface height, the wind
speed, significant wave height and all required geophysical corrections and related flags. Also the sea Ice
freeboard measurement is included. The measurements in the standard data file provide the measurements in low
(1 Hz = approx. 7km) and high resolution (20 Hz = approx. 300 m), in LRM mode or in SAR mode, for both C-band and
Ku band. The SAR mode is the default mode. The reduced measurement data file contains 1 Hz measurements only. The
enhanced measurement data file contains also the waveforms and associated parameters and the pseudo LRM
measurements when in SAR mode. Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU
Copernicus programme. This collection contains reprocessed data from baseline collection 004. Operational data can
be found in the corresponding collection.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "WAT", "REPROCESSED", "BC005"]
eodag:sensor_type: RADAR
license: other
title: SRAL Level 2 Altimetry Global (version BC005) - Sentinel-3 - Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-05-05T00:00:00Z", "2023-03-09T00:00:00Z"]]}}
S3_LAN:
description: LAN or SR_2_LAN___
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "LAN"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-2 LAN
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_LAN_HY:
description: |
Sentinel-3 STM payload includes two main instruments: the Sentinel-3 Radar ALtimeter (SRAL) and a MicroWave
Radiometer (MWR). SRAL is providing continuous topography measurements of the Earth's surface. It is the first
radar altimeter operating exclusively with delay-Doppler capabilities, which provides a significant improvement of
the along-track resolution compared to conventional Low Resolution Mode (LRM) altimeters. This enhancement is
highly valuable over continental and sea ice surfaces, considering their heterogeneous characteristics.
ESA and the Sentinel-3 Mission Performance Cluster (MPC) have developed in 2021-2022 dedicated delay-Doppler and
Level-2 processing chains for the generation of new Sentinel-3 STM LAND level-2 products over inland waters,
sea-ice, and land ice areas. The main objective of these so-called “Thematic Products” is to address the specific
needs of the user communities related to the three different Thematic surfaces.
For Hydrology Thematic Products, the coverage includes all the continental surfaces, except the Antarctica ice
sheet, and Greenland ice sheet interior. Over coastal zones the 50 km common area between Land and Marine products
remains. Therefore, the Hydrology products cover up to 25 km over surfaces considered as Marine.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "LAN", "HYDROLOGY"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-2 LAN HYDRO
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_LAN_SI:
description: |
Sentinel-3 STM payload includes two main instruments: the Sentinel-3 Radar ALtimeter (SRAL) and a MicroWave
Radiometer (MWR). SRAL is providing continuous topography measurements of the Earth's surface. It is the first
radar altimeter operating exclusively with delay-Doppler capabilities, which provides a significant improvement of
the along-track resolution compared to conventional Low Resolution Mode (LRM) altimeters. This enhancement is
highly valuable over continental and sea ice surfaces, considering their heterogeneous characteristics.
ESA and the Sentinel-3 Mission Performance Cluster (MPC) have developed in 2021-2022 dedicated delay-Doppler and
Level-2 processing chains for the generation of new Sentinel-3 STM LAND level-2 products over inland waters,
sea-ice, and land ice areas. The main objective of these so-called “Thematic Products” is to address the specific
needs of the user communities related to the three different Thematic surfaces.
Each Sentinel-3 STM Land Thematic Product has a dedicated geographical coverage, defined in a Thematic Mask. For Sea
Ice Thematic Products, the mask remains static, and the coverage was calculated by the Expert Support
Laboratories (ESL) of the Sentinel-3 MPC, based on the maximum of sea ice extent given a NSIDC sea ice climatology.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "LAN", "SEA", "ICE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-2 LAN SEA ICE
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_LAN_LI:
description: |
Sentinel-3 STM payload includes two main instruments: the Sentinel-3 Radar ALtimeter (SRAL) and a MicroWave
Radiometer (MWR). SRAL is providing continuous topography measurements of the Earth's surface. It is the first
radar altimeter operating exclusively with delay-Doppler capabilities, which provides a significant improvement of
the along-track resolution compared to conventional Low Resolution Mode (LRM) altimeters. This enhancement is
highly valuable over continental and sea ice surfaces, considering their heterogeneous characteristics.
ESA and the Sentinel-3 Mission Performance Cluster (MPC) have developed in 2021-2022 dedicated delay-Doppler and
Level-2 processing chains for the generation of new Sentinel-3 STM LAND level-2 products over inland waters,
sea-ice, and land ice areas. The main objective of these so-called “Thematic Products” is to address the specific
needs of the user communities related to the three different Thematic surfaces.
Each Sentinel-3 STM Land Thematic Product has a dedicated geographical coverage, defined in a Thematic Mask. For
Land Ice Thematic Products, the mask includes the Antarctica and Greenland ice sheets, along with glacier areas as
defined in the Randolph Glacier Inventory (RGI) database.
instruments: ["SRAL"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SRAL", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "LAN", "LAND", "ICE"]
eodag:sensor_type: RADAR
license: other
title: SENTINEL3 SRAL Level-2 LAN LAND ICE
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
# MARK: MFG
MFG_GSA_57:
description: |
Release 2 of the Thematic Climate Data Record (TCDR) of the Meteosat First
Generation (MFG) Level 2 land surface albedo. The variables estimated are
black-sky albedo (BSA) and white-sky albedo (WSA) with the corresponding
uncertainties as explained in the Product User Guide (PUM). The data record
validation and limitations are provided in the Validation Report (VR). The
products are available in netCDF4 format. This release contains products
generated with Meteosat-7.
instruments: ["MVIRI"]
constellation: MFG
platform: MFG
processing:level: L2
keywords: ["MVIRI", "L2", "MFG", "Climate", "Thematic"]
eodag:sensor_type: OPTICAL
license: other
title: GSA Level 2 Climate Data Record Release 2 - MFG - 57 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2006-12-07T00:00:00Z", "2017-03-30T23:59:59Z"]]}}
MFG_GSA_63:
description: |
Release 2 of the Thematic Climate Data Record (TCDR) of the Meteosat First
Generation (MFG) Level 2 land surface albedo. The variables estimated are
black-sky albedo (BSA) and white-sky albedo (WSA) with the corresponding
uncertainties as explained in the Product User Guide (PUM). The data record
validation and limitations are provided in the Validation Report (VR). The
products are available in netCDF4 format. This release contains products
generated with Meteosat-5.
instruments: ["MVIRI"]
constellation: MFG
platform: MFG
processing:level: L2
keywords: ["MVIRI", "L2", "MFG", "Climate", "Thematic"]
eodag:sensor_type: OPTICAL
license: other
title: GSA Level 2 Climate Data Record Release 2 - MFG - 63 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1998-07-10T00:00:00Z", "2007-04-10T23:59:59Z"]]}}
HIRS_FDR_1_MULTI:
description: |
This is Release 2 of the Fundamental Data Record (FDR) brightness temperatures
from the High Resolution Infrared Radiation Sounder (HIRS) on board NOAA and Metop
satellites. The data record covers more than 40 years from 29 October 1978 to 31
December 2022. Release 2 provides recalibrated Level 1c brightness temperatures
based on the V4.0 calibration method developed by Cao et al. (2007). This method
was implemented into the NWP-SAF software ATOVS and AVHRR processing Package (AAPP).
This software was consistently used to recalibrate and reprocess data from all HIRS
instruments on board TIROS-N, NOAA-06 to NOAA-19, Metop-A, and Metop-B. Input HIRS
data were collected from NOAA/CLASS and ECMWF archives and merged to produce a
longer time series of some of the satellites.
instruments: ["HIRS"]
constellation: Metop,TIROS,NOAA
platform: Metop,TIROS,NOAA
processing:level: L1C
keywords: ["HIRS", "L1C", "HIRS", "TIROS", "Metop", "NOAA", "Sounder", "FDR"]
eodag:sensor_type: Sounder
license: other
title: HIRS Level 1C Fundamental Data Record Release 2 - Multimission - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1978-10-29T00:00:00Z", "2020-12-31T23:59:59Z"]]}}
S6_RADIO_OCCULTATION:
description: |
Jason-CS/Sentinel-6 Radio Occultation Level 1B product, providing a bending
angle versus impact parameter profile, as well as other relevant
information derived from the observation.
instruments: ["GNSS-RO"]
constellation: Sentinel-6
platform: Sentinel-6
processing:level: L1B
keywords: ["Sentinel-6", "L1B", "GNSS-RO", "Radio", "Occultation"]
eodag:sensor_type: Radio Occultation
license: other
title: Radio Occultation Level 1B Products - Sentinel-6
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2021-11-19T00:00:00Z", null]]}}
MTG_LI_AF:
description: |
LI Level 2 Accumulated Flashes (AF) complements the LI Level 2 Accumulated
Flash Area (AFA) by providing one with the variation of the number of
events within those regions reported to have lightning flashes in the
Accumulated Flash Area (AFA). Accumulated Flashes provide users with data
about the mapping of the number of LI events/detections rather than the
mapping of flashes. One should keep in mind that the absolute value within
each pixel of the Accumulated Flashes has no real physical meaning; it is
rather a proxy for the pixel-by-pixel variation of the number of events. It
is worth noting that one can derive the flash rate over a region
encompassing a complete lightning feature (not within an FCI grid pixel) in
Accumulated Flashes; this stems from the definition in Accumulated
(gridded) data.
instruments: ["LI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "LI", "AF", "Lightning", "Weather", "Flashes"]
eodag:sensor_type: Lightning Imager
license: other
title: LI Accumulated Flashes - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-07-08T00:00:00Z", null]]}}
MTG_LI_LFL:
description: |
LI Level 2 Lightning Flashes (LFL) contains LI flashes. The definition of a
flash is shared by LI and GLM; collections of groups that are correlated in
space and time within the two windows of 330 milliseconds (temporal window)
and 16.5km (space window), respectively. Even if the definition of a flash
is not uniform among all lightning location systems, the simple fact that a
flash is a collection of groups/strokes correlated in space and time
somewhat mitigates the differences in the way different types of lightning
sensors interpret different lightning processes. This makes flash datasets
of different lightning location systems more comparable than group/stroke
datasets.
instruments: ["LI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "LI", "LFL", "Lightning", "Weather", "Flashes"]
eodag:sensor_type: Lightning Imager
license: other
title: LI Lightning Flashes - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-07-08T00:00:00Z", null]]}}
MTG_LI_LGR:
description: |
LI Level 2 Lightning Groups (LGR) contains LI groups. These are closely
related to other similar space-born instruments. The definition of a group is
shared by LI, GLM and ISS-LIS groups: collections of pixel-based lightning
events that are acquired within the same acquisition frame and are spatially
clustered. LI groups provide users with information about the time-slicing
imaging (over the LI acquisition time, ie one millisecond) of lightning
optical emissions. When comparing LI groups with either GLM or ISS-LIS
groups, users must consider the differences in design between instruments,
such as integration time and spatial sampling/resolution. Both GLM and
ISS-LIS acquire over two milliseconds. When observing the same storm, this
difference in design can potentially create considerable differences in the
total number of groups, as well as differences between the acquisition times
of the groups. In addition, differences will be found also for the
geolocation of groups. In general, the discrepancies mentioned above are
expected to be of the order of a few milliseconds for the group time and of
the order of a few kilometres for the group geolocation.
instruments: ["LI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "LI", "LGR", "Lightning", "Weather", "groups"]
eodag:sensor_type: Lightning Imager
license: other
title: LI Lightning Groups - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-07-08T00:00:00Z", null]]}}
MTG_LI_AFA:
description: |
LI Level 2 Accumulated Flash Area (AFA) provides the user with data about
flash mapping by using the area covered by the optical emission of each
flash in LI Level 2 Lightning Flashes (LFL). It is important to keep in
mind that each flash is treated as a flat (uniform) optical emission in
this data. Accumulated Flash Area allows one to monitor the regions within
a cloud top from which lightning-related optical emissions over 30 sec are
emerging and accumulating and to know the number of flashes that were
observed within the FCI grid pixels composing those regions. For example,
from the Accumulated Flash Area, one can derive the flash rate for each
pixel of the FCI 2km grid. This is a considerable improvement compared to
the simple description of the flash using the variable flash_footprint
available in Lightning Flashes.
instruments: ["LI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "LI", "AFA", "Lightning", "Weather", "Flashes", "Accumulated"]
eodag:sensor_type: Lightning Imager
license: other
title: LI Accumulated Flash Area - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-07-08T00:00:00Z", null]]}}
MTG_LI_AFR:
description: |
LI Level 2 Accumulated Flash Radiance (AFR) is meant to describe the
pixel-by-pixel variation of the optical emission accumulated over 30 sec
within the FCI 2km grid. This stems from the events contributing to LI
Level 2 Accumulated Flashes (AF) (each one contributing with its radiance)
and it can be thought of as the 'appearance' of the accumulated optical
emissions over 30 sec as seen by LI.
instruments: ["LI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "LI", "AFR", "Lightning", "Weather", "Flashes", "Accumulated", "Radiance"]
eodag:sensor_type: Lightning Imager
license: other
title: LI Accumulated Flash Radiance - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-07-08T00:00:00Z", null]]}}
MTG_LI_LEF:
description: |
LI Level 2 Ligthning Events Filtered (LEF) is one of the initial products,
along with Lightning Flashes (LFL) and Lightning Groups (LGR), and provides
the finest scale over which LI can monitor lightning activity. Lightning
Flashes and Lightning Groups contain two variables that provide the size of
each group and flash in units of LI pixels, ie number_of_events and
flash_footprint, respectively. To compute the exact physical size of a
group/flash, users should use the information available in Ligthning Events
Filtered. One can derive such a descriptor only by knowing which events
compose a group/flash and employing the physical size of the projection of
each event on the Earth's surface for the computation.
instruments: ["LI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "LI", "LEF", "Lightning", "Weather", "Flashes", "Filtered"]
eodag:sensor_type: Lightning Imager
license: other
title: LI Lightning Events Filtered - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-07-08T00:00:00Z", null]]}}
MTG_FCI_FDHSI:
description: |
The rectified (Level 1c) Meteosat FCI full disc image data in normal
spatial (FDHSI) resolution. The FCI instrument consists of 16 imaging
spectral channels ranging from 0.4 µm to 13.3 µm with the channel at 3.8 µm
having an extended dynamic range dedicated to fire monitoring. The spatial
resolution is 1km for visible and near-infrared channels and 2 km for
infrared channels. FCI Level 1c rectified radiance dataset consists of a
set of files that contain the level 1c science data rectified to a
reference grid together with the auxiliary data associated with the
processing configuration and the quality assessment of the dataset. Level
1c image data here corresponds to initially geolocated and radiometrically
pre-processed image data, without full georeferencing and cal/val in
spatial and spectral domains applied. The data are ready for further
processing and testing, e.g. value chains and initial tests for extracting
meteorological products, however, we generally do not recommend the
generation of Level 2 products due to known limitations in the Level 1c
data.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L1
keywords: ["MTG", "L1", "FCI", "FDHSI", "Atmosphere", "Ocean", "Land"]
eodag:sensor_type: Imager
license: other
title: FCI Level 1c Normal Resolution Image Data - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-09-24T00:00:00Z", null]]}}
MTG_FCI_HRFI:
description: |
The rectified (Level 1c) Meteosat FCI full disc image data in high spatial
(HRFI) resolution.The FCI instrument consists of 16 imaging spectral
channels ranging from 0.4 µm to 13.3 µm with the channel at 3.8 µm having
an extended dynamic range dedicated to fire monitoring. The high-resolution
HRFI dataset has 4 spectral channels at VIS 0.6 µm, NIR 2.2 µm, IR 3.8 µm
and IR 13.3 µm with a spatial resolution of 0.5 km for visible and
near-infrared channels and 1 km for infrared channels. FCI Level 1c
rectified radiance dataset consists of a set of files that contain the
level 1c science data rectified to a reference grid together with the
auxiliary data associated with the processing configuration and the quality
assessment of the dataset. Level 1c image data here corresponds to
initially geolocated and radiometrically pre-processed image data, without
full georeferencing and cal/val in spatial and spectral domains applied.
The data are ready for further processing and testing, e.g. value chains
and initial tests for extracting meteorological products, however, we
generally do not recommend the generation of Level 2 products due to known
limitations in the Level 1c data. A selection of single channel data are
visualised in our EUMETView service.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L1
keywords: ["MTG", "L1", "FCI", "HRFI", "Atmosphere", "Ocean", "Land"]
eodag:sensor_type: Imager
license: other
title: FCI Level 1c High Resolution Image Data - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-09-24T00:00:00Z", null]]}}
MTG_FCI_ASR_BUFR:
description: |
The All-Sky Radiance (ASR) product is a segmented product that provides
FCI Level 1C data statistics within processing segments referred to as
Field-of-Regard (FoR). The statistics are computed on the L1C radiances
(for all FCI channels), brightness temperatures (for the eight IR
channels) and reflectances (for the eight visible and near-infrared
channels) and include the mean value, standard deviation, minimum and
maximum values within the FoR. The ASR product is available in BUFR
and netCDF format, every 10 minutes, at a spatial resolution of 16x16
pixels (IR) and 32x32 pixels (VIS).
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "FCI", "ASR", "Radiance", "BUFR"]
eodag:sensor_type: Imager
license: other
title: All Sky Radiance (BUFR) - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2025-01-22T00:00:00Z", null]]}}
MTG_FCI_ASR_NETCDF:
description: |
The All-Sky Radiance (ASR) product is a segmented product that provides
FCI Level 1C data statistics within processing segments referred to as
Field-of-Regard (FoR). The statistics are computed on the L1C radiances
(for all FCI channels), brightness temperatures (for the eight IR
channels) and reflectances (for the eight visible and near-infrared
channels) and include the mean value, standard deviation, minimum and
maximum values within the FoR. The ASR product is available in BUFR
and netCDF format, every 10 minutes, at a spatial resolution of 16x16
pixels (IR) and 32x32 pixels (VIS).
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "FCI", "ASR", "Radiance", "netCDF"]
eodag:sensor_type: Imager
license: other
title: All Sky Radiance (netCDF) - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2025-01-22T00:00:00Z", null]]}}
MTG_FCI_AMV_BUFR:
description: |
The Atmospheric Motion Vector (AMV) product is realised by tracking
clouds or water vapour features in consecutive FCI satellite images
based on feature tracking between each pair of consecutive repeat
cycles, leading to two intermediate AMV products for an image triplet.
The final product is then derived from these two intermediate products,
and includes information on wind speed, direction, height, and quality.
AMVs are extracted from the FCI VIS 0.8, IR 3.8 (night only), IR 10.5,
WV 6.3 and WV 7.3 channels. The AMV product is available in BUFR and
netCDF format, every 30 minutes.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "FCI", "AMV", "Clouds", "BUFR"]
eodag:sensor_type: Imager
license: other
title: Atmospheric Motion Vectors (BUFR) - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2025-01-22T00:00:00Z", null]]}}
MTG_FCI_AMV_NETCDF:
description: |
The Atmospheric Motion Vector (AMV) product is realised by tracking
clouds or water vapour features in consecutive FCI satellite images
based on feature tracking between each pair of consecutive repeat
cycles, leading to two intermediate AMV products for an image triplet.
The final product is then derived from these two intermediate products,
and includes information on wind speed, direction, height, and quality.
AMVs are extracted from the FCI VIS 0.8, IR 3.8 (night only), IR 10.5,
WV 6.3 and WV 7.3 channels. The AMV product is available in BUFR and
netCDF format, every 30 minutes.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "FCI", "AMV", "Clouds", "netCDF"]
eodag:sensor_type: Imager
license: other
title: Atmospheric Motion Vectors (netCDF) - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2025-01-22T00:00:00Z", null]]}}
MTG_FCI_CLM:
description: |
The central aim of the cloud mask (CLM) product is to identify cloudy and
cloud free FCI Level 1c pixels with high confidence. The product also
provides information on the presence of snow/sea ice, volcanic ash and
dust. This information is crucial both for spatiotemporal analyses of the
cloud coverage and for the subsequent retrieval of other meteorological
products that are only valid for cloudy (e.g. cloud properties) or clear
pixels (e.g. clear sky reflectance maps or global instability indices).
The algorithm is based on multispectral threshold techniques applied to
each pixel of the image. CLM is available in netCDF and GRIB format,
every 10 minutes, at a spatial resolution of 2 km at nadir.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "FCI", "CLM", "Clouds"]
eodag:sensor_type: Imager
license: other
title: Cloud Mask - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2025-01-22T00:00:00Z", null]]}}
MTG_FCI_GII:
description: |
The Global Instability Index (GII) product provides information about
instability of the atmosphere and thus can identify regions of convective
potential. GII is a segmented product that uses an optimal estimation
scheme to fit clear-sky vertical profiles of temperature and humidity,
constrained by NWP forecast products, to FCI observations in the seven
channels WV6.3, WV7.3, IR8.7, IR9.7, IR10.5, IR12.3, and IR13.3. The
retrieved profiles are then used to compute atmospheric instability
indices: Lifted Index, K Index, Layer Precipitable Water, Total
Precipitable Water. The GII product is available in netCDF format, every
10 minutes, in 3x3 pixels (IR channels), leading to a spatial resolution
of 6 km at nadir.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "FCI", "GII", "atmosphere"]
eodag:sensor_type: Imager
license: other
title: Global Instability Indices - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2025-01-22T00:00:00Z", null]]}}
MTG_FCI_OCA:
description: |
The Optimal Cloud Analysis (OCA) product uses an optimal estimation
retrieval scheme to retrieve cloud properties (phase, height and
microphysical properties) from visible, near-infrared and thermal
infrared FCI channels. The optimal estimation framework aims to ensure
that measurements and any prior information may be given appropriate
weight in the solution depending on error characteristics whether
instrumental or from modelling sources. The product can also contain
information on dust and volcanic ash clouds if these are flagged in the
corresponding Cloud Analysis Product. The OCA product is available in
netCDF format, every 10 minutes, at 2km spatial resolution at nadir.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "FCI", "OCA", "Clouds"]
eodag:sensor_type: Imager
license: other
title: Optimal Cloud Analysis - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2025-01-22T00:00:00Z", null]]}}
MTG_FCI_OLR:
description: |
The Outgoing Longwave Radiation (OLR) product is important for Earth
radiation budget studies as well as for weather and climate model
validation purposes, since variations in OLR reflect the response of the
Earth-atmosphere system to solar diurnal forcing. The product is based on
a statistical relationship linking the radiance measured in each FCI
infrared channel to the top-of-atmosphere outgoing longwave flux
integrated over the full infrared spectrum. The computation is done for
each pixel considering the cloud cover characteristics (clear sky,
semi-transparent and opaque cloud cover). The OLR product is available in
netCDF format, every 10 minutes, at 2 km spatial resolution at nadir.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["MTG", "L2", "FCI", "OLR", "Radiation", "LW"]
eodag:sensor_type: Imager
license: other
title: Outgoing LW radiation at TOA - MTG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2025-01-22T00:00:00Z", null]]}}
MSG_SEVIRI_RSS_AMV_CDR_V1:
description: |
Release 1 of the Climate Data Record (CDR) of MSG imager SEVIRI
(Spinning Enhanced Visible Infra-Red Imager) High-Resolution Wind (HRW)
from the Rapid Scan Service (RSS). This release covers the MSG SEVIRI
RSS record from three different satellite platforms Meteosat-8 to -10.
The CDR covers 12 years’ worth of data from 2008-05-06 until 2020-04-03
and there is a product every five minutes in NetCDF format. It provides
information on Atmospheric motion vectors (AMV) speed (ms-1),
direction (degree), and height (given in pressure units Pa) of vectors
which are derived by tracking cloud or water vapour features between
two images in time. AMV are wind vectors retrieved at all heights below
the tropopause, derived from High Resolution Visible (HRV), Visible 0.6
microns Water Vapour (6.2 microns and 7.3 microns) and 10.8 microns.
Infrared channels. In all channels, winds are retrieved by tracking cloud
features. For the two WV channels, water vapour features are also tracked
(clear sky winds).\nThe data record has been generated using the NWC SAF
software for geostationary satellites (NWC/GEO).
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["Wind", "Level 2 Data", "SEVIRI", "MSG"]
eodag:sensor_type: Optical
license: CC-BY-4.0
title: SEVIRI Rapid Scan Atmospheric Motion Vectors Climate Data Record Release 1 - MSG
extent: {"spatial": {"bbox": [[-64.0, 16.0, 70.0, 70.0]]}, "temporal": {"interval": [["2008-05-06T00:00:00Z", "2020-04-03T00:00:00Z"]]}}
MSG_SEVIRI_RSS_HR_IMG_L1_5_V1:
description: |
Release 1 of the Fundamental Climate Data Record (FCDR) of MSG imager
SEVIRI (Spinning Enhanced Visible Infra-Red Imager) level 1.5 counts
and recalibration coefficients (only for the infrared channels) from
the Rapid Scan Service (RSS). This release covers the MSG SEVIRI RSS
record from three different satellite platforms Meteosat-8 to -10.
Both operational and recalibrated calibration coefficients are provided.
The FCDR covers 13 years’ worth of data from 2008-05-06 until 2021-09-17,
but recalibrated parameters are only available until 2020-04. To improve
usability, the data are reformatted to NetCDF and additionally contain
variable grouping, geolocation information, and sun/satellite angles.
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L1
keywords: ["Calibration", "Level 1 Data", "Fundamental Climate Data Record", "Radiation", "SEVIRI", "MSG"]
eodag:sensor_type: Optical
license: proprietary
title: SEVIRI Rapid Scan High Rate Level 1.5 Image Data Climate Data Record Release 1 - MSG
extent: {"spatial": {"bbox": [[-71.22, 12.78, 90.22, 81.26]]}, "temporal": {"interval": [["2008-05-06T00:00:00Z", "2021-09-17T00:00:00Z"]]}}
MSG_SEVIRI_SARAH_CDR_V003:
description: |
The third edition of the Surface Solar Radiation Data Set - Heliosat
(SARAH-3) is a satellite-based climate data record of the solar
surface irradiance (SIS), the surface direct irradiance ((direct
horizontal and direct normalized, SID and DNI), the sunshine duration
(SDU), the photosynthetically active radiation (PAR), daylight (DAL),
and the effective cloud albedo derived (CAL) from satellite-observations
of the visible channels of the MVIRI and the SEVIRI instruments onboard
the geostationary Meteosat satellites. SARAH-3 covers the time period
1 Jan 1983 - 31 Dec 2020 as climate data record (CDR) and is
operationally extended as Interim Climate Data Record (ICDR) to the
present with a latency of 5 days; the data cover the region ±65°
longitude and ±65° latitude. The products are available as monthly
(P1M) and daily (P1D) means, and as 30-min instantaneous data (PT30M)
(sunshine duration is available as monthly and daily sum) on a regular
latitude/longitude grid with a spatial resolution of 0.05° x 0.05°
degrees. The data record is complemented with a comprehensive
documentation of the algorithms used and the generation of the data
record. Validation report and user guidance are also available.\n\n
All product are available via the CM SAF Web User Interface. The
daily and monthly mean surface incoming shortwave radiation (SIS)
and the daily and monthly sums of sunshine duration (SDU) are also
provided via EUMETCast.\n\nNote: the CDR coverage ends on the
31/12/2020. Dates after 31/12/2020 are covered by the ICDR.
instruments: ["SEVIRI", "MVIRI"]
constellation: MSG
platform: MSG,MFG
processing:level:
keywords: ["Climate", "Interim Climate Data Record", "Radiation", "Thematic Climate Data Record", "SEVIRI", "MSG"]
eodag:sensor_type: Optical
license: CC-BY-4.0
title: Surface Radiation Data Set - Heliosat (SARAH) - Edition 3
extent: {"spatial": {"bbox": [[-65.0, -65.0, 65.0, 65.0]]}, "temporal": {"interval": [["1983-01-01T00:00:00Z", null]]}}
MTG_FCI_ACTIVE_FIRE_L2_V1:
description: |
The Active Fire Monitoring product indicates the presence of fire within a pixel.
The key input to the algorithm is the FCI channel IR-3.8, which is very sensitive to hot spots caused by fire.
instruments: ["FCI"]
constellation: MTG
platform: MTG
processing:level: L2
keywords: ["Fire", "Level 2 Data", "MTG"]
eodag:sensor_type: Imager
license: CC-BY-4.0
title: Active Fire Monitoring (netCDF) - MTG - 0 degree
extent: {"spatial": {"bbox": [[-67.5, -67.5, 67.5, 67.5]]}, "temporal": {"interval": [["2025-07-03T00:00:00Z", null]]}}
MULT_PMW_IR_GIRAFE_PRECIP_CDR_V001:
description: |
The GIRAFE v1 climate data record (CDR) provides precipitation estimates
derived from a combination of passive microwave (PMW) observations onboard
polar orbiting satellites and infrared (IR) observations onboard
geostationary satellites. GIRAFE v1 covers the time period 2002/01/01
until 2022/12/31. The PMW input to GIRAFE v1 is from various microwave
imager and sounder instruments. Precipitation rate estimates are retrieved
from the observed PMW brightness temperatures by precipitation retrieval
algorithms HOAPS, PNPR-CLIM*, and PRPS. The resulting archives of
instantaneous precipitation rate estimates are homogenized using quantile
mapping. The IR input to GIRAFE comes from the five geostationary positions
forming the Geo-Ring, providing observations along all geographical longitudes.
The spatially and temporally highly resolved IR input is trained to detect the
occurrence of precipitation using the PMW-based instantaneous precipitation
rate estimates. Conditional precipitation rates are computed based on PMW
observations only. At latitudes higher than 55°N/S where Geo-Ring IR pixels
are extremely distorted, GIRAFE v1 relies only on the PMW input. GIRAFE v1 is
a gridded product which is available globally at a spatial resolution of 1° x 1°
and at a temporal resolution of 24h as accumulated precipitation computed from
the (IR-based) fraction of precipitation and the conditional precipitation rate.
Additionally, 1° x 1° monthly mean values of the daily accumulated precipitation
are provided. The daily accumulated precipitation features a dedicated sampling
uncertainty at the same 1° x 1° x 24 h resolution which is based on the analysis
of decorrelation scales in space and time in the IR-based precipitation fields.
This is a Thematic Climate Data Record (TCDR).\n*The PNPR-CLIM algorithm has
been developed by CNR-ISAC in the C3S_312b_Lot1 Copernicus project.
instruments: ["GOES Imager", "VISSR", "JAMI", "MVIRI", "AHI", "MTSAT-2 Imager", "ABI", "SEVIRI"]
constellation: MTG
platform: GOES,Himawari,MFG,MSG
processing:level: L3
keywords: ["GOES", "Himawari", "MFG", "MSG", "Climate", "Precipitation", "Level 3 Data", "GIRAFE"]
eodag:sensor_type: Optical,Imager
license: CC-BY-4.0
title: "GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1"
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-01-01T00:00:00Z", "2022-12-31T00:00:00Z"]]}}
# MARK: S3 SYNERGY
# Synergy data products are generally combinations of OLCI and SLSTR instruments
S3_SY_SYN:
description: |
The Level-2 SYN product (SY_2_SYN) is produced by the Synergy Level-1/2 SDR software and contains
surface reflectance and aerosol parameters over land. All measurement datasets are provided on the
OLCI image grid, similar to the one included in the OLCI L1b product.
Some sub-sampled annotations and atmospheric datasets are provided on the OLCI tie-points grid.
Several associated variables are also provided in annotation data files.
instruments: ["SYNERGY"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SYNERGY", "SY", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "SYN"]
eodag:sensor_type: OPTICAL,RADAR
license: other
title: SENTINEL3 SYNERGY Level-2 SYN
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SY_AOD:
description: |
The Level-2 SYN AOD product (SY_2_AOD) is produced by a dedicated processor including the whole
SYN L1 processing module and a global synergy level 2 processing module retrieving, over land and
sea, aerosol optical thickness. The resolution of this product is wider than classic S3 products,
as the dataset are provided on a 4.5 km² resolution
instruments: ["SYNERGY"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SYNERGY", "SY", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "AOD"]
eodag:sensor_type: OPTICAL,RADAR
license: other
title: SENTINEL3 SYNERGY Level-2 AOD
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SY_V10:
description: |
The Level-2 VG1 and V10 SYN products (SY_2_VG1 and SY_2_V10 respectively) are produced by the
SYNERGY Level-2 processor and contain 1 km VEGETATION-like product, 1 and 10 days synthesis surface
reflectances and NDVI. The product grid and the four spectral bands are similar to the SYN Level-2
VGP product.
instruments: ["SYNERGY"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2W
keywords: ["SYNERGY", "SY", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "V10"]
eodag:sensor_type: OPTICAL,RADAR
license: other
title: SENTINEL3 SYNERGY Level-2 V10
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SY_VG1:
description: |
The Level-2 VG1 and V10 SYN products (SY_2_VG1 and SY_2_V10 respectively) are produced by the
SYNERGY Level-2 processor and contain 1 km VEGETATION-like product, 1 and 10 days synthesis surface
reflectances and NDVI. The product grid and the four spectral bands are similar to the SYN Level-2
VGP product.
instruments: ["SYNERGY"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SYNERGY", "SY", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "VG1"]
eodag:sensor_type: OPTICAL,RADAR
license: other
title: SENTINEL3 SYNERGY Level-2 VG1
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
S3_SY_VGP:
description: |
The Level-2 VGP SYN product (SY_2_VGP) is produced by the Global Synergy Level-1/2 software and
contains 1 km VEGETATION-like product TOA reflectances. The "1 km VEGETATION-like product" label
means that measurements are provided on a regular latitude-longitude grid, with an equatorial
sampling distance of approximately 1 km. This product is restricted in longitude, including only filled ones.
instruments: ["SYNERGY"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL3
platform: S3A,S3B
processing:level: L2
keywords: ["SYNERGY", "SY", "SENTINEL", "SENTINEL3", "S3", "S3A", "S3B", "L2", "VGP"]
eodag:sensor_type: OPTICAL,RADAR
license: other
title: SENTINEL3 SYNERGY Level-2 VGP
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-02-16T00:00:00Z", null]]}}
# MARK: Sentinel 5P --------------------------------------------------------------------
S5P_L1B2_IR_ALL:
description: |
Solar irradiance spectra for all bands (UV1-6 and SWIR)
The TROPOMI instrument is a space-borne, nadir-viewing, imaging spectrometer covering wavelength bands between the
ultraviolet and the shortwave infrared. The instrument, the single payload of the Sentinel-5P spacecraft, uses
passive remote sensing techniques to attain its objective by measuring, at the Top Of Atmosphere (TOA), the solar
radiation reflected by and radiated from the earth. The instrument operates in a push-broom configuration
(non-scanning), with a swath width of ~2600 km on the Earth's surface. The typical pixel size (near nadir) will
be 7x3.5 km2 for all spectral bands, with the exception of the UV1 band (7x28 km2) and SWIR bands (7x7 km2).
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B, L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "IR", "SIR", "SWIR", "Irradiances", "UVN"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B and Level 2 Irradiances for the SWIR and UNV bands
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_IR_ALL:
description: |
Solar irradiance spectra for all bands (UV1-6 and SWIR)
The TROPOMI instrument is a space-borne, nadir-viewing, imaging spectrometer covering wavelength bands between the
ultraviolet and the shortwave infrared. The instrument, the single payload of the Sentinel-5P spacecraft, uses
passive remote sensing techniques to attain its objective by measuring, at the Top Of Atmosphere (TOA), the solar
radiation reflected by and radiated from the earth. The instrument operates in a push-broom configuration
(non-scanning), with a swath width of ~2600 km on the Earth's surface. The typical pixel size (near nadir) will
be 7x3.5 km2 for all spectral bands, with the exception of the UV1 band (7x28 km2) and SWIR bands (7x7 km2).
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "IR", "SIR", "SWIR", "Irradiances", "UVN"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Irradiances for the SWIR and UNV bands
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_IR_ALL:
description: |
The TROPOMI instrument is a space-borne, nadir-viewing, imaging spectrometer covering wavelength bands between the
ultraviolet and the shortwave infrared. The instrument, the single payload of the Sentinel-5P spacecraft, uses
passive remote sensing techniques to attain its objective by measuring, at the Top Of Atmosphere (TOA), the solar
radiation reflected by and radiated from the earth. The instrument operates in a push-broom configuration
(non-scanning), with a swath width of ~2600 km on the Earth's surface. The typical pixel size (near nadir) will
be 7x3.5 km2 for all spectral bands, with the exception of the UV1 band (7x28 km2) and SWIR bands (7x7 km2).
Level 2 data provides total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, formaldehyde,
tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Data
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-04-01T00:00:00Z", null]]}}
S5P_L1B_IR_SIR:
description: |
Solar irradiance spectra for the SWIR bands (band 7 and band 8).
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "IR", "SIR", "SWIR", "Irradiances"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Irradiances for the SWIR bands
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_IR_UVN:
description: |
Solar irradiance spectra for the UVN bands (band 1 through band 6).
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "IR", "UVN", "Irradiances"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Irradiances for the UVN bands
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_RA_BD1:
description: |
Sentinel-5 Precursor Level 1B Radiances for spectral band 1.
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "RA", "Radiances", "BD1", "BAND1", "B01"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 1
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_RA_BD2:
description: |
Sentinel-5 Precursor Level 1B Radiances for spectral band 2.
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "RA", "Radiances", "BD2", "BAND2", "B02"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_RA_BD3:
description: |
Sentinel-5 Precursor Level 1B Radiances for spectral band 3.
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "RA", "Radiances", "BD3", "BAND3", "B03"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 3
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_RA_BD4:
description: |
Sentinel-5 Precursor Level 1B Radiances for spectral band 4.
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "RA", "Radiances", "BD4", "BAND4", "B04"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 4
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_RA_BD5:
description: |
Sentinel-5 Precursor Level 1B Radiances for spectral band 5.
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "RA", "Radiances", "BD5", "BAND5", "B05"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 5
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_RA_BD6:
description: |
Sentinel-5 Precursor Level 1B Radiances for spectral band 6.
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "RA", "Radiances", "BD6", "BAND6", "B06"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 6
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_RA_BD7:
description: |
Sentinel-5 Precursor Level 1B Radiances for spectral band 7.
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "RA", "Radiances", "BD7", "BAND7", "B07"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 7
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L1B_RA_BD8:
description: |
Sentinel-5 Precursor Level 1B Radiances for spectral band 8.
TROPOMI utilises a single telescope to form an image of the target area onto a rectangular slit that acts as the
entrance slit of the spectrometer system. There are four different spectrometers, each with its own optics and
detector: mediumwave ultraviolet (UV), longwave ultraviolet combined with visible (UVIS), near infrared (NIR), and
shortwave infrared (SWIR). The spectrometers for UV, UVIS and NIR are jointly referred to as UVN. Radiation for the
SWIR spectrometer is transferred by an optical relay part in the UVN system from the telescope to an interface
position (the pupil stop) for the SWIR spectrometer. This is done because of the more stringent thermal requirements
on the SWIR part of the instrument.
Each of the detectors is divided in two halves, which yields a total of eight spectral bands.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L1", "L1B", "TROPOMI", "RA", "Radiances", "BD8", "BAND8", "B08"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 8
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_NO2:
description: |
The TROPOMI instrument, single payload onboard Sentinel-5 Precursor, retrieves operationally tropospheric and
stratospheric NO2 column products. The TROPOMI NO2 data products pose an improvement over previous NO2 data sets,
particularly in their unprecedented spatial resolution, but also in the separation of the stratospheric and
tropospheric contributions of the retrieved slant columns, and in the calculation of the air-mass factors used to
convert slant to total columns.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "NO2", "Nitrogen", "Dioxide"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Nitrogen Dioxide
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_CLOUD:
description: |
The TROPOMI instrument, single payload onboard Sentinel-5 Precursor, retrieves operationally the most important
quantities for cloud correction of satellite trace gas retrievals: cloud fraction, cloud optical thickness (albedo),
and cloud-top pressure (height). Cloud parameters from TROPOMI are not only used for enhancing the accuracy of trace
gas retrievals, but also to extend the satellite data record of cloud information derived from oxygen A-band
measurements initiated with GOME.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "CLOUD"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Cloud
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_O3:
description: |
Ozone (O3) is of crucial importance for the equilibrium of the Earth's atmosphere. In the stratosphere, the ozone
layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient
cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation.
Ozone is also an important greenhouse-gas contributor to ongoing climate change.
These products are provided in NetCDF-CF format and contain total ozone, ozone temperature, and error information
including averaging kernels.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "O3", "Ozone"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Ozone
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_CO:
description: |
The TROPOMI instrument, single payload onboard Sentinel-5 Precursor, retrieves the CO global abundance exploiting
clear-sky and cloudy-sky Earth radiance measurements in the 2.3 µm spectral range of the shortwave infrared (SWIR)
part of the solar spectrum. TROPOMI clear sky observations provide CO total columns with sensitivity to the
tropospheric boundary layer. For cloudy atmospheres, the column sensitivity changes according to the light path.
The TROPOMI CO retrieval uses the same method employed by SCIAMACHY.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "CO", "Carbon", "Monoxide"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Carbon Monoxide
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_AER_AI:
description: |
TROPOMI aerosol index is referred to as the Ultraviolet Aerosol Index (UVAI). The relatively simple calculation of
the Aerosol Index is based on wavelength dependent changes in Rayleigh scattering in the UV spectral range where
ozone absorption is very small. UVAI can also be calculated in the presence of clouds so that daily, global coverage
is possible. This is ideal for tracking the evolution of episodic aerosol plumes from dust outbreaks, volcanic ash,
and biomass burning.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "AER", "AI", "Ultraviolet", "Aerosol", "Index"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Ultraviolet Aerosol Index
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_O3_PR:
description: |
Retrieved ozone profiles are used to monitor the evolution of stratospheric and tropospheric ozone. Such monitoring
is important as the ozone layer protects life on Earth against harmful UV radiation. The ozone layer is recovering
from depletion due to manmade Chlorofluorocarbons (CFCs). Tropospheric ozone is toxic and it plays an important role
in tropospheric chemistry. Also, ozone is a greenhouse gas and is therefore also relevant for climate change.
The main parameters in the file are the retrieved ozone profile at 33 levels and the retrieved sub-columns of ozone
in 6 layers. In addition, the total ozone column and tropospheric ozone columns are provided. For the ozone profile,
the precision and smoothing errors, the a-priori profile and the averaging kernel are also provided.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "O3", "PR", "Ozone", "Profile"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Ozone Profile
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_O3_TCL:
description: |
Ozone in the tropical troposphere plays various important roles. The intense UV radiation and high humidity in the
tropics stimulate the formation of the hydroxyl radical (OH) by the photolysis of ozone. OH is the most important
oxidant in the troposphere because it reacts with virtually all trace gases, such as CO, CH4 and other hydrocarbons.
The tropics are also characterized by large emissions of nitrogen oxides (NOx), carbon monoxide (CO) and
hydrocarbons, both from natural and anthropogenic sources. Ozone that is formed over regions where large amounts of
these ozone precursors are emitted, can be transported over great distances and affects areas far from the source.
The TROPOMI tropospheric ozone product is a level-2c product that represents three day averaged tropospheric ozone
columns on a 0.5° by 1° latitude-longitude grid for the tropical region between 20°N and 20°S. The TROPOMI
tropospheric ozone column product uses the TROPOMI Level-2 total OZONE and CLOUD products as input.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "O3", "TCL", "Tropospheric", "Ozone"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Tropospheric Ozone
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_AER_LH:
description: |
The TROPOMI Aerosol Layer Height product focuses on retrieval of vertically localised aerosol layers in the free
troposphere, such as desert dust, biomass burning aerosol, or volcanic ash plumes. The height of such layers is
retrieved for cloud-free conditions. Height information for aerosols in the free troposphere is particularly
important for aviation safety. Scientific applications include radiative forcing studies, long-range transport
modelling and studies of cloud formation processes. Aerosol height information also helps to interpret the UV
Aerosol Index (UVAI) in terms of aerosol absorption as the index is strongly height-dependent.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "AER", "LH", "Aerosol", "Layer", "Height"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Aerosol Layer Height
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_HCHO:
description: |
Formaldehyde is an intermediate gas in almost all oxidation chains of Non-Methane Volatile Organic Compounds
(NMVOC), leading eventually to CO2. NMVOCs are, together with NOx, CO and CH4, among the most important precursors
of tropospheric O3. The major HCHO source in the remote atmosphere is CH4 oxidation. Over the continents, the
oxidation of higher NMVOCs emitted from vegetation, fires, traffic and industrial sources results in important and
localised enhancements of the HCHO levels.
In addition to the main product results, such as HCHO slant column, vertical column and air mass factor, the level 2
data files contain several additional parameters and diagnostic information.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "HCHO", "Formaldehyde"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Formaldehyde
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_CH4:
description: |
Methane (CH4) is, after carbon dioxide (CO2), the most important contributor to the anthropogenically enhanced
greenhouse effect. Roughly three-quarters of methane emissions are anthropogenic and as such it is important to
continue the record of satellite-based measurements. TROPOMI aims at providing CH4 column concentrations with high
sensitivity to the Earth's surface, good spatio/temporal coverage, and sufficient accuracy to facilitate inverse
modelling of sources and sinks.
The output product consists of the retrieved methane column and a row vector referred to as the column averaging
kernel A. The column averaging kernel describes how the retrieved column relates to the true profile and should be
used in validation exercises (when possible) or use of the product in source/sink inverse modelling. The output
product also contains altitude levels of the layer interfaces to which the column averaging kernel corresponds.
Additional output for Level-2 data products: viewing geometry, precision of retrieved methane, residuals of the fit,
quality flags (cloudiness, terrain roughness etc.) and retrieved albedo and aerosol properties. The latter
properties are required for a posteriori filtering and for estimation of total retrieval error.
The Sentinel-5 Precursor mission flies in loose formation (about 3.5 - 5 minutes behind) with the S-NPP
(SUOMI-National Polar-orbiting Partnership) mission to use VIIRS (Visible Infrared Imaging Radiometer Suite) cloud
information to select cloud free TROPOMI pixels for high quality methane retrieval.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "CH4", "Methane"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Methane
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_NP_BD3:
description: |
S5P-NPP Cloud for spectral band 3.
The S5P level 2 methane product is dependent on having information on cloud occurrence at spatial resolution finer
than that achievable from TROPOMI itself. This information is also useful for other purposes, including assessing
the influence of cloud on other L2 products and issues related to spatial co-registration. A level 2 auxiliary
product was therefore developed to describe cloud in the TROPOMI field of view (FOV), using co-located observations
of VIIRS (Visible Infra-red Imaging Radiometer Suite) on the U.S. S-NPP (Suomi - National Polar-orbiting
Partnership). S5P flies in a so-called loose formation with the S-NPP with a temporal separation between them of
less than 5 minutes.
The main information contained in the S5P-NPP product is:
1. A statistical summary for each S5P FOV of the NPP-VIIRS L2 Cloud Mask (VCM).
2. The mean and standard deviation of the sun-normalised radiance in a number of VIIRS moderate resolution bands.
This information is provided for three S5P spectral bands (to account for differences in spatial sampling).
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "NP", "NPP", "Cloud", "BD3", "B03", "BAND3"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 NPP Cloud for band 3
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_NP_BD6:
description: |
S5P-NPP Cloud for spectral band 6.
The S5P level 2 methane product is dependent on having information on cloud occurrence at spatial resolution finer
than that achievable from TROPOMI itself. This information is also useful for other purposes, including assessing
the influence of cloud on other L2 products and issues related to spatial co-registration. A level 2 auxiliary
product was therefore developed to describe cloud in the TROPOMI field of view (FOV), using co-located observations
of VIIRS (Visible Infra-red Imaging Radiometer Suite) on the U.S. S-NPP (Suomi - National Polar-orbiting
Partnership). S5P flies in a so-called loose formation with the S-NPP with a temporal separation between them of
less than 5 minutes.
The main information contained in the S5P-NPP product is:
1. A statistical summary for each S5P FOV of the NPP-VIIRS L2 Cloud Mask (VCM).
2. The mean and standard deviation of the sun-normalised radiance in a number of VIIRS moderate resolution bands.
This information is provided for three S5P spectral bands (to account for differences in spatial sampling).
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "NP", "NPP", "Cloud", "BD6", "B06", "BAND6"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 NPP Cloud for band 6
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_NP_BD7:
description: |
S5P-NPP Cloud for spectral band 7.
The S5P level 2 methane product is dependent on having information on cloud occurrence at spatial resolution finer
than that achievable from TROPOMI itself. This information is also useful for other purposes, including assessing
the influence of cloud on other L2 products and issues related to spatial co-registration. A level 2 auxiliary
product was therefore developed to describe cloud in the TROPOMI field of view (FOV), using co-located observations
of VIIRS (Visible Infra-red Imaging Radiometer Suite) on the U.S. S-NPP (Suomi - National Polar-orbiting
Partnership). S5P flies in a so-called loose formation with the S-NPP with a temporal separation between them of
less than 5 minutes.
The main information contained in the S5P-NPP product is:
1. A statistical summary for each S5P FOV of the NPP-VIIRS L2 Cloud Mask (VCM).
2. The mean and standard deviation of the sun-normalised radiance in a number of VIIRS moderate resolution bands.
This information is provided for three S5P spectral bands (to account for differences in spatial sampling).
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "NP", "NPP", "Cloud", "BD7", "B07", "BAND7"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 NPP Cloud for band 7
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
S5P_L2_SO2:
description: |
Sulphur dioxide (SO2) enters the Earth's atmosphere through both natural (~30%) and anthropogenic processes (~70%).
It plays a role in chemistry on a local and global scale and its impact ranges from short term pollution to effects
on climate.
Beside the total column of SO2, enhanced levels of SO2 are flagged within the products. The recognition of enhanced
SO2 values is essential in order to detect and monitor volcanic eruptions and anthropogenic pollution sources.
Volcanic SO2 emissions may also pose a threat to aviation, along with volcanic ash.
instruments: ["TROPOMI"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL5P
platform: S5P
processing:level: L2
keywords: ["SENTINEL", "SENTINEL5P", "S5P", "L2", "TROPOMI", "SO2", "Sulphur", "Dioxide"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Sentinel-5 Precursor Level 2 Sulphur Dioxide
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-10-13T00:00:00Z", null]]}}
# MARK: Sentinel 6
S6_P4_L1AHR_F06:
description: |
This is a reprocessed dataset at baseline F06, which is continued by the NRT/NTC data stream from 29/April/2022
onwards. The Level-1A product contains Level 1 intermediate output of the HR processor (RAW and RMC). It
includes geo-located bursts of Ku echoes (at ~9 kHz) with all instrument calibrations applied. It includes the full
rate complex waveforms input to the delay/Doppler or SAR processor. This product is most relevant to altimetry
specialists, working on fundamental SAR processing techniques and calibration studies. Sentinel-6 is part of a
series of Sentinel satellites, under the umbrella of the EU Copernicus programme. It is a collaborative Copernicus
mission, implemented and co-funded by the European Commission, ESA, EUMETSAT and the USA, through NASA and the
National Oceanic and Atmospheric Administration (NOAA).
instruments: ["Poseidon-4"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL6-A
platform: S6A
processing:level: L1A
keywords: ["SENTINEL", "SENTINEL6", "S6", "S6A", "LEO", "L1A", "ALTIMETRIC", "HR", "POSEIDON4", "P4", "F06"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Sentinel 6 - Poseidon-4 Altimetry Level 1A High Resolution Reprocessed at F06
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-12-17T00:00:00Z", null]]}}
S6_P4_L1BLR_F06:
description: |
This is a reprocessed dataset at baseline F06, which is continued by the NRT/NTC data stream from 29/April/2022
onwards. The Level-1B LR product is output of the LR processor. It includes geo-located, and fully calibrated
pulse-limited low-resolution Ku-band and C-band waveforms. This product is most relevant to geophysical retrieval
algorithm developers (over ocean, land and ice surfaces), surface characterisations studies (e.g. impact of sea
state bias, wave directional effects etc) and Quality Control systems. Sentinel-6 is part of a series of Sentinel
satellites, under the umbrella of the EU Copernicus programme. It is a collaborative Copernicus mission, implemented
and co-funded by the European Commission, ESA, EUMETSAT and the USA, through NASA and the National Oceanic and
Atmospheric Administration (NOAA).
instruments: ["Poseidon-4"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL6-A
platform: S6A
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL6", "S6", "S6A", "LEO", "L1B", "ALTIMETRIC", "LR", "POSEIDON4", "P4", "F06"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Sentinel 6 - Poseidon-4 Altimetry Level 1B Low Resolution Reprocessed at F06
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-12-17T00:00:00Z", null]]}}
S6_P4_L1BAHR_F06:
description: |
This is a reprocessed dataset at baseline F06, which is continued by the NRT/NTC data stream from 29/April/2022
onwards. The Level-1B HR product is output of the HR processor. It includes geo-located, and fully calibrated
multi-looked high-resolution Ku-band waveforms. This product is most relevant to geophysical retrieval algorithm
developers (over ocean, land and ice surfaces), surface characterisations studies (e.g. impact of sea state bias,
wave directional effects etc.) and Quality Control systems. Sentinel-6 is part of a series of Sentinel satellites,
under the umbrella of the EU Copernicus programme. It is a collaborative Copernicus mission, implemented and
co-funded by the European Commission, ESA, EUMETSAT and the USA, through NASA and the National Oceanic and
Atmospheric Administration (NOAA).
instruments: ["Poseidon-4"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL6-A
platform: S6A
processing:level: L1B
keywords: ["SENTINEL", "SENTINEL6", "S6", "S6A", "LEO", "L1B", "ALTIMETRIC", "HR", "POSEIDON4", "P4", "F06"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Sentinel 6 - Poseidon-4 Altimetry Level 1B High Resolution Reprocessed at F06
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-12-17T00:00:00Z", null]]}}
S6_P4_L2LR_F06:
description: |
This is a reprocessed dataset at baseline F06, which is continued by the NRT/NTC data stream from 29/April/2022
onwards. The product contain the typical altimetry measurements, like the altimeter range, the sea surface height,
the wind speed, significant wave height and all required geophysical corrections and related flags derived from LR.
Two measurement data files are available (standard and reduced), each with a different number of variables. The
standard data file includes 1 Hz and 20 Hz measurements for the Ku- and C-bands as well as geophysical corrections
at 1 Hz and some at 20 Hz. The reduced data file contains only 1 Hz measurements for the Ku- and C-bands as well as
geophysical corrections at 1 Hz. These products are suitable for users seeking information on sea state and those
creating downstream added value products from multiple altimeters. Sentinel-6 is part of a series of Sentinel
satellites, under the umbrella of the EU Copernicus programme. It is a collaborative Copernicus mission, implemented
and co-funded by the European Commission, ESA, EUMETSAT and the USA, through NASA and the National Oceanic and
Atmospheric Administration (NOAA).
instruments: ["Poseidon-4"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL6-A
platform: S6A
processing:level: L2
keywords: ["SENTINEL", "SENTINEL6", "S6", "S6A", "LEO", "L2", "ALTIMETRIC", "LR", "POSEIDON4", "P4", "F06"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Sentinel 6 - Poseidon-4 Altimetry Level 2 Low Resolution Reprocessed at F06
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-12-17T00:00:00Z", null]]}}
S6_P4_L2HR_F06:
description: |
This is a reprocessed dataset at baseline F06, which is continued by the NRT/NTC data stream from 29/April/2022
onwards. The level-2 high resolution products contain the typical altimetry measurements, like the altimeter range,
the sea surface height, the wind speed, significant wave height and all required geophysical corrections and related
flags derived either from RAW or RMC, or the combination of both. Two measurement data files are available
(standard and reduced), each with a different number of variables. The standard data file includes 1 Hz and 20 Hz
measurements for the Ku- band as well as geophysical corrections at 1 Hz and some at 20 Hz. The reduced data file
contains only 1 Hz measurements for the Ku- and C-bands as well as geophysical corrections at 1 Hz. Note that the
HR data products only contain Ku-band measurements. These products are suitable for users seeking information on
sea state and those creating downstream added value products from multiple altimeters. Particularly for those
seeking the highest resolution measurements. Sentinel-6 is part of a series of Sentinel satellites, under the
umbrella of the EU Copernicus programme. It is a collaborative Copernicus mission, implemented and co-funded by the
European Commission, ESA, EUMETSAT and the USA, through NASA and the National Oceanic and Atmospheric
Administration (NOAA).
instruments: ["Poseidon-4"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL6-A
platform: S6A
processing:level: L2
keywords: ["SENTINEL", "SENTINEL6", "S6", "S6A", "LEO", "L2", "ALTIMETRIC", "HR", "POSEIDON4", "P4", "F06"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Sentinel 6 - Poseidon-4 Altimetry Level 2 High Resolution Reprocessed at F06
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-12-17T00:00:00Z", null]]}}
S6_AMR_L2_F06:
description: |
This is a reprocessed dataset at baseline F06, which is continued by the NRT/NTC data stream from 29/April/2022
onwards. AMR-C Level 2 Products as generated by the AMR-C CFI Processor. These products include antenna and
brightness temperatures, wet tropospheric correction, water vapour content, and a rain flag. Sentinel-6 is part of
a series of Sentinel satellites, under the umbrella of the EU Copernicus programme. It is a collaborative
Copernicus mission, implemented and co-funded by the European Commission, ESA, EUMETSAT and the USA, through NASA
and the National Oceanic and Atmospheric Administration (NOAA).
instruments: ["AMR-C"]
providers: [{"url":"https://earth.esa.int","name":"European Space Agency (ESA)","roles":["producer","processor","licensor"]}]
constellation: SENTINEL6-A
platform: S6A
processing:level: L2
keywords: ["SENTINEL", "SENTINEL6", "S6", "S6A", "LEO", "L2", "AMR-C", "RADIOMETER", "MICROWAVE", "F06"]
eodag:sensor_type: RADIOMETER
license: other
title: Sentinel 6 - Climate-quality Advanced Microwave Radiometer Level 2 Products Reprocessed at F06
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-11-28T00:00:00Z", null]]}}
# MARK: VENUS --------------------------------------------------------------------------
VENUS_L1C:
description: |
A light description of Venus L1 data is available at http://www.cesbio.ups-tlse.fr/multitemp/?page_id=12984
instruments: []
constellation: VENUS
platform: VENUS
processing:level: L1C
keywords: ["VENUS", "L1", "L1C"]
eodag:sensor_type: OPTICAL
license: other
title: Venus Level1-C
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-08-02T00:00:00Z", null]]}}
VENUS_L2A_MAJA:
description: |
Level2 products provide surface reflectances after atmospheric correction, along with masks of clouds and their
shadows. Data is processed by MAJA (before called MACCS) for THEIA land data center.
instruments: []
providers: [{"url":"https://www.cnes.fr","name":"Centre National d'Etudes Spatiales (CNES)","roles":["processor"]}]
constellation: VENUS
platform: VENUS
processing:level: L2A
keywords: ["VENUS", "L2", "L2A"]
eodag:sensor_type: OPTICAL
license: other
title: Venus Level2-A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-08-02T00:00:00Z", null]]}}
VENUS_L3A_MAJA:
description: ""
instruments: []
providers: [{"url":"https://www.cnes.fr","name":"Centre National d'Etudes Spatiales (CNES)","roles":["processor"]}]
constellation: VENUS
platform: VENUS
processing:level: L3A
keywords: ["VENUS", "L3", "L3A"]
eodag:sensor_type: OPTICAL
license: other
title: Venus Level3-A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-08-02T00:00:00Z", null]]}}
# MARK: ECMWF --------------------------------------------------------------------------
TIGGE_CF_SFC:
description: |
TIGGE (THORPEX Interactive Grand Global Ensemble) Surface Control forecast
from ECMWF
instruments: []
constellation: TIGGE
platform: TIGGE
processing:level:
keywords: ["THORPEX", "TIGGE", "CF", "SFC", "ECMWF"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: TIGGE ECMWF Surface Control forecast
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2006-10-01T00:00:00Z", null]]}}
# MARK: COPERNICUS ADS -----------------------------------------------------------------
CAMS_GAC_FORECAST:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmosphere", "Atmospheric", "Forecast", "GAC"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global atmospheric composition forecasts
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", null]]}}
CAMS_EU_AIR_QUALITY_FORECAST:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmosphere", "Atmospheric", "Air", "Forecast", "EEA"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS European air quality forecasts
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2022-01-03T00:00:00Z", null]]}}
CAMS_GFE_GFAS:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmosphere", "Atmospheric", "Fire", "FRP", "DM", "MODIS", "NASA", "EOS", "ECMWF", "GFAS"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["2003-01-01T00:00:00Z", null]]}}
CAMS_SOLAR_RADIATION:
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).
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Solar", "Radiation"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS solar radiation time-series
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2004-01-02T00:00:00Z", null]]}}
CAMS_GREENHOUSE_INVERSION:
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).
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmosphere", "Atmospheric", "IPCC", "CO2", "CH4", "N2O"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global inversion-optimised greenhouse gas fluxes and concentrations
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1979-01-01T00:00:00Z", null]]}}
CAMS_EAC4_MONTHLY:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmosphere", "Atmospheric", "EWMCF", "EAC4"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global reanalysis (EAC4) monthly averaged fields
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", null]]}}
CAMS_EU_AIR_QUALITY_RE:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmosphere", "Atmospheric", "Air", "EEA"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS European air quality reanalyses
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-01-01T00:00:00Z", null]]}}
CAMS_EAC4:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmosphere", "Atmospheric", "EWMCF", "EAC4"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global reanalysis (EAC4)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", null]]}}
CAMS_GRF_AUX:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmospheric", "Atmosphere", "RF", "CO2", "CH4", "O3", "Aerosol"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global radiative forcing - auxilliary variables
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", null]]}}
CAMS_GRF:
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".
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmospheric", "Atmosphere", "RF", "CO2", "CH4", "O3", "Aerosol"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global radiative forcings
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", null]]}}
CAMS_GREENHOUSE_EGG4_MONTHLY:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmospheric", "Atmosphere", "CO2", "CH4", "Greenhouse", "ECMWF", "EGG4"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", null]]}}
CAMS_GREENHOUSE_EGG4:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmospheric", "Atmosphere", "CO2", "CH4", "GHG", "ECMWF", "EGG4"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global greenhouse gas reanalysis (EGG4)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2003-01-01T00:00:00Z", null]]}}
CAMS_GLOBAL_EMISSIONS:
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.
instruments: []
constellation: CAMS
platform: CAMS
processing:level:
keywords: ["Copernicus", "ADS", "CAMS", "Atmosphere", "Atmospheric", "Emissions", "Pollutants", "GHG"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: CAMS global emission inventories
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2000-01-01T00:00:00Z", null]]}}
# MARK: COPERNICUS CDS -----------------------------------------------------------------
AG_ERA5:
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.
constellation: ERA5
instruments: []
platform: ERA5
processing:level:
keywords: ["ECMWF", "Reanalysis", "ERA5", "CDS", "Atmospheric", "climate", "land", "agriculture", "AgERA5", "surface"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Agrometeorological indicators from 1979 to present derived from reanalysis
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1979-01-01T00:00:00Z", null]]}}
ERA5_SL:
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).
instruments: []
constellation: ERA5
platform: ERA5
processing:level:
keywords: ["ECMWF", "Reanalysis", "ERA5", "CDS", "Atmospheric", "land", "sea", "hourly", "single", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: ERA5 hourly data on single levels from 1940 to present
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1940-01-01T09:00:00Z", null]]}}
ERA5_SL_MONTHLY:
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).
instruments: []
constellation: ERA5
platform: ERA5
processing:level:
keywords: ["Climate", "ECMWF", "Reanalysis", "ERA5", "CDS", "Atmospheric", "land", "sea", "monthly", "single", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["1940-01-01T00:00:00Z", null]]}}
ERA5_PL:
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)
instruments: []
constellation: ERA5
platform: ERA5
processing:level:
keywords: ["ECMWF", "Reanalysis", "ERA5", "CDS", "Atmospheric", "land", "sea", "hourly", "pressure", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: ERA5 hourly data on pressure levels from 1940 to present
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1940-01-01T00:00:00Z", null]]}}
ERA5_PL_MONTHLY:
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).
instruments: []
constellation: ERA5
platform: ERA5
processing:level:
keywords: ["Climate", "ECMWF", "Reanalysis", "ERA5", "CDS", "Atmospheric", "land", "sea", "monthly", "pressure", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["1940-01-01T00:00:00Z", null]]}}
ERA5_LAND:
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
constellation: ERA5
instruments: []
platform: ERA5
processing:level:
keywords: ["ECMWF", "Reanalysis", "ERA5", "CDS", "Atmospheric", "land", "hourly", "evolution"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: ERA5-Land hourly data from 1950 to present
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1950-01-02T00:00:00Z", null]]}}
ERA5_LAND_MONTHLY:
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
constellation: ERA5
instruments: []
platform: ERA5
processing:level:
keywords: ["ECMWF", "Reanalysis", "ERA5", "CDS", "Atmospheric", "land", "monthly", "evolution"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: ERA5-Land monthly averaged data from 1950 to present
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1950-01-01T00:00:00Z", null]]}}
UERRA_EUROPE_SL:
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
constellation: SURFEX
instruments: []
platform: SURFEX
processing:level:
keywords: ["Climate", "ECMWF", "Reanalysis", "Regional", "Europe", "UERRA", "UERRA-HARMONIE", "SURFEX", "MESCAN-SURFEX", "CDS", "Atmospheric", "single", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["1961-01-01T00:00:00Z", null]]}}
GLACIERS_ELEVATION_AND_MASS_CHANGE:
description: |
This dataset provides in situ and remote sensing derived glacier changes from individual glaciers globally.
The dataset represents the latest homogenized state-of-the-art glacier change data collected by scientists
and the national correspondents of each country as provided to the World Glacier Monitoring Service (WGMS).
The product is an extract of the WGMS Fluctuations of Glacier (FoG) database and consists of two data sets
providing time series of glacier changes: glacier elevation change series from the geodetic method and glacier
mass-balance series from the glaciological method
constellation: INSITU
instruments: []
platform: INSITU
processing:level:
keywords: ["ECMWF", "WGMS", "INSITU", "CDS", "C3S", "glacier", "elevation", "mass", "change"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Glaciers elevation and mass change data from 1850 to present from the Fluctuations of Glaciers Database
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1850-01-01T00:00:00Z", null]]}}
GLACIERS_DIST_RANDOLPH:
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.
constellation:
instruments: []
platform: INSITU
processing:level:
keywords: ["ECMWF", "WGMS", "INSITU", "CDS", "C3S", "glacier", "randolph", "distribution", "inventory"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["2000-01-01T00:00:00Z", "2000-12-31T23:59:00Z"]]}}
GRIDDED_GLACIERS_MASS_CHANGE:
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
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "WGMS", "INSITU", "CDS", "C3S", "glacier", "randolph", "mass", "gridded"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["1975-01-01T00:00:00Z", null]]}}
SATELLITE_CARBON_DIOXIDE:
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)
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "carbon-dioxide"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["2002-10-01T00:00:00Z", "2022-12-31T23:59:59Z"]]}}
SATELLITE_FIRE_BURNED_AREA:
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.
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "burned"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["2001-01-01T00:00:00Z", "2022-04-01T23:59:59Z"]]}}
SATELLITE_METHANE:
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)
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "methane"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Methane data from 2003 to present derived from satellite observations
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-10-01T00:00:00Z", null]]}}
SATELLITE_SEA_ICE_EDGE_TYPE:
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:
instruments: []
constellation:
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "sea", "ice"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["1978-10-25T00:00:00Z", "2023-05-02T23:59:59Z"]]}}
SATELLITE_SEA_ICE_THICKNESS:
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).
instruments: []
constellation:
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "sea", "ice"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["2002-10-01T00:00:00Z", null]]}}
SATELLITE_SEA_ICE_CONCENTRATION:
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.
instruments: []
constellation:
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "sea", "ice"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["1978-10-25T00:00:00Z", null]]}}
SEASONAL_POSTPROCESSED_PL:
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
keywords: ["ECMWF", "CDS", "C3S", "seasonal", "forecast", "anomalies", "pressure", "levels"]
constellation:
instruments: []
platform:
processing:level:
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal forecast anomalies on pressure levels
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-09-01T00:00:00Z", null]]}}
SATELLITE_SEA_LEVEL_GLOBAL:
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
constellation:
instruments: []
platform:
processing:level:
keywords: ["Climate", "ECMWF", "CDS", "C3S", "sea", "level", "global"]
eodag:sensor_type: HYDROLOGICAL
license: other
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": [["1993-01-01T00:00:00Z", "2022-08-04T23:59:59Z"]]}}
SEASONAL_POSTPROCESSED_SL:
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
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "seasonal", "forecast", "anomalies", "single", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal forecast anomalies on single levels
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-09-01T00:00:00Z", null]]}}
SEASONAL_ORIGINAL_SL:
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
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "seasonal", "forecast", "daily", "single", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal forecast daily and subdaily data on single levels
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-01-01T00:00:00Z", null]]}}
SEASONAL_ORIGINAL_PL:
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
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "seasonal", "forecast", "subdaily", "pressure", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal forecast subdaily data on pressure levels
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-01-01T00:00:00Z", null]]}}
SEASONAL_MONTHLY_PL:
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
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "seasonal", "forecast", "monthly", "pressure", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal forecast monthly statistics on pressure levels
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-01-01T00:00:00Z", null]]}}
SEASONAL_MONTHLY_SL:
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
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "seasonal", "forecast", "monthly", "single", "levels"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal forecast monthly statistics on single levels
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-01-01T00:00:00Z", "2023-05-01T00:00:00Z"]]}}
SIS_HYDRO_MET_PROJ:
description: |
This dataset provides precipitation and near surface air temperature for Europe as Essential Climate
Variables (ECVs) and as a set of Climate Impact Indicators (CIIs) based on the ECVs. \nECV datasets
provide the empirical evidence needed to understand the current climate and predict future changes.
\nCIIs contain condensed climate information which facilitate relatively quick and efficient subsequent
analysis. Therefore, CIIs make climate information accessible to application focussed users within a
sector.\nThe ECVs and CIIs provided here were derived within the water management sectoral information
service to address questions specific to the water sector. However, the products are provided in a generic
form and are relevant for a range of sectors, for example agriculture and energy.\nThe data represent
the current state-of-the-art in Europe for regional climate modelling and indicator production. Data
from eight model simulations included in the Coordinated Regional Climate Downscaling Experiment (CORDEX)
were used to calculate a total of two ECVs and five CIIs at a spatial resolution of 0.11° x 0.11° and 5km
x 5km.\nThe ECV data meet the technical specification set by the Global Climate Observing System (GCOS),
as such they are provided on a daily time step. They are bias adjusted using the EFAS gridded observations
as a reference dataset. Note these are model output data, not observation data as is the general case for
ECVs.\nThe CIIs are provided as mean values over a 30-year time period. For the reference period
(1971-2000) data is provided as absolute values, for the future periods the data is provided as absolute
values and as the relative or absolute change from the reference period. The future periods cover 3 fixed
time periods (2011-2040, 2041-2070 and 2071-2100) and 3 \"degree scenario\" periods defined by when global
warming exceeds a given threshold (1.5 °C, 2.0 °C or 3.0 °C). The global warming is calculated from the
global climate model (GCM) used, therefore the actual time period of the degree scenarios will be different
for each GCM.\nThis dataset is produced and quality assured by the Swedish Meteorological and Hydrological
Institute on behalf of the Copernicus Climate Change Service. \n\nVariables in the dataset/application
are:\n2m air temperature, Highest 5-day precipitation amount, Longest dry spells, Number of dry spells,
Precipitation
constellation:
instruments: []
platform:
processing:level:
keywords: ["ECMWF", "CDS", "C3S", "hydrology", "meterology", "water", "precipitation", "temperature"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1970-01-01T00:00:00Z", "2100-12-31T23:59:00Z"]]}}
CMIP6_CLIMATE_PROJECTIONS:
description: |
This catalogue entry provides daily and monthly global climate projections data from a large number of experiments,
models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project
(CMIP6). CMIP6 data underpins the Intergovernmental Panel on Climate Change 6th Assessment Report. The use of these
data is mostly aimed at:\n\naddressing outstanding scientific questions that arose as part of the IPCC reporting
process; improving the understanding of the climate system; providing estimates of future climate change and related
uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and
exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different
models are in simulating the recent past.\n\nThe term "experiments" refers to the three main categories of CMIP6
simulations:\n\nHistorical experiments which cover the period where modern climate observations exist. These
experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison
with scenario runs for the future. The period covered is typically 1850-2014. Climate projection experiments
following the combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway
(RCP). The SSP scenarios provide different pathways of the future climate forcing. The period covered is typically
2015-2100.\n\nThis catalogue entry provides both two- and three-dimensional data, along with an option to apply
spatial and/or temporal subsetting to data requests. This is a new feature of the global climate projection dataset,
which relies on compute processes run simultaneously in the ESGF nodes, where the data are originally located. The
data are produced by the participating institutes of the CMIP6 project.
constellation: CMIP6
instruments: ["CMIP6"]
platform: CMIP6
processing:level:
eodag:sensor_type:
keywords: ["ECMWF","CDS","C3S","CMIP6","historical","projection","global"]
license: other
title: CMIP6 climate projections
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1850-01-01T00:00:00Z", "2100-12-31T23:59:00Z"]]}}
# MARK: CEMS
FIRE_HISTORICAL:
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
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "EFFIS", "fire", "historical", "ERA5", "european", "sustainability", "CEMS", "system"]
eodag:sensor_type: ATMOSPHERIC
license: other
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": [["1940-03-01T00:00:00Z", null]]}}
FIRE_SEASONAL:
description: |
This dataset offers modeled daily fire danger time series, driven by seasonal weather forecasts. It provides long-range
predictions of meteorological conditions conducive to the initiation, spread, and persistence of fires. The fire danger
metrics included in this dataset are part of an extensive dataset produced by the Copernicus Emergency Management Service (CEMS)
for the European Forest Fire Information System (EFFIS) and the Global Wildfire Information System (GWIS). EFFIS and GWIS
are used for monitoring and forecasting fire danger at both European and global scales. The dataset incorporates fire danger
indices from the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI),
and the Australian McArthur (Mark 5) rating systems.
This dataset was generated by driving the Global ECMWF Fire Forecast (GEFF) model with seasonal weather ensemble forecasts
from the European Centre for Medium-Range Weather Forecasts (ECMWF) System 5 (SEAS5) prediction system.These forecasts initially
consist of 25 ensemble members until December 2016, referred to as re-forecasts. After that period, they consist of seasonal
forecasts with 51 members. It is important to note that the re-forecast dataset was initialized using ERA-Interim analysis data,
while forecast simulations from 2016 onward are initialized using ECMWF operational analysis. Therefore, it is suggested that
the period 1981-2016 be used as a reference period, while the period 2017-to present as a real time forecast.
For both the re-forecast (1981-2016) and forecast periods (2017-present), the temporal resolution is daily forecasts at 12:00
local time, available once a month, with a prediction horizon of 216 days (equivalent to 7 months). The data records in this
dataset will be extended over time as seasonal forcing data becomes available. Once the SEAS5 operation ceases, the dataset
will be updated with the next ECMWF seasonal system (SEAS6). It is essential to note that this is not a real-time service,
as real-time forecasts are accessible through the EFFIS web services.
These seasonal forecasts can be used to assess the performance of the forecasting system or to develop tools for statistically
correcting forecast errors. ECMWF produces this dataset as the computational center for fire danger forecasting within the
Copernicus Emergency Management Service (CEMS) on behalf of the Joint Research Centre, which serves as the managing entity for this service.
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "EFFIS", "GWIS", "fire", "danger", "seasonal", "GEFF"]
eodag:sensor_type:
license: other
title: Seasonal forecast of fire danger indices from the Copernicus Emergency Management Service
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-02-01T00:00:00Z", null]]}}
GLOFAS_FORECAST:
description: |
This dataset contains global modelled daily data of river discharge forced with meteorological forecasts.
The data was produced by the Global Flood Awareness System (GloFAS), which is part of the Copernicus Emergency
Management Service (CEMS). River discharge, or river flow as it is also known, is defined as the amount of water
that flows through a river section at a given time. \nThis dataset is simulated by forcing a hydrological modelling
chain with input from ECMWF ensemble forecast combined with the ECMWF extended-range ensemble forecast up to 30 days.
Data availability for the GloFAS forecast is from 2019-11-05 up to near real time.\n\nVariables in the
dataset/application are:\nRiver discharge in the last 24 hours\n\nVariables in the dataset/application
are:\nUpstream area
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "GloFAS", "forecast", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: River discharge and related forecasted data by the Global Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2019-11-05T00:00:00Z", null]]}}
GLOFAS_HISTORICAL:
description: |
This dataset contains global modelled daily data of river discharge from the Global Flood Awareness System (GloFAS),
which is part of the Copernicus Emergency Management Service (CEMS). River discharge, or river flow as it is also known,
is defined as the amount of water that flows through a river section at a given time. \nThis dataset is simulated by
forcing a hydrological modelling chain with inputs from a global reanalysis. Data availability for the historical
simulation is from 1979-01-01 up to near real time.\n\nVariables in the dataset/application are:\nRiver discharge in the
last 24 hours\n\nVariables in the dataset/application are:\nUpstream area
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "GloFAS", "historical", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: River discharge and related historical data from the Global Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1979-01-01T00:00:00Z", null]]}}
GLOFAS_REFORECAST:
description: |
This dataset provides a gridded modelled time series of river discharge, forced with medium-
to sub-seasonal range meteorological reforecasts. The data is a consistent representation of a
key hydrological variable across the global domain, and is a product of the Global Flood Awareness
System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the
upstream area (see the related variables table and associated link in the documentation).\nThis
dataset was produced by forcing a hydrological modelling chain with input from the European
Centre for Medium-range Weather Forecasts (ECMWF) 11-member ensemble ECMWF-ENS reforecasts.
Reforecasts are forecasts run over past dates, and those presented here are used for providing
a suitably long time period against which the skill of the 30-day real-time operational forecast
can be assessed. The reforecasts are initialised twice weekly with lead times up to 46 days, at
24-hour steps for 20 years in the recent history. For more specific information on the how the
reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available
through the Climate Data Store (CDS), are the operational forecasts, historical simulations that
can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for
users looking for long term forecasts. For users looking specifically for European hydrological
data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations.
All these datasets are part of the operational flood forecasting within the Copernicus Emergency
Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the
last 24 hours\n\nVariables in the dataset/application are:\nUpstream area
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "GloFAS", "reforecast", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Reforecasts of river discharge and related data by the Global Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-01-03T00:00:00Z", "2023-11-25T23:59:59Z"]]}}
GLOFAS_SEASONAL:
description: |
This dataset provides a gridded modelled time series of river discharge, forced with seasonal
range meteorological forecasts. The data is a consistent representation of a key hydrological
variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS).
It is accompanied by an ancillary file for interpretation that provides the upstream area (see the
related variables table and associated link in the documentation).\nThis dataset was produced by
forcing the LISFLOOD hydrological model at a 0.1° (~11 km at the equator) resolution with downscaled
runoff forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF) 51-member
ensemble seasonal forecasting system, SEAS5. The forecasts are initialised on the first of each
month with a 24-hourly time step, and cover 123 days.\nCompanion datasets, also available through
the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used
to derive the hydrological climatology, and medium-range and seasonal reforecasts. The latter dataset
enables research, local skill assessment and post-processing of the seasonal forecasts. In addition,
the seasonal reforecasts are also used to derive a specific range dependent climatology for the
seasonal system. For users looking specifically for European hydrological data, we refer to the
European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are
part of the operational flood forecasting within the Copernicus Emergency Management Service
(CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24
hours\n\nVariables in the dataset/application are:\nUpstream area
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "GloFAS", "seasonal", "forecast", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal forecasts of river discharge and related data by the Global Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-12-01T00:00:00Z", null]]}}
GLOFAS_SEASONAL_REFORECAST:
description: |
This dataset provides a gridded modelled time series of river discharge forced with seasonal
range meteorological reforecasts. The data is a consistent representation of a key hydrological
variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS).
It is accompanied by an ancillary file for interpretation that provides the upstream area (see the
related variables table and associated link in the documentation).\nThis dataset was produced by
forcing a hydrological modelling chain with input from the European Centre for Medium-range Weather
Forecasts (ECMWF) ensemble seasonal forecasting system, SEAS5. For the period of 1981 to 2016 the
number of ensemble members is 25, whilst reforecasts produced for 2017 onwards use a 51-member
ensemble. Reforecasts are forecasts run over past dates, with those presented here used for
producing the seasonal river discharge thresholds. In addition, they provide a suitably long time
period against which the skill of the seasonal forecast can be assessed. The reforecasts are
initialised monthly and run for 123 days, with a 24-hourly time step. For more specific information
on the how the seasonal reforecast dataset is produced we refer to the documentation.\nCompanion
datasets, also available through the Climate Data Store (CDS), include the seasonal forecasts, for
which the dataset provided here can be useful for local skill assessment and post-processing. For
users looking for shorter term forecasts there are also medium-range forecasts and reforecasts
available, as well as historical simulations that can be used to derive the hydrological
climatology. For users looking specifically for European hydrological data, we refer to the European
Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of
the operational flood forecasting within the Copernicus Emergency Management Service
(CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24
hours\n\nVariables in the dataset/application are:\nUpstream area"
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "GloFAS", "seasonal", "forecast", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-01-27T00:00:00Z", null]]}}
EFAS_FORECAST:
description: |
This dataset provides gridded modelled hydrological time series forced with medium-range
meteorological forecasts. The data is a consistent representation of the most important
hydrological variables across the European Flood Awareness System (EFAS) domain. The
temporal resolution is sub-daily high-resolution and ensemble forecasts of:\n\nRiver
discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides
static data on soil depth for the three soil layers. Soil moisture and river discharge
data are accompanied by ancillary files for interpretation (see related variables and
links in the documentation).\nThis data set was produced by forcing the LISFLOOD
hydrological model at a 5x5km resolution with meteorological forecasts. The forecasts are
initialised twice daily at 00 and 12 UTC with time steps of 6 or 24 hours and lead times
between 5 and 15 days depending on the forcing numerical weather prediction model. The
forcing meteorological data are high-resolution and ensemble forecasts from the European
Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members, high-resolution
forecasts from the Deutsches Wetter Dienst (DWD) and the ensemble forecasts from the COSMO
Local Ensemble Prediction System (COSMO-LEPS) with 20 ensemble members. The hydrological
forecasts are available from 2018-10-10 up until present with a 30-day delay. The real-time
data is only available to EFAS partners.\nCompanion datasets, also available through the
CDS, are historical simulations which can be used to derive the hydrological climatology
and for verification; reforecasts for research, local skill assessment and post-processing;
and seasonal forecasts and reforecasts for users looking for longer leadtime forecasts.
For users looking for global hydrological data, we refer to the Global Flood Awareness
System (GloFAS) forecasts and historical simulations. All these datasets are part of the
operational flood forecasting within the Copernicus Emergency Management Service
(CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours,
River discharge in the last 6 hours, Snow depth water equivalent, Soil depth, Volumetric
soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "EFAS", "forecast", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: River discharge and related forecasted data by the European Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-10-11T00:00:00Z", null]]}}
EFAS_HISTORICAL:
description: |
This dataset provides gridded modelled daily hydrological time series forced with
meteorological observations. The data set is a consistent representation of the most
important hydrological variables across the European Flood Awareness System (EFAS) domain.
The temporal resolution is up to 30 years modelled time series of:\n\nRiver discharge\nSoil
moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil
depth for the three soil layers. Soil moisture and river discharge data are accompanied by
ancillary files for interpretation (see related variables and links in the
documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model with
gridded observational data of precipitation and temperature at a 5x5 km resolution across
the EFAS domain. The most recent version\nuses a 6-hourly time step, whereas older versions
uses a 24-hour time step. It is available from 1991-01-01 up until near-real time, with a
delay of 6 days. The real-time data is only available to EFAS partners.\nCompanion datasets,
also available through the CDS, are forecasts for users who are looking medium-range
forecasts, reforecasts for research, local skill assessment and post-processing, and
seasonal forecasts and reforecasts for users looking for long-term forecasts. For users
looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS)
forecasts and historical simulations. All these datasets are part of the operational flood
forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the
dataset/application are:\nRiver discharge in the last 24 hours, River discharge in the last
6 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in
the dataset/application are:\nOrography, Upstream area
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "EFAS", "historical", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: River discharge and related historical data from the European Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1991-01-01T06:00:00Z", null]]}}
EFAS_REFORECAST:
description: |
This dataset provides gridded modelled hydrological time series forced with medium- to
sub-seasonal range meteorological reforecasts. The data is a consistent representation of
the most important hydrological variables across the European Flood Awareness System (EFAS)
domain. The temporal resolution is 20 years of sub-daily reforecasts initialised twice
weekly (Mondays and Thursdays) of:\n\nRiver discharge\nSoil moisture for three soil
layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three
soil layers. Soil moisture and river discharge data are accompanied by ancillary files for
interpretation (see related variables and links in the documentation).\nThis dataset was
produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with ensemble
meteorological reforecasts from the European Centre of Medium-range Weather Forecasts
(ECMWF). Reforecasts are forecasts run over past dates and are typically used to assess
the skill of a forecast system or to develop tools for statistical error correction of the
forecasts. The reforecasts are initialised twice weekly with lead times up to 46 days, at
6-hourly time steps for 20 years. For more specific information on the how the reforecast
dataset is produced we refer to the documentation.\nCompanion datasets, also available
through the Climate Data Store (CDS), are the operational forecasts, historical simulations
which can be used to derive the hydrological climatology, and seasonal forecasts and
reforecasts for users looking for long term forecasts. For users looking for global
hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts an
historical simulations. All these datasets are part of the operational flood forecasting
within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the
dataset/application are:\nRiver discharge, Snow depth water equivalent, Soil depth,
Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream
area
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "EFAS", "reforecast", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Reforecasts of river discharge and related data by the European Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-01-03T00:00:00Z", "2023-11-21T23:59:59Z"]]}}
EFAS_SEASONAL:
description: |
This dataset provides gridded modelled daily hydrological time series forced with seasonal
meteorological forecasts. The dataset is a consistent representation of the most important
hydrological variables across the European Flood Awareness (EFAS) domain. The temporal
resolution is daily forecasts initialised once a month consisting of:\n\nRiver discharge\nSoil
moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on
soil depth for the three soil layers. Soil moisture and river discharge data are accompanied
by ancillary files for interpretation (see related variables and links in the
documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a
5x5km resolution with seasonal meteorological ensemble forecasts. The forecasts are
initialised on the first of each month with a lead time of 215 days at 24-hour time steps.
The meteorological data are seasonal forecasts (SEAS5) from the European Centre of
Medium-range Weather Forecasts (ECMWF) with 51 ensemble members. The forecasts are available
from November 2020.\nCompanion datasets, also available through the Climate Data Store (CDS),
are seasonal reforecasts for research, local skill assessment and post-processing of the
seasonal forecasts. There are also medium-range forecasts for users who want to look at
shorter time ranges. These are accompanied by historical simulations which can be used to
derive the hydrological climatology, and medium-range reforecasts. For users looking for
global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts
and historical simulations. All these datasets are part of the operational flood forecasting
within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the
dataset/application are:\nRiver discharge in the last 24 hours, Snow depth water equivalent,
Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application
are:\nOrography, Upstream area
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "EFAS", "seasonal", "forecast", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal forecasts of river discharge and related data by the European Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-11-01T00:00:00Z", "2023-09-30T00:00:00Z"]]}}
EFAS_SEASONAL_REFORECAST:
description: |
This dataset provides modelled daily hydrological time series forced with seasonal meteorological reforecasts.
The dataset is a consistent representation of the most important hydrological variables across the European
Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month over the
reforecast period 1991-2020 of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water
equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river
discharge data are accompanied by ancillary files for interpretation (see related variables and links in the
documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km gridded
resolution with seasonal meteorological ensemble reforecasts. Reforecasts are forecasts run over past dates
and are typically used to assess the skill of a forecast system or to develop tools for statistical error
correction of the forecasts. The reforecasts are initialised on the first of each month with a lead time of
215 days at 24-hour time steps. The forcing meteorological data are seasonal reforecasts from the European
Centre of Medium-range Weather Forecasts (ECMWF), consisting of 25 ensemble members up until December 2016,
and after that 51 members. Hydrometeorological reforecasts are available from 1991-01-01 up until 2020-10-01.
\nCompanion datasets, also available through the Climate Data Store (CDS), are seasonal forecasts, for which
the seasonal reforecasts can be useful for local skill assessment and post-processing of the seasonal forecasts.
For users looking for shorter time ranges there are medium-range forecasts and reforecasts, as well as
historical simulations which can be used to derive the hydrological climatology. For users looking for global
hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations.
All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management
Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, Snow
depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application
are:\nOrography, Upstream area"
instruments: []
constellation: CEMS
platform: CEMS
processing:level:
keywords: ["ECMWF", "CEMS", "EFAS", "seasonal", "reforecast", "river", "discharge"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Seasonal reforecasts of river discharge and related data by the European Flood Awareness System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1991-01-01T00:00:00Z", "2020-10-01T00:00:00Z"]]}}
# MARK: COPERNICUS Digital Elevation Model
COP_DEM_GLO30_DGED:
description: |
Defence Gridded Elevation Data (DGED, 32 Bit floating point) formatted Copernicus DEM GLO-30 data.
The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth
including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10,
GLO-30 and GLO-90. GLO-30 provides worldwide coverage at 30 meters.Data were acquired through the TanDEM-X mission
between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026.
instruments: []
constellation: TerraSAR
platform:
processing:level:
keywords: ["TerraSAR", "TanDEM-X", "DEM", "surface", "GLO-30", "DSM", "GDGED"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Copernicus DEM GLO-30 DGED
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2010-06-21T00:00:00Z", null]]}}
COP_DEM_GLO30_DTED:
description: |
Digital Terrain Elevation Data (DTED, 16 Bit signed integer) formatted Copernicus DEM GLO-30 data.
The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth
including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10,
GLO-30 and GLO-90. GLO-30 provides worldwide coverage at 30 meters.Data were acquired through the TanDEM-X mission
between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026.
instruments: []
constellation: TerraSAR
platform:
processing:level:
keywords: ["TerraSAR", "TanDEM-X", "DEM", "surface", "GLO-30", "DSM", "DTED"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Copernicus DEM GLO-30 DTED
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2010-06-21T00:00:00Z", null]]}}
COP_DEM_GLO90_DGED:
description: |
Defence Gridded Elevation Data (DGED, 32 Bit floating point) formatted Copernicus DEM GLO-90 data.
The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth
including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10,
GLO-30 and GLO-90. GLO-90 provides worldwide coverage at 90 meters.Data were acquired through the TanDEM-X mission
between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026.
instruments: []
constellation: TerraSAR
platform:
processing:level:
keywords: ["TerraSAR", "TanDEM-X", "DEM", "surface", "GLO-90", "DSM", "GDGED"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Copernicus DEM GLO-90 DGED
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2010-06-21T00:00:00Z", null]]}}
COP_DEM_GLO90_DTED:
description: |
Digital Terrain Elevation Data (DTED, 16 Bit signed integer) formatted Copernicus DEM GLO-90 data.
The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth
including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10,
GLO-30 and GLO-90. GLO-90 provides worldwide coverage at 90 meters.Data were acquired through the TanDEM-X mission
between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026.
instruments: []
constellation: TerraSAR
platform:
processing:level:
keywords: ["TerraSAR", "TanDEM-X", "DEM", "surface", "GLO-90", "DSM", "DTED"]
eodag:sensor_type: ALTIMETRIC
license: other
title: Copernicus DEM GLO-90 DTED
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2010-06-21T00:00:00Z", null]]}}
# MARK: Copernicus Land Monitoring Service
CLMS_CORINE:
description: |
The CORINE Land Cover (CLC) inventory was initiated in 1985 (reference year 1990). Updates have been produced in
2000, 2006, 2012, and 2018. It consists of an inventory of land cover in 44 classes. CLC uses a Minimum Mapping
Unit (MMU) of 25 hectares (ha) for areal phenomena and a minimum width of 100 m for linear phenomena. The time
series are complemented by change layers, which highlight changes in land cover with an MMU of 5 ha. Different
MMUs mean that the change layer has higher resolution than the status layer. Due to differences in MMUs the
difference between two status layers will not equal to the corresponding CLC-Changes layer. If you are interested
in CLC-Changes between two neighbour surveys always use the CLC-Change layer.
instruments: []
constellation: Sentinel-2, LANDSAT, SPOT-4/5, IRS P6 LISS III
platform: S2, L5, L7, L8, SPOT4, SPOT5
processing:level:
keywords: ["Land-cover", "LCL", "CORINE", "CLMS"]
eodag:sensor_type:
license: other
title: CORINE Land Cover
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1986-01-01T00:00:00Z", null]]}}
CLMS_GLO_FCOVER_333M:
description: |
The Fraction of Vegetation Cover (FCover) corresponds to the fraction of ground covered by green vegetation.
Practically, it quantifies the spatial extent of the vegetation. Because it is independent from the illumination
direction and it is sensitive to the vegetation amount, FCover is a very good candidate for the replacement of
classical vegetation indices for the monitoring of ecosystems. The product at 333m resolution is provided in
Near Real Time and consolidated in the next six periods.
instruments: ["OLCI", "PROBA-V"]
constellation: Sentinel-3
platform:
processing:level:
keywords: ["Land", "Fraction-of-vegetation-cover", "OLCI", "PROBA-V", "Sentinel-3"]
eodag:sensor_type:
license: other
title: Global 10-daily Fraction of Vegetation Cover 333m
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-01-10T00:00:00Z", null]]}}
CLMS_GLO_NDVI_333M:
description: |
The Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined
as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the
reflectance in the red band. It is closely related to FAPAR and is little scale dependant.
instruments: ["PROBA-V"]
constellation:
platform:
processing:level:
keywords: ["Land", "NDVI", "PROBA-V"]
eodag:sensor_type:
license: other
title: Global 10-daily Normalized Difference Vegetation Index 333M
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-01-01T00:00:00Z", "2021-01-01T23:59:59Z"]]}}
CLMS_GLO_NDVI_1KM_LTS:
description: |
The Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined
as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the
reflectance in the red band. The time series of dekadal (10-daily) NDVI 1km version 2 observations over the
period 1999-2017 is used to calculate Long Term Statistics (LTS) for each of the 36 10-daily periods (dekads)
of the year. The calculated LTS include the minimum, median, maximum, average, standard deviation and the number
of observations in the covered time series period. These LTS can be used as a reference for actual NDVI observations,
which allows evaluating whether vegetation conditions deviate from a 'normal' situation.
instruments: ["VEGETATION", "PROBA-V"]
constellation: SPOT
platform:
processing:level:
keywords: ["Land", "NDVI", "LTS", "SPOT", "VEGETATION", "PROBA-V"]
eodag:sensor_type:
license: other
title: "Normalized Difference Vegetation Index: global Long Term Statistics (raster 1km) - version 2, Apr 2019"
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-01-01T00:00:00Z", null]]}}
CLMS_GLO_DMP_333M:
description: |
Dry matter Productivity (DMP) is an indication of the overall growth rate or dry biomass increase of the vegetation
and is directly related to ecosystem Net Primary Productivity (NPP), however its units (kilograms of gross dry
matter per hectare per day) are customized for agro-statistical purposes. Compared to the Gross DMP (GDMP), or its
equivalent Gross Primary Productivity, the main difference lies in the inclusion of the autotrophic respiration.
Like the FAPAR products that are used as input for the GDMP estimation, these GDMP products are provided in Near
Real Time, with consolidations in the next periods, or as offline product.
instruments: ["OLCI", "PROBA-V"]
constellation: Sentinel-3
platform:
processing:level:
keywords: ["Land", "Dry-matter-productivity", "DMP", "OLCI", "PROBA-V", "Sentinel-3"]
eodag:sensor_type:
license: other
title: 10-daily Dry Matter Productivity 333M
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-01-10T00:00:00Z", null]]}}
CLMS_GLO_GDMP_333M:
description: |
Gross dry matter Productivity (GDMP) is an indication of the overall growth rate or dry biomass increase of the
vegetation and is directly related to ecosystem Gross Primary Productivity (GPP), that reflects the ecosystem's
overall production of organic compounds from atmospheric carbon dioxide, however its units (kilograms of gross dry
matter per hectare per day) are customized for agro-statistical purposes. Like the FAPAR products that are used as
input for the GDMP estimation, these GDMP products are provided in Near Real Time, with consolidations in the next
periods, or as offline product.
instruments: ["OLCI", "PROBA-V"]
constellation: Sentinel-3
platform:
processing:level:
keywords: ["Land", "Gross-dry-matter-productivity", "GDMP", "GPP", "OLCI", "PROBA-V", "Sentinel-3"]
eodag:sensor_type:
license: other
title: 10-daily Gross Dry Matter Productivity 333M
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-01-10T00:00:00Z", null]]}}
CLMS_GLO_LAI_333M:
description: |
LAI was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit
horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems.
LAI is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations
will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation,
it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where
the understory may represent a very significant contribution to the total canopy LAI. The derived LAI corresponds
therefore to the total green LAI, including the contribution of the green elements of the understory. The product at
333m resolution is provided in Near Real Time and consolidated in the next six periods.
instruments: ["OLCI", "PROBA-V"]
constellation: Sentinel-3
platform:
processing:level:
keywords: ["Land", "Leaf-area-index", "LAI", "OLCI", "PROBA-V", "Sentinel-3"]
eodag:sensor_type:
license: other
title: Global 10-daily Leaf Area Index 333m
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-01-10T00:00:00Z", null]]}}
CLMS_GLO_FAPAR_333M:
description: |
The FAPAR quantifies the fraction of the solar radiation absorbed by plants for photosynthesis. It refers only to
the green and living elements of the canopy. The FAPAR depends on the canopy structure, vegetation element optical
properties, atmospheric conditions and angular configuration. To overcome this latter dependency, a daily integrated
FAPAR value is assessed. FAPAR is very useful as input to a number of primary productivity models and is recognized
as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). The product at 333m resolution
is provided in Near Real Time and consolidated in the next six periods.
instruments: ["OLCI", "PROBA-V"]
constellation: Sentinel-3
platform:
processing:level:
keywords: ["Land", "Fraction-of-absorbed-PAR", "FAPAR", "OLCI", "PROBA-V", "Sentinel-3"]
eodag:sensor_type:
license: other
title: Global 10-daily Fraction of Absorbed PAR 333m
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-01-10T00:00:00Z", null]]}}
EEA_DAILY_SWI_1KM:
description: |
The Soil Water Index (SWI) quantifies the moisture condition at various depths in the soil. It is mainly driven by
the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on
small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale
areas, with the presence of vegetation adding complexity to the interpretation. Soil moisture is a key parameter in
numerous environmental studies including hydrology, meteorology and agriculture, and is recognized as an Essential
Climate Variable (ECV) by the Global Climate Observing System (GCOS). The SWI product provides daily information about
moisture conditions in different soil layers. It includes a quality flag (QFLAG) indicating the availability of SSM
measurements for SWI calculations, and a Surface State Flag (SSF) indicating frozen or snow covered soils.
instruments: ["C-SAR", "Metop-ASCAT"]
constellation: Sentinel-1
platform:
processing:level:
keywords: ["SWI", "QFLAG", "SSF", "C-SAR", "Metop-ASCAT", "Sentinel-1"]
eodag:sensor_type: RADAR
license: other
title: "Soil Water Index: continental Europe daily (raster 1km) - version 1, Apr 2019"
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", null]]}}
EEA_DAILY_SSM_1KM:
description: |
Surface Soil Moisture (SSM) is the relative water content of the top few centimetres soil, describing how wet or
dry the soil is in its topmost layer, expressed in percent saturation. It is measured by satellite radar sensors and
allows insights in local precipitation impacts and soil conditions. SSM is a key driver of water and heat fluxes
between the ground and the atmosphere, regulating air temperature and humidity. Moreover, in its role as water supply,
it is vital to vegetation health. Vice versa, SSM is very sensitive to external forcing in the form of precipitation,
temperature, solar irradiation, humidity, and wind. SSM is thus both an integrator of climatic conditions and a driver
of local weather and climate, and plays a major role in global water-, energy- and carbon- cycles. Knowledge on the
dynamics of soil moisture is important in the understanding of processes in many environmental and socio-economic fields,
e.g., its impact on vegetation vitality, crop yield, droughts or exposure to flood threats.
instruments: ["C-SAR", "Metop-ASCAT"]
constellation: Sentinel-1
platform:
processing:level:
keywords: ["SSM", "C-SAR", "Metop-ASCAT", "Sentinel-1"]
eodag:sensor_type: RADAR
license: other
title: "Surface Soil Moisture: continental Europe daily (raster 1km) - version 1, Apr 2019"
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", null]]}}
EEA_HRL_TCF:
description: |
Tree Cover Fraction (TCF) is defined as the percentage of ground covered by tree canopy when viewed from above.
It is a biophysical variable that characterizes the land surface and is important for many applications in
environmental and climate studies. TCF is derived from Sentinel-2 imagery at 20m resolution using a machine
learning approach. The TCF product is part of the pan-European High Resolution Vegetation Phenology and Productivity
(HR-VPP) component of the Copernicus Land Monitoring Service (CLMS).
instruments: []
constellation: Sentinel-2
platform: S2A,S2B,S2C
processing:level:
keywords: ["Land","Tree-cover-fraction","TCF","Sentinel-2","S2A","S2B","S2C"]
eodag:sensor_type: RADAR
license: other
title: Tree Cover Fraction, UTM projection
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", null]]}}
CLMS_HRVPP_ST:
description: |
The Seasonal Trajectories product is a filtered time series of Plant Phenology Index (PPI), with regular time step, part of the Copernicus Land Monitoring Service (CLMS) HR-VPP product suite.
Plant Phenology Index (PPI) is a physically based vegetation index that is optimized for monitoring vegetation phenology and has a linear relationship with green leaf area index.
The Seasonal Trajectories (ST) products are derived from a function fitting of the time series of the PPI vegetation index and hence provide a filtered PPI time series, with regular 10-day time step, together with related quality information (QFLAG).
The PPI Seasonal Trajectories are derived from the PPI vegetation index and hence based on Copernicus Sentinel-2 satellite observations.
The ST products are made available as a set of raster files with 10m and 100m resolution, in ETRS89/LAEA projection corresponding to the High Resolution Layers tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom, and for the period from 2017 until today, with yearly updates.
These Seasonal Trajectories are part of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS).
instruments: []
constellation: Sentinel-2
platform: S2A,S2B,S2C
processing:level:
keywords: ["Land", "Plant-phenology-index", "Phenology", "Vegetation", "Sentinel-2", "S2A", "S2B", "S2C"]
eodag:sensor_type:
license: other
title: Seasonal Trajectories, 10-daily, ST projection
links:
- rel: describedby
href: https://land.copernicus.eu/en/technical-library/hr-vpp-data-access-manual/@@download/file
type: application/pdf
title: User Manual
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [[null, null]]}}
CLMS_HRVPP_ST_LAEA:
description: |
The Seasonal Trajectories product is a filtered time series of Plant Phenology Index (PPI), with regular time step, part of the Copernicus Land Monitoring Service (CLMS) HR-VPP product suite.
Plant Phenology Index (PPI) is a physically based vegetation index that is optimized for monitoring vegetation phenology and has a linear relationship with green leaf area index.
The Seasonal Trajectories (ST) products are derived from a function fitting of the time series of the PPI vegetation index and hence provide a filtered PPI time series, with regular 10-day time step, together with related quality information (QFLAG).
The PPI Seasonal Trajectories are derived from the PPI vegetation index and hence based on Copernicus Sentinel-2 satellite observations.
The ST products are made available as a set of raster files with 10m and 100m resolution, in ETRS89/LAEA projection corresponding to the High Resolution Layers tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom, and for the period from 2017 until today, with yearly updates.
These Seasonal Trajectories are part of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS).
instruments: []
constellation: Sentinel-2
platform: S2A,S2B,S2C
processing:level:
keywords: ["Land", "Plant-phenology-index", "Phenology", "Vegetation", "Sentinel-2", "S2A", "S2B", "S2C"]
eodag:sensor_type:
license: other
title: Seasonal Trajectories, 10-daily, LAEA projection
links:
- rel: describedby
href: https://land.copernicus.eu/en/technical-library/hr-vpp-data-access-manual/@@download/file
type: application/pdf
title: User Manual
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [[null, null]]}}
CLMS_HRVPP_VPP:
description: |
Vegetation Phenology and Productivity (VPP) comprise 13 parameters up to 2 seasons that characterize the vegetation growth cycle. It is part of the Copernicus Land Monitoring Service (CLMS) HR-VPP product suite.
A set of 13 Vegetation Phenology and Productivity parameters are derived, for up to two growing seasons. These parameters include the start, end and length of the growing season, the dates when the vegetation index reaches its minimum and maximum values, the maximum value itself and its distance to the minimum (amplitude), the slopes of the green-up and green-down periods and the seasonal and total productivity.
The Vegetation and Productivity parameters are extracted from the filtered PPI time-series, the HR-VPP Seasonal Trajectories, and hence based on Copernicus Sentinel-2 satellite observations.
The VPP parameters are made available as a set of raster files with 10m and 100m resolution, in ETRS89/LAEA projection corresponding to the High Resolution Layers tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from 2017 until today, with yearly updates.
This VPP product is part of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS).
instruments: []
constellation: Sentinel-2
platform: S2A,S2B,S2C
processing:level:
keywords: ["Land", "Plant-phenology-index", "Phenology", "Vegetation", "Sentinel-2", "S2A", "S2B", "S2C"]
eodag:sensor_type:
license: other
title: Vegetation Phenology and Productivity, yearly, UTM projection
links:
- rel: describedby
href: https://land.copernicus.eu/en/technical-library/hr-vpp-data-access-manual/@@download/file
type: application/pdf
title: User Manual
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [[null, null]]}}
CLMS_HRVPP_VPP_LAEA:
description: |
Vegetation Phenology and Productivity (VPP) comprise 13 parameters up to 2 seasons that characterize the vegetation growth cycle. It is part of the Copernicus Land Monitoring Service (CLMS) HR-VPP product suite.
A set of 13 Vegetation Phenology and Productivity parameters are derived, for up to two growing seasons. These parameters include the start, end and length of the growing season, the dates when the vegetation index reaches its minimum and maximum values, the maximum value itself and its distance to the minimum (amplitude), the slopes of the green-up and green-down periods and the seasonal and total productivity.
The Vegetation and Productivity parameters are extracted from the filtered PPI time-series, the HR-VPP Seasonal Trajectories, and hence based on Copernicus Sentinel-2 satellite observations.
The VPP parameters are made available as a set of raster files with 10m and 100m resolution, in ETRS89/LAEA projection corresponding to the High Resolution Layers tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from 2017 until today, with yearly updates.
This VPP product is part of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS).
instruments: []
constellation: Sentinel-2
platform: S2A,S2B,S2C
processing:level:
keywords: ["Land", "Plant-phenology-index", "Phenology", "Vegetation", "Sentinel-2", "S2A", "S2B", "S2C"]
eodag:sensor_type:
license: other
title: Vegetation Phenology and Productivity, yearly, LAEA projection
links:
- rel: describedby
href: https://land.copernicus.eu/en/technical-library/hr-vpp-data-access-manual/@@download/file
type: application/pdf
title: User Manual
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [[null, null]]}}
# MARK: AERIS --------------------------------------------------------------------------
AERIS_IAGOS:
description: |
The mission of IAGOS is to provide high quality data throughout the tropopshere
and lower stratosphere, and scientific expertise to understand the evolution of
atmospheric composition, air quality, and climate.
instruments: ["IAGOS-CORE", "IAGOS-MOZAIC", "IAGOS-CARIBIC"]
constellation:
platform:
processing:level: L2
keywords: ["AERIS", "AIRCRAFT", "ATMOSPHERIC", "IAGOS", "L2"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: In-service Aircraft for a Global Observing System
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1994-08-01T00:00:00Z", null]]}}
# MARK: Global Surface Water------------------------------------------------------------------
GSW_CHANGE:
description: |
The Global Surface Water Occurrence Change Intensity map provides information on where
surface water occurrence increased, decreased or remained the same between 1984-1999
and 2000-2021. Both the direction of change and its intensity are documented.
instruments: []
constellation: GSW
platform: GSW
processing:level:
keywords: ["PEKEL", "Global-Surface-Water", "Change", "Landsat"]
eodag:sensor_type: HYDROLOGICAL
license: proprietary
title: Global Surface Water Occurrence Change Intensity 1984-2020
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1984-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}
GSW_EXTENT:
description: |
The Global Surface Water Maximum Water Extent shows all the locations ever detected as
water over a 38-year period (1984-2021)
instruments: []
constellation: GSW
platform: GSW
processing:level:
keywords: ["PEKEL", "Global-Surface-Water", "Extent", "Landsat"]
eodag:sensor_type: HYDROLOGICAL
license: proprietary
title: Global Surface Water Maximum Water Extent 1984-2021
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1984-01-01T00:00:00Z", "2021-12-31T23:59:59Z"]]}}
GSW_OCCURRENCE:
description: |
The Global Surface Water Occurrence shows where surface water occurred between 1984
and 2021 and provides information concerning overall water dynamics. This product
captures both the intra and inter-annual variability and changes.
instruments: []
constellation: GSW
platform: GSW
processing:level:
keywords: ["PEKEL", "Global-Surface-Water", "Occurrence", "Landsat"]
eodag:sensor_type: HYDROLOGICAL
license: proprietary
title: Global Surface Water Occurrence 1984-2021
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1984-01-01T00:00:00Z", "2021-12-31T23:59:59Z"]]}}
GSW_RECURRENCE:
description: |
The Global Surface Water Recurrence provides information concerning the inter-annual
behaviour of water surfaces and captures the frequency with which water returns from
year to year.
instruments: []
constellation: GSW
platform: GSW
processing:level:
keywords: ["PEKEL", "Global-Surface-Water", "Recurrence", "Landsat"]
eodag:sensor_type: HYDROLOGICAL
license: proprietary
title: Global Surface Water Recurrence 1984-2021
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1984-01-01T00:00:00Z", "2021-12-31T23:59:59Z"]]}}
GSW_SEASONALITY:
description: |
The Global Surface Water Seasonality map provides information concerning the intra-annual
behaviour of water surfaces for a single year (2021) and shows permanent and seasonal
water and the number of months water was present.
instruments: []
constellation: GSW
platform: GSW
processing:level:
keywords: ["PEKEL", "Global-Surface-Water", "Seasonality", "Landsat"]
eodag:sensor_type: HYDROLOGICAL
license: proprietary
title: Global Surface Water Seasonality 2014-2020
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-01-01T00:00:00Z", "2020-12-31T23:59:59Z"]]}}
GSW_TRANSITIONS:
description: |
The Global Surface Water Transitions map provides information on the change in surface
water seasonality between the first and last years (between 1984 and 2021) and captures
changes between the three classes of not water, seasonal water and permanent water.
instruments: []
constellation: GSW
platform: GSW
processing:level:
keywords: ["PEKEL", "Global-Surface-Water", "Transitions", "Landsat"]
eodag:sensor_type: HYDROLOGICAL
license: proprietary
title: Global Surface Water Transitions 1984-2021
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1984-01-01T00:00:00Z", "2021-12-31T23:59:59Z"]]}}
# MARK: Eurostat --------------------------------------------------------------------------
EUSTAT_GREENHOUSE_GAS_EMISSION_AGRICULTURE:
description: |
This indicator tracks trends in greenhouse gas (GHG) emissions by agriculture, estimated
and reported under the United Nations Framework Convention on Climate Change (UNFCCC),
the Kyoto Protocol and the Decision 525/2013/EC. The annual data collection covers in
principle all Member States of the European Union as well as some other European countries
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "Agriculture", "Greenhouse-gas", "CO2", "Emission", "Air-pollutants"]
eodag:sensor_type:
license: proprietary
title: Eurostat - Greenhouse gas emissions from agriculture
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2011-01-01T00:00:00Z", "2022-12-31T23:59:59Z"]]}}
EUSTAT_POP_AGE_SEX_NUTS2:
description: |
Each year Eurostat collects demographic data at regional level from 37 countries as part
of the Unified Demography (Unidemo) project. UNIDEMO is Eurostat's main annual demographic
data collection and aims to gather information on demography and migration. This dataset
contains information about the population by sex, age and region of residence (NUTS 2 level).
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "Population", "Age", "Sex", "NUTS-2", "Unidemo", "Demographic"]
eodag:sensor_type:
license: proprietary
title: Population on 1 January by age, sex and NUTS 2 region
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2023-12-31T23:59:59Z"]]}}
EUSTAT_POP_AGE_GROUP_SEX_NUTS3:
description: |
Each year Eurostat collects demographic data at regional level from 37 countries as part
of the Unified Demography (Unidemo) project. UNIDEMO is Eurostat's main annual demographic
data collection and aims to gather information on demography and migration. This dataset
contains information about the population by sex, age and region of residence (NUTS 3 level).
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "Population", "Age", "Sex", "NUTS-3", "Unidemo", "Demographic"]
eodag:sensor_type:
license: proprietary
title: Population on 1 January by age, sex and NUTS 3 region
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2014-01-01T00:00:00Z", "2023-12-31T23:59:59Z"]]}}
EUSTAT_POP_CHANGE_DEMO_BALANCE_CRUDE_RATES_NUTS3:
description: |
Each year Eurostat collects demographic data at regional level from 37 countries as part
of the Unified Demography (Unidemo) project. UNIDEMO is Eurostat's main annual demographic
data collection and aims to gather information on demography and migration. This dataset
contains information about demographic balance and crude rates of a population at regional
level (NUTS 3 level).
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "Population", "Age", "Sex", "NUTS-3", "Unidemo", "Demographic"]
eodag:sensor_type:
license: proprietary
title: Population change - Demographic balance and crude rates at regional level (NUTS 3)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2000-01-01T00:00:00Z", "2023-12-31T23:59:59Z"]]}}
EUSTAT_SHARE_ENERGY_FROM_RENEWABLE:
description: |
This dataset covers the indicator for monitoring progress towards renewable energy targets of
the Europe 2020 strategy implemented by Directive 2009/28/EC on the promotion of the use of
energy from renewable sources. The annual data collection covers in principle all Member States
of the European Union. Time series starts in the year 2004. Due to the change of legal basis,
a break in series occurs between 2020 and 2021. The calculation is based on data
collected in the framework of Regulation (EC) No 1099/2008 on energy statistics and complemented
by specific supplementary data transmitted by national administrations to Eurostat. In some
countries the statistical systems are not yet fully developed to meet all requirements of
Directive 2009/28/EC, in particular with respect to ambient heat captured from the environment
by heat pumps renewable cooling or sustainability of solid and gaseous biofuels. This is indicator
is a Sustainable Development Goal (SDG). It has been chosen for the assessment of the progress
towards the objectives and targets of the EU Sustainable Development Strategy. The data collection
covers the full spectrum of the Member States of the European Union.The share of energy from
renewable sources is calculated for four indicators: Transport (RES-T), Heating and Cooling
(RES-H&C), Electricity (RES-E), Overall RES share (RES)
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "Energy", "Renewable", "Transport", "Heating", "Cooling", "Electricity"]
eodag:sensor_type:
license: proprietary
title: Share of energy from renewable sources
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2004-01-01T00:00:00Z", "2023-12-31T23:59:59Z"]]}}
EUSTAT_AVAILABLE_BEDS_HOSPITALS_NUTS2:
description: |
Non-expenditure healthcare data provide information on institutions providing healthcare in countries, on resources used and on output produced in the framework of healthcare provision. \nData on healthcare form a major element of public health information as they describe the capacities available for different types of healthcare provision as well as potential 'bottlenecks' observed. The quantity and quality of healthcare services provided and the work sharing established between the different institutions are a subject of ongoing debate in all countries. Sustainability - continuously providing the necessary monetary and personal resources needed - and meeting the challenges of ageing societies are the primary perspectives used when analysing and using the data. \nThe resource-related data refer to both human and technical resources, i.e. they relate to: \n- Health care staff: 'manpower' active in the health care sector (doctors, dentists, nurses, etc.);\n- Heath workforce migration: migration movements of doctors and nurses;\n- Healthcare facilities: technical capacity dimensions (hospital beds, beds in nursing and residential care facilities, etc.).\nThe output-related data ('activities') refer to contacts between patients and the healthcare system, and to the treatment thereby received. Data are available for hospital discharges of in-patients and day cases, average length of stay of in-patients, consultations with medical professionals, and medical procedures performed in hospitals.\nAnnual national and regional data are provided in absolute numbers, percentages, and in population-standardised rates (per 100 000 inhabitants).\nWherever applicable, the definitions and classifications of the System of Health Accounts (SHA) are followed, e.g. International Classification for Health Accounts - Providers of health care (ICHA-HP). For hospital discharges, the International Shortlist for Hospital Morbidity Tabulation (ISHMT) is used. Surgical procedures are classified according to a shortlist mapped to ICD-9-CM.\nThese healthcare data are largely based on administrative data sources in the countries. Therefore, they reflect the country-specific way of organising healthcare and may not always be completely comparable.
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "Health-care", "Hospital", "Bed", "Health"]
eodag:sensor_type:
license: proprietary
title: Available beds in hospitals by NUTS 2 region
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-01-01T00:00:00Z", "2022-12-31T23:59:59Z"]]}}
EUSTAT_BATHING_SITES_WATER_QUALITY:
description: |
The indicator measures the number and proportion of coastal and inland bathing sites with excellent water quality. The indicator assessment is based on microbiological parameters (intestinal enterococci and Escherichia coli). The new Bathing Water Directive requires Member States to identify and assess the quality of all inland and marine bathing waters and to classify these waters as ‘poor’, ‘sufficient’, ‘good’ or ‘excellent’.
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "Bath", "Water", "Water-quality"]
eodag:sensor_type:
license: proprietary
title: Bathing sites with excellent water quality by location
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2011-01-01T00:00:00Z", "2023-12-31T23:59:59Z"]]}}
EUSTAT_POP_DENSITY_NUTS3:
description: |
Eurostat’s annual data collections on population. Member States send population data to Eurostat data as on of 31 December for the reference year under Regulation 1260/2013 on European demographic statistics. The data are conventionally published by Eurostat as population on 1 January of the following year (reference year + 1). \nThe aim is to collect annual mandatory and voluntary demographic data from the national statistical institutes. Mandatory data are those defined by the legislation listed under ‘6.1. Institutional mandate — legal acts and other agreements’. \nThe completeness of the demographic data collected on a voluntary basis depends on the availability and completeness of information provided by the national statistical institutes.\nFor more information on mandatory/voluntary data collection, see 6.1. Institutional mandate — legal acts and other agreements. \nThe following statistics are available. \nPopulation on 1 January by sex and by:\n- single age and educational attainment / marital status / broad group of citizenship / broad group of country of birth;\n - five-year age group and citizenship / country of birth;\n - citizenship and broad group of country of birth / country of birth and broad group of citizenship;\n - broad age group and NUTS 3 (under regional data population folder);\n - single age and NUTS 2 (under regional data population folder);\n - five-year age group and NUTS 2 / NUTS 3 (under regional data population folder).\nPopulation structure statistics: median age of population, proportion of population by various age groups, old age dependency ratio.
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "Population", "Density", "NUTS-3"]
eodag:sensor_type:
license: proprietary
title: Population density by NUTS 3 region
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2021-12-31T23:59:59Z"]]}}
EUSTAT_SOIL_SEALING_INDEX:
description: |
The indicator estimates the increase in sealed soil surfaces with impervious materials due to urban development and construction (e.g. buildings, constructions and laying of completely or partially impermeable artificial material, such as asphalt, metal, glass, plastic or concrete). This provides an indication of the rate of soil sealing, when areas change land use towards artificial and urban land use. The indicator builds on data from the imperviousness High Resolution Layer (a product of the Copernicus Land Monitoring Service). The indicator is presented in the following units: Index 2006=100 % of total surface total sealed surface in km2.
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "soil", "soil-sealing", "SDG", "EU-Sustainable-Development-Goals"]
eodag:sensor_type:
license: proprietary
title: Soil sealing index
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2006-01-01T00:00:00Z", "2018-12-31T23:59:59Z"]]}}
EUSTAT_SURFACE_TERRESTRIAL_PROTECTED_AREAS:
description: |
The indicator measures the surface of terrestrial protected areas. The indicator comprises nationally designated protected areas and Natura 2000 sites. A nationally designated area is an area protected by national legislation. The Natura 2000 network comprises both marine and terrestrial protected areas designated under the EU Habitats and Birds Directives with the goal to maintain or restore a favourable conservation status for habitat types and species of EU interest.
instruments: []
constellation: Eurostat
platform: Eurostat
processing:level:
keywords: ["Eurostat", "CO2", "terrestrial", "protected-areas"]
eodag:sensor_type:
license: proprietary
title: Surface of the terrestrial protected areas
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-01-01T00:00:00Z", "2022-12-31T23:59:59Z"]]}}
# MARK: ISIMIP -------------------------------------------------------------------------
ISIMIP_CLIMATE_FORCING_ISIMIP3B:
description: |
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for
the collation of a consistent set of climate impact data across sectors and scales. It
also provides a unique opportunity for considering interactions between climate change
impacts across sectors through consistent scenarios.\n\nThe ISIMIP3b part of the third
simulation round is dedicated to a quantification of climate-related risks at different
levels of global warming and socio-economic change. ISIMIP3b group I simulations are based
on historical climate change as simulated in CMIP6 combined with observed historical
socio-economic forcing. ISIMIP3b group II simulations are based on climate change according
to the CMIP6 future projections combined with socio-economic forcings fixed at 2015 levels.
ISIMIP3b group III simulations additionally account for future changes in socio-economic
forcing.\n\nThis collection contains bias-adjusted atmospheric climate input data,
atmospheric composition input data as well as ocean and lightning input data.
instruments: []
constellation: ISIMIP
platform: ISIMIP
processing:level:
keywords: ["ISIMIP", "CLIMATE-FORCING", "ISIMIP3b", "atmospheric", "climate", "HRMC"]
eodag:sensor_type:
license: other
title: ISIMIP3b climate input data
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1601-01-01T00:00:00Z", "2500-12-31T23:59:59Z"]]}}
ISIMIP_SOCIO_ECONOMIC_FORCING_ISIMIP3B:
description: |
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for
the collation of a consistent set of climate impact data across sectors and scales. It also
provides a unique opportunity for considering interactions between climate change impacts
across sectors through consistent scenarios.\n\nThe ISIMIP3b part of the third simulation
round is dedicated to a quantification of climate-related risks at different levels of global
warming and socio-economic change. ISIMIP3b group I simulations are based on historical climate
change as simulated in CMIP6 combined with observed historical socio-economic forcing. ISIMIP3b
group II simulations are based on climate change according to the CMIP6 future projections
combined with socio-economic forcings fixed at 2015 levels. ISIMIP3b group III simulations
additionally account for future changes in socio-economic forcing. This collection contains
fishing, lake fraction, land use, land transition, water abstraction and wood harvesting input
data as well as information about crops and fertilizers
instruments: []
constellation: ISIMIP
platform: ISIMIP
processing:level:
keywords: ["ISIMIP", "SOCIO-ECONOMIC-FORCING", "ISIMIP3b", "socioeconomic"]
eodag:sensor_type:
license: other
title: ISIMIP3b socio-economic input data
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1601-01-01T00:00:00Z", "2100-12-31T23:59:59Z"]]}}
# MARK: METEOBLUE ----------------------------------------------------------------------
NEMSGLOBAL_TCDC:
description: |
Total cloud cover from NOAAmodel Environment Monitoring System (NEMS) global model.
NEMSGLOBAL has 30km spatial and 1h temporal resolutions and produces seamless
datasets from 1984 to 7 days ahead.
instruments: []
constellation: NEMSGLOBAL
platform: NEMSGLOBAL
processing:level:
keywords: ["meteoblue", "NEMS", "NEMSGLOBAL", "CLOUD", "COVER", "TOTAL", "TCDC", "DAILY", "MEAN"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: NEMSGLOBAL Total Cloud Cover daily mean
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1984-01-01T00:00:00Z", null]]}}
NEMSAUTO_TCDC:
description: |
Total cloud cover from NOAAmodel Environment Monitoring System (NEMS) automatic
domain switch. NEMSAUTO is the automatic delivery of the highest resolution meteoblue
model available for any requested period of time and location. The NEMS model family
are improved NMM successors (operational since 2013). NEMS is a multi-scale model
(used from global down to local domains) and significantly improves cloud-development
and precipitation forecast.
Note that Automatic domain switching is only supported for multi point queries.
Support for polygons may follow later.
instruments: []
constellation: NEMSAUTO
platform: NEMSAUTO
processing:level:
keywords: ["meteoblue", "NEMS", "NEMSAUTO", "CLOUD", "COVER", "TOTAL", "TCDC", "DAILY", "MEAN"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: NEMSAUTO Total Cloud Cover daily mean
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1984-01-01T00:00:00Z", null]]}}
# MARK: DE DT Outputs ------------------------------------------------------------------
DT_EXTREMES:
description: |
The Digital Twin on Weather-Induced and Geophysical Extremes provides capabilities
for the assessment and prediction of environmental extremes in support of risk
assessment and management.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Weather", "Geophysical", "Extremes"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Weather and Geophysical Extremes Digital Twin (DT)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2024-04-04T00:00:00Z", "2024-06-07T00:00:00Z"]]}}
DT_CLIMATE_ADAPTATION:
description: |
The Digital Twin on Climate Change Adaptation support the analysis and testing of
scenarios. This in turn will support sustainable development and climate adaptation
and mitigation policy-making at multi-decadal timescales, at regional and national
levels.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate", "Change", "Adaptation"]
eodag:sensor_type: ATMOSPHERIC
license: other
title: Climate Change Adaptation Digital Twin (DT)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", null]]}}
DT_CLIMATE_G1_CMIP6_HIST_ICON_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Historical Simulation' data based on the 'ICON' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "CMIP6", "ICON", "Historical Simulation"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - ICON
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2019-12-31T23:59:59Z"]]}}
DT_CLIMATE_G1_CMIP6_HIST_IFS_NEMO_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Historical Simulation' data based on the 'IFS-NEMO' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "CMIP6", "IFS-NEMO","Historical Simulation"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-NEMO
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2002-02-28T23:59:59Z"]]}}
DT_CLIMATE_G1_HIGHRESMIP_CONT_IFS_FESOM_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Control Simulation' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "HIGHRESMIP", "IFS-FESOM","Control Simulation"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-FESOM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2004-12-31T23:59:59Z"]]}}
DT_CLIMATE_G1_HIGHRESMIP_CONT_IFS_NEMO_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Control Simulation' data based on the 'IFS-NEMO' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "HIGHRESMIP", "IFS-NEMO","Control Simulation"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-NEMO
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2007-04-30T23:59:59Z"]]}}
DT_CLIMATE_G1_SCENARIOMIP_SSP3_7_0_ICON_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Future Projection' data based on the 'ICON' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "SCENARIOMIP", "ICON","Future Projection", "SSP3-7.0"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - ICON
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-09-01T00:00:00Z", "2039-12-31T23:59:59Z"]]}}
DT_CLIMATE_G1_SCENARIOMIP_SSP3_7_0_IFS_FESOM_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Future Projection' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "SCENARIOMIP", "IFS-FESOM","Future Projection", "SSP3-7.0"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-FESOM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2039-12-31T23:59:59Z"]]}}
DT_CLIMATE_G1_SCENARIOMIP_SSP3_7_0_IFS_NEMO_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Future Projection' data based on the 'IFS-NEMO' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "SCENARIOMIP", "IFS-NEMO","Future Projection", "SSP3-7.0"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-NEMO
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", "2039-12-31T23:59:59Z"]]}}
DT_CLIMATE_G1_STORY_NUDGING_CONT_IFS_FESOM_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story nudging", "IFS-FESOM","Control Simulation", "Storyline Simulation Present Climate"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2024-11-15T23:59:59Z"]]}}
DT_CLIMATE_G1_STORY_NUDGING_HIST_IFS_FESOM_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story nudging", "IFS-FESOM","Historical Simulation", "Storyline Simulation Present Climate"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2024-11-15T23:59:59Z"]]}}
DT_CLIMATE_G1_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R1:
description: |
The Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story nudging", "IFS-FESOM","Tplus2.0K", "Storyline Simulation Present Climate"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2024-11-15T23:59:59Z"]]}}
DT_CLIMATE_G1_CMIP6_HIST_IFS_FESOM_R1:
description: |
The nextGEMS data is aligned with the Climate Change Adaptation Digital Twin. The Climate
Change Adaptation Digital Twin provides global climate projections and sector-specific
information over multiple decades at high resolution via a unified framework combining
advanced Earth system models, impact assessments, and observations. This nextGEMS Collection
gives access to 'Historical Simulation' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "CMIP6", "IFS-FESOM","Historical Simulation", "nextGEMS"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: nextGEMS - Historical Simulation - IFS-FESOM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2019-12-31T23:59:59Z"]]}}
DT_CLIMATE_G1_SCENARIOMIP_SSP3_7_0_IFS_FESOM_R2:
description: |
The nextGEMS data is aligned with the Climate Change Adaptation Digital Twin. The
Climate Change Adaptation Digital Twin provides global climate projections and
sector-specific information over multiple decades at high resolution via a unified
framework combining advanced Earth system models, impact assessments, and observations.
This nextGEMS Collection gives access to 'Future Projection' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "SCENARIOMIP", "IFS-FESOM","Future Projection", "nextGEMS", "SSP3-7.0"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: nextGEMS - Future Projection - IFS-FESOM
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2021-01-01T00:00:00Z", "2049-12-31T23:59:59Z"]]}}
# DT_CLIMATE_ADAPTATION Phase 2
DT_CLIMATE_G2_BASELINE_CONT_ICON_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Control Simulation' data based on the 'ICON' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "baseline", "ICON", "cont", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - ICON - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "1999-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_BASELINE_CONT_IFS_FESOM_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Control Simulation' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "baseline", "IFS-FESOM", "cont", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-FESOM - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "1999-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_BASELINE_CONT_IFS_NEMO_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Control Simulation' data based on the 'IFS-NEMO' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "baseline", "IFS-NEMO", "cont", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-NEMO - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "1999-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_BASELINE_HIST_ICON_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Historical Simulation' data based on the 'ICON' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "baseline", "ICON", "hist", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - ICON - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2014-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_BASELINE_HIST_IFS_FESOM_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Historical Simulation' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "baseline", "IFS-FESOM", "hist", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-FESOM - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2014-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_BASELINE_HIST_IFS_NEMO_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Historical Simulation' data based on the 'IFS-NEMO' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "baseline", "IFS-NEMO", "hist", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-NEMO - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1990-01-01T00:00:00Z", "2014-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_PROJECTIONS_SSP3_7_0_ICON_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Future Projection' data based on the 'ICON' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "projections", "ICON", "SSP3-7.0", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - ICON - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2049-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_PROJECTIONS_SSP3_7_0_IFS_FESOM_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Future Projection' data based on the 'IFS-FESOM' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "projections", "IFS-FESOM", "SSP3-7.0", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-FESOM - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2049-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_PROJECTIONS_SSP3_7_0_IFS_NEMO_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Future Projection' data based on the 'IFS-NEMO' model.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "projections", "IFS-NEMO", "SSP3-7.0", "Phase 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-NEMO - Generation-2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2049-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model, realization 1.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "cont", "Phase 2", "Realization 1"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization 1
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R2:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model, realization 2.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "cont", "Phase 2", "Realization 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization 2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R3:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model, realization 3.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "cont", "Phase 2", "Realization 3"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization 3
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R4:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model, realization 4.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "cont", "Phase 2", "Realization 4"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization 4
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R5:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model, realization 5.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "cont", "Phase 2", "Realization 5"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization 5
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model, realization 1.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "hist", "Phase 2", "Realization 1"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization 1
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R2:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model, realization 2.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "hist", "Phase 2", "Realization 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization 2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R3:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model, realization 3.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "hist", "Phase 2", "Realization 3"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization 3
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R4:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model, realization 4.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "hist", "Phase 2", "Realization 4"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization 4
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R5:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model, realization 5.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "hist", "Phase 2", "Realization 5"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization 5
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R1:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model, realization 1.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "Tplus2.0K", "Phase 2", "Realization 1"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization 1
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R2:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model, realization 2.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "Tplus2.0K", "Phase 2", "Realization 2"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization 2
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R3:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model, realization 3.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "Tplus2.0K", "Phase 2", "Realization 3"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization 3
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R4:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model, realization 4.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "Tplus2.0K", "Phase 2", "Realization 4"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization 4
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R5:
description: |
The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal
timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector
applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales
(1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of
global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the
leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding
of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)
\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated
inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and
further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information
on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)
\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.
Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050.
See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations
\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to
the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and
[Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)".
This Generation-2 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model, realization 5.
instruments: []
constellation: Digital Twin
platform: DT
processing:level:
keywords: ["DT", "DE", "LUMI", "Destination-Earth", "Digital-Twin", "Climate Change", "story-nudging", "IFS-FESOM", "Tplus2.0K", "Phase 2", "Realization 5"]
eodag:sensor_type: ATMOSPHERIC
license: CC-BY-4.0
title: Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization 5
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-01-01T00:00:00Z", "2025-12-31T23:59:59Z"]]}}
# MARK: METOP --------------------------------------------------------------------------
METOP_AMSU_L1:
description: |
The Advanced Microwave Sounding Unit-A (AMSU-A) is a 15-channel microwave radiometer
that is used for measuring global atmospheric temperature profiles and will provide
information on atmospheric water in all of its phases (with the exception of small
ice particles, which are transparent at microwave frequencies). AMSU-A will provide
information even in cloudy conditions. AMSU-A measures Earth radiance at frequencies
(in GHz) as listed under the instrument channel information.
instruments: ["AMSU-A"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "AMSU-A", "SOUNDER", "L1", "L1B", "WATER", "ATHMOSPHERE", "TEMPERATURE", "AMSxxx1B", "AMSUL1"]
eodag:sensor_type: SOUNDER
license: other
title: AMSU-A Level 1B - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2008-03-01T00:00:00Z", null]]}}
METOP_OSI_104:
description: |
Equivalent neutral 10m winds over the global oceans, with specific sampling to
provide as many observations as possible near the coasts. Better than using this
archived NRT product, please use the reprocessed ASCAT winds data records
(METOP_OSI_150A, METOP_OSI_150B).
For Metop-A, t is recommended that the reprocessed ASCAT winds data records
(10.15770/EUM_SAF_OSI_0007) are used instead of this archived NRT product for the
period before 1 April 2014.
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L2
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L2", "WEATHER", "OCEAN-SURFACE-WIND", "OCEAN", "RADAR-BACKSCATTER-NRCS", "OSI-104", "ASCAT12+", "OSI-104-C", "OSI-104-B", "OASWC12"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Coastal Winds at 12.5 km Swath Grid - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-04-16T00:00:00Z", null]]}}
METOP_OSI_150A:
description: |
The ASCAT Wind Product contains stress equivalent 10m winds (speed and direction)
over the global oceans. The winds are obtained through the processing of reprocessed
scatterometer backscatter data originating from the ASCAT instrument on EUMETSAT's
Metop satellite.
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L2
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L2", "WEATHER", "OCEAN-SURFACE-WIND", "OCEAN", "RADAR-BACKSCATTER-NRCS", "OSI-150-A", "OR1ASW025", "REPASC25"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT L2 25 km Winds Data Record Release 1 - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", "2014-04-01T00:00:00Z"]]}}
METOP_OSI_150B:
description: |
The ASCAT Wind Product contains stress equivalent 10m winds (speed and direction)
over the global oceans. The winds are obtained through the processing of reprocessed
scatterometer backscatter data originating from the ASCAT instrument on EUMETSAT's
Metop satellite.
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L2
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L2", "WEATHER", "OCEAN-SURFACE-WIND", "OCEAN", "RADAR-BACKSCATTER-NRCS", "OSI-150-B", "OR1ASWC12", "REPASC12+"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT L2 12.5 km Winds Data Record Release 1 - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", "2014-04-01T00:00:00Z"]]}}
METOP_ASCSZF1B:
description: |
The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed
and direction over the oceans, and the main operational application is the
assimilation of ocean winds in NWP models. Other operational applications, based on
the use of measurements of the backscattering coefficient, are sea ice edge
detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface
parameters. This product consists of geo-located radar backscatter values along the
six ASCAT beams. The different beam measurements are not collocated into a regular
swath grid and the individual measurements are not spatially averaged. The
resolution of each of the 255 backscatter values per each beam varies slightly along
the beam, but it is approximately 10km (in the along beam direction) x 25 km (across
the beam). This product is usually referred to as 'ASCAT Level 1B Full resolution
product'. Note that some of the data are reprocessed. Please refer to the associated
product validation reports or product release notes for further information.
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L1", "OCEAN", "RADAR-BACKSCATTER-NRCS", "ASCSZF1B"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Level 1 Sigma0 Full Resolution - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-05-31T00:00:00Z", null]]}}
METOP_ASCSZR1B:
description: |
The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed
and direction over the oceans, and the main operational application is the
assimilation of ocean winds in NWP models. Other operational applications, based on
the use of measurements of the backscattering coefficient, are sea ice edge
detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface
parameters. The product is available from the archive in 2 different spatial
resolutions; 25 km and 12.5 km. Note that some of the data are reprocessed. Please
refer to the associated product validation reports or product release notes for
further information. Near real-time distribution discontinued on 29/09/2015 but the
product contents are now available in the corresponding Level 2 product 'ASCAT Soil
Moisture at 12.5 km Swath Grid'.
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L1", "LAND", "OCEAN", "RADAR-BACKSCATTER-NRCS", "ASCSZR1B"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Level 1 Sigma0 resampled at 12.5 km Swath Grid - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-03-01T00:00:00Z", null]]}}
METOP_ASCSZO1B:
description: |
The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed
and direction over the oceans, and the main operational application is the
assimilation of ocean winds in NWP models. Other operational applications, based on
the use of measurements of the backscattering coefficient, are sea ice edge
detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface
parameters. The product is available from the archive in 2 different spatial
resolutions; 25 km and 12.5 km. Note that some of the data are reprocessed. Please
refer to the associated product validation reports or product release notes for
further information. Near real-time distribution discontinued on 29/09/2015 but the
product contents are now available in the corresponding Level 2 product 'ASCAT Soil
Moisture at 25 km Swath Grid'.
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L1", "LAND", "OCEAN", "RADAR-BACKSCATTER-NRCS", "ASCSZO1B"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Level 1 Sigma0 resampled at 25 km Swath Grid - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-03-01T00:00:00Z", null]]}}
METOP_ASCSZFR02:
description: |
Reprocessed L1B data from the Advanced Scatterometer (ASCAT) on METOP-A, resampled
at full resolution (SZF). Normalized radar cross section (NRCS) of the Earth surface
together with measurement time, location (latitude and longitude) and geometrical
information (incidence and azimuth angles). The prime objective of the Advanced
SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and
the main operational application is the assimilation of ocean winds in NWP models.
Other operational applications, based on the use of measurements of the
backscattering coefficient, are sea ice edge detection and monitoring, monitoring
sea ice, snow cover, soil moisture and surface parameters. This product is also
available at 12.5 and 25 km Swath Grids. This is a Fundamental Climate Data Record
(FCDR).
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L1", "CLIMATE", "FUNDAMENTAL-CLIMATE-DATA-RECORD", "ASCSZF1B0200", "ASCSZFR02"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Level 1 SZF Climate Data Record Release 2 - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", "2014-04-01T00:00:00Z"]]}}
METOP_ASCSZOR02:
description: |
Reprocessed L1B data from the Advanced Scatterometer (ASCAT) on METOP-A, resampled
at 25 km Swath Grid (SZO). Normalized radar cross section (NRCS) triplets of the
Earth surface together with measurement time, location (latitude and longitude) and
geometrical information (incidence and azimuth angles). The prime objective of the
Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the
oceans, and the main operational application is the assimilation of ocean winds in
NWP models. Other operational applications, based on the use of measurements of the
backscattering coefficient, are sea ice edge detection and monitoring, monitoring
sea ice, snow cover, soil moisture and surface parameters. This product is also
available at full resolution and at 12.5 km Swath Grid. This is a Fundamental
Climate Data Record (FCDR).
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L1", "CLIMATE", "FUNDAMENTAL-CLIMATE-DATA-RECORD", "ASCSZOR02", "ASCSZO1B0200"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Level 1 SZO Climate Data Record Release 2 - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", "2014-04-01T00:00:00Z"]]}}
METOP_ASCSZRR02:
description: |
Reprocessed L1B data from the Advanced Scatterometer (ASCAT) on METOP-A, resampled
at 12.5 km Swath Grid (SZR). Normalized radar cross section (NRCS) triplets of the
Earth surface together with measurement time, location (latitude and longitude) and
geometrical information (incidence and azimuth angles). The prime objective of the
Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the
oceans, and the main operational application is the assimilation of ocean winds in
NWP models. Other operational applications, based on the use of measurements of the
backscattering coefficient, are sea ice edge detection and monitoring, monitoring
sea ice, snow cover, soil moisture and surface parameters. This product is also
available at full resolution and at 25 km Swath Grid. This is a Fundamental Climate
Data Record (FCDR).
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L1", "CLIMATE", "FUNDAMENTAL-CLIMATE-DATA-RECORD", "ASCSZR1B0200", "ASCSZRR02"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Level 1 SZR Climate Data Record Release 2 - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", "2014-04-01T00:00:00Z"]]}}
METOP_SOMO12:
description: |
The Soil Moisture (SM) product is derived from the Advanced SCATterometer (ASCAT)
backscatter observations and given in swath orbit geometry (12.5 km sampling). This
SM product provides an estimate of the water content of the 0-5 cm topsoil layer,
expressed in degree of saturation between 0 and 100 [%]. The algorithm used to
derive this parameter is based on a linear relationship of SM and scatterometer
backscatter and uses change detection techniques to eliminate the contributions of
vegetation, land cover and surface topography, considered invariant from year to
year. Seasonal vegetation effects are modelled by exploiting the multi-angle viewing
capabilities of ASCAT. The SM processor has been developed by Vienna University of
Technology (TU Wien). Note that some of the data are reprocessed. Please refer to
the associated product validation reports or product release notes for further
information.
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L2
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L2", "LAND", "SOIL-MOISTURE", "SOMO12", "ASCSMR02", "SSM-ASCAT-C-NRT-O12.5", "H101", "H16", "H104"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Soil Moisture at 12.5 km Swath Grid in NRT - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-06-01T00:00:00Z", null]]}}
METOP_SOMO25:
description: |
The Soil Moisture (SM) product is derived from the Advanced SCATterometer (ASCAT)
backscatter observations and given in swath orbit geometry (25 km sampling). This SM
product provides an estimate of the water content of the 0-5 cm topsoil layer,
expressed in degree of saturation between 0 and 100 [%]. The algorithm used to
derive this parameter is based on a linear relationship of SM and scatterometer
backscatter and uses change detection techniques to eliminate the contributions of
vegetation, land cover and surface topography, considered invariant from year to
year. Seasonal vegetation effects are modelled by exploiting the multi-angle viewing
capabilities of ASCAT. The SM processor has been developed by Vienna University of
Technology (TU Wien). Note that some of the data are reprocessed. Please refer to
the associated product validation reports or product release notes for further
information.
instruments: ["ASCAT"]
constellation: METOP
platform: METOP
processing:level: L2
keywords: ["METOP", "ASCAT", "SCATTEROMETER", "L2", "LAND", "SOIL-MOISTURE", "ASCSMO02", "H102", "H103", "SOMO25", "H105", "SSM-ASCAT-C-NRT-O25"]
eodag:sensor_type: SCATTEROMETER
license: other
title: ASCAT Soil Moisture at 25 km Swath Grid in NRT - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-06-01T00:00:00Z", null]]}}
METOP_AVHRRGACR02:
description: |
This is the second release of the reprocessed polar Atmospheric Motion Vectors (AMV)
Thematic Climate Data Record (TCDR) from the Advanced Very High Resolution
Radiometer (AVHRR) in Global Area Coverage (GAC), from TIROS-N, NOAA-06, 07, 08, 09,
10, 11, 12, 14, 15, 16, 17, 18 and 19 and Metop-A and -B. It contains AMVs at all
heights below the tropopause, derived from images in the Infrared channel at 10.8
microns. Vectors are retrieved by tracking the motion of clouds in two consecutive
images. The height assignment of the AMVs is calculated using the Cross-Correlation
Contribution (CCC) function to determine the height using the pixels that contribute
the most to the vectors. A quality indicator is derived for each vector to assess
the reliability of the retrieval. Products are stored in netCDF4 format and cover
the period from January 1979 to September 2019.
This is a Thematic Climate Data Record (TCDR).
instruments: ["AVHRR"]
constellation: METOP,TIROS,NOAA
platform: METOP,TIROS,NOAA
processing:level: L2
keywords: ["METOP", "AVHRR", "RADIOMETER", "L2", "WIND", "CLIMATE", "ATMOSPHERE", "THEMATIC-CLIMATE-DATA-RECORD", "AVHGAC020200"]
eodag:sensor_type: RADIOMETER
license: other
title: AVHRR GAC Atmospheric Motion Vectors Climate Data Record Release 2 - Multimission - Polar
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1979-01-01T00:00:00Z", "2019-09-01T00:00:00Z"]]}}
METOP_AVHRRL1:
description: |
The Advanced Very High Resolution Radiometer (AVHRR) operates at 5 different channels simultaneously in the visible
and infrared bands, with wavelengths specified in the instrument channels description. Channel 3 switches between 3a
and 3b for daytime and nighttime. As a high-resolution imager (about 1.1 km near nadir) its main purpose is to
provide cloud and surface information such as cloud coverage, cloud top temperature, surface temperature over land
and sea, and vegetation or snow/ice. In addition, AVHRR products serve as input for the level 2 processing of IASI
and ATOVS.
instruments: ["AVHRR"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "AVHRR", "RADIOMETER", "L1", "ATMOSPHERE", "OCEAN", "AVHXXX1B", "AVHRRL1"]
eodag:sensor_type: RADIOMETER
license: other
title: AVHRR Level 1B - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2008-03-01T00:00:00Z", null]]}}
METOP_LSA_002:
description: |
The EDLST (EPS Daily Land Surface Temperature) provides a composite of day-time and nigh-time retrievals of LST
based on clear-sky measurements from the Advanced Very High Resolution Radiometer (AVHRR) on-board EUMETSAT polar
system satellites, the Metop series.
instruments: ["AVHRR"]
constellation: METOP
platform: METOP
processing:level: L3
keywords: ["METOP", "AVHRR", "RADIOMETER", "L3", "LAND-SURFACE-TEMPERATURE", "SURFACE-RADIATION-BUDGET", "LAND", "EDLST", "LSA-002"]
eodag:sensor_type: RADIOMETER
license: other
title: Daily Land Surface Temperature - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", null]]}}
METOP_GLB_SST_NC:
description: |
Global Metop/AVHRR sub-skin Sea Surface Temperature (GBL SST) is a 12 hourly synthesis on a 0.05° global grid. The
product format is compliant with the Data Specification (GDS) version 2 from the Group for High Resolution Sea
Surface Temperatures (GHRSST).
instruments: ["AVHRR"]
constellation: METOP
platform: METOP
processing:level: L3
keywords: ["METOP", "AVHRR", "RADIOMETER", "L3", "OCEAN", "SEA-SURFACE-TEMPERATURE", "OSSTGLBN", "OSI-201-B", "GLB-SST", "OSSTGLB"]
eodag:sensor_type: RADIOMETER
license: other
title: Global L3C AVHRR Sea Surface Temperature (GHRSST) - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-07-12T00:00:00Z", null]]}}
METOP_GOMEL1:
description: |
The Global Ozone Monitoring Experiment-2 (GOME-2) spectrometer measures profiles and total columns of ozone and of
other atmospheric constituents that are related to the depletion of ozone in the stratosphere and its production in
the troposphere, as well as to natural and anthropogenic sources of pollution.
instruments: ["GOME-2"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "GOME-2", "SPECTROMETER", "L1", "ATMOSPHERE", "GOMEL1", "GOMXXX1B"]
eodag:sensor_type: SPECTROMETER
license: other
title: GOME-2 Level 1B - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", null]]}}
METOP_GOMEL1R03:
description: |
This is release 3 of the Global Ozone Monitoring Experiment 2 (GOME-2) Level 1B Fundamental Data Record from Metop-A
and -B. GOME-2 is an optical spectrometer. GOME-2 senses the Earth's backscattered radiance and extra-terrestrial
solar irradiance in the ultraviolet and visible part of the spectrum (240 nm - 790 nm) at a high spectral resolution
between 0.26 nm and 0.51 nm. There are 4096 spectral points from four detector channels transferred for each
individual GOME-2 measurement. This is a Fundamental Data Record (FDR). Disclaimer: GOME2-A channel 3 should be
careful to use for the period: April 2007 until March 2009 when doing DOAS retrievals.
instruments: ["GOME-2"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "GOME-2", "SPECTROMETER", "L1", "CLIMATE", "FUNDAMENTAL-DATA-RECORD", "FDR", "CLOUDS", "ATMOSPHERE", "RADIATION", "GOMXXX1B0300"]
eodag:sensor_type: SPECTROMETER
license: other
title: GOME-2 Level 1B Fundamental Data Record Release 3 - Metop-A and -B
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-04-01T00:00:00Z", "2020-08-01T00:00:00Z"]]}}
METOP_IASTHR011:
description: |
This is the release 1.1 of the climate data record of "all-sky" temperature and humidity profiles and their
associated quality parameters. The CDR was processed using the latest operational EUMETSAT algorithms available
(V6.5.4, 12/2019). It consists of the outputs of the statistical retrieval module Piece Wise Linear Regression only.
This provides a homogeneous CDR throughout the time period.
On the 8 August 2023, year 2022 was added to the CDR.
This is a Thematic Climate Data Record (TCDR).
instruments: ["IASI"]
constellation: METOP
platform: METOP
processing:level: L2
keywords: ["METOP", "IASI", "INTERFEROMETER", "L2", "CLIMATE", "TEMPERATURE", "ATMOSPHERE", "HUMIDITY", "LAND-SURFACE-TEMPERATURE", "THEMATIC-CLIMATE-DATA-RECORD", "SEA-SURFACE-TEMPERATURE", "IASTHPW30101"]
eodag:sensor_type: INTERFEROMETER
license: other
title: IASI All Sky Temperature and Humidity Profiles - Climate Data Record Release 1.1 - Metop-A and -B
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-07-10T00:00:00Z", "2023-01-01T00:00:00Z"]]}}
METOP_IASSND02:
description: |
The main objective of the Infrared Atmospheric Sounding Interferometer (IASI) is to provide high resolution
atmospheric emission spectra to derive temperature and humidity profiles with high spectral and vertical resolution
and accuracy. Additionally, it is used for the determination of trace gases such as ozone, as well as land and sea
surface temperature, emissivity and cloud properties. This combined product (IASI Atmospheric Temperature Water
Vapour and Surface Skin Temperature; IASI Cloud Parameters; IASI Ozone and IASI Trace Gases contains temperature
Profiles, Humidity Profiles, Surface Temperature, Surface Emissivity, Fractional Cloud Cover, Cloud Top Temperature,
Cloud Top Pressure, Cloud Phase, Total Column Ozone, Columnar ozone amounts in thick layers, Total column N2O, CO,
CH4, CO2 - all combined in one product.
instruments: ["IASI"]
constellation: METOP
platform: METOP
processing:level: L2
keywords: ["METOP", "IASI", "INTERFEROMETER", "L2", "CLIMATE", "TEMPERATURE", "ATMOSPHERE", "HUMIDITY", "IASSND02"]
eodag:sensor_type: INTERFEROMETER
license: other
title: IASI Combined Sounding Products - Metop
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2008-02-13T00:00:00Z", null]]}}
METOP_IASIL1C_ALL:
description: |
This product covers all spectral samples. The main objective of the Infrared Atmospheric Sounding Interferometer
(IASI) is to provide high resolution atmospheric emission spectra to derive temperature and humidity profiles with
high spectral and vertical resolution and accuracy. Additionally it is used for the determination of trace gases
such as ozone, nitrous oxide, carbon dioxide and methane, as well as land and sea surface temperature, emissivity
and cloud properties. The IASI L1c product contains infra-red radiance spectra at 0.5cm-1 resolution. The EUMETCast
product has for each sounder pixel 8461 spectral samples covering the range between 645.0 cm-1 and 2760 cm-1.
instruments: ["IASI"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "IASI", "INTERFEROMETER", "L1", "L1C", "ATMOSPHERE", "IASIL1C-ALL", "IASxxx1C"]
eodag:sensor_type: INTERFEROMETER
license: other
title: IASI Level 1C - All Spectral Samples - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2009-03-23T00:00:00Z", null]]}}
METOP_MHSL1:
description: |
The Microwave Humidity Sounder (MHS) is a 5 channel instrument used to provide input to the retrieval of surface
temperatures, emissivities, and atmospheric humidity. In combination with AMSU-A information it can also be used to
process precipitation rates and related cloud properties, as well as to detect sea ice and snow coverage.
instruments: ["MHS"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "MHS", "SOUNDER", "L1", "L1B", "ATMOSPHERE", "MHSxxx1B", "MHSL1"]
eodag:sensor_type: SOUNDER
license: other
title: MHS Level 1B - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2009-03-23T00:00:00Z", null]]}}
METOP_HIRSL1:
description: |
The High Resolution Infrared Sounder (HIRS) operates at 20 channels (19 channels in the infrared and one in the
visible). Its main purpose is to provide input for the vertical temperature and humidity profile retrievals. In
addition, the HIRS pixel resolution serves as the standard grid resolution for all ATOVS level 2 products.
instruments: ["HIRS"]
constellation: METOP
platform: METOP
processing:level: L1
keywords: ["METOP", "HIRS", "SOUNDER", "L1", "L1B", "ATMOSPHERE", "HIRxxx1B", "HIRSL1"]
eodag:sensor_type: SOUNDER
license: other
title: HIRS Level 1B - Metop - Global
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2009-03-23T00:00:00Z", null]]}}
# MARK: MSG --------------------------------------------------------------------------
MSG_MFG_GSA_0:
description: |
Release 2 of the Thematic Climate Data Record (TCDR) of the Meteosat First
Generation (MFG) and Meteosat Second Generation (MSG) Level 2 land surface
albedo. The variables estimated are black-sky albedo (BSA) and white-sky
albedo (WSA) with the corresponding uncertainties as explained in the
Product User Guide (PUM). The data record validation and limitations are
provided in the Validation Report (VR). The products are available in
netCDF4 format. This release contains products generated with Meteosat-2 to
Meteosat-10.
instruments: ["MVIRI", "SEVIRI"]
constellation: MFG,MSG
platform: MFG,MSG
processing:level: L2
keywords: ["MVIRI", "SEVIRI", "L2", "MFG", "MSG", "Climate", "Thematic", "Meteosat", "TCDR"]
eodag:sensor_type: OPTICAL
license: other
title: GSA Level 2 Climate Data Record Release 2 - MFG and MSG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-02-10T00:00:00Z", "2017-12-31T23:59:59Z"]]}}
MSG_OCA_CDR:
description: |
The OCA Release 1 Climate Data Record (CDR) covers the MSG observation
period from 2004 up to 2019, providing a homogenous cloud properties time
series. It is generated at full Meteosat repeat cycle (15 minutes)
fequency. Cloud properties retrieved by OCA are cloud top pressure, cloud
optical thickness, and cloud effective radius, together with uncertainties.
The OCA algorithm has been slightly adapted for climate data record
processing. The adaptation mainly consists in the usage of different
inputs, because the one used for Near Real Time (NRT) were not available
for the reprocessing (cloud mask, clear sky reflectance map) and also not
homogenous (reanalysis) over the complete time period. it extends the NRT
data record more than 9 years back in time. This is a Thematic Climate Data
Record (TCDR).
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["MSG", "L2", "SEVIRI", "Climate", "Clouds", "Atmosphere", "Observation", "Thematic", "TCDR", "OCA"]
eodag:sensor_type: MSG
license: other
title: Optimal Cloud Analysis Climate Data Record Release 1 - MSG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2004-01-19T00:00:00Z", "2019-08-31T23:59:59Z"]]}}
MSG_CLM:
description: |
The Cloud Mask product describes the scene type (either 'clear' or 'cloudy') on a pixel level. Each pixel is classified
as one of the following four types: clear sky over water, clear sky over land, cloud, or not processed (off Earth disc).
Applications & Uses: The main use is in support of Nowcasting applications, where it frequently serves as a basis for other
cloud products, and the remote sensing of continental and ocean surfaces.
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["MSG", "SEVIRI", "OPTICAL", "WEATHER", "CLOUDS", "ATMOSPHERE", "VISUALISATION", "L2", "MSGCLMK", "CLM"]
eodag:sensor_type: OPTICAL
license: other
title: Cloud Mask - MSG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-09-01T00:00:00Z", null]]}}
MSG_CLM_IODC:
description: |
The Cloud Mask product describes the scene type (either 'clear' or 'cloudy') on a pixel level. Each pixel is classified
as one of the following four types: clear sky over water, clear sky over land, cloud, or not processed (off Earth disc).
Applications & Uses: The main use is in support of Nowcasting applications, where it frequently serves as a basis for other
cloud products, and the remote sensing of continental and ocean surfaces.
From 1 June 2022, Meteosat-9 at 45.5° E is the prime satellite for the IODC service, replacing Meteosat-8 (located at 41.5° E while in operation).
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["MSG", "SEVIRI", "OPTICAL", "WEATHER", "CLOUDS", "ATMOSPHERE", "ATMOSPHERIC", "COMPOSITION", "VISUALISATION", "L2", "MSGCLMK", "CLM"]
eodag:sensor_type: OPTICAL
license: other
title: Cloud Mask - MSG - Indian Ocean
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-02-01T00:00:00Z", null]]}}
MSG_GSAL2R02:
description: |
Release 2 of the Thematic Climate Data Record (TCDR) of the Meteosat First Generation (MFG) and Meteosat Second Generation (MSG)
Level 2 land surface albedo. The variables estimated are black-sky albedo (BSA) and white-sky albedo (WSA) with the corresponding
uncertainties as explained in the Product User Guide (PUM). The data record validation and limitations are provided in the
Validation Report (VR). The products are available in netCDF4 format. This release contains products generated with Meteosat-2
to Meteosat-10.
instruments: ["MVIRI", "SEVIRI"]
constellation: MSG,MFG
platform: MSG,MFG
processing:level: L2
keywords: ["MSG", "MFG", "SEVIRI", "MVIRI", "OPTICAL", "CLIMATE", "L2", "MxGGSA000200"]
eodag:sensor_type: OPTICAL
license: other
title: GSA Level 2 Climate Data Record Release 2 - MFG and MSG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1982-02-10T00:00:00Z", null]]}}
MSG_HRSEVIRI:
description: |
Rectified (level 1.5) Meteosat SEVIRI image data. The data is transmitted as High Rate transmissions in 12 spectral channels.
Level 1.5 image data corresponds to the geolocated and radiometrically pre-processed image data, ready for further processing,
e.g. the extraction of meteorological products. Any spacecraft specific effects have been removed, and in particular, linearisation
and equalisation of the image radiometry has been performed for all SEVIRI channels. The on-board blackbody data has been processed.
Both radiometric and geometric quality control information is included. Images are made available with different timeliness according
to their latency: quarter-hourly images if latency is more than 3 hours and hourly images if latency is less than 3 hours
(for a total of 87 images per day). To enhance the perception for areas which are on the night side of the Earth a different mapping
with increased contrast is applied for IR3.9 product. The greyscale mapping is based on the EBBT which allows to map the ranges 200 K
to 300 K for the night and 250 K to 330 K for the day.
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L1
keywords: ["MSG", "SEVIRI", "OPTICAL", "OCEAN", "ATMOSPHERE", "VISUALISATION", "L1", "MSG15", "HRSEVIRI"]
eodag:sensor_type: OPTICAL
license: other
title: High Rate SEVIRI Level 1.5 Image Data - MSG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2004-01-19T00:00:00Z", null]]}}
MSG_HRSEVIRI_IODC:
description: |
Rectified (level 1.5) Meteosat SEVIRI image data. The data is transmitted as High Rate transmissions in 12 spectral channels.
Level 1.5 image data corresponds to the geolocated and radiometrically pre-processed image data, ready for further processing,
e.g. the extraction of meteorological products. Any spacecraft specific effects have been removed, and in particular, linearisation
and equalisation of the image radiometry has been performed for all SEVIRI channels. The on-board blackbody data has been processed.
Both radiometric and geometric quality control information is included. Images are made available with different timeliness according
to the latency: quarter-hourly images with a latency of more than 3 hours and hourly images if latency is less than 3 hours
(for a total of 87 images per day). To enhance the perception for areas which are on the night side of the Earth a different mapping
with increased contrast is applied for IR3.9 product. The greyscale mapping is based on the EBBT which allows to map the ranges 200 K
to 300 K for the night and 250 K to 330 K for the day.
From 1 June 2022, Meteosat-9 at 45.5° E is the prime satellite for the IODC service, replacing Meteosat-8 (located at 41.5° E while in operation).
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L1
keywords: ["MSG", "SEVIRI", "OPTICAL", "OCEAN", "ATMOSPHERE", "VISUALISATION", "L1", "MSG15", "HRSEVIRI", "IODC"]
eodag:sensor_type: OPTICAL
license: other
title: High Rate SEVIRI Level 1.5 Image Data - MSG - Indian Ocean
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2017-02-01T00:00:00Z", null]]}}
MSG_RSS_CLM:
description: |
The Rapid Scanning Services (RSS) Cloud Mask product describes the scene type (either 'clear' or 'cloudy') on a pixel level. Each pixel
is classified as one of the following four types: clear sky over water, clear sky over land, cloud, or not processed (off Earth disc).
Applications & Uses: The main use is in support of Nowcasting applications, where it frequently serves as a basis for other cloud products,
and the remote sensing of continental and ocean surfaces.
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["RSS-CLM", "MSGCLMK", "MSG", "SEVIRI", "OPTICAL", "CLOUDS", "ATMOSPHERE", "L2"]
eodag:sensor_type: OPTICAL
license: other
title: Rapid Scan Cloud Mask - MSG
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2013-02-28T00:00:00Z", null]]}}
MSG_MSG15_RSS:
description: |
Rectified (level 1.5) Meteosat SEVIRI Rapid Scan image data. The baseline scan region is a reduced area of the top 1/3 of a nominal
repeat cycle, covering a latitude range from approximately 15 degrees to 70 degrees. The service generates repeat cycles at
5-minute intervals (the same as currently used for weather radars). The dissemination of RSS data is similar to the normal dissemination,
with image segments based on 464 lines and compatible with the full disk level 1.5 data scans. Epilogue and prologue
(L1.5 Header and L1.5 Trailer) have the same structure. Calibration is as in Full Earth Scan. Image rectification is to 9.5 degreesE.
The scans start at 00:00, 00:05, 00:10, 00:15 ... etc. (5 min scan). The differences from the nominal Full Earth scan are that for
channels 1 - 11, only segments 6 - 8 are disseminated and for the High Resolution Visible Channel only segments 16 - 24 are disseminated.
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L1
keywords: ["MSG15-RSS", "MSG15", "MSG", "SEVIRI", "OPTICAL", "OCEAN", "ATMOSPHERE", "LAND", "L1"]
eodag:sensor_type: OPTICAL
license: other
title: Rapid Scan High Rate SEVIRI Level 1.5 Image Data - MSG
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2008-05-13T00:00:00Z", null]]}}
MSG_LSA_FRM:
description: |
Fire risk by merging NWP & remotely sensed (FRP) data. The product includes 24h, 48h, 72h, 96h and 120h forecasts of: risk of fire (5 classes)
and the probability of ignitions reaching energy releases over 2000GJ (both covering Southern Europe); Fire Weather Index (FWI)
and respective components estimated for the whole MSG disk.
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["LSA-504.2", "FRMV2", "FIRE", "VEGETATION", "LAND", "MSG", "SEVIRI", "OPTICAL", "LAND", "L2"]
eodag:sensor_type: OPTICAL
license: other
title: Fire Risk Map - Released Energy Based - MSG
# Note: There's a mismatch between the temporal extent mentioned on the
# product description page (22/07/2021 to now) and the available time range
# displayed on the download page (2023-09-21 - 2024-10-20).
#
# Using the available time range start date here.
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2023-09-21T00:00:00Z", null]]}}
MSG_LSA_LST_CDR:
description: |
The full archive of MSG/SEVIRI data was reprocessed to provide the user community a consistent, homogeneous and continuous Data Record of
the 15-min Land Surface Temperature (LST) for the period 2004-2015. This Data Record was obtained with the best version of its equivalent
NRT product (MLST) which can also complement the time series from 2016 onwards.
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L3
keywords: ["LSA-050", "MLST-R", "FIRE", "VEGETATION", "LAND", "MSG", "SEVIRI", "OPTICAL", "LAND", "L3"]
eodag:sensor_type: OPTICAL
license: other
title: Land Surface Temperature Climate Data Record - MSG
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2004-01-21T00:00:00Z", "2015-12-31T00:00:00Z"]]}}
MSG_LSA_LSTDE:
description: |
Land Surface Temperature (LST) is the radiative skin temperature over land. LST plays an important role in the physics of land surface as
it is involved in the processes of energy and water exchange with the atmosphere. LST is useful for the scientific community, namely for
those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related
to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies.
Land Surface Emissivity (EM), a crucial parameter for LST retrieval from space, is independently estimated as a function of (satellite derived)
Fraction of Vegetation Cover (FVC) and land cover classification. In the most recent version of the dataset, information on the expected
deviation of LST estimates from SEVIRI/MSG with respect to a reference view - here considered to be nadir view - has been added to the
original product (LSA-001) as an extra data layer (LSA-004).
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["LSA-004", "LSA-001", "MLST_DIR", "FIRE", "VEGETATION", "LAND", "MSG", "SEVIRI", "OPTICAL", "LAND", "L2"]
eodag:sensor_type: OPTICAL
license: other
title: Land Surface Temperature with Directional Effects - MSG
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2005-01-16T00:00:00Z", null]]}}
MSG_AMVR02:
description: |
This is the second release of the reprocessed Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) Atmospheric Motion Vectors (AMV)
Thematic Climate Data Record (TCDR). It contains AMV at all heights below the tropopause, derived from images in 2 channels
(Water Vapour 6.2, Infrared 10.8) of the instrument MVIRI on board MFG and SEVIRI on board MSG. Vectors are retrieved by tracking the motion
of clouds and other atmospheric constituents such as water vapour patterns. The height assignment of the AMVs is calculated using the
Cross-Correlation Contribution (CCC) function to determine the height using the pixels that contribute the most to the vectors. The final vector
is estimated averaging the speed and height over 4 consecutive images. A quality indicator is derived for each vector to assess the reliability
of the retrieval. Products are stored in netCDF4 format and generated from Meteosat-2 to Meteosat-11 satellites, covering the period from
September 1981 to August 2019. This is a Thematic Climate Data Record (TCDR).
instruments: ["SEVIRI", "MVIRI"]
constellation: MSG,MFG
platform: MSG,MFG
processing:level: L2
keywords: ["WIND", "CLIMATE", "ATMOSPHERE", "OBSERVATION", "THEMATIC", "OPTICAL", "MXGAMV000200", "AMVR20000", "L2"]
eodag:sensor_type: OPTICAL
license: other
title: Atmospheric Motion Vectors Climate Data Record Release 2 - MFG and MSG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-09-03T00:00:00Z", null]]}}
MSG_CTH:
description: |
The product indicates the height of highest cloud. Based on a subset of the information derived during Scenes and Cloud Analysis,
but also makes use of other external meteorological data. Applications and Users: Aviation meteorology.
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["MSG", "SEVIRI", "OPTICAL", "WEATHER", "CLOUDS", "ATMOSPHERE", "VISUALISATION", "L2", "MSGCLTH", "CTH"]
eodag:sensor_type: OPTICAL
license: other
title: Cloud Top Height - MSG - 0 degree
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-09-01T00:00:00Z", null]]}}
MSG_CTH_IODC:
description: |
The product indicates the height of highest cloud. Based on a subset of the information derived during Scenes and Cloud Analysis,
but also makes use of other external meteorological data. Applications and Users: Aviation meteorology. From 1 June 2022,
Meteosat-9 at 45.5° E is the prime satellite for the IODC service, replacing Meteosat-8 (located at 41.5° E while in operation).
instruments: ["SEVIRI"]
constellation: MSG
platform: MSG
processing:level: L2
keywords: ["MSG", "SEVIRI", "OPTICAL", "WEATHER", "CLOUDS", "ATMOSPHERE", "VISUALISATION", "L2", "MSGCLTH", "CTH"]
eodag:sensor_type: OPTICAL
license: other
title: Cloud Top Height - MSG - Indian Ocean
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-09-01T00:00:00Z", null]]}}
# Copernicus Marine
MO_GLOBAL_ANALYSISFORECAST_PHY_001_024:
description: |
The Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global
ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year's time series
sliding window. This product includes daily and monthly mean files of temperature, salinity, currents, sea level,
mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean
surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a
1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are
ranging from 0 to 5500 meters. This product also delivers a special dataset for surface current which also includes
wave and tidal drift called SMOC (Surface merged Ocean Current). DOI (product) : https://doi.org/10.48670/moi-00016
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "analysis", "forecast", "marine", "physics"]
eodag:sensor_type:
license: other
title: Global Ocean Physics Analysis and Forecast
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2019-01-01T00:00:00Z", null]]}}
MO_GLOBAL_ANALYSISFORECAST_BGC_001_028:
description: |
The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing
10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two
full year's time series sliding window. This product includes daily and monthly mean files of biogeochemical
parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production,
phytoplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output
files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular
projection. 50 vertical levels are ranging from 0 to 5700 meters. + NEMO version (v3.6_STABLE) + Forcings:
GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. + Outputs mean fields are interpolated on a standard
regular grid in NetCDF format. + Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and
dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC +
Quality/Accuracy/Calibration information: See the related
QuID[http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-028.pdf] DOI (product): https://doi.org/10.48670/moi-00015
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "analysis", "forecast", "marine", "biogeochemical", "biogeochemistry"]
eodag:sensor_type:
license: other
title: Global Ocean Biogeochemistry Analysis and Forecast
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2021-10-01T00:00:00Z", null]]}}
MO_GLOBAL_ANALYSISFORECAST_WAV_001_027:
description: |
The operational global ocean analysis and forecast system of Météo-France with a resolution of 1/12 degree is
providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes
3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period,
direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell
waves. The global wave system of Météo-France is based on the wave model MFWAM which is a third generation wave
model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The
model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project « my wave »
(Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data
ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles.
At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10°. The operational
model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The
wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM
uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a
day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum
in primary and secondary swells. DOI (product) : https://doi.org/10.48670/moi-00017
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "analysis", "forecast", "marine", "waves", "surface"]
eodag:sensor_type:
license: other
title: Global Ocean Waves Analysis and Forecast
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2021-01-01T00:00:00Z", null]]}}
MO_GLOBAL_MULTIYEAR_BGC_001_033:
description: |
The Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [https://www.cls.fr CLS] on behalf
of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional
groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu).
No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth,
depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.
Forcings sources: + Ocean currents and temperature (CMEMS multiyear product) + Net Primary Production computed from
chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS
multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high
latitudes (CMEMS biogeochemistry multiyear product)
Vertical coverage: + Epipelagic layer + Upper mesopelagic layer + Lower mesopelagic layer (max. 1000m)
DOI (product) : https://doi.org/10.48670/moi-00020
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "hindcast", "marine", "biomass", "LMTL"]
eodag:sensor_type:
license: other
title: Global ocean low and mid trophic levels biomass content hindcast
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1998-01-01T00:00:00Z", "2022-12-31T23:59:59Z"]]}}
MO_GLOBAL_MULTIYEAR_WAV_001_032:
description: |
GLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1993. This product
also bears the name of WAVERYS within the GLO-HR MFC. for correspondence to other global multi-year products like GLORYS.
BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave
spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5° irregular grid. Average wave
quantities derived from this wave spectrum, such as the SWH (significant wave height) or the average wave period, are
delivered on a regular 1/5° grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from
a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the
GLORYS12 physical ocean reanalysis and assimilates significant wave height observed from historical altimetry missions
and directional wave spectra from Sentinel 1 SAR from 2017 onwards. DOI (product): https://doi.org/10.48670/moi-00022
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "reanalysis", "forecast", "marine", "waves", "WAVERYS"]
eodag:sensor_type:
license: other
title: Global Ocean Waves Reanalysis
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2024-01-31T21:00:00Z"]]}}
MO_GLOBAL_MULTIYEAR_PHY_ENS_001_031:
description: |
You can find here the CMEMS Global Ocean Ensemble Reanalysis product at ¼ degree resolution: monthly means of Temperature, Salinity,
Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded
descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data
assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations
(i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in
the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate
than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering “altimetric era” period
(starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n GLORYS2V4 from Mercator Ocean (Fr);
\n ORAS5 from ECMWF;\n GloSea5 from Met Office (UK);\n and C-GLORSv7 from CMCC (It);\n
\nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products
available for users are monthly or daily mean averages describing the ocean.
\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00024
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "ensemble", "multiyear", "reanalysis", "temperature", "currents", "salinity", "ice", "physics"]
eodag:sensor_type:
license: other
title: Global Ocean Ensemble Physics Reanalysis
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", "2022-12-31T00:00:00Z"]]}}
MO_INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048:
description: |
This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types
of instruments are distributed:\n The near-surface zonal and meridional velocities calculated along the trajectories of
the drifting buoys which are part of the DBCP's Global Drifter Program. These data are delivered together with wind stress
components and surface temperature. \n The near-surface zonal and meridional total velocities, and near-surface radial
velocities, measured by High Frequency radars that are part of the European High Frequency radar Network. These data are
delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of
Precision (GDOP), quality flags and metadata.\n The zonal and meridional velocities, at parking depth and in surface,
calculated along the trajectories of the floats which are part of the Argo Program.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00041
instruments: []
constellation:
platform:
processing:level: L2
keywords: ["CMEMS", "Mercator", "ocean", "insitu", "NRT", "currents", "global", "L2"]
eodag:sensor_type:
license: other
title: Global Ocean- in-situ Near real time observations of ocean currents
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1997-01-01T00:00:00Z", null]]}}
MO_INSITU_GLO_PHY_TS_OA_NRT_013_002:
description: |
For the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near
real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting
a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation
source for operational models, observing seasonal cycle and inter-annual variability.
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00037
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "insitu", "NRT", "gridded", "monthly", "global", "L4", "analysis"]
eodag:sensor_type:
license: other
title: Global Ocean- Real time in-situ observations objective analysis
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2023-01-15T00:00:00Z", "2024-12-01T00:00:00Z"]]}}
MO_INSITU_GLO_PHY_TS_OA_MY_013_052:
description: |
Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global
product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical
estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational
models, observing seasonal cycle and inter-annual variability.\n\n**DOI (product):** \nhttps://doi.org/10.17882/46219
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "insitu", "delayed", "gridded", "global", "L4", "analysis", "temperature", "salinity", "CORA"]
eodag:sensor_type:
license: other
title: Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1960-01-01T00:00:00Z", "2023-12-01T00:00:00Z"]]}}
MO_MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:
description: |
This product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp)
and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25°x0.25° horizontal resolution,
over 36 levels from the surface to 1000 m depth. A neural network method estimates both the vertical distribution of
Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface
ocean color satellite measurements with hydrological properties and additional relevant drivers.
DOI (product): https://doi.org/10.48670/moi-00046 Product Citation: Please refer to our Technical FAQ for citing
products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "marine", "POC", "organic", "carbon", "particulate", "chlorophyll", "backscattering", "bbp", "Chla"]
eodag:sensor_type:
license: other
title: Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1998-01-07T00:00:00Z", "2021-12-29T00:00:00Z"]]}}
MO_MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:
description: |
This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2,
pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated
uncertainties on a 0.25° x 0.25° regular grid. The product is obtained from an ensemble-based forward feed neural network
approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and
sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction.
Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014).
Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2
and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\nProduct Citation: Please refer to our
Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.
\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00047\n\nReferences:\n\n Chau, T. T. T., Gehlen, M., and
Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air-sea CO2 fluxes over the global
coastal and open oceans, Biogeosciences, 19, 1087-1109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "surface", "carbon", "L4", "REP"]
eodag:sensor_type:
license: other
title: Global Ocean Surface Carbon
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1985-01-01T00:00:00Z", "2023-12-01T00:00:00Z"]]}}
MO_MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009:
description: |
This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and
carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide),
computed for each Argo float equipped with an oxygen sensor.\nThe method called CANYON (Carbonate system and Nutrients
concentration from hYdrological properties and Oxygen using a Neural-network) is based on a neural network trained using
high-quality nutrient data collected over the last 30 years (GLODAPv2 database, https://www.glodap.info/). The method is
applied to each Argo float equipped with an oxygen sensor using as input the properties measured by the float (pressure,
temperature, salinity, oxygen), and its date and position.\n\nProduct Citation: Please refer to our Technical FAQ for
citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\nDOI (product):
\nhttps://doi.org/10.48670/moi-00048\n\nReferences:\n\n Sauzede R., H. C. Bittig, H. Claustre, O. Pasqueron de Fommervault,
J.-P. Gattuso, L. Legendre and K. S. Johnson, 2017: Estimates of Water-Column Nutrient Concentrations and Carbonate System
Parameters in the Global Ocean: A novel Approach Based on Neural Networks. Front. Mar. Sci. 4:128.
doi: 10.3389/fmars.2017.00128.\n Bittig H. C., T. Steinhoff, H. Claustre, B. Fiedler, N. L. Williams, R. Sauzède,
A. Körtzinger and J.-P. Gattuso,2018: An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables
and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Front. Mar. Sci. 5:328. doi: 10.3389/fmars.2018.00328.\n
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "vertical", "nutrients", "carbon", "carbonate", "L3"]
eodag:sensor_type:
license: other
title: Nutrient and carbon profiles vertical distribution
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2002-09-01T00:00:00Z", "2022-08-31T00:00:00Z"]]}}
MO_MULTIOBS_GLO_PHY_MYNRT_015_003:
description: |
This product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components
(geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and
at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents
and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5 in NRT). 1 hourly product,
daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been
consolidated during the Globcurrent project (funded by the ESA User Element Program).
\n\nProduct Citation:\nPlease refer to our Technical FAQ for citing products:
http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.
\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00327\n\nReferences:\n\n Rio, M.-H., S. Mulet, and N. Picot:
Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new
insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, doi:10.1002/2014GL061773, 2014.\n
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "REP", "NRT", "geostrophic", "currents", "GLOBCURRENT", "L4"]
eodag:sensor_type:
license: other
title: Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", null]]}}
MO_MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:
description: |
This product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD)
at 1/8° of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images
from multiple satellite sources as NASA's Soil Moisture Active Passive (SMAP) and ESA's Soil Moisture Ocean Salinity (SMOS) satellites
with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR)
and includes 4 datasets:\n cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data
\n cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n cmems_obs-mob_glo_phy-sss_my_multi_P1D,
which provides multi-year reprocessed (REP) daily data \n cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP)
monthly data \n\nProduct citation: \nPlease refer to our Technical FAQ for citing products:
http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\nDOI
(product): \nhttps://doi.org/10.48670/moi-00051\n\nReferences:\n\n Droghei, R., B. Buongiorno Nardelli, and
R. Santoleri, 2016: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface.
J. Atmos. Oceanic Technol. doi:10.1175/JTECH-D-15-0194.1.\n Buongiorno Nardelli, B., R. Droghei, and R. Santoleri,
2016: Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data.
Rem. Sens. Environ., doi:10.1016/j.rse.2015.12.052.\n Droghei, R., B. Buongiorno Nardelli, and R. Santoleri,
2018: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993-2016), Front. Mar. Sci., 5(March),
1-13, doi:10.3389/fmars.2018.00084.\n Sammartino, Michela, Salvatore Aronica, Rosalia Santoleri, and Bruno Buongiorno Nardelli. (2022).
Retrieving Mediterranean Sea Surface Salinity Distribution and Interannual Trends from Multi-Sensor Satellite and In Situ Data,
Remote Sensing 14, 2502: https://doi.org/10.3390/rs14102502.\n
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "surface", "salinity", "density", "NRT", "daily", "REP", "L4"]
eodag:sensor_type:
license: other
title: Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", null]]}}
MO_MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:
description: |
You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature,
Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/4 degree regular grid and on 50 depth levels from
the surface down to the bottom. The product includes 4 datasets: \n dataset-armor-3d-nrt-weekly, which delivers near-real-time (NRT)
weekly data\n dataset-armor-3d-nrt-monthly, which delivers near-real-time (NRT) monthly data\n dataset-armor-3d-rep-weekly, which
delivers multi-year reprocessed (REP) weekly data \n dataset-armor-3d-rep-monthly, which delivers multi-year reprocessed (REP)
monthly data\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00052\n\n\nProduct Citation: \nPlease refer to our
Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.
\n\nReferences:\n\n Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature
and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845-857.\n Mulet, S., M.-H. Rio,
A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite
data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77-80(0):70-81.\n
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "REP", "NRT", "ARMOR3D", "temperature", "salinity", "heights", "Geostrophic", "currents", "L4"]
eodag:sensor_type:
license: other
title: Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1993-01-01T00:00:00Z", null]]}}
MO_MULTIOBS_GLO_PHY_UVW_3D_MYNRT_015_007:
description: |
You can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the
Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4° horizontal resolution, from
the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation
of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 ) and ERA5 surface fluxes.
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00053\n\n
\n\nReferences:\n\n Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical
Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711–1723. https://doi.org/10.5194/essd-12-1711-2020.
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "ARMOR3D", "weekly", "ERA5", "quasi-geostrophic", "currents", "L4", "OMEGA3D"]
eodag:sensor_type:
license: other
title: Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)
extent: {"spatial": {"bbox": [[-179.87, -82.12, 179.88, 89.88]]}, "temporal": {"interval": [["1993-01-06T00:00:00Z", null]]}}
MO_SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:
description: |
For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers three global sea ice products in operational mode:
sea ice concentration, sea ice edge, sea ice type (OSI-401 OSI-402 and OSI-403). These products are delivered daily at
10km resolution in a polar stereographic projection covering the Northern Hemisphere and the Southern Hemisphere. It is
the Sea Ice operational nominal product for the Global Ocean. In addition, a sea ice drift product is delivered at 60km
resolution in a polar stereographic projection covering the Northern and Southern Hemispheres. The sea ice motion vectors
have a time-span of 2 days.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00134
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "ice", "arctic", "antarctic", "concentration", "edge", "type", "L4"]
eodag:sensor_type:
license: other
title: Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2019-05-04T00:00:00Z", null]]}}
MO_SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:
description: |
DTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR,
Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours,
the nominal product grid resolution is a 10km.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00135
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "NRT", "gridded", "MCC", "DTU", "displacement", "L4"]
eodag:sensor_type:
license: other
title: Global Ocean - High Resolution SAR Sea Ice Drift
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2019-05-04T00:00:00Z", null]]}}
MO_SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:
description: |
The CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from
October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration
is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and
dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020)
and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00136\n
\nReferences:\n\n [http://osisaf.met.no/docs/osisaf_cdop2_ss2_pum_sea-ice-conc-reproc_v2p2.pdf]\n
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "ice", "concentration", "CDR", "ICDR", "REP", "reprocessed", "L4"]
eodag:sensor_type:
license: other
title: Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1978-10-25T00:00:00Z", null]]}}
MO_SEALEVEL_GLO_PHY_L4_NRT_008_046:
description: |
Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is
estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available.
Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages
for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents
(absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission
altimeter data processing system. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00149
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "gridded", "surface", "heights", "SLA", "NRT", "L4"]
eodag:sensor_type:
license: other
title: GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2022-01-01T00:00:00Z", null]]}}
MO_SEALEVEL_GLO_PHY_MDT_008_063:
description: |
Mean Dynamic Topography that combines the global CNES-CLS-2022 MDT, the Black Sea CMEMS2020 MDT and the Med Sea CMEMS2020 MDT.
It is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid. This is consistent with the
reference time period also used in the DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00150
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "sealevel", "topography", "surface", "height", "L4"]
eodag:sensor_type:
license: other
title: GLOBAL OCEAN MEAN DYNAMIC TOPOGRAPHY
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1993-01-06T00:00:00Z", null]]}}
MO_SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:
description: |
For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature
from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature
observations from multiple satellite sensors, daily, over a 0.1° resolution global grid. It includes observations by polar
orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11)
satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor
median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain "per sensor type" data:
Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00164
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "surface", "temperature", "L3", "PIR", "PMW", "GIR"]
eodag:sensor_type:
license: other
title: ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-12-31T00:00:00Z", null]]}}
MO_SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:
description: |
For the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of: Foundation
Sea Surface Temperature at 0.05° x 0.05° horizontal grid resolution, using in-situ and satellite data from both infrared and
microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met
Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations
to determine the sea surface temperature.\nA high resolution (1/20° - approx. 6 km) daily analysis of sea surface temperature (SST)
is produced for the global ocean and some lakes.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00165\n\nReferences:
\n\n Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.;
Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and
Ice Concentration Analyses. Remote Sens. 2020, 12, 720. doi: 10.3390/rs12040720\n Donlon, C.J., Martin, M., Stark, J., Roberts-Jones,
J., Fiedler, E., and Wimmer, W., 2012, The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing
of the Environment. doi: 10.1016/j.rse.2010.10.017 2011.\n John D. Stark, Craig J. Donlon, Matthew J. Martin and Michael E. McCulloch,
2007, OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system., Oceans 07 IEEE Aberdeen,
conference proceedings. Marine challenges: coastline to deep sea. Aberdeen, Scotland.IEEE.\n
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "surface", "temperature", "L4", "OSTIA"]
eodag:sensor_type:
license: other
title: Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2007-01-01T00:00:00Z", null]]}}
MO_SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:
description: |
The OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation
sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution,
using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free
of diurnal variability.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00168\n
\nReferences:\n\n Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.;
Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation
Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720\n
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "surface", "temperature", "concentration", "L4", "OSTIA", "reprocessed", "REP"]
eodag:sensor_type:
license: other
title: Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-10-01T00:00:00Z", "2022-05-31T00:00:00Z"]]}}
MO_SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:
description: |
The ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm
depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of
sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface
Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution)
daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR
satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice
concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00169\n\nReferences:\n\n Good, S., Fiedler, E., Mao, C., Martin,
M.J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., Worsfold, M. The Current Configuration of the
OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens.
2020, 12, 720, doi:10.3390/rs12040720.\n Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær,
G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen,
L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78,
doi:10.5194/tc-13-49-2019, 2019.\n Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface
temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\n
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "surface", "temperature", "ESA", "SST", "CCI", "C3S", "L4", "reprocessed", "REP"]
eodag:sensor_type:
license: other
title: ESA SST CCI and C3S reprocessed sea surface temperature analyses
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1981-09-01T00:00:00Z", "2022-10-31T00:00:00Z"]]}}
MO_WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002:
description: |
Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear
propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered.
The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal
direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags
are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant
wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation
point are also available together with a quality flag based on the consistency between each propagated observation and the overall
swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It serves in near-real time
the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes near-real-time data
from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two
formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations
available in a 3-hour time range, and for both formats, propagated information from source to land.
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00178
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "NRT", "wave", "L3", "WAVE-TAC", "SAR", "spectral", "mono-mission"]
eodag:sensor_type:
license: other
title: Global Ocean L 3 Spectral Parameters From Nrt Satellite Measurements
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-04-22T00:00:00Z", null]]}}
MO_WAVE_GLO_PHY_SWH_L3_NRT_014_001:
description: |
Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a
rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter
values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH,
sigma0, range, off nadir angle…). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022,
Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the
measurement noise.\n\nAs a support of information to the significant wave height, wind speed measured by the altimeters is also
processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on
different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.
\n\nThis product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the
main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT,
produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2,
SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.\n\nOne file containing valid SWH
is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED)
and the wind speed (wind_speed).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00179
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "NRT", "wave", "height", "L3", "wind", "speed", "WAVE-TAC", "mono-mission"]
eodag:sensor_type:
license: other
title: GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2021-01-01T00:00:00Z", null]]}}
MO_WAVE_GLO_PHY_SWH_L4_NRT_014_003:
description: |
Near-Real-Time gridded multi-mission merged satellite significant wave height. Only valid data are included. This product
is processed in Near-Real-Time by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS level-3
SWH datasets (see the product WAVE_GLO_WAV_L3_SWH_NRT_OBSERVATIONS_014_001).\nIt merges along-track SWH data from the following
missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT and HaiYang-2B. The resulting gridded product has
a 2° horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics
computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate
of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for
their spatial and temporal proximity.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00180
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "NRT", "wave", "height", "L4", "gridded", "WAVE-TAC", "multi-mission"]
eodag:sensor_type:
license: other
title: GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-01-01T00:00:00Z", null]]}}
MO_WIND_GLO_PHY_CLIMATE_L4_MY_012_003:
description: |
For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal
spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields,
corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT,
QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT satellite instruments. The product provides monthly mean stress-equivalent wind and
stress variables as well as their standard deviation. The number of observations used to calculate the monthly averages are
included in the product.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00181
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "L4", "surface", "wind", "stress", "monthly", "Scatterometer"]
eodag:sensor_type:
license: other
title: Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1999-08-01T00:00:00Z", "2024-01-01T00:00:00Z"]]}}
MO_WIND_GLO_PHY_L3_NRT_012_002:
description: |
For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers
with resolutions corresponding to the L2 swath products:\n\n0.5 degrees grid for the 50 km scatterometer L2 inputs,
\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer
swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.
\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products
follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\nThe ASCAT scatterometers on Metop-A
(discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\nThe OSCAT scatterometer on Scatsat-1 at 0.25
and 0.5 degrees (discontinued on 28/2/2021); \nThe HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00182
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "L3", "surface", "wind", "daily", "gridded", "NRT", "Scatterometer"]
eodag:sensor_type:
license: other
title: Global Ocean Daily Gridded Sea Surface Winds from Scatterometer
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2016-01-01T00:00:00Z", null]]}}
MO_WIND_GLO_PHY_L3_MY_012_005:
description: |
For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers
with resolutions corresponding to the L2 swath products:\n0.5 degrees grid for the 50 km scatterometer L2 inputs,
\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer
swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately.
\n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products
follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer
on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI
scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00183
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "L3", "surface", "wind", "daily", "gridded", "reprocessed", "REP", "Scatterometer"]
eodag:sensor_type:
license: other
title: Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1991-08-01T00:00:00Z", "2023-12-31T00:00:00Z"]]}}
MO_WIND_GLO_PHY_L4_NRT_012_004:
description: |
For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial
resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range
Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields
are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind
and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences
(for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00305
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "L4", "surface", "wind", "stress", "hourly", "NRT", "Scatterometer", "Metop"]
eodag:sensor_type:
license: other
title: Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2020-07-01T00:00:00Z", null]]}}
MO_WIND_GLO_PHY_L4_MY_012_006:
description: |
For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal
spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5
reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for
persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B,
Metop-C ASCAT (0.125 degrees), QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind
and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences
(for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.
\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00185
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "L4", "surface", "wind", "stress", "hourly", "REP", "reprocessed", "Scatterometer", "Metop", "QuikSCAT", "ERS"]
eodag:sensor_type:
license: other
title: Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1994-06-01T00:00:00Z", "2024-01-21T23:00:00Z"]]}}
MO_OCEANCOLOUR_GLO_BGC_L3_MY_009_107:
description: |
For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products
based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the **""multi""** products.\n
Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**) and Reflectance (**RRS**).\n\n
Temporal resolutions: **daily**, **monthly**.\n* Spatial resolutions: **4 km** (multi).\n
Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).
\n\nTo find these products in the catalogue, use the search keyword **""ESA-CCI""**.
\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00282
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "L3", "bio-geo-chemical", "BGC", "chlorophyll", "phytoplankton", "reflectance"]
eodag:sensor_type: multi
license: other
title: Global Ocean Colour Plankton and Reflectances MY L3 daily observations
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1997-09-04T00:00:00Z", "2024-04-30T00:00:00Z"]]}}
MO_OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:
description: |
For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)**
products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B
for the **"multi"** products, and S3A & S3B only for the **"olci"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton
Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**),
Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**
\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in
datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n
\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **"GlobColour"**.
\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00278
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "colour", "L3", "bio-geo-chemical", "BGC", "Copernicus-GlobColour", "NRT"]
eodag:sensor_type:
license: other
title: Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2023-04-25T00:00:00Z", null]]}}
MO_OCEANCOLOUR_GLO_BGC_L3_MY_009_103:
description: |
For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)**
products based on the **Copernicus-GlobColour** processor.\n Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for
the **"multi"** products, and S3A & S3B only for the **"olci"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional
types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate
Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n Temporal resolutions: **daily**.\n Spatial resolutions:
**4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**)
and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue,
use the search keyword **"GlobColour"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00280
instruments: []
constellation:
platform:
processing:level: L3
keywords: ["CMEMS", "Mercator", "ocean", "global", "colour", "L3", "bio-geo-chemical", "BGC", "Copernicus-GlobColour", "MY", "multi-years"]
eodag:sensor_type:
license: other
title: Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1997-09-04T00:00:00Z", null]]}}
MO_OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:
description: |
For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)**
products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the
**"multi"** products, and S3A & S3B only for the **"olci"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional
types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse
Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n
\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a
"cloud free" product.\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.
\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets
called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword
**"GlobColour"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00279
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "colour", "L4", "bio-geo-chemical", "BGC", "Copernicus-GlobColour", "NRT", "monthly", "interpolated"]
eodag:sensor_type:
license: other
title: Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2023-04-01T00:00:00Z", null]]}}
MO_OCEANCOLOUR_GLO_BGC_L4_MY_009_104:
description: |
For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)**
products based on the **Copernicus-GlobColour** processor.\n Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the
**""multi""** products, and S3A & S3B only for the **""olci""** products.\n Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional
types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation
(**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n Temporal resolutions:
**monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a ""cloud free"" product.\n
Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n Recent products are organized
in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).
\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **""GlobColour""**."
\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00281
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "colour", "L4", "bio-geo-chemical", "BGC", "Copernicus-GlobColour", "MY", "multi-years", "monthly", "interpolated"]
eodag:sensor_type:
license: other
title: Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", null]]}}
MO_OCEANCOLOUR_GLO_BGC_L4_MY_009_108:
description: |
For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products
based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the **""multi""** products.
\n Variables: Chlorophyll-a (**CHL**).\n\n* Temporal resolutions: **monthly**.\n* Spatial resolutions: **4 km** (multi).
\n Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets
called Multi-Years (**MY**).\n\nTo find these products in the catalogue, use the search keyword **""ESA-CCI""**.
\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00283
instruments: []
constellation:
platform:
processing:level: L4
keywords: ["CMEMS", "Mercator", "ocean", "global", "colour", "L4", "bio-geo-chemical", "BGC", "chlorophyll", "MY", "multi-years", "monthly"]
eodag:sensor_type: multi
license: other
title: Global Ocean Colour Plankton MY L4 monthly observations
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1997-09-01T00:00:00Z", "2024-04-01T00:00:00Z"]]}}
# MARK: spacenet ------------------------------------------------------------------------
SPACENET_BUILDINGS_DETECTION_V1:
description: |
The first SpaceNet challenge focused on large-scale building detection. It provided 2,544 km² of high-resolution WorldView satellite imagery over Rio de Janeiro, Brazil, along with 382,534 building footprint labels. Both 3-band and 8-band imagery were included to support algorithm development for automated building extraction.
instruments: []
constellation: SpaceNet
platform: SN1
processing:level:
keywords: ["Rio", "Building", "Building Detection v1", "Building Detection", "v1", "SpaceNet", "Chipped Training Dataset"]
eodag:sensor_type: OPTICAL
license: CC-BY-4.0
title: "SpaceNet 1: Building Detection v1"
extent: {"spatial": {"bbox": [[-43.7753747662, -23.0106794336, -43.4847465904, -22.8256549561]]}, "temporal": {"interval": [["2016-02-29T00:00:00Z", null]]}}
SPACENET_BUILDINGS_DETECTION_V2:
description: |
The SpaceNet 2 dataset expands the building detection challenge to multiple cities worldwide, including Las Vegas, Paris, Shanghai, and Khartoum. It consists of high-resolution WorldView satellite imagery with annotations for more than 300,000 building footprints across 665 km². The dataset is designed to evaluate model generalization and robustness in detecting buildings across diverse urban landscapes.
instruments: []
constellation: SpaceNet
platform: SN2
processing:level:
keywords: ["Vegas", "Paris", "Shanghai", "Khartoum", "Building", "Building Detection v2", "Building Detection", "v2", "SpaceNet", "Chipped Training Dataset"]
eodag:sensor_type: OPTICAL
license: CC-BY-4.0
title: "SpaceNet 2: Building Detection v2"
extent: {"spatial": {"bbox": [[-115.3075176, 15.5138111999, 121.7498742, 49.0582359 ], [-115.3075176, 36.1212776997, -115.1513226, 36.2616777], [2.18641139997, 48.9757509, 2.30048639992, 49.0582359], [121.5901692, 31.1978709001, 121.7498742, 31.4277759], [32.4858384, 15.5138111999, 32.5665684, 15.7402062]]}, "temporal": {"interval": [["2015-04-13T00:00:00Z", null]]}}
SPACENET_ROADS_NETWORK_DETECTION:
description: |
The SpaceNet 3 dataset was created for the Road Detection and Routing Challenge. It contains more than 8,000 km of road centerlines with detailed attributes, including road type, surface type, and number of lanes. All annotations were digitized from 30 cm GSD WorldView-3 imagery across four cities: Las Vegas, Paris, Shanghai, and Khartoum. This dataset enables the development of algorithms not only for road extraction but also for generating usable routing networks from satellite imagery.
instruments: []
constellation: SpaceNet
platform: SN3
processing:level:
keywords: ["Vegas", "Paris", "Shanghai", "Khartoum", "Road", "Road Network Detection", "SpaceNet", "Chipped Training Dataset"]
eodag:sensor_type: OPTICAL
license: CC-BY-4.0
title: "SpaceNet 3: Road Network Detection"
extent: {"spatial": {"bbox": [[ -115.3075176, 15.5138111999, 121.7498742, 49.0582359], [-115.3075176, 36.1212776997, -115.1513226, 36.2616777], [2.18641139997, 48.9757509, 2.30048639992, 49.0582359], [121.5901692, 31.1978709001, 121.7498742, 31.4277759], [32.4858384, 15.5138111999, 32.5665684, 15.7402062]]}, "temporal": {"interval": [["2015-04-13T00:00:00Z", null]]}}
SPACENET_OFF_NADIR_BUILDING:
description: |
SpaceNet 4 introduced imagery collected at multiple viewing angles (off-nadir), making building footprint extraction significantly more challenging due to distortions, shadows, and perspective changes. The dataset focused on Atlanta, USA, with labeled building footprints provided across different look angles.
instruments: []
constellation: SpaceNet
platform: SN4
processing:level:
keywords: ["Atlanta", "Buildings", "Chipped Training Dataset", "Off-Nadir Buildings", "Off-Nadir", "v1", "SpaceNet"]
eodag:sensor_type: OPTICAL
license: CC-BY-4.0
title: "SpaceNet 4: Off-Nadir Buildings"
extent: {"spatial": {"bbox": [[ 732701.0, 3720189.0, 748901.0, 3744039.0]]}, "temporal": {"interval": [["2016-02-29T00:00:00Z", null]]}}
SPACENET_ROADS_NETWORK_ROUTE_TRAVEL:
description: |
SpaceNet 5 focuses on automated road network extraction and route travel time estimation from satellite imagery. The publicly available dataset includes high-resolution imagery and road labels for two cities: Moscow, Russia, and Mumbai, India. These annotations enable development of algorithms for extracting road networks and generating connected graphs suitable for routing applications.
instruments: []
constellation: SpaceNet
platform: SN5
processing:level:
keywords: ["Moscow", "Road", "Road Network Extraction", "Road Network", "Route Travel Time Estimation", "Route Travel", "SpaceNet"]
eodag:sensor_type: OPTICAL
license: CC-BY-4.0
title: "SpaceNet 5: Automated Road Network Extraction and Route Travel Time Estimation from Satellite Imagery"
extent: {"spatial": {"bbox": [[-43.7753747662, -23.0106794336, 72.89403328656945, 55.746917105720065], [37.62123533463809, 55.60938704097397, 37.78303540799408, 55.746917105720065], [72.7844259905958, 18.885500500608433, 72.89403328656945, 19.0645257518164]]}, "temporal": {"interval": [["2016-02-29T00:00:00Z", null]]}}
SPACENET_ALL_WEATHER_MAPPING:
description: |
SpaceNet 6 (MSAW) introduced multi-sensor data for the first time, combining Synthetic Aperture Radar (SAR) imagery from Capella Space with optical WorldView-2 imagery. The dataset, centered on Atlanta, provides over 48,000 building footprint labels. Training data includes both SAR and optical images, while testing is SAR-only, encouraging robust building detection under all-weather conditions.
instruments: ["Capella-SAR", "WorldView-2"]
constellation: SpaceNet
platform: SN6
processing:level:
keywords: ["Rotterdam", "Buildings", "Building Detection", "SpaceNet", "Multi-Sensor", "All-Weather Mapping", "SAR", "Optical"]
eodag:sensor_type: OPTICAL,RADAR
license: CC-BY-4.0
title: "SpaceNet 6: All-Weather Mapping"
extent: {"spatial": {"bbox": [[590111.9563080214, 5745470.427302527, 596702.0547804891, 5753195.130788647]]}, "temporal": {"interval": [["2019-08-04T00:00:00Z", "2019-08-23T00:00:00Z"]]}}
# Copernicus Global Human Settlement
GHS_BUILT_S:
description: |
The GHS-BUILT-S spatial raster dataset depicts the distribution of the built-up (BU) surfaces estimates between
1975 and 2030 in 5 year intervals and two functional use components a) the total BU surface and b) the non-
residential (NRES) BU surface. The data is made by spatial-temporal interpolation of five observed collections
of multiple-sensor, multiple-platform satellite imageries: Landsat (MSS, TM, ETM sensor) data supports the
1975, 1990, 2000, and 2014 epochs, while a Sentinel-2 (S2) image composite (GHS-composite-S2 R2020A)
supports the 2018 epoch.
instruments:
constellation: Sentinel-2, LANDSAT
platform:
processing:level:
keywords: ["GHS", "Settlement", "Built-up", "Surface", "GHS-BUILT-S", "Landsat", "Sentinel-2"]
eodag:sensor_type: multi
license: other
title: GHS built-up surface spatial raster dataset, derived from Sentinel-2 composite and Landsat, multi-temporal (1975-2030)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2030-12-31T00:00:00Z"]]}}
GHS_BUILT_H:
description: |
The spatial raster dataset depicts the spatial distribution of the building heights as extracted from the filtering of a composite
of global digital elevation models (DEM) and the filtering of satellite imagery using linear regression techniques.
The used input DEMs are the ALOS World 3D - 30m (AW3D30, 2006-2011) and the Shuttle Radar Topography Mission 30m (SRTM30, 2000).
The building heights extracted from these sources are updated using the support of the shadow markers extracted from the Sentinel2 image
data composite of the year 2018 (GHS-composite-S2 R2020A).
instruments:
constellation: Sentinel-2
platform:
processing:level:
keywords: ["GHS", "Settlement", "Built-up", "Height", "GHS-BUILT-H", "AW3D3", "SRTM30", "Sentinel-2"]
eodag:sensor_type: multi
license: other
title: GHS-BUILT-H R2023A - GHS building height, derived from AW3D30, SRTM30, and Sentinel2 composite (2018)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2018-12-31T00:00:00Z"]]}}
GHS_BUILT_V:
description: |
The spatial raster dataset depicts the distribution of built-up volumes, expressed as number of cubic metres.
The data report about the total built-up volume and the built-up volume allocated to dominant non-residential (NRES) uses.
Data are spatial-temporal interpolated or extrapolated from 1975 to 2030 in 5 years intervals.
instruments:
constellation: Sentinel-2, LANDSAT, TanDEM-X
platform:
processing:level:
keywords: ["GHS", "Settlement", "Built-up", "Volume", "GHS-BUILT-V", "TanDEM-X", "Landsat", "Sentinel-2"]
eodag:sensor_type: multi
license: other
title: GHS built-up volume grids derived from joint assessment of Sentinel2, Landsat, and global DEM data, multitemporal (1975-2030)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2030-12-31T00:00:00Z"]]}}
GHS_BUILT_C:
description: |
The spatial raster dataset delineates the boundaries of the human settlements at 10m resolution, and describe
their inner characteristics in terms of the morphology of the built environment and the functional use.
The Morphological Settlement Zone (MSZ) delineates the spatial domain of all the human settlements at the neighboring
scale of approx. 100m, based on the spatial generalization of the built-up surface fraction (BUFRAC) function.
The objective is to fill the open spaces that are surrounded by large patches of built space.
MSZ, open spaces, and built spaces basic class abstractions are derived by mathematical morphology spatial filtering
(opening, closing, regional maxima) from the BUFRAC function.
instruments:
constellation: Sentinel-2
platform:
processing:level:
keywords: ["GHS", "Settlement", "Built-up", "Boundaries", "GHS-BUILT-C", "MSZ", "BUFRAC", "Sentinel-2"]
eodag:sensor_type: multi
license: other
title: GHS Settlement Characteristics, derived from Sentinel2 composite (2018) and other GHS R2023A data
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2018-12-31T00:00:00Z"]]}}
GHS_LAND:
description: |
Land fraction per pixel as derived from Sentinel2 data composite and OpenStreetMap (OSM) data.
instruments:
constellation: Sentinel-2, Landsat
platform:
processing:level:
keywords: ["GHS", "Settlement", "Land", "OSM", "Sentinel-2", "Landsat"]
eodag:sensor_type: multi
license: other
title: Land fraction as derived from Sentinel2 image composite (2018) and OSM data
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2018-01-01T00:00:00Z", "2018-12-31T00:00:00Z"]]}}
GHS_POP:
description: |
The spatial raster dataset depicts the distribution of residential population, expressed as the number of people per cell.
Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11
were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of
built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.
instruments:
constellation:
platform:
processing:level:
keywords: ["GHS", "Settlement", "Population", "residential", "CIESIN", "GPW"]
eodag:sensor_type: multi
license: other
title: GHS population grid multitemporal (1975-2030)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2030-12-31T00:00:00Z"]]}}
GHS_SMOD:
description: |
The layers present the application of the Degree of Urbanisation stage I methodology recommended by UN Statistical Commission to
the global population grid generated by the JRC in the epochs 1975-2030 (5 years timestep).
They have been generated by integration of built-up surface extracted from Landsat and Sentinel-2 image data processing (GHS-BUILT-S R2023),
and population data derived from the CIESIN GPW v4.11 (GHS-POP R2023).
This product (v2) is an update of the data released in 2023 (v1) based on GHS-BUILT-S, GHS-POP and uses an updated definition of
Semi-Dennse Urban Clusters. The Settlement Model is provided at the detailed level (Second Level - L2). First level can be obtained aggregating L2.
instruments:
constellation: Landsat, Sentinel-2
platform:
processing:level:
keywords: ["GHS", "Settlement", "Urbanisation", "Sentinel-2", "Landsat", "CIESIN", "GPW"]
eodag:sensor_type: multi
license: other
title: GHS settlement layers, application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A, multitemporal (1975-2030)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2030-12-31T00:00:00Z"]]}}
GHS_ESM:
description: |
The European Settlement Map is a spatial raster dataset that is mapping human settlements in Europe based on
Copernicus Very High Resolution optical coverage for reference year 2015 (VHR_IMAGE_2015).The ESM_2015 product
exploits the Copernicus VHR_IMAGE_2015 dataset made of satellite images Pleiades, Deimos-02, WorldView-2,
WorldView-3, GeoEye-01 and Spot 6/7 ranging from 2014 to 2016.
instruments:
constellation: Deimos, Pleiades, WorldView, SPOT
platform: Deimos-02, Pleiades-1A, Pleiades-1B, WorldView-2, WorldView-3, GeoEye-01, Spot 6, Spot 7
processing:level:
keywords: ["GHS", "Settlement", "ESM", "Europe", "VHR_IMAGE_2015"]
eodag:sensor_type: multi
license: other
title: European Settlement Map from Copernicus Very High Resolution data for reference year 2015
extent: {"spatial": {"bbox": [[-31.39, 27.55, 44.93, 71.27]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2015-12-31T00:00:00Z"]]}}
GHS_DUC:
description: |
This dataset contains the Global Degree of Urbanisation Classification of administrative units delineated by the Global ADMinistrative layer version 4.1 (GADM41)
according to the application of stage II of the Degree of Urbanisation.
The classification is based on the Global Human Settlement Layer (GHSL) global datasets GHS R2023: the settlement classification GHS-SMOD (GHS-SMOD_GLOBE_R2023A) and
the population grid GHS-POP (GHS-POP_GLOBE_R2023A) layers for target years 1975-2030 (5 years timestep).
This dataset contains a package composed by one excel table, a summary table gathering global summary statistics and by countries and territories in GADM4.1,
and 72 CSV files, one per GADM level (from 0 to 5) and epoch (1975-2030), with all administrative units classified according to the Degree of Urbanisation.
The spatial delineation of GADM 4.1 administrative units is available on the GADM official website. Please select the whole world shapefile as six separate
layers (one for each level of subdivision/aggregation). The vector layers can be used to join each classification table (csv) with the actual spatial units.
Version 2 includes the improvement of the classification of small spatial units by Degree of Urbanisation level 2 as described in the updated (EUROSTAT manual).
instruments:
constellation:
platform:
processing:level:
keywords: ["GHS", "Settlement", "Urbanisation", "Classification", "GADM"]
eodag:sensor_type: multi
license: other
title: GHS Degree of Urbanisation Classification, application of the Degree of Urbanisation methodology (stage II) to GADM 4.1 layer, multitemporal (1975-2030)
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2030-12-31T00:00:00Z"]]}}
GHS_FUA:
description: |
The GHS Functional Urban Areas (GHS-FUA) delineate the spatial entities representing the commuting area of the Urban Centres of 2015.
The Functional Urban Areas for the epoch 2015 are estimated through an automated classification procedure developed in collaboration
with the OECD, based on objective characteristics (i.e. travel time to the Urban Centres, area of the Urban Centres, local population and country GDP per capita).
Information on the Urban Centres are derived from the GHS-UCDB R2019A dataset (GHS_STATS_UCDB2015MT_GLOBE_R2019A_V1_0); population information
are calculated from the 2015 GHS-POP R2019A dataset (GHS_POP_E2015_GLOBE_R2019A_V1_0); travel time from each cell towards the edge of
the closest (in time) Urban Centre is estimated by using the global friction matrix; the Urban Centre GDP is inherited from the
country GDP per capita (World Development Indicators: NY.GDP.PCAP.CD), as provided by The World Bank.
instruments:
constellation:
platform:
processing:level:
keywords: ["GHS", "Settlement", "FUA", "functional areas", "Centres"]
eodag:sensor_type: multi
license: other
title: GHS functional urban areas, derived from GHS-UCDB R2019A, (2015), R2019A
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["2015-01-01T00:00:00Z", "2015-12-31T00:00:00Z"]]}}
GHS_BUILT_LAUSTAT:
description: |
This product contains the summary statistics of GHS-BUILT-S multi-temporal (1975-2020) at Local Administrative Unit Level (LAU)
from the 2020 layer provided by GISCO. For each LAU the table contains the sum of built-up surface (GHS-BUILT-S) between
1975 and 2020 in 5 years interval, expressed in square kilometres.
instruments:
constellation:
platform:
processing:level:
keywords: ["GHS", "Settlement", "BUILT", "LAU", "GISCO", "Surface"]
eodag:sensor_type: multi
license: other
title: GHS built-up surface statistics in European LAU, multitemporal (1975-2020)
extent: {"spatial": {"bbox": [[-79.7, -26.64, 62.29, 88.92]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2020-12-31T00:00:00Z"]]}}
GHS_ENACT_POP:
description: |
This spatial raster dataset depicts the seasonal nighttime and daytime population grids for 2011 (coverage: EU28-2013) produced in the ENACT project.
Values are expressed as decimals (Float) representing the number of people per cell. The data is published at 1 km resolution in
Lambert Azimuthal Equal Area (EPSG:3035, 12 monthly nighttime grids and 12 daytime grids), and at 30 arc-seconds in WGS-84 (EPSG:4326, 12 monthly
nighttime grids and 12 daytime grids). The compressed ZIP files contain TIF files and short documentation.
instruments:
constellation:
platform:
processing:level:
keywords: ["GHS", "Settlement", "ENACT", "Population", "grid"]
eodag:sensor_type: multi
license: other
title: ENACT-POP R2020A - ENACT 2011 Population Grid
extent: {"spatial": {"bbox": [[-53.51, 24.791, 58.715, 70.916]]}, "temporal": {"interval": [["2011-01-01T00:00:00Z", "2011-12-31T00:00:00Z"]]}}
GHS_UCDB_DOMAIN:
description: |
The GHS Urban Centre Database (GHS-UCDB) describes spatial entities called “Urban Centres” described through a set of multi-temporal thematic
attributes from the GHSL data combined with other free and open data sets. The Urban Centres are defined by specific cut-off values on
resident population and built-up surface share in a 1x1 km uniform global grid. The input data is generated by the GHSL, and the
operating parameters are set in the frame of the “Degree of Urbanisation” (DEGURBA) methodology. The data is grouped by thematic domain.
instruments:
constellation:
platform:
processing:level:
keywords: ["GHS", "Settlement", "UCDB", "Urbanisation", "thematic domain", "DEGURBA"]
eodag:sensor_type: multi
license: other
title: Stats in the City - the GHS Urban Centre Database UCDB R2024A by Thematic Domain
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2030-12-31T00:00:00Z"]]}}
GHS_UCDB_REGION:
description: |
The GHS Urban Centre Database (GHS-UCDB) describes spatial entities called “Urban Centres” described through a set of multi-temporal thematic
attributes from the GHSL data combined with other free and open data sets. The Urban Centres are defined by specific cut-off values on
resident population and built-up surface share in a 1x1 km uniform global grid. The input data is generated by the GHSL, and the
operating parameters are set in the frame of the “Degree of Urbanisation” (DEGURBA) methodology. The data is grouped by region.
instruments:
constellation:
platform:
processing:level:
keywords: ["GHS", "Settlement", "UCDB", "Urbanisation", "region", "DEGURBA"]
eodag:sensor_type: multi
license: other
title: Stats in the City - the GHS Urban Centre Database UCDB R2024A by Region
extent: {"spatial": {"bbox": [[-180.0, -90.0, 180.0, 90.0]]}, "temporal": {"interval": [["1975-01-01T00:00:00Z", "2030-12-31T00:00:00Z"]]}}
# MARK: GENERIC ------------------------------------------------------------------------
GENERIC_COLLECTION:
description:
instruments: []
constellation:
platform:
processing:level:
keywords:
eodag:sensor_type:
license:
title:
extent: {"spatial": {"bbox": [[-180, -90, 180, 90]]}, "temporal": {"interval": [[null, null]]}}
The following table lists the metadata parameters of the collections, and shows whether these collections are available for providers or not.
Collections information (CSV)#
collection |
constellation |
processing:level |
aws_eos |
cop_ads |
cop_cds |
cop_dataspace |
cop_dataspace_s3 |
cop_ewds |
cop_ghsl |
cop_marine |
creodias |
creodias_s3 |
dedl |
dedt_lumi |
dedt_mn5 |
dlr_eoc_geoservice |
earth_search |
earth_search_gcs |
ecmwf |
eocat |
eumetsat_ds |
fedeo_ceda |
geodes |
geodes_s3 |
hydroweb_next |
meteoblue |
planetary_computer |
sara |
theia |
usgs |
usgs_satapi_aws |
wekeo_cmems |
wekeo_ecmwf |
wekeo_main |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AERIS_IAGOS |
L2 |
available |
||||||||||||||||||||||||||||||||
AG_ERA5 |
ERA5 |
available |
available |
|||||||||||||||||||||||||||||||
CAMS_EAC4 |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_EAC4_MONTHLY |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_EU_AIR_QUALITY_FORECAST |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_EU_AIR_QUALITY_RE |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_GAC_FORECAST |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_GFE_GFAS |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_GLOBAL_EMISSIONS |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_GREENHOUSE_EGG4 |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_GREENHOUSE_EGG4_MONTHLY |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_GREENHOUSE_INVERSION |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_GRF |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_GRF_AUX |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CAMS_SOLAR_RADIATION |
CAMS |
available |
available |
available |
||||||||||||||||||||||||||||||
CLMS_CORINE |
Sentinel-2, LANDSAT, SPOT-4/5, IRS P6 LISS III |
available |
available |
|||||||||||||||||||||||||||||||
CLMS_GLO_DMP_333M |
Sentinel-3 |
available |
available |
|||||||||||||||||||||||||||||||
CLMS_GLO_FAPAR_333M |
Sentinel-3 |
available |
available |
|||||||||||||||||||||||||||||||
CLMS_GLO_FCOVER_333M |
Sentinel-3 |
available |
available |
|||||||||||||||||||||||||||||||
CLMS_GLO_GDMP_333M |
Sentinel-3 |
available |
available |
|||||||||||||||||||||||||||||||
CLMS_GLO_LAI_333M |
Sentinel-3 |
available |
available |
|||||||||||||||||||||||||||||||
CLMS_GLO_NDVI_1KM_LTS |
SPOT |
available |
available |
|||||||||||||||||||||||||||||||
CLMS_GLO_NDVI_333M |
available |
available |
||||||||||||||||||||||||||||||||
CLMS_HRVPP_ST |
Sentinel-2 |
available |
||||||||||||||||||||||||||||||||
CLMS_HRVPP_ST_LAEA |
Sentinel-2 |
available |
||||||||||||||||||||||||||||||||
CLMS_HRVPP_VPP |
Sentinel-2 |
available |
||||||||||||||||||||||||||||||||
CLMS_HRVPP_VPP_LAEA |
Sentinel-2 |
available |
||||||||||||||||||||||||||||||||
CMIP6_CLIMATE_PROJECTIONS |
CMIP6 |
available |
||||||||||||||||||||||||||||||||
COP_DEM_GLO30_DGED |
TerraSAR |
available |
available |
available |
available |
available |
||||||||||||||||||||||||||||
COP_DEM_GLO30_DTED |
TerraSAR |
available |
available |
available |
available |
|||||||||||||||||||||||||||||
COP_DEM_GLO90_DGED |
TerraSAR |
available |
available |
available |
available |
available |
||||||||||||||||||||||||||||
COP_DEM_GLO90_DTED |
TerraSAR |
available |
available |
available |
available |
|||||||||||||||||||||||||||||
DT_CLIMATE_ADAPTATION |
Digital Twin |
available |
available |
|||||||||||||||||||||||||||||||
DT_CLIMATE_G1_CMIP6_HIST_ICON_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_CMIP6_HIST_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_CMIP6_HIST_IFS_NEMO_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_HIGHRESMIP_CONT_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_HIGHRESMIP_CONT_IFS_NEMO_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_SCENARIOMIP_SSP3_7_0_ICON_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_SCENARIOMIP_SSP3_7_0_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_SCENARIOMIP_SSP3_7_0_IFS_FESOM_R2 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_SCENARIOMIP_SSP3_7_0_IFS_NEMO_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_STORY_NUDGING_CONT_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_STORY_NUDGING_HIST_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G1_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_BASELINE_CONT_ICON_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_BASELINE_CONT_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_BASELINE_CONT_IFS_NEMO_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_BASELINE_HIST_ICON_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_BASELINE_HIST_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_BASELINE_HIST_IFS_NEMO_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_PROJECTIONS_SSP3_7_0_ICON_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_PROJECTIONS_SSP3_7_0_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_PROJECTIONS_SSP3_7_0_IFS_NEMO_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R2 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R3 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R4 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_CONT_IFS_FESOM_R5 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R2 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R3 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R4 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_HIST_IFS_FESOM_R5 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R1 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R2 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R3 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R4 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_CLIMATE_G2_STORY_NUDGING_TPLUS2_0K_IFS_FESOM_R5 |
Digital Twin |
available |
||||||||||||||||||||||||||||||||
DT_EXTREMES |
Digital Twin |
available |
available |
|||||||||||||||||||||||||||||||
EEA_HRL_TCF |
Sentinel-2 |
available |
||||||||||||||||||||||||||||||||
EFAS_FORECAST |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
EFAS_HISTORICAL |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
EFAS_REFORECAST |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
EFAS_SEASONAL |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
EFAS_SEASONAL_REFORECAST |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
ERA5_LAND |
ERA5 |
available |
available |
available |
||||||||||||||||||||||||||||||
ERA5_LAND_MONTHLY |
ERA5 |
available |
available |
available |
||||||||||||||||||||||||||||||
ERA5_PL |
ERA5 |
available |
available |
available |
||||||||||||||||||||||||||||||
ERA5_PL_MONTHLY |
ERA5 |
available |
available |
available |
||||||||||||||||||||||||||||||
ERA5_SL |
ERA5 |
available |
available |
available |
||||||||||||||||||||||||||||||
ERA5_SL_MONTHLY |
ERA5 |
available |
available |
available |
||||||||||||||||||||||||||||||
EUSTAT_AVAILABLE_BEDS_HOSPITALS_NUTS2 |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_BATHING_SITES_WATER_QUALITY |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_GREENHOUSE_GAS_EMISSION_AGRICULTURE |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_POP_AGE_GROUP_SEX_NUTS3 |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_POP_AGE_SEX_NUTS2 |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_POP_CHANGE_DEMO_BALANCE_CRUDE_RATES_NUTS3 |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_POP_DENSITY_NUTS3 |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_SHARE_ENERGY_FROM_RENEWABLE |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_SOIL_SEALING_INDEX |
Eurostat |
available |
||||||||||||||||||||||||||||||||
EUSTAT_SURFACE_TERRESTRIAL_PROTECTED_AREAS |
Eurostat |
available |
||||||||||||||||||||||||||||||||
FIRE_HISTORICAL |
CEMS |
available |
available |
available |
||||||||||||||||||||||||||||||
FIRE_SEASONAL |
CEMS |
available |
||||||||||||||||||||||||||||||||
GENERIC_PRODUCT_TYPE |
available |
|||||||||||||||||||||||||||||||||
GHS_BUILT_C |
Sentinel-2 |
available |
||||||||||||||||||||||||||||||||
GHS_BUILT_H |
Sentinel-2 |
available |
||||||||||||||||||||||||||||||||
GHS_BUILT_LAUSTAT |
available |
|||||||||||||||||||||||||||||||||
GHS_BUILT_S |
Sentinel-2, LANDSAT |
available |
||||||||||||||||||||||||||||||||
GHS_BUILT_V |
Sentinel-2, LANDSAT, TanDEM-X |
available |
||||||||||||||||||||||||||||||||
GHS_DUC |
available |
|||||||||||||||||||||||||||||||||
GHS_ENACT_POP |
available |
|||||||||||||||||||||||||||||||||
GHS_ESM |
Deimos, Pleiades, WorldView, SPOT |
available |
||||||||||||||||||||||||||||||||
GHS_FUA |
available |
|||||||||||||||||||||||||||||||||
GHS_LAND |
Sentinel-2, Landsat |
available |
||||||||||||||||||||||||||||||||
GHS_POP |
available |
|||||||||||||||||||||||||||||||||
GHS_SMOD |
Landsat, Sentinel-2 |
available |
||||||||||||||||||||||||||||||||
GHS_UCDB_DOMAIN |
available |
|||||||||||||||||||||||||||||||||
GHS_UCDB_REGION |
available |
|||||||||||||||||||||||||||||||||
GLACIERS_DIST_RANDOLPH |
available |
available |
available |
|||||||||||||||||||||||||||||||
GLOFAS_FORECAST |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
GLOFAS_HISTORICAL |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
GLOFAS_REFORECAST |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
GLOFAS_SEASONAL |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
GLOFAS_SEASONAL_REFORECAST |
CEMS |
available |
available |
|||||||||||||||||||||||||||||||
GRIDDED_GLACIERS_MASS_CHANGE |
available |
available |
available |
|||||||||||||||||||||||||||||||
GSW_CHANGE |
GSW |
available |
||||||||||||||||||||||||||||||||
GSW_EXTENT |
GSW |
available |
||||||||||||||||||||||||||||||||
GSW_OCCURRENCE |
GSW |
available |
||||||||||||||||||||||||||||||||
GSW_RECURRENCE |
GSW |
available |
||||||||||||||||||||||||||||||||
GSW_SEASONALITY |
GSW |
available |
||||||||||||||||||||||||||||||||
GSW_TRANSITIONS |
GSW |
available |
||||||||||||||||||||||||||||||||
HIRS_FDR_1_MULTI |
Metop,TIROS,NOAA |
L1C |
available |
|||||||||||||||||||||||||||||||
ISIMIP_CLIMATE_FORCING_ISIMIP3B |
ISIMIP |
available |
||||||||||||||||||||||||||||||||
ISIMIP_SOCIO_ECONOMIC_FORCING_ISIMIP3B |
ISIMIP |
available |
||||||||||||||||||||||||||||||||
L8_OLI_TIRS_C1L1 |
LANDSAT8 |
L1 |
available |
available |
||||||||||||||||||||||||||||||
LANDSAT_C2L1 |
LANDSAT |
L1 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
LANDSAT_C2L2 |
LANDSAT |
L1 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
LANDSAT_C2L2ALB_BT |
LANDSAT |
L2 |
available |
|||||||||||||||||||||||||||||||
LANDSAT_C2L2ALB_SR |
LANDSAT |
L2 |
available |
|||||||||||||||||||||||||||||||
LANDSAT_C2L2ALB_ST |
LANDSAT |
L2 |
available |
|||||||||||||||||||||||||||||||
LANDSAT_C2L2ALB_TA |
LANDSAT |
L2 |
available |
|||||||||||||||||||||||||||||||
LANDSAT_C2L2_SR |
LANDSAT |
L2 |
available |
|||||||||||||||||||||||||||||||
LANDSAT_C2L2_ST |
LANDSAT |
L2 |
available |
|||||||||||||||||||||||||||||||
METOP_AMSU_L1 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_ASCSZF1B |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_ASCSZFR02 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_ASCSZO1B |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_ASCSZOR02 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_ASCSZR1B |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_ASCSZRR02 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_AVHRRGACR02 |
METOP,TIROS,NOAA |
L2 |
available |
available |
||||||||||||||||||||||||||||||
METOP_AVHRRL1 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_GLB_SST_NC |
METOP |
L3 |
available |
available |
||||||||||||||||||||||||||||||
METOP_GOMEL1 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_GOMEL1R03 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_HIRSL1 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_IASIL1C_ALL |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_IASSND02 |
METOP |
L2 |
available |
available |
||||||||||||||||||||||||||||||
METOP_IASTHR011 |
METOP |
L2 |
available |
available |
||||||||||||||||||||||||||||||
METOP_LSA_002 |
METOP |
L3 |
available |
available |
||||||||||||||||||||||||||||||
METOP_MHSL1 |
METOP |
L1 |
available |
available |
||||||||||||||||||||||||||||||
METOP_OSI_104 |
METOP |
L2 |
available |
available |
||||||||||||||||||||||||||||||
METOP_OSI_150A |
METOP |
L2 |
available |
available |
||||||||||||||||||||||||||||||
METOP_OSI_150B |
METOP |
L2 |
available |
available |
||||||||||||||||||||||||||||||
METOP_SOMO12 |
METOP |
L2 |
available |
available |
||||||||||||||||||||||||||||||
METOP_SOMO25 |
METOP |
L2 |
available |
available |
||||||||||||||||||||||||||||||
MFG_GSA_57 |
MFG |
L2 |
available |
|||||||||||||||||||||||||||||||
MFG_GSA_63 |
MFG |
L2 |
available |
|||||||||||||||||||||||||||||||
MODIS_MCD43A4 |
Terra+Aqua |
L3 |
available |
available |
||||||||||||||||||||||||||||||
MO_GLOBAL_ANALYSISFORECAST_BGC_001_028 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_GLOBAL_ANALYSISFORECAST_PHY_001_024 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_GLOBAL_ANALYSISFORECAST_WAV_001_027 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_GLOBAL_MULTIYEAR_BGC_001_033 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_GLOBAL_MULTIYEAR_PHY_ENS_001_031 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_GLOBAL_MULTIYEAR_WAV_001_032 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_INSITU_GLO_PHY_TS_OA_MY_013_052 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_INSITU_GLO_PHY_TS_OA_NRT_013_002 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048 |
L2 |
available |
available |
|||||||||||||||||||||||||||||||
MO_MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_MULTIOBS_GLO_BIO_BGC_3D_REP_015_010 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_MULTIOBS_GLO_PHY_MYNRT_015_003 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_MULTIOBS_GLO_PHY_UVW_3D_MYNRT_015_007 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_OCEANCOLOUR_GLO_BGC_L3_MY_009_103 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_OCEANCOLOUR_GLO_BGC_L3_MY_009_107 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_OCEANCOLOUR_GLO_BGC_L3_NRT_009_101 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_OCEANCOLOUR_GLO_BGC_L4_MY_009_104 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_OCEANCOLOUR_GLO_BGC_L4_MY_009_108 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_OCEANCOLOUR_GLO_BGC_L4_NRT_009_102 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SEALEVEL_GLO_PHY_L4_NRT_008_046 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SEALEVEL_GLO_PHY_MDT_008_063 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SST_GLO_SST_L4_REP_OBSERVATIONS_010_011 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_SST_GLO_SST_L4_REP_OBSERVATIONS_010_024 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_WAVE_GLO_PHY_SWH_L3_NRT_014_001 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_WAVE_GLO_PHY_SWH_L4_NRT_014_003 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_WIND_GLO_PHY_CLIMATE_L4_MY_012_003 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_WIND_GLO_PHY_L3_MY_012_005 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_WIND_GLO_PHY_L3_NRT_012_002 |
L3 |
available |
available |
|||||||||||||||||||||||||||||||
MO_WIND_GLO_PHY_L4_MY_012_006 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MO_WIND_GLO_PHY_L4_NRT_012_004 |
L4 |
available |
available |
|||||||||||||||||||||||||||||||
MSG_AMVR02 |
MSG,MFG |
L2 |
available |
available |
||||||||||||||||||||||||||||||
MSG_CLM |
MSG |
L2 |
available |
available |
||||||||||||||||||||||||||||||
MSG_CLM_IODC |
MSG |
L2 |
available |
available |
||||||||||||||||||||||||||||||
MSG_CTH |
MSG |
L2 |
available |
|||||||||||||||||||||||||||||||
MSG_CTH_IODC |
MSG |
L2 |
available |
|||||||||||||||||||||||||||||||
MSG_GSAL2R02 |
MSG,MFG |
L2 |
available |
available |
||||||||||||||||||||||||||||||
MSG_HRSEVIRI |
MSG |
L1 |
available |
available |
||||||||||||||||||||||||||||||
MSG_HRSEVIRI_IODC |
MSG |
L1 |
available |
available |
||||||||||||||||||||||||||||||
MSG_LSA_FRM |
MSG |
L2 |
available |
available |
||||||||||||||||||||||||||||||
MSG_LSA_LSTDE |
MSG |
L2 |
available |
available |
||||||||||||||||||||||||||||||
MSG_LSA_LST_CDR |
MSG |
L3 |
available |
available |
||||||||||||||||||||||||||||||
MSG_MFG_GSA_0 |
MFG,MSG |
L2 |
available |
|||||||||||||||||||||||||||||||
MSG_MSG15_RSS |
MSG |
L1 |
available |
available |
||||||||||||||||||||||||||||||
MSG_OCA_CDR |
MSG |
L2 |
available |
|||||||||||||||||||||||||||||||
MSG_RSS_CLM |
MSG |
L2 |
available |
available |
||||||||||||||||||||||||||||||
MSG_SEVIRI_RSS_AMV_CDR_V1 |
MSG |
L2 |
available |
|||||||||||||||||||||||||||||||
MSG_SEVIRI_RSS_HR_IMG_L1_5_V1 |
MSG |
L1 |
available |
|||||||||||||||||||||||||||||||
MSG_SEVIRI_SARAH_CDR_V003 |
MSG |
available |
||||||||||||||||||||||||||||||||
MTG_FCI_ACTIVE_FIRE_L2_V1 |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_AMV_BUFR |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_AMV_NETCDF |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_ASR_BUFR |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_ASR_NETCDF |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_CLM |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_FDHSI |
MTG |
L1 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_GII |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_HRFI |
MTG |
L1 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_OCA |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_FCI_OLR |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_LI_AF |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_LI_AFA |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_LI_AFR |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_LI_LEF |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_LI_LFL |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MTG_LI_LGR |
MTG |
L2 |
available |
|||||||||||||||||||||||||||||||
MULT_PMW_IR_GIRAFE_PRECIP_CDR_V001 |
MTG |
L3 |
available |
|||||||||||||||||||||||||||||||
NAIP |
National Agriculture Imagery Program |
N/A |
available |
available |
available |
|||||||||||||||||||||||||||||
NEMSAUTO_TCDC |
NEMSAUTO |
available |
||||||||||||||||||||||||||||||||
NEMSGLOBAL_TCDC |
NEMSGLOBAL |
available |
||||||||||||||||||||||||||||||||
S1_AUX_GNSSRD |
SENTINEL1 |
AUX |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S1_AUX_MOEORB |
SENTINEL1 |
AUX |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S1_AUX_POEORB |
SENTINEL1 |
AUX |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S1_AUX_PREORB |
SENTINEL1 |
AUX |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S1_AUX_PROQUA |
SENTINEL1 |
AUX |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S1_AUX_RESORB |
SENTINEL1 |
AUX |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S1_SAR_GRD |
SENTINEL1 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||
S1_SAR_GRD_COG |
SENTINEL1 |
L1 |
available |
available |
||||||||||||||||||||||||||||||
S1_SAR_L3_DH_MCM |
SENTINEL1 |
L3 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S1_SAR_L3_IW_MCM |
SENTINEL1 |
L3 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S1_SAR_OCN |
SENTINEL1 |
L2 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S1_SAR_RAW |
SENTINEL1 |
RAW |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S1_SAR_SLC |
SENTINEL1 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||||||||||
S2_MSI_L1C |
SENTINEL2 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||||||
S2_MSI_L2A |
SENTINEL2 |
L2 |
available |
available |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||||||||||
S2_MSI_L2A_COG |
SENTINEL2 |
L2 |
available |
|||||||||||||||||||||||||||||||
S2_MSI_L2A_MAJA |
SENTINEL2 |
L2 |
available |
available |
||||||||||||||||||||||||||||||
S2_MSI_L2B_MAJA_SNOW |
SENTINEL2 |
L2 |
available |
available |
||||||||||||||||||||||||||||||
S2_MSI_L2B_MAJA_WATER |
SENTINEL2 |
L2 |
available |
available |
||||||||||||||||||||||||||||||
S3_EFR |
SENTINEL3 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_EFR_BC002 |
SENTINEL3 |
L1 |
available |
|||||||||||||||||||||||||||||||
S3_ERR |
SENTINEL3 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_ERR_BC002 |
SENTINEL3 |
L1 |
available |
|||||||||||||||||||||||||||||||
S3_LAN |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||||
S3_LAN_HY |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S3_LAN_LI |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S3_LAN_SI |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S3_OLCI_L2LFR |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||
S3_OLCI_L2LRR |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||
S3_OLCI_L2WFR |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_OLCI_L2WFR_BC003 |
SENTINEL3 |
L2 |
available |
|||||||||||||||||||||||||||||||
S3_OLCI_L2WRR |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_OLCI_L2WRR_BC003 |
SENTINEL3 |
L2 |
available |
|||||||||||||||||||||||||||||||
S3_OL_2_WFRBC003 |
SENTINEL3 |
L2 |
available |
|||||||||||||||||||||||||||||||
S3_RAC |
SENTINEL3 |
L1 |
available |
|||||||||||||||||||||||||||||||
S3_SLSTR_L1RBT |
SENTINEL3 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_SLSTR_L1RBT_BC003 |
SENTINEL3 |
L1 |
available |
|||||||||||||||||||||||||||||||
S3_SLSTR_L1RBT_BC004 |
SENTINEL3 |
L1 |
available |
|||||||||||||||||||||||||||||||
S3_SLSTR_L2 |
SENTINEL3 |
L2 |
available |
|||||||||||||||||||||||||||||||
S3_SLSTR_L2AOD |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||
S3_SLSTR_L2FRP |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||
S3_SLSTR_L2LST |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||||
S3_SLSTR_L2WST |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||
S3_SLSTR_L2WST_BC003 |
SENTINEL3 |
L2 |
available |
|||||||||||||||||||||||||||||||
S3_SRA |
SENTINEL3 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_SRA_1A_BC004 |
SENTINEL3 |
L1A |
available |
|||||||||||||||||||||||||||||||
S3_SRA_1A_BC005 |
SENTINEL3 |
L1A |
available |
|||||||||||||||||||||||||||||||
S3_SRA_1B_BC004 |
SENTINEL3 |
L1B |
available |
|||||||||||||||||||||||||||||||
S3_SRA_1B_BC005 |
SENTINEL3 |
L1B |
available |
|||||||||||||||||||||||||||||||
S3_SRA_A |
SENTINEL3 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_SRA_BS |
SENTINEL3 |
L1 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_SRA_BS_BC004 |
SENTINEL3 |
L1B |
available |
|||||||||||||||||||||||||||||||
S3_SRA_BS_BC005 |
SENTINEL3 |
L1B |
available |
|||||||||||||||||||||||||||||||
S3_SY_AOD |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S3_SY_SYN |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S3_SY_V10 |
SENTINEL3 |
L2W |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S3_SY_VG1 |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S3_SY_VGP |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
|||||||||||||||||||||||||||
S3_WAT |
SENTINEL3 |
L2 |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||||||||||||||||
S3_WAT_BC004 |
SENTINEL3 |
L2 |
available |
|||||||||||||||||||||||||||||||
S3_WAT_BC005 |
SENTINEL3 |
L2 |
available |
|||||||||||||||||||||||||||||||
S5P_L1B_IR_ALL |
SENTINEL5P |
L1B |
available |
available |
||||||||||||||||||||||||||||||
S5P_L1B_IR_SIR |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_IR_UVN |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_RA_BD1 |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_RA_BD2 |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_RA_BD3 |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_RA_BD4 |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_RA_BD5 |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_RA_BD6 |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_RA_BD7 |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L1B_RA_BD8 |
SENTINEL5P |
L1B |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_AER_AI |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_AER_LH |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_CH4 |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_CLOUD |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_CO |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_HCHO |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_IR_ALL |
SENTINEL5P |
L2 |
available |
available |
||||||||||||||||||||||||||||||
S5P_L2_NO2 |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_NP_BD3 |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_NP_BD6 |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_NP_BD7 |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_O3 |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_O3_PR |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_O3_TCL |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S5P_L2_SO2 |
SENTINEL5P |
L2 |
available |
available |
available |
available |
||||||||||||||||||||||||||||
S6_RADIO_OCCULTATION |
Sentinel-6 |
L1B |
available |
|||||||||||||||||||||||||||||||
SATELLITE_CARBON_DIOXIDE |
available |
available |
available |
|||||||||||||||||||||||||||||||
SATELLITE_FIRE_BURNED_AREA |
available |
available |
||||||||||||||||||||||||||||||||
SATELLITE_METHANE |
available |
available |
available |
|||||||||||||||||||||||||||||||
SATELLITE_SEA_ICE_CONCENTRATION |
available |
available |
available |
|||||||||||||||||||||||||||||||
SATELLITE_SEA_ICE_EDGE_TYPE |
available |
available |
available |
|||||||||||||||||||||||||||||||
SATELLITE_SEA_ICE_THICKNESS |
available |
available |
available |
|||||||||||||||||||||||||||||||
SATELLITE_SEA_LEVEL_GLOBAL |
available |
available |
available |
|||||||||||||||||||||||||||||||
SEASONAL_MONTHLY_PL |
available |
available |
available |
|||||||||||||||||||||||||||||||
SEASONAL_MONTHLY_SL |
available |
available |
available |
|||||||||||||||||||||||||||||||
SEASONAL_ORIGINAL_PL |
available |
available |
available |
|||||||||||||||||||||||||||||||
SEASONAL_ORIGINAL_SL |
available |
available |
available |
|||||||||||||||||||||||||||||||
SEASONAL_POSTPROCESSED_PL |
available |
available |
available |
|||||||||||||||||||||||||||||||
SEASONAL_POSTPROCESSED_SL |
available |
available |
available |
|||||||||||||||||||||||||||||||
SIS_HYDRO_MET_PROJ |
available |
available |
||||||||||||||||||||||||||||||||
SPACENET_ALL_WEATHER_MAPPING |
SpaceNet |
available |
||||||||||||||||||||||||||||||||
SPACENET_BUILDINGS_DETECTION_V1 |
SpaceNet |
available |
||||||||||||||||||||||||||||||||
SPACENET_BUILDINGS_DETECTION_V2 |
SpaceNet |
available |
||||||||||||||||||||||||||||||||
SPACENET_OFF_NADIR_BUILDING |
SpaceNet |
available |
||||||||||||||||||||||||||||||||
SPACENET_ROADS_NETWORK_DETECTION |
SpaceNet |
available |
||||||||||||||||||||||||||||||||
SPACENET_ROADS_NETWORK_ROUTE_TRAVEL |
SpaceNet |
available |
||||||||||||||||||||||||||||||||
TIGGE_CF_SFC |
TIGGE |
available |
||||||||||||||||||||||||||||||||
UERRA_EUROPE_SL |
SURFEX |
available |
available |
available |