EO product types#
eodag maintains a catalog of EO product types including some of their metadata. Each product type 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 product type Sentinel 2 Level-2A.
S2_MSI_L2A:
abstract: |
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).
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L2
sensorType: OPTICAL
license: proprietary
title: SENTINEL2 Level-2A
missionStartDate: "2015-06-23T00:00:00Z"
This product type catalog can be obtained from the API:
from eodag import EODataAccessGateway
dag = EODataAccessGateway()
dag.list_product_types()
Or from the CLI:
eodag list
The catalog is used in different ways by eodag:
Product types made available for a given provider (search/download) are listed in its configuration. This allows to unify the product type identifier among the providers.
eodag search --conf peps_conf.yml -p S2_MSI_L2A eodag search --conf creodias_conf.yml -p 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 product type that best matches the criteria. The actual search is then performed with this product type.
eodag search --sensorType OPTICAL --processingLevel L2from eodag import EODataAccessGateway dag = EODataAccessGateway() dag.search(sensorType="OPTICAL", processingLevel="L2")
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.
product_types.yml
CBERS4_MUX_L2:
abstract: |
China-Brazil Earth Resources Satellite, CBERS-4 MUX camera Level-2 product. System corrected images, expect some
translation error.
instrument: MUX
platform: CBERS
platformSerialIdentifier: CBERS-4
processingLevel: L2
keywords: MUX,CBERS,CBERS-4,L2
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-12-07T00:00:00Z"
title: CBERS-4 MUX Level-2
CBERS4_AWFI_L2:
abstract: |
China-Brazil Earth Resources Satellite, CBERS-4 AWFI camera Level-2 product. System corrected images, expect some
translation error.
instrument: AWFI
platform: CBERS
platformSerialIdentifier: CBERS-4
processingLevel: L2
keywords: AWFI,CBERS,CBERS-4,L2
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-12-07T00:00:00Z"
title: CBERS-4 AWFI Level-2
CBERS4_PAN5M_L2:
abstract: |
China-Brazil Earth Resources Satellite, CBERS-4 PAN5M camera Level-2 product. System corrected images, expect some
translation error.
instrument: PAN5M
platform: CBERS
platformSerialIdentifier: CBERS-4
processingLevel: L2
keywords: PAN5M,CBERS,CBERS-4,L2
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-12-07T00:00:00Z"
title: CBERS-4 PAN5M Level-2
CBERS4_PAN10M_L2:
abstract: |
China-Brazil Earth Resources Satellite, CBERS-4 PAN10M camera Level-2 product. System corrected images, expect some
translation error.
instrument: PAN10M
platform: CBERS
platformSerialIdentifier: CBERS-4
processingLevel: L2
keywords: PAN10M,CBERS,CBERS-4,L2
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-12-07T00:00:00Z"
title: CBERS-4 PAN10M Level-2
CBERS4_MUX_L4:
abstract: |
China-Brazil Earth Resources Satellite, CBERS-4 MUX camera Level-4 product. Orthorectified with ground control
points.
instrument: MUX
platform: CBERS
platformSerialIdentifier: CBERS-4
processingLevel: L4
keywords: MUX,CBERS,CBERS-4,L4
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-12-07T00:00:00Z"
title: CBERS-4 MUX Level-4
CBERS4_AWFI_L4:
abstract: |
China-Brazil Earth Resources Satellite, CBERS-4 AWFI camera Level-4 product. Orthorectified with ground control
points.
instrument: AWFI
platform: CBERS
platformSerialIdentifier: CBERS-4
processingLevel: L4
keywords: AWFI,CBERS,CBERS-4,L4
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-12-07T00:00:00Z"
title: CBERS-4 AWFI Level-4
CBERS4_PAN5M_L4:
abstract: |
China-Brazil Earth Resources Satellite, CBERS-4 PAN5M camera Level-4 product. Orthorectified with ground control
points.
instrument: PAN5M
platform: CBERS
platformSerialIdentifier: CBERS-4
processingLevel: L4
keywords: PAN5M,CBERS,CBERS-4,L4
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-12-07T00:00:00Z"
title: CBERS-4 PAN5M Level-4
CBERS4_PAN10M_L4:
abstract: |
China-Brazil Earth Resources Satellite, CBERS-4 PAN10M camera Level-4 product. Orthorectified with ground control
points.
instrument: PAN10M
platform: CBERS
platformSerialIdentifier: CBERS-4
processingLevel: L4
keywords: PAN10M,CBERS,CBERS-4,L4
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-12-07T00:00:00Z"
title: CBERS-4 PAN10M Level-4
# Landasat --------------------------------------------------------------------
# https://www.usgs.gov/faqs/what-naming-convention-landsat-collections-level-1-scenes
L57_REFLECTANCE:
abstract: |
Landsat 5,7,8 L2A data (old format) distributed by Theia (2014 to 2017-03-20) using MUSCATE prototype,
Lamber 93 projection.
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L5,L7,L8
processingLevel: L2A
keywords: OLI,TIRS,LANDSAT,L5,L7,L8,L2,L2A,MUSCATE
sensorType: OPTICAL
license: proprietary
missionStartDate: "2014-01-01T00:00:00Z"
missionEndDate: "2017-03-20T00:00:00Z"
title: Landsat 5,7,8 Level-2A
L8_REFLECTANCE:
abstract: |
Landsat 8 L2A data distributed by Theia since 2017-03-20 using operational version of MUSCATE, UTM projection,
and tiled using Sentinel-2 tiles.
instrument: OLI,TIRS
platform: LANDSAT8
platformSerialIdentifier: L8
processingLevel: L2A
keywords: OLI,TIRS,LANDSAT,LANDSAT8,L8,L2,L2A,MUSCATE
sensorType: OPTICAL
license: proprietary
missionStartDate: "2013-02-11T00:00:00Z"
title: Landsat 8 Level-2A
L8_OLI_TIRS_C1L1:
abstract: |
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
instrument: OLI,TIRS
platform: LANDSAT8
platformSerialIdentifier: L8
processingLevel: L1
keywords: OLI,TIRS,LANDSAT,LANDSAT8,L8,L1,C1,COLLECTION1
sensorType: OPTICAL
license: proprietary
missionStartDate: "2013-02-11T00:00:00Z"
title: Landsat 8 Level-1
LANDSAT_C2L1:
abstract: |
The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L1,L2,L3,L4,L5,L6,L7,L8
processingLevel: L1
keywords: OLI,TIRS,LANDSAT,L1,L2,L3,L4,L5,L6,L7,L8,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat Collection 2 Level-1 Product
missionStartDate: "1972-07-25T00:00:00Z"
LANDSAT_C2L2:
abstract: |
Collection 2 Landsat OLI/TIRS Level-2 Science Products (L2SP) include
Surface Reflectance and Surface Temperature scene-based products.
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L8,L9
processingLevel: L1
keywords: OLI,TIRS,LANDSAT,L8,L9,L2,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat OLI and TIRS Collection 2 Level-2 Science Products 30-meter multispectral data.
missionStartDate: "2013-02-11T00:00:00Z"
LANDSAT_C2L2_SR:
abstract: |
The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected
from Earth's surface to the Landsat sensor.
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L4,L5,L7,L8
processingLevel: L2
keywords: OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,SR,surface,reflectance,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product
missionStartDate: "1982-08-22T00:00:00Z"
LANDSAT_C2L2_ST:
abstract: |
The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L4,L5,L7,L8
processingLevel: L2
keywords: OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,ST,surface,temperature,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product
missionStartDate: "1982-08-22T00:00:00Z"
LANDSAT_C2L2ALB_BT:
abstract: |
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.
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L4,L5,L7,L8
processingLevel: L2
keywords: OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,BT,Brightness,Temperature,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product
missionStartDate: "1982-08-22T00:00:00Z"
LANDSAT_C2L2ALB_SR:
abstract: |
The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected
from Earth's surface to the Landsat sensor.
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L4,L5,L7,L8
processingLevel: L2
keywords: OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,L2ALB,SR,Surface,Reflectance,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product
missionStartDate: "1982-08-22T00:00:00Z"
LANDSAT_C2L2ALB_ST:
abstract: |
The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L4,L5,L7,L8
processingLevel: L2
keywords: OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,L2ALB,Surface,Temperature,ST,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product
missionStartDate: "1982-08-22T00:00:00Z"
LANDSAT_C2L2ALB_TA:
abstract: |
The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1
radiance product.
instrument: OLI,TIRS
platform: LANDSAT
platformSerialIdentifier: L4,L5,L7,L8
processingLevel: L2
keywords: OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,L2ALB,TA,Top,Atmosphere,Reflectance,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product
missionStartDate: "1982-08-22T00:00:00Z"
LANDSAT_TM_C1:
abstract: |
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).
instrument: TM
platform: LANDSAT
platformSerialIdentifier: L4,L5
processingLevel: L1
keywords: TM,LANDSAT,L4,L5,L1,C1,COLLECTION1
sensorType: OPTICAL
license: proprietary
title: Landsat 4-5 Thematic Mapper (TM) Collection-1 Level-1 Data Products
missionStartDate: "1982-08-22T00:00:00Z"
missionEndDate: "2013-06-06T00:00:00Z"
LANDSAT_TM_C2L1:
abstract: |
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).
instrument: TM
platform: LANDSAT
platformSerialIdentifier: L4,L5
processingLevel: L1
keywords: TM,LANDSAT,L4,L5,L1,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat 4-5 Thematic Mapper (TM) Collection-2 Level-1 Data Products
missionStartDate: "1982-08-22T00:00:00Z"
missionEndDate: "2013-06-06T00:00:00Z"
LANDSAT_TM_C2L2:
abstract: |
Collection 2 Landsat 4-5 Thematic Mapper (TM) Level-2 Science Products (L2SP) include
Surface Reflectance and Surface Temperature scene-based products.
instrument: TM
platform: LANDSAT
platformSerialIdentifier: L4,L5
processingLevel: L2
keywords: TM,LANDSAT,L4,L5,L2,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Landsat 4-5 Thematic Mapper (TM) Collection-2 Level-2 Data Products
missionStartDate: "1982-08-22T00:00:00Z"
missionEndDate: "2013-06-06T00:00:00Z"
LANDSAT_ETM_C1:
abstract: |
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).
instrument: ETM+
platform: LANDSAT
platformSerialIdentifier: L7
processingLevel: L1
keywords: ETM,ETM+,LANDSAT,L7,L1,C1,COLLECTION1
sensorType: OPTICAL
license: proprietary
title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-1 Level-1 data from Landsat 7
missionStartDate: "1999-04-15T00:00:00Z"
LANDSAT_ETM_C2L1:
abstract: |
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).
instrument: ETM+
platform: LANDSAT
platformSerialIdentifier: L7
processingLevel: L1
keywords: ETM,ETM+,LANDSAT,L7,L1,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-2 Level-1 data from Landsat 7
missionStartDate: "1999-04-15T00:00:00Z"
LANDSAT_ETM_C2L2:
abstract: |
Collection 2 Landsat 7 ETM+ Level-2 Science Products (L2SP) include Surface Reflectance and Surface Temperature
scene-based products.
instrument: ETM+
platform: LANDSAT
platformSerialIdentifier: L7
processingLevel: L2
keywords: ETM,ETM+,LANDSAT,L7,L2,C2,COLLECTION2
sensorType: OPTICAL
license: proprietary
title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-2 Level-2 data from Landsat 7
missionStartDate: "1999-04-15T00:00:00Z"
# MODIS -----------------------------------------------------------------------
MODIS_MCD43A4:
abstract: |
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.
instrument: MODIS
platform: Terra+Aqua
platformSerialIdentifier: EOS AM-1+PM-1
processingLevel: L3
keywords: MODIS,Terra,Aqua,EOS,AM-1+PM-1,L3,MCD43A4
sensorType: OPTICAL
license: proprietary
missionStartDate: "2000-03-05T00:00:00Z"
title: MODIS MCD43A4
# OSO -------------------------------------------------------------------------
OSO:
abstract: |
An overview of OSO Land Cover data is given on https://www.theia-land.fr/en/ceslist/land-cover-sec/
and the specific description of OSO products is available on
https://www.theia-land.fr/product/carte-doccupation-des-sols-de-la-france-metropolitaine/
instrument:
platform:
platformSerialIdentifier:
processingLevel: L3B
keywords: L3B,OSO,land,cover
sensorType:
license: proprietary
missionStartDate: "2016-01-01T00:00:00Z"
title: OSO Land Cover
# NAIP -----------------------------------------------------------------------
NAIP:
abstract: |
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.
instrument: film and digital cameras
platform: National Agriculture Imagery Program
platformSerialIdentifier: NAIP
processingLevel: N/A
keywords: film,digital,cameras,Agriculture,NAIP
sensorType: OPTICAL
license: proprietary
missionStartDate: "2003-01-01T00:00:00Z"
title: National Agriculture Imagery Program
# Pleiades - ------------------------------------------------------------------
PLD_PAN:
abstract: Pleiades Panchromatic (Pan)
instrument: PHR
platform: PLEIADES
platformSerialIdentifier: P1A,P1B
processingLevel: PRIMARY
keywords: PHR,PLEIADES,P1A,P1B,PRIMARY,PLD,PAN,Panchromatic
sensorType: OPTICAL
license: proprietary
missionStartDate: "2011-12-17T00:00:00Z"
title: Pleiades Panchromatic
PLD_XS:
abstract: Pleiades Multispectral (XS)
instrument: PHR
platform: PLEIADES
platformSerialIdentifier: P1A,P1B
processingLevel: PRIMARY
keywords: PHR,PLEIADES,P1A,P1B,PRIMARY,PLD,XS,Multispectral
sensorType: OPTICAL
license: proprietary
missionStartDate: "2011-12-17T00:00:00Z"
title: Pleiades Multispectral
PLD_BUNDLE:
abstract: Pleiades Bundle (Pan, XS)
instrument: PHR
platform: PLEIADES
platformSerialIdentifier: P1A,P1B
processingLevel: PRIMARY
keywords: PHR,PLEIADES,P1A,P1B,PRIMARY,PLD,BUNDLE,Pan,Xs
sensorType: OPTICAL
license: proprietary
missionStartDate: "2011-12-17T00:00:00Z"
title: Pleiades Bundle
PLD_PANSHARPENED:
abstract: Pleiades Pansharpened (Pan+XS)
instrument: PHR
platform: PLEIADES
platformSerialIdentifier: P1A,P1B
processingLevel: PRIMARY
keywords: PHR,PLEIADES,P1A,P1B,PRIMARY,PLD,PANSHARPENED,Pan,Xs
sensorType: OPTICAL
license: proprietary
missionStartDate: "2011-12-17T00:00:00Z"
title: Pleiades Pansharpened
# Sentinel 1 ------------------------------------------------------------------
S1_SAR_OCN:
abstract: |
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
instrument: SAR
platform: SENTINEL1
platformSerialIdentifier: S1A,S1B
processingLevel: L2
keywords: SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L2,OCN,SAFE
sensorType: RADAR
license: proprietary
title: SENTINEL1 Level-2 OCN
missionStartDate: "2014-04-03T00:00:00Z"
S1_SAR_GRD:
abstract: |
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
instrument: SAR
platform: SENTINEL1
platformSerialIdentifier: S1A,S1B
processingLevel: L1
keywords: SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L1,GRD,SAFE
sensorType: RADAR
license: proprietary
title: SENTINEL1 Level-1 Ground Range Detected
missionStartDate: "2014-04-03T00:00:00Z"
S1_SAR_GRD_JP2:
abstract: |
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
instrument: SAR
platform: SENTINEL1
platformSerialIdentifier: S1A,S1B
processingLevel: L1
keywords: SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L1,GRD,JP2
sensorType: RADAR
license: proprietary
title: SENTINEL1 Level-1 Ground Range Detected
missionStartDate: "2014-04-03T00:00:00Z"
S1_SAR_SLC:
abstract: |
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
instrument: SAR
platform: SENTINEL1
platformSerialIdentifier: S1A,S1B
processingLevel: L1
keywords: SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L1,SLC,SAFE
sensorType: RADAR
license: proprietary
title: SENTINEL1 Level-1 Single Look Complex
missionStartDate: "2014-04-03T00:00:00Z"
S1_SAR_RAW:
abstract: |
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
instrument: SAR
platform: SENTINEL1
platformSerialIdentifier: S1A,S1B
processingLevel: L0
keywords: SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L0,RAW,SAFE
sensorType: RADAR
license: proprietary
title: SENTINEL1 SAR Level-0
missionStartDate: "2014-04-03T00:00:00Z"
# Sentinel 2 ------------------------------------------------------------------
S2_MSI_L1C:
abstract: |
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
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L1
keywords: MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L1,L1C,SAFE
sensorType: OPTICAL
license: proprietary
missionStartDate: "2015-06-23T00:00:00Z"
title: SENTINEL2 Level-1C
S2_MSI_L1C_JP2:
abstract: |
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
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L1
keywords: MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L1C,JP2
sensorType: OPTICAL
license: proprietary
missionStartDate: "2015-06-23T00:00:00Z"
title: SENTINEL2 Level-1C
S2_MSI_L2A:
abstract: |
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
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L2
keywords: MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,SAFE
sensorType: OPTICAL
license: proprietary
title: SENTINEL2 Level-2A
missionStartDate: "2018-03-26T00:00:00Z"
S2_MSI_L2AP:
abstract: |
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.
Level-2AP are the pilot products of Level-2A product generated by ESA until March 2018. After March, they are
operational products
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L2
keywords: MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,SAFE, pilot
sensorType: OPTICAL
license: proprietary
title: SENTINEL2 Level-2A pilot
missionStartDate: "2017-05-23T00:00:00Z"
missionEndDate: "2018-03-25T00:00:00Z"
S2_MSI_L2A_JP2:
abstract: |
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
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L2
keywords: MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,JP2
sensorType: OPTICAL
license: proprietary
title: SENTINEL2 Level-2A
missionStartDate: "2015-06-23T00:00:00Z"
S2_MSI_L2A_COG:
abstract: |
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.
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L2
keywords: MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,COG
sensorType: OPTICAL
license: proprietary
title: SENTINEL2 Level-2A
missionStartDate: "2015-06-23T00:00:00Z"
S2_MSI_L2A_MAJA:
abstract: |
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
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L2
keywords: MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,MAJA
sensorType: OPTICAL
license: proprietary
title: SENTINEL2 Level-2A
missionStartDate: "2015-06-23T00:00:00Z"
S2_MSI_L2B_MAJA_SNOW:
abstract: |
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
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L2
keywords: MSI,MAJA,SENTINEL,sentinel2,S2,S2A,S2B,L2,L2B,SNOW
sensorType: OPTICAL
license: proprietary
missionStartDate: "2015-06-23T00:00:00Z"
title: SENTINEL2 snow product
S2_MSI_L2B_MAJA_WATER:
abstract: |
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
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L2
keywords: MSI,MAJA,SENTINEL,sentinel2,S2,S2A,S2B,L2,L2B,WATER
sensorType: OPTICAL
license: proprietary
missionStartDate: "2015-06-23T00:00:00Z"
title: SENTINEL2 L2B-WATER
S2_MSI_L3A_WASP:
abstract: |
The Level-3A product provides a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis
is based on a weighted arithmetic mean of clear observations.
The data processing is produced by WASP (Weighted Average Synthesis Processor), by MUSCATE data center at CNES,
in the framework of THEIA data center. The full description of the product format is available at
https://theia.cnes.fr/atdistrib/documents/THEIA-ST-411-0419-CNES_01-04_Format_Specification_of_MUSCATE_Level-3A_Products-signed.pdf
instrument: MSI
platform: SENTINEL2
platformSerialIdentifier: S2A,S2B
processingLevel: L3
keywords: MSI,SENTINEL,sentinel2,S2,S2A,S2B,L3,L3A,WASP
sensorType: OPTICAL
license: proprietary
missionStartDate: "2015-06-23T00:00:00Z"
title: SENTINEL2 Level-3A
# Sentinel 3 ------------------------------------------------------------------
# S3 OLCI L1
S3_EFR:
abstract: |
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.
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,EFR
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 EFR
missionStartDate: "2016-02-16T00:00:00Z"
S3_ERR:
abstract: |
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.
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,ERR
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 ERR
missionStartDate: "2016-02-16T00:00:00Z"
S3_RAC:
abstract: |
Sentinel 3 OLCI products output during Radiometric Calibration mode
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,L2,RAC
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 RAC
missionStartDate: "2016-02-16T00:00:00Z"
# S3 OLCI L2
S3_OLCI_L2LRR:
abstract: |
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
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2LRR,LRR
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 OLCI Level-2 Land Reduced Resolution
missionStartDate: "2016-02-16T00:00:00Z"
S3_OLCI_L2LFR:
abstract: |
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
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2LFR,LFR
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 OLCI Level-2 Land Full Resolution
missionStartDate: "2016-02-16T00:00:00Z"
S3_OLCI_L2WRR:
abstract: |
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
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WRR,WRR
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 OLCI Level-2 Water Reduced Resolution
missionStartDate: "2016-02-16T00:00:00Z"
S3_OLCI_L2WRR_BC003:
abstract: |
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.
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WRR,WRR,REPROCESSED,BC003
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 OLCI Level-2 Water Reduced Resolution Reprocessed from BC003
missionStartDate: "2016-02-16T00:00:00Z"
S3_OLCI_L2WFR:
abstract: |
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
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WFR,WFR
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 OLCI Level-2 Water Full Resolution
missionStartDate: "2016-02-16T00:00:00Z"
S3_OLCI_L2WFR_BC003:
abstract: |
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
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WFR,WFR,REPROCESSED,BC003
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 OLCI Level-2 Water Full Resolution Reprocessed from BC003
missionStartDate: "2016-02-16T00:00:00Z"
S3_OLCI_L4BALTIC:
abstract: |
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
instrument: OLCI
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L4
keywords: OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L4,BGC,CHL,BALTIC
sensorType: OPTICAL
license: proprietary
title: SENTINEL3 OLCI Baltic Sea Surface Ocean Colour Plankton
missionStartDate: "2023-04-10T00:00:00Z"
# S3 SLSTR
S3_SLSTR_L1RBT:
abstract: |
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
instrument: SLSTR
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1
keywords: SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,L1RBT,RBT
sensorType: ATMOSPHERIC
license: proprietary
title: SENTINEL3 SLSTR Level-1
missionStartDate: "2016-02-16T00:00:00Z"
S3_SLSTR_L1RBT_BC004:
abstract: |
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.
instrument: SLSTR
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1
keywords: SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,L1RBT,RBT,VIS,NIR,SWIR,BT,TIR,FIR,Reprocessed,BC004
sensorType: ATMOSPHERIC
license: proprietary
title: SENTINEL3 SLSTR Level-1 RBT - Reprocessed from BC004
missionStartDate: "2018-05-09T00:00:00Z"
S3_SLSTR_L2LST:
abstract: |
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)
instrument: SLSTR
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2LST,LST
sensorType: ATMOSPHERIC
license: proprietary
title: SENTINEL3 SLSTR Level-2 LST
missionStartDate: "2016-02-16T00:00:00Z"
S3_SLSTR_L2WST:
abstract: |
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)
instrument: SLSTR
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WST,WST
sensorType: ATMOSPHERIC
license: proprietary
title: SENTINEL3 SLSTR Level-2 WST
missionStartDate: "2016-02-16T00:00:00Z"
S3_SLSTR_L2WST_BC003:
abstract: |
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.
instrument: SLSTR
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WST,WST,REPROCESSED,BC003
sensorType: ATMOSPHERIC
license: proprietary
title: SENTINEL3 SLSTR Level-2 WST Reprocessed from BC003
missionStartDate: "2016-04-18T00:00:00Z"
S3_SLSTR_L2AOD:
abstract: |
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.
instrument: SLSTR
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2AOD,AOD
sensorType: ATMOSPHERIC
license: proprietary
title: SENTINEL3 SLSTR Level-2 AOD
missionStartDate: "2016-02-16T00:00:00Z"
S3_SLSTR_L2FRP:
abstract: |
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.
instrument: SLSTR
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2FRP,FRP
sensorType: ATMOSPHERIC
license: proprietary
title: SENTINEL3 SLSTR Level-2 FRP
missionStartDate: "2016-02-16T00:00:00Z"
S3_SLSTR_L2:
abstract: |
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.
instrument: SLSTR
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2FRP,FRP,L2WCT,WCT,L2WST,WST,L2AOD,AOD
sensorType: ATMOSPHERIC
license: proprietary
title: SENTINEL3 SLSTR Level-2
missionStartDate: "2017-07-05T00:00:00Z"
# S3 SRAL
S3_SRA:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1
keywords: SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-1
missionStartDate: "2016-02-16T00:00:00Z"
S3_SRA_A:
abstract: |
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)
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1
keywords: SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-1 SRA_A
missionStartDate: "2016-02-16T00:00:00Z"
S3_SRA_BS:
abstract: |
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
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1
keywords: SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-1 SRA_BS
missionStartDate: "2016-02-16T00:00:00Z"
S3_SRA_1A_BC004:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1A
keywords: SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,L1A,REPROCESSED,BC004
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-1A Unpacked - Reprocessed from BC004
missionStartDate: "2016-03-01T00:00:00Z"
S3_SRA_1B_BC004:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1B
keywords: SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,L1B,REPROCESSED,BC004
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-1B - Reprocessed from BC004
missionStartDate: "2016-03-01T00:00:00Z"
S3_SRA_BS_BC004:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L1B
keywords: SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,L1B,REPROCESSED,STACK,ECHOES,BC004
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-1B Stack Echoes - Reprocessed from BC004
missionStartDate: "2016-03-01T00:00:00Z"
S3_WAT:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,WAT
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-2 WAT
missionStartDate: "2016-02-16T00:00:00Z"
S3_WAT_BC004:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,WAT,REPROCESSED,BC004
sensorType: RADAR
license: proprietary
title: SRAL Level 2 Altimetry Global - Reprocessed from BC004
missionStartDate: "2016-03-01T00:00:00Z"
S3_LAN:
abstract: LAN or SR_2_LAN___ (peps)
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,LAN
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-2 LAN
missionStartDate: "2016-02-16T00:00:00Z"
S3_LAN_HY:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,LAN,HYDROLOGY
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-2 LAN HYDRO
missionStartDate: "2016-02-16T00:00:00Z"
S3_LAN_SI:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,LAN,SEA,ICE
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-2 LAN SEA ICE
missionStartDate: "2016-02-16T00:00:00Z"
S3_LAN_LI:
abstract: |
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.
instrument: SRAL
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,LAN,LAND,ICE
sensorType: RADAR
license: proprietary
title: SENTINEL3 SRAL Level-2 LAN LAND ICE
missionStartDate: "2016-02-16T00:00:00Z"
# S3 SYNERGY
# Synergy data products are generally combinations of OLCI and SLSTR instruments
S3_SY_SYN:
abstract: |
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.
instrument: SYNERGY
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,SYN
sensorType: OPTICAL,RADAR
license: proprietary
title: SENTINEL3 SYNERGY Level-2 SYN
missionStartDate: "2016-02-16T00:00:00Z"
S3_SY_AOD:
abstract: |
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
instrument: SYNERGY
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: L2
keywords: SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,AOD
sensorType: OPTICAL,RADAR
license: proprietary
title: SENTINEL3 SYNERGY Level-2 AOD
missionStartDate: "2016-02-16T00:00:00Z"
S3_SY_V10:
abstract: |
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.
instrument: SYNERGY
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: LEVEL-2W
keywords: SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,V10
sensorType: OPTICAL,RADAR
license: proprietary
title: SENTINEL3 SYNERGY Level-2 V10
missionStartDate: "2016-02-16T00:00:00Z"
S3_SY_VG1:
abstract: |
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.
instrument: SYNERGY
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: LEVEL-2
keywords: SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,VG1
sensorType: OPTICAL,RADAR
license: proprietary
title: SENTINEL3 SYNERGY Level-2 VG1
missionStartDate: "2016-02-16T00:00:00Z"
S3_SY_VGP:
abstract: |
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.
instrument: SYNERGY
platform: SENTINEL3
platformSerialIdentifier: S3A,S3B
processingLevel: LEVEL-2
keywords: SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,VGP
sensorType: OPTICAL,RADAR
license: proprietary
title: SENTINEL3 SYNERGY Level-2 VGP
missionStartDate: "2016-02-16T00:00:00Z"
# Sentinel 5P ------------------------------------------------------------------
S5P_L1B2_IR_ALL:
abstract: |
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).
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B, L2
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,IR,SIR,SWIR,Irradiances,UVN
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B and Level 2 Irradiances for the SWIR and UNV bands
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_IR_ALL:
abstract: |
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).
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,IR,SIR,SWIR,Irradiances,UVN
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Irradiances for the SWIR and UNV bands
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_IR_ALL:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Data
missionStartDate: "2018-04-01T00:00:00Z"
S5P_L1B_IR_SIR:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,IR,SIR,SWIR,Irradiances
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Irradiances for the SWIR bands
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_IR_UVN:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,IR,UVN,Irradiances
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Irradiances for the UVN bands
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_RA_BD1:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD1,BAND1,B01
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 1
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_RA_BD2:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD2,BAND2,B02
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 2
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_RA_BD3:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD3,BAND3,B03
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 3
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_RA_BD4:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD4,BAND4,B04
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 4
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_RA_BD5:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD5,BAND5,B05
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 5
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_RA_BD6:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD6,BAND6,B06
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 6
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_RA_BD7:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD7,BAND7,B07
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 7
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L1B_RA_BD8:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L1B
keywords: SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD8,BAND8,B08
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 1B Radiances for spectral band 8
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_NO2:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,NO2,Nitrogen,Dioxide
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Nitrogen Dioxide
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_CLOUD:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,CLOUD
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Cloud
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_O3:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,O3,Ozone
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Ozone
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_CO:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,CO,Carbon,Monoxide
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Carbon Monoxide
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_AER_AI:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,AER,AI,Ultraviolet,Aerosol,Index
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Ultraviolet Aerosol Index
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_O3_PR:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,O3,PR,Ozone,Profile
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Ozone Profile
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_O3_TCL:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,O3,TCL,Tropospheric,Ozone
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Tropospheric Ozone
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_AER_LH:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,AER,LH,Aerosol,Layer,Height
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Aerosol Layer Height
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_HCHO:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,HCHO,Formaldehyde
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Formaldehyde
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_CH4:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,CH4,Methane
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Methane
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_NP_BD3:
abstract: |
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).
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,NP,NPP,Cloud,BD3,B03,BAND3
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 NPP Cloud for band 3
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_NP_BD6:
abstract: |
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).
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,NP,NPP,Cloud,BD6,B06,BAND6
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 NPP Cloud for band 6
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_NP_BD7:
abstract: |
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).
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,NP,NPP,Cloud,BD7,B07,BAND7
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 NPP Cloud for band 7
missionStartDate: "2017-10-13T00:00:00Z"
S5P_L2_SO2:
abstract: |
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.
instrument: TROPOMI
platform: SENTINEL5P
platformSerialIdentifier: S5P
processingLevel: L2
keywords: SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,SO2,Sulphur,Dioxide
sensorType: ATMOSPHERIC
license: proprietary
title: Sentinel-5 Precursor Level 2 Sulphur Dioxide
missionStartDate: "2017-10-13T00:00:00Z"
# Sentinel 6
S6_P4_L1AHR_F06:
abstract: |
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).
instrument: Poseidon-4
platform: SENTINEL6-A
platformSerialIdentifier: S6A
processingLevel: L1A
keywords: SENTINEL,SENTINEL6,S6,S6A,LEO,L1A,ALTIMETRIC,HR,POSEIDON4,P4,F06
sensorType: ALTIMETRIC
license: proprietary
title: Sentinel 6 - Poseidon-4 Altimetry Level 1A High Resolution Reprocessed at F06
missionStartDate: "2020-12-17T00:00:00Z"
S6_P4_L1BLR_F06:
abstract: |
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).
instrument: Poseidon-4
platform: SENTINEL6-A
platformSerialIdentifier: S6A
processingLevel: L1B
keywords: SENTINEL,SENTINEL6,S6,S6A,LEO,L1B,ALTIMETRIC,LR,POSEIDON4,P4,F06
sensorType: ALTIMETRIC
license: proprietary
title: Sentinel 6 - Poseidon-4 Altimetry Level 1B Low Resolution Reprocessed at F06
missionStartDate: "2020-12-17T00:00:00Z"
S6_P4_L1BAHR_F06:
abstract: |
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).
instrument: Poseidon-4
platform: SENTINEL6-A
platformSerialIdentifier: S6A
processingLevel: L1B
keywords: SENTINEL,SENTINEL6,S6,S6A,LEO,L1B,ALTIMETRIC,HR,POSEIDON4,P4,F06
sensorType: ALTIMETRIC
license: proprietary
title: Sentinel 6 - Poseidon-4 Altimetry Level 1B High Resolution Reprocessed at F06
missionStartDate: "2020-12-17T00:00:00Z"
S6_P4_L2LR_F06:
abstract: |
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).
instrument: Poseidon-4
platform: SENTINEL6-A
platformSerialIdentifier: S6A
processingLevel: L2
keywords: SENTINEL,SENTINEL6,S6,S6A,LEO,L2,ALTIMETRIC,LR,POSEIDON4,P4,F06
sensorType: ALTIMETRIC
license: proprietary
title: Sentinel 6 - Poseidon-4 Altimetry Level 2 Low Resolution Reprocessed at F06
missionStartDate: "2020-12-17T00:00:00Z"
S6_P4_L2HR_F06:
abstract: |
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).
instrument: Poseidon-4
platform: SENTINEL6-A
platformSerialIdentifier: S6A
processingLevel: L2
keywords: SENTINEL,SENTINEL6,S6,S6A,LEO,L2,ALTIMETRIC,HR,POSEIDON4,P4,F06
sensorType: ALTIMETRIC
license: proprietary
title: Sentinel 6 - Poseidon-4 Altimetry Level 2 High Resolution Reprocessed at F06
missionStartDate: "2020-12-17T00:00:00Z"
S6_AMR_L2_F06:
abstract: |
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).
instrument: AMR-C
platform: SENTINEL6-A
platformSerialIdentifier: S6A
processingLevel: L2
keywords: SENTINEL,SENTINEL6,S6,S6A,LEO,L2,AMR-C,RADIOMETER,MICROWAVE,F06
sensorType: RADIOMETER
license: proprietary
title: Sentinel 6 - Climate-quality Advanced Microwave Radiometer Level 2 Products Reprocessed at F06
missionStartDate: "2020-11-28T00:00:00Z"
# SPOT ------------------------------------------------------------------------
SPOT_SWH:
abstract: |
The Spot World Heritage (SWH) programme objective is the free availability for non-commercial use of orthorectified
products derived from multispectral images of more than 5 years old from the Spot 1-5 satellites family.
More informations on https://www.theia-land.fr/en/product/spot-world-heritage/
instrument:
platform: SPOT1-5
platformSerialIdentifier: SPOT1-5
processingLevel: L1C
keywords: SPOT,SPOT1,SPOT2,SPOT3,SPOT4,SPOT5,L1C
sensorType: OPTICAL
license: proprietary
title: Spot World Heritage
missionStartDate: "1986-02-22T00:00:00Z"
SPOT_SWH_OLD:
abstract: |
Spot world heritage Old format.
instrument:
platform: SPOT1-5
platformSerialIdentifier: SPOT1-5
processingLevel: L1C
keywords: SPOT,SPOT1,SPOT2,SPOT3,SPOT4,SPOT5,L1C
sensorType: OPTICAL
license: proprietary
title: Spot World Heritage
missionStartDate: "1986-02-22T00:00:00Z"
SPOT5_SPIRIT:
abstract: |
SPOT 5 stereoscopic survey of Polar Ice.
instrument:
platform: SPOT5
platformSerialIdentifier: SPOT5
processingLevel: L1A
keywords: SPOT,SPOT5,L1A
sensorType: OPTICAL
license: proprietary
title: Spot 5 SPIRIT
missionStartDate: "2002-05-04T00:00:00Z"
# VENUS ------------------------------------------------------------------------
VENUS_L1C:
abstract: |
A light description of Venus L1 data is available at http://www.cesbio.ups-tlse.fr/multitemp/?page_id=12984
instrument:
platform: VENUS
platformSerialIdentifier: VENUS
processingLevel: L1C
keywords: VENUS,L1,L1C
sensorType: OPTICAL
license: proprietary
title: Venus Level1-C
missionStartDate: "2017-08-02T00:00:00Z"
VENUS_L2A_MAJA:
abstract: |
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.
instrument:
platform: VENUS
platformSerialIdentifier: VENUS
processingLevel: L2A
keywords: VENUS,L2,L2A
sensorType: OPTICAL
license: proprietary
title: Venus Level2-A
missionStartDate: "2017-08-02T00:00:00Z"
VENUS_L3A_MAJA:
abstract: ""
instrument:
platform: VENUS
platformSerialIdentifier: VENUS
processingLevel: L3A
keywords: VENUS,L3,L3A
sensorType: OPTICAL
license: proprietary
title: Venus Level3-A
missionStartDate: "2017-08-02T00:00:00Z"
# ECMWF -----------------------------------------------------------------------
TIGGE_CF_SFC:
abstract: |
TIGGE (THORPEX Interactive Grand Global Ensemble) Surface Control forecast
from ECMWF
instrument:
platform: TIGGE
platformSerialIdentifier: TIGGE
processingLevel:
keywords: THORPEX,TIGGE,CF,SFC,ECMWF
sensorType: ATMOSPHERIC
license: proprietary
title: TIGGE ECMWF Surface Control forecast
missionStartDate: "2003-01-01T00:00:00Z"
# COPERNICUS ADS ----------------------------------------------------------------------
CAMS_GAC_FORECAST:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Forecast,GAC
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global atmospheric composition forecasts
missionStartDate: "2015-01-02T00:00:00Z"
CAMS_EU_AIR_QUALITY_FORECAST:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Air,Forecast,EEA
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS European air quality forecasts
missionStartDate: "2021-01-01T00:00:00Z"
CAMS_GFE_GFAS:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Fire,FRP,DM,MODIS,NASA,EOS,ECMWF,GFAS
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global biomass burning emissions based on fire radiative power (GFAS)
missionStartDate: "2003-01-01T00:00:00Z"
CAMS_SOLAR_RADIATION:
abstract: |
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).
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Solar,Radiation
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS solar radiation time-series
missionStartDate: "2004-01-02T00:00:00Z"
CAMS_GREENHOUSE_INVERSION:
abstract: |
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).
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmosphere,Atmospheric,IPCC,CO2,CH4,N2O
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global inversion-optimised greenhouse gas fluxes and concentrations
missionStartDate: "1979-01-01T00:00:00Z"
CAMS_EAC4_MONTHLY:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmosphere,Atmospheric,EWMCF,EAC4
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global reanalysis (EAC4) monthly averaged fields
missionStartDate: "2003-01-01T00:00:00Z"
CAMS_EU_AIR_QUALITY_RE:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Air,EEA
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS European air quality reanalyses
missionStartDate: "2013-01-01T00:00:00Z"
CAMS_EAC4:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmosphere,Atmospheric,EWMCF,EAC4
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global reanalysis (EAC4)
missionStartDate: "2003-01-01T00:00:00Z"
CAMS_GRF_AUX:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmospheric,Atmosphere,RF,CO2,CH4,O3,Aerosol
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global radiative forcing - auxilliary variables
missionStartDate: "2003-01-01T00:00:00Z"
CAMS_GRF:
abstract: |
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".
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmospheric,Atmosphere,RF,CO2,CH4,O3,Aerosol
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global radiative forcings
missionStartDate: "2003-01-01T00:00:00Z"
CAMS_GREENHOUSE_EGG4_MONTHLY:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmospheric,Atmosphere,CO2,CH4,Greenhouse,ECMWF,EGG4
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields
missionStartDate: "2003-01-01T00:00:00Z"
CAMS_GREENHOUSE_EGG4:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmospheric,Atmosphere,CO2,CH4,GHG,ECMWF,EGG4
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global greenhouse gas reanalysis (EGG4)
missionStartDate: "2003-01-01T00:00:00Z"
CAMS_GLOBAL_EMISSIONS:
abstract: |
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.
instrument:
platform: CAMS
platformSerialIdentifier: CAMS
processingLevel:
keywords: Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Emissions,Pollutants,GHG
sensorType: ATMOSPHERIC
license: proprietary
title: CAMS global emission inventories
missionStartDate: "2000-01-01T00:00:00Z"
# COPERNICUS CDS ----------------------------------------------------------------------
ERA5_SL:
abstract: |
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).
instrument:
platform: ERA5
platformSerialIdentifier: ERA5
processingLevel:
keywords: ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,sea,hourly,single,levels
sensorType: ATMOSPHERIC
license: proprietary
title: ERA5 hourly data on single levels from 1940 to present
missionStartDate: "1940-01-01T00:00:00Z"
ERA5_SL_MONTHLY:
abstract: |
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).
instrument:
platform: ERA5
platformSerialIdentifier: ERA5
processingLevel:
keywords: Climate,ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,sea,monthly,single,levels
sensorType: ATMOSPHERIC
license: proprietary
title: ERA5 monthly averaged data on single levels from 1940 to present
missionStartDate: "1940-01-01T00:00:00Z"
ERA5_PL:
abstract: |
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)
instrument:
platform: ERA5
platformSerialIdentifier: ERA5
processingLevel:
keywords: ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,sea,hourly,pressure,levels
sensorType: ATMOSPHERIC
license: proprietary
title: ERA5 hourly data on pressure levels from 1940 to present
missionStartDate: "1940-01-01T00:00:00Z"
ERA5_PL_MONTHLY:
abstract: |
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).
instrument:
platform: ERA5
platformSerialIdentifier: ERA5
processingLevel:
keywords: Climate,ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,sea,monthly,pressure,levels
sensorType: ATMOSPHERIC
license: proprietary
title: ERA5 monthly averaged data on pressure levels from 1940 to present
missionStartDate: "1940-01-01T00:00:00Z"
ERA5_LAND:
abstract: |
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
platform: ERA5
instrument:
platformSerialIdentifier: ERA5
processingLevel:
keywords: ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,hourly,evolution
sensorType: ATMOSPHERIC
license: proprietary
title: ERA5-Land hourly data from 1950 to present
missionStartDate: "1950-01-01T00:00:00Z"
ERA5_LAND_MONTHLY:
abstract: |
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
platform: ERA5
instrument:
platformSerialIdentifier: ERA5
processingLevel:
keywords: ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,monthly,evolution
sensorType: ATMOSPHERIC
license: proprietary
title: ERA5-Land monthly averaged data from 1950 to present
missionStartDate: "1950-01-01T00:00:00Z"
UERRA_EUROPE_SL:
abstract: |
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
platform: SURFEX
instrument:
platformSerialIdentifier: SURFEX
processingLevel:
keywords: Climate,ECMWF,Reanalysis,Regional,Europe,UERRA,UERRA-HARMONIE,SURFEX,MESCAN-SURFEX,CDS,Atmospheric,single,levels
sensorType: ATMOSPHERIC
license: proprietary
title: UERRA regional reanalysis for Europe on single levels from 1961 to 2019
missionStartDate: "1961-01-01T00:00:00Z"
GLACIERS_ELEVATION_AND_MASS_CHANGE:
abstract: |
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
platform: INSITU
instrument:
platformSerialIdentifier: INSITU
processingLevel:
keywords: ECMWF,WGMS,INSITU,CDS,C3S,glacier,elevation,mass,change
sensorType: ATMOSPHERIC
license: proprietary
title: Glaciers elevation and mass change data from 1850 to present from the Fluctuations of Glaciers Database
missionStartDate: "1850-01-01T00:00:00Z"
GLACIERS_DIST_RANDOLPH:
abstract: |
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.
platform:
instrument:
platformSerialIdentifier: INSITU
processingLevel:
keywords: ECMWF,WGMS,INSITU,CDS,C3S,glacier,randolph,distribution,inventory
sensorType: ATMOSPHERIC
license: proprietary
title: Glaciers distribution data from the Randolph Glacier Inventory for year 2000
missionStartDate: "2000-01-01T00:00:00Z"
missionEndDate: "2000-12-31T23:59:00Z"
GRIDDED_GLACIERS_MASS_CHANGE:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,WGMS,INSITU,CDS,C3S,glacier,randolph,mass,gridded
sensorType: ATMOSPHERIC
license: proprietary
title: Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database
missionStartDate: "1975-01-01T00:00:00Z"
SATELLITE_CARBON_DIOXIDE:
abstract: |
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)
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,carbon-dioxide
sensorType: ATMOSPHERIC
license: proprietary
title: Carbon dioxide data from 2002 to present derived from satellite observations
missionStartDate: "2002-10-01T00:00:00Z"
SATELLITE_METHANE:
abstract: |
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)
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,methane
sensorType: ATMOSPHERIC
license: proprietary
title: Methane data from 2002 to present derived from satellite observations
missionStartDate: "2002-10-01T00:00:00Z"
SATELLITE_SEA_ICE_EDGE_TYPE:
abstract: |
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
platform:
instrument:
platform:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,sea,ice
sensorType: ATMOSPHERIC
license: proprietary
title: Sea ice edge and type daily gridded data from 1978 to present derived from satellite observations
missionStartDate: "1979-01-01T00:00:00Z"
missionEndDate: "2023-05-02-02T23:59:59"
SEASONAL_POSTPROCESSED_PL:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal forecast anomalies on pressure levels
missionStartDate: "2017-09-01T00:00:00Z"
SATELLITE_SEA_LEVEL_BLACK_SEA:
abstract: |
Sea level anomaly is the height of water over the mean sea surface in a given time and region. Up-to-date
altimeter standards are used to estimate the sea level anomalies with a mapping algorithm dedicated to the
Black sea region. Anomalies are computed with respect to a twenty-year mean reference period (1993-2012).
The steady number of reference satellite used in the production of this dataset contributes to the long-term
stability of the sea level record. Improvements of the accuracy, sampling of meso-scale processes and of the
high-latitude coverage were achieved by using a few additional satellite missions. New data are provided with
a delay of about 4-5 months relatively to near-real time or interim sea level products. This delay is mainly
due to the timeliness of the input data, the centred processing temporal window and the validation process.
However, this processing and validation adds stability and accuracy to the sea level variables and make them
adapted to climate applications. This dataset includes uncertainties for each grid cell. More details about
the sea level retrieval, additional filters, optimisation procedures, and the error estimation are given in
the Documentation section. 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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: Climate,ECMWF,CDS,C3S,sea,level,Black Sea
sensorType: HYDROLOGICAL
license: proprietary
title: Sea level daily gridded data from satellite observations for the Black Sea from 1993 to 2020
missionStartDate: "1993-01-01T00:00:00Z"
SATELLITE_SEA_LEVEL_GLOBAL:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: Climate,ECMWF,CDS,C3S,sea,level,global
sensorType: HYDROLOGICAL
license: proprietary
title: Sea level gridded data from satellite observations for the global ocean
missionStartDate: "1993-01-01T00:00:00Z"
missionEndDate: "2022-08-04T23:59:59Z"
SATELLITE_SEA_LEVEL_MEDITERRANEAN:
abstract: |
Sea level anomaly is 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). Up-to-date altimeter
standards are used to estimate the sea level anomalies with a mapping algorithm specifically dedicated to the
Mediterranean Sea. The steady number of reference satellite used in the production of this dataset contributes to
the long-term stability of the sea level record. Improvements of the accuracy, sampling of meso-scale processes and
of the high-latitude coverage were achieved by using a few additional satellite missions. New data are provided with
a delay of about 4-5 months relatively to near-real time or interim sea level products. This delay is mainly due to
the timeliness of the input data, the centred processing temporal window and the validation process. However, this
processing and validation adds stability and accuracy to the sea level variables and make them adapted to climate
applications. This dataset includes uncertainties for each grid cell. More details about the sea level retrieval,
additional filters, optimisation procedures, and the error estimation are given in the Documentation section.
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: Climate,ECMWF,CDS,C3S,sea,level,mediterranean
sensorType: HYDROLOGICAL
license: proprietary
title: Sea level daily gridded data from satellite observations for the Mediterranean Sea
missionStartDate: "1993-01-01T00:00:00Z"
missionEndDate: "2018-11-01T:23:59:59Z"
SEASONAL_POSTPROCESSED_SL:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,seasonal,forecast,anomalies,single,levels
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal forecast anomalies on single levels
missionStartDate: "2017-09-01T00:00:00Z"
SEASONAL_ORIGINAL_SL:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,seasonal,forecast,daily,single,levels
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal forecast daily and subdaily data on single levels
missionStartDate: "1981-01-01T00:00:00Z"
SEASONAL_ORIGINAL_PL:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,seasonal,forecast,subdaily,pressure,levels
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal forecast subdaily data on pressure levels
missionStartDate: "1981-01-01T00:00:00Z"
SEASONAL_MONTHLY_PL:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,seasonal,forecast,monthly,pressure,levels
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal forecast monthly statistics on pressure levels
missionStartDate: "1981-01-01T00:00:00Z"
SEASONAL_MONTHLY_SL:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,seasonal,forecast,monthly,single,levels
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal forecast monthly statistics on single levels
missionStartDate: "1981-01-01T00:00:00Z"
missionEndDate: "2023-05-01T00:00:00Z"
SIS_HYDRO_MET_PROJ:
abstract: |
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
platform:
instrument:
platformSerialIdentifier:
processingLevel:
keywords: ECMWF,CDS,C3S,hydrology,meterology,water,precipitation,temperature
sensorType: ATMOSPHERIC
license: proprietary
title: Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections
missionStartDate: "1970-01-01T00:00:00Z"
missionEndDate: "2100-12-31T23:59:00Z"
# CEMS
FIRE_HISTORICAL:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,EFFIS,fire,historical,ERA5,european,sustainability,CEMS,system
sensorType: ATMOSPHERIC
license: proprietary
title: Fire danger indices historical data from the Copernicus Emergency Management Service
missionStartDate: "1940-01-03T00:00:00Z"
GLOFAS_FORECAST:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,GloFAS,forecast,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: River discharge and related forecasted data by the Global Flood Awareness System
missionStartDate: "2021-05-26T00:00:00Z"
GLOFAS_HISTORICAL:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,GloFAS,historical,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: River discharge and related historical data from the Global Flood Awareness System
missionStartDate: "1979-01-01T00:00:00Z"
GLOFAS_REFORECAST:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,GloFAS,reforecast,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: Reforecasts of river discharge and related data by the Global Flood Awareness System
missionStartDate: "2003-03-27T00:00:00Z"
missionEndDate: "2018-12-30T23:59:00Z"
GLOFAS_SEASONAL:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,GloFAS,seasonal,forecast,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal forecasts of river discharge and related data by the Global Flood Awareness System
missionStartDate: "2021-06-01T00:00:00Z"
GLOFAS_SEASONAL_REFORECAST:
abstract: |
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"
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,GloFAS,seasonal,forecast,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System
missionStartDate: "1981-01-01T00:00:00Z"
EFAS_FORECAST:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,EFAS,forecast,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: River discharge and related forecasted data by the European Flood Awareness System
missionStartDate: "2018-10-11T00:00:00Z"
EFAS_HISTORICAL:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,EFAS,historical,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: River discharge and related historical data from the European Flood Awareness System
missionStartDate: "1992-01-02T00:00:00Z"
EFAS_REFORECAST:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,EFAS,reforecast,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: Reforecasts of river discharge and related data by the European Flood Awareness System
missionStartDate: "2003-03-27T00:00:00Z"
missionEndDate: "2018-12-30T00:00:00Z"
EFAS_SEASONAL:
abstract: |
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
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,EFAS,seasonal,forecast,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal forecasts of river discharge and related data by the European Flood Awareness System
missionStartDate: "2020-11-01T00:00:00Z"
EFAS_SEASONAL_REFORECAST:
abstract: |
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"
instrument:
platform: CEMS
platformSerialIdentifier: CEMS
processingLevel:
keywords: ECMWF,CEMS,EFAS,seasonal,reforecast,river,discharge
sensorType: ATMOSPHERIC
license: proprietary
title: Seasonal reforecasts of river discharge and related data by the European Flood Awareness System
missionStartDate: "1991-01-01T00:00:00Z"
missionEndDate: "2020-10-01T00:00:00Z"
# COPERNICUS Digital Elevation Model
COP_DEM_GLO30_DGED:
abstract: |
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.
instrument:
platform: TerraSAR
platformSerialIdentifier:
processingLevel:
keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,GDGED
sensorType: ALTIMETRIC
license: proprietary
title: Copernicus DEM GLO-30 DGED
missionStartDate: "2010-06-21T00:00:00Z"
COP_DEM_GLO30_DTED:
abstract: |
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.
instrument:
platform: TerraSAR
platformSerialIdentifier:
processingLevel:
keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,DTED
sensorType: ALTIMETRIC
license: proprietary
title: Copernicus DEM GLO-30 DTED
missionStartDate: "2010-06-21T00:00:00Z"
COP_DEM_GLO90_DGED:
abstract: |
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.
instrument:
platform: TerraSAR
platformSerialIdentifier:
processingLevel:
keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,GDGED
sensorType: ALTIMETRIC
license: proprietary
title: Copernicus DEM GLO-90 DGED
missionStartDate: "2010-06-21T00:00:00Z"
COP_DEM_GLO90_DTED:
abstract: |
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.
instrument:
platform: TerraSAR
platformSerialIdentifier:
processingLevel:
keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,DTED
sensorType: ALTIMETRIC
license: proprietary
title: Copernicus DEM GLO-90 DTED
missionStartDate: "2010-06-21T00:00:00Z"
# Copernicus Land Monitoring Service
CLMS_CORINE:
abstract: |
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.
instrument:
platform: Sentinel-2, LANDSAT, SPOT-4/5, IRS P6 LISS III
platformSerialIdentifier: S2, L5, L7, L8, SPOT4, SPOT5
processingLevel:
keywords: Land-cover,LCL,CORINE,CLMS
sensorType:
license: proprietary
title: CORINE Land Cover
missionStartDate: "1986-01-01T00:00:00Z"
CLMS_GLO_FCOVER_333M:
abstract: |
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.
instrument: OLCI,PROBA-V
platform: Sentinel-3
platformSerialIdentifier:
processingLevel:
keywords: Land,Fraction-of-vegetation-cover,OLCI,PROBA-V,Sentinel-3
sensorType:
license: proprietary
title: Global 10-daily Fraction of Vegetation Cover 333m
missionStartDate: "2014-01-10T00:00:00Z"
CLMS_GLO_NDVI_333M:
abstract: |
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.
instrument: PROBA-V
platform:
platformSerialIdentifier:
processingLevel:
keywords: Land,NDVI,PROBA-V
sensorType:
license: proprietary
title: Global 10-daily Normalized Difference Vegetation Index 333M
missionStartDate: "2014-01-01T00:00:00Z"
missionEndDate: "2021-01-01T23:59:59Z"
CLMS_GLO_NDVI_1KM_LTS:
abstract: |
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.
instrument: VEGETATION,PROBA-V
platform: SPOT
platformSerialIdentifier:
processingLevel:
keywords: Land,NDVI,LTS,SPOT,VEGETATION,PROBA-V
sensorType:
license: proprietary
title: "Normalized Difference Vegetation Index: global Long Term Statistics (raster 1km) - version 2, Apr 2019"
missionStartDate: "1999-01-01T00:00:00Z"
CLMS_GLO_DMP_333M:
abstract: |
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.
instrument: OLCI,PROBA-V
platform: Sentinel-3
platformSerialIdentifier:
processingLevel:
keywords: Land,Dry-matter-productivity,DMP,OLCI,PROBA-V,Sentinel-3
sensorType:
license: proprietary
title: 10-daily Dry Matter Productivity 333M
missionStartDate: "2014-01-10T00:00:00Z"
CLMS_GLO_GDMP_333M:
abstract: |
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.
instrument: OLCI,PROBA-V
platform: Sentinel-3
platformSerialIdentifier:
processingLevel:
keywords: Land,Gross-dry-matter-productivity,GDMP,GPP,OLCI,PROBA-V,Sentinel-3
sensorType:
license: proprietary
title: 10-daily Gross Dry Matter Productivity 333M
missionStartDate: "2014-01-10T00:00:00Z"
CLMS_GLO_LAI_333M:
abstract: |
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.
instrument: OLCI,PROBA-V
platform: Sentinel-3
platformSerialIdentifier:
processingLevel:
keywords: Land,Leaf-area-index,LAI,OLCI,PROBA-V,Sentinel-3
sensorType:
license: proprietary
title: Global 10-daily Leaf Area Index 333m
missionStartDate: "2014-01-10T00:00:00Z"
CLMS_GLO_FAPAR_333M:
abstract: |
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.
instrument: OLCI,PROBA-V
platform: Sentinel-3
platformSerialIdentifier:
processingLevel:
keywords: Land,Fraction-of-absorbed-PAR,FAPAR,OLCI,PROBA-V,Sentinel-3
sensorType:
license: proprietary
title: Global 10-daily Fraction of Absorbed PAR 333m
missionStartDate: "2014-01-10T00:00:00Z"
EEA_DAILY_SWI_1KM:
abstract: |
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.
instrument: C-SAR,Metop ASCAT
platform: Sentinel-1
platformSerialIdentifier:
processingLevel:
keywords: SWI,QFLAG,SSF,C-SAR,Metop-ASCAT,Sentinel-1
sensorType: RADAR
license: proprietary
title: "Soil Water Index: continental Europe daily (raster 1km) - version 1, Apr 2019"
missionStartDate: "2015-01-01T00:00:00Z"
EEA_DAILY_SSM_1KM:
abstract: |
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.
instrument: C-SAR,Metop ASCAT
platform: Sentinel-1
platformSerialIdentifier:
processingLevel:
keywords: SSM,C-SAR,Metop-ASCAT,Sentinel-1
sensorType: RADAR
license: proprietary
title: "Surface Soil Moisture: continental Europe daily (raster 1km) - version 1, Apr 2019"
missionStartDate: "2015-01-01T00:00:00Z"
EEA_DAILY_VI:
abstract: |
Vegetation Indices (VI) comprises four daily vegetation indices (PPI, NDVI, LAI and FAPAR) and quality information,
that are part of the Copernicus Land Monitoring Service (CLMS) HR-VPP product suite. The 10m resolution, daily updated
Plant Phenology Index (PPI), Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Fraction of Absorbed
Photosynthetically Active Radiation (fAPAR) are derived from Copernicus Sentinel-2 satellite observations. They are
provided together with a related quality indicator (QFLAG2) that flags clouds, shadows, snow, open water and other areas
where the VI retrieval is less reliable. These Vegetation Indices are made available as a set of raster files with 10 x 10m
resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38
countries and the United Kingdom and for the period from 2017 until today, with daily updates. The Vegetation Indices
are part of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus
Land Monitoring Service (CLMS).
instrument:
platform: Sentinel-2
platformSerialIdentifier: S2A, S2B
processingLevel:
keywords: Land,Plant-phenology-index,Phenology,Vegetation,Sentinel-2,S2A,S2B
sensorType: RADAR
license: proprietary
title: Vegetation Indices, daily, UTM projection
missionStartDate:
# METEOBLUE --------------------------------------------------------------------------
NEMSGLOBAL_TCDC:
abstract: |
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.
instrument:
platform: NEMSGLOBAL
platformSerialIdentifier: NEMSGLOBAL
processingLevel:
keywords: meteoblue,NEMS,NEMSGLOBAL,CLOUD,COVER,TOTAL,TCDC,DAILY,MEAN
sensorType: ATMOSPHERIC
license: proprietary
title: NEMSGLOBAL Total Cloud Cover daily mean
missionStartDate: "1984-01-01T00:00:00Z"
NEMSAUTO_TCDC:
abstract: |
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.
instrument:
platform: NEMSAUTO
platformSerialIdentifier: NEMSAUTO
processingLevel:
keywords: meteoblue,NEMS,NEMSAUTO,CLOUD,COVER,TOTAL,TCDC,DAILY,MEAN
sensorType: ATMOSPHERIC
license: proprietary
title: NEMSAUTO Total Cloud Cover daily mean
missionStartDate: "1984-01-01T00:00:00Z"
# GENERIC----------------------------------------------------------------------
GENERIC_PRODUCT_TYPE:
abstract:
instrument:
platform:
platformSerialIdentifier:
processingLevel:
keywords:
sensorType:
license:
title:
missionStartDate:
The following table lists the metadata parameters of the product types, and shows whether these product types are available for providers or not. The table allows you to display desired columns only, sort, and filter its content.
Product types information (CSV)#
product type |
abstract |
instrument |
platform |
platformSerialIdentifier |
processingLevel |
keywords |
sensorType |
license |
title |
missionStartDate |
_id |
astraea_eod |
aws_eos |
cop_ads |
cop_cds |
cop_dataspace |
creodias |
creodias_s3 |
earth_search |
earth_search_cog |
earth_search_gcs |
ecmwf |
hydroweb_next |
meteoblue |
onda |
peps |
planetary_computer |
sara |
theia |
usgs |
usgs_satapi_aws |
wekeo |
wekeo_cmems |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CAMS_EAC4 |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmosphere,Atmospheric,EWMCF,EAC4 |
ATMOSPHERIC |
proprietary |
CAMS global reanalysis (EAC4) |
2003-01-01T00:00:00Z |
CAMS_EAC4 |
available |
available |
||||||||||||||||||||||
CAMS_EAC4_MONTHLY |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmosphere,Atmospheric,EWMCF,EAC4 |
ATMOSPHERIC |
proprietary |
CAMS global reanalysis (EAC4) monthly averaged fields |
2003-01-01T00:00:00Z |
CAMS_EAC4_MONTHLY |
available |
available |
||||||||||||||||||||||
CAMS_EU_AIR_QUALITY_FORECAST |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Air,Forecast,EEA |
ATMOSPHERIC |
proprietary |
CAMS European air quality forecasts |
2021-01-01T00:00:00Z |
CAMS_EU_AIR_QUALITY_FORECAST |
available |
available |
||||||||||||||||||||||
CAMS_EU_AIR_QUALITY_RE |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Air,EEA |
ATMOSPHERIC |
proprietary |
CAMS European air quality reanalyses |
2013-01-01T00:00:00Z |
CAMS_EU_AIR_QUALITY_RE |
available |
available |
||||||||||||||||||||||
CAMS_GAC_FORECAST |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Forecast,GAC |
ATMOSPHERIC |
proprietary |
CAMS global atmospheric composition forecasts |
2015-01-02T00:00:00Z |
CAMS_GAC_FORECAST |
available |
available |
||||||||||||||||||||||
CAMS_GFE_GFAS |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Fire,FRP,DM,MODIS,NASA,EOS,ECMWF,GFAS |
ATMOSPHERIC |
proprietary |
CAMS global biomass burning emissions based on fire radiative power (GFAS) |
2003-01-01T00:00:00Z |
CAMS_GFE_GFAS |
available |
|||||||||||||||||||||||
CAMS_GLOBAL_EMISSIONS |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmosphere,Atmospheric,Emissions,Pollutants,GHG |
ATMOSPHERIC |
proprietary |
CAMS global emission inventories |
2000-01-01T00:00:00Z |
CAMS_GLOBAL_EMISSIONS |
available |
available |
||||||||||||||||||||||
CAMS_GREENHOUSE_EGG4 |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmospheric,Atmosphere,CO2,CH4,GHG,ECMWF,EGG4 |
ATMOSPHERIC |
proprietary |
CAMS global greenhouse gas reanalysis (EGG4) |
2003-01-01T00:00:00Z |
CAMS_GREENHOUSE_EGG4 |
available |
available |
||||||||||||||||||||||
CAMS_GREENHOUSE_EGG4_MONTHLY |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmospheric,Atmosphere,CO2,CH4,Greenhouse,ECMWF,EGG4 |
ATMOSPHERIC |
proprietary |
CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields |
2003-01-01T00:00:00Z |
CAMS_GREENHOUSE_EGG4_MONTHLY |
available |
available |
||||||||||||||||||||||
CAMS_GREENHOUSE_INVERSION |
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). |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmosphere,Atmospheric,IPCC,CO2,CH4,N2O |
ATMOSPHERIC |
proprietary |
CAMS global inversion-optimised greenhouse gas fluxes and concentrations |
1979-01-01T00:00:00Z |
CAMS_GREENHOUSE_INVERSION |
available |
available |
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CAMS_GRF |
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”. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmospheric,Atmosphere,RF,CO2,CH4,O3,Aerosol |
ATMOSPHERIC |
proprietary |
CAMS global radiative forcings |
2003-01-01T00:00:00Z |
CAMS_GRF |
available |
available |
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CAMS_GRF_AUX |
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. |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Atmospheric,Atmosphere,RF,CO2,CH4,O3,Aerosol |
ATMOSPHERIC |
proprietary |
CAMS global radiative forcing - auxilliary variables |
2003-01-01T00:00:00Z |
CAMS_GRF_AUX |
available |
available |
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CAMS_SOLAR_RADIATION |
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). |
CAMS |
CAMS |
Copernicus,ADS,CAMS,Solar,Radiation |
ATMOSPHERIC |
proprietary |
CAMS solar radiation time-series |
2004-01-02T00:00:00Z |
CAMS_SOLAR_RADIATION |
available |
available |
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CBERS4_AWFI_L2 |
China-Brazil Earth Resources Satellite, CBERS-4 AWFI camera Level-2 product. System corrected images, expect some translation error. |
AWFI |
CBERS |
CBERS-4 |
L2 |
AWFI,CBERS,CBERS-4,L2 |
OPTICAL |
proprietary |
CBERS-4 AWFI Level-2 |
2014-12-07T00:00:00Z |
CBERS4_AWFI_L2 |
available |
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CBERS4_AWFI_L4 |
China-Brazil Earth Resources Satellite, CBERS-4 AWFI camera Level-4 product. Orthorectified with ground control points. |
AWFI |
CBERS |
CBERS-4 |
L4 |
AWFI,CBERS,CBERS-4,L4 |
OPTICAL |
proprietary |
CBERS-4 AWFI Level-4 |
2014-12-07T00:00:00Z |
CBERS4_AWFI_L4 |
available |
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CBERS4_MUX_L2 |
China-Brazil Earth Resources Satellite, CBERS-4 MUX camera Level-2 product. System corrected images, expect some translation error. |
MUX |
CBERS |
CBERS-4 |
L2 |
MUX,CBERS,CBERS-4,L2 |
OPTICAL |
proprietary |
CBERS-4 MUX Level-2 |
2014-12-07T00:00:00Z |
CBERS4_MUX_L2 |
available |
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CBERS4_MUX_L4 |
China-Brazil Earth Resources Satellite, CBERS-4 MUX camera Level-4 product. Orthorectified with ground control points. |
MUX |
CBERS |
CBERS-4 |
L4 |
MUX,CBERS,CBERS-4,L4 |
OPTICAL |
proprietary |
CBERS-4 MUX Level-4 |
2014-12-07T00:00:00Z |
CBERS4_MUX_L4 |
available |
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CBERS4_PAN10M_L2 |
China-Brazil Earth Resources Satellite, CBERS-4 PAN10M camera Level-2 product. System corrected images, expect some translation error. |
PAN10M |
CBERS |
CBERS-4 |
L2 |
PAN10M,CBERS,CBERS-4,L2 |
OPTICAL |
proprietary |
CBERS-4 PAN10M Level-2 |
2014-12-07T00:00:00Z |
CBERS4_PAN10M_L2 |
available |
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CBERS4_PAN10M_L4 |
China-Brazil Earth Resources Satellite, CBERS-4 PAN10M camera Level-4 product. Orthorectified with ground control points. |
PAN10M |
CBERS |
CBERS-4 |
L4 |
PAN10M,CBERS,CBERS-4,L4 |
OPTICAL |
proprietary |
CBERS-4 PAN10M Level-4 |
2014-12-07T00:00:00Z |
CBERS4_PAN10M_L4 |
available |
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CBERS4_PAN5M_L2 |
China-Brazil Earth Resources Satellite, CBERS-4 PAN5M camera Level-2 product. System corrected images, expect some translation error. |
PAN5M |
CBERS |
CBERS-4 |
L2 |
PAN5M,CBERS,CBERS-4,L2 |
OPTICAL |
proprietary |
CBERS-4 PAN5M Level-2 |
2014-12-07T00:00:00Z |
CBERS4_PAN5M_L2 |
available |
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CBERS4_PAN5M_L4 |
China-Brazil Earth Resources Satellite, CBERS-4 PAN5M camera Level-4 product. Orthorectified with ground control points. |
PAN5M |
CBERS |
CBERS-4 |
L4 |
PAN5M,CBERS,CBERS-4,L4 |
OPTICAL |
proprietary |
CBERS-4 PAN5M Level-4 |
2014-12-07T00:00:00Z |
CBERS4_PAN5M_L4 |
available |
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CLMS_CORINE |
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. |
Sentinel-2, LANDSAT, SPOT-4/5, IRS P6 LISS III |
S2, L5, L7, L8, SPOT4, SPOT5 |
Land-cover,LCL,CORINE,CLMS |
proprietary |
CORINE Land Cover |
1986-01-01T00:00:00Z |
CLMS_CORINE |
available |
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CLMS_GLO_DMP_333M |
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. |
OLCI,PROBA-V |
Sentinel-3 |
Land,Dry-matter-productivity,DMP,OLCI,PROBA-V,Sentinel-3 |
proprietary |
10-daily Dry Matter Productivity 333M |
2014-01-10T00:00:00Z |
CLMS_GLO_DMP_333M |
available |
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CLMS_GLO_FAPAR_333M |
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. |
OLCI,PROBA-V |
Sentinel-3 |
Land,Fraction-of-absorbed-PAR,FAPAR,OLCI,PROBA-V,Sentinel-3 |
proprietary |
Global 10-daily Fraction of Absorbed PAR 333m |
2014-01-10T00:00:00Z |
CLMS_GLO_FAPAR_333M |
available |
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CLMS_GLO_FCOVER_333M |
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. |
OLCI,PROBA-V |
Sentinel-3 |
Land,Fraction-of-vegetation-cover,OLCI,PROBA-V,Sentinel-3 |
proprietary |
Global 10-daily Fraction of Vegetation Cover 333m |
2014-01-10T00:00:00Z |
CLMS_GLO_FCOVER_333M |
available |
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CLMS_GLO_GDMP_333M |
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. |
OLCI,PROBA-V |
Sentinel-3 |
Land,Gross-dry-matter-productivity,GDMP,GPP,OLCI,PROBA-V,Sentinel-3 |
proprietary |
10-daily Gross Dry Matter Productivity 333M |
2014-01-10T00:00:00Z |
CLMS_GLO_GDMP_333M |
available |
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CLMS_GLO_LAI_333M |
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. |
OLCI,PROBA-V |
Sentinel-3 |
Land,Leaf-area-index,LAI,OLCI,PROBA-V,Sentinel-3 |
proprietary |
Global 10-daily Leaf Area Index 333m |
2014-01-10T00:00:00Z |
CLMS_GLO_LAI_333M |
available |
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CLMS_GLO_NDVI_1KM_LTS |
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. |
VEGETATION,PROBA-V |
SPOT |
Land,NDVI,LTS,SPOT,VEGETATION,PROBA-V |
proprietary |
Normalized Difference Vegetation Index: global Long Term Statistics (raster 1km) - version 2, Apr 2019 |
1999-01-01T00:00:00Z |
CLMS_GLO_NDVI_1KM_LTS |
available |
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CLMS_GLO_NDVI_333M |
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. |
PROBA-V |
Land,NDVI,PROBA-V |
proprietary |
Global 10-daily Normalized Difference Vegetation Index 333M |
2014-01-01T00:00:00Z |
CLMS_GLO_NDVI_333M |
available |
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COP_DEM_GLO30_DGED |
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. |
TerraSAR |
TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,GDGED |
ALTIMETRIC |
proprietary |
Copernicus DEM GLO-30 DGED |
2010-06-21T00:00:00Z |
COP_DEM_GLO30_DGED |
available |
available |
available |
available |
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COP_DEM_GLO30_DTED |
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. |
TerraSAR |
TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,DTED |
ALTIMETRIC |
proprietary |
Copernicus DEM GLO-30 DTED |
2010-06-21T00:00:00Z |
COP_DEM_GLO30_DTED |
available |
available |
available |
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COP_DEM_GLO90_DGED |
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. |
TerraSAR |
TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,GDGED |
ALTIMETRIC |
proprietary |
Copernicus DEM GLO-90 DGED |
2010-06-21T00:00:00Z |
COP_DEM_GLO90_DGED |
available |
available |
available |
available |
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COP_DEM_GLO90_DTED |
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. |
TerraSAR |
TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,DTED |
ALTIMETRIC |
proprietary |
Copernicus DEM GLO-90 DTED |
2010-06-21T00:00:00Z |
COP_DEM_GLO90_DTED |
available |
available |
available |
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EEA_DAILY_VI |
Vegetation Indices (VI) comprises four daily vegetation indices (PPI, NDVI, LAI and FAPAR) and quality information, that are part of the Copernicus Land Monitoring Service (CLMS) HR-VPP product suite. The 10m resolution, daily updated Plant Phenology Index (PPI), Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are derived from Copernicus Sentinel-2 satellite observations. They are provided together with a related quality indicator (QFLAG2) that flags clouds, shadows, snow, open water and other areas where the VI retrieval is less reliable. These Vegetation Indices are made available as a set of raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from 2017 until today, with daily updates. The Vegetation Indices are part of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). |
Sentinel-2 |
S2A, S2B |
Land,Plant-phenology-index,Phenology,Vegetation,Sentinel-2,S2A,S2B |
RADAR |
proprietary |
Vegetation Indices, daily, UTM projection |
EEA_DAILY_VI |
available |
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EFAS_FORECAST |
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:nnRiver dischargenSoil moisture for three soil layersnSnow water equivalentnnIt 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).nnVariables 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 moisturennVariables in the dataset/application are:nOrography, Upstream area |
CEMS |
CEMS |
ECMWF,CEMS,EFAS,forecast,river,discharge |
ATMOSPHERIC |
proprietary |
River discharge and related forecasted data by the European Flood Awareness System |
2018-10-11T00:00:00Z |
EFAS_FORECAST |
available |
available |
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EFAS_HISTORICAL |
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:nnRiver dischargenSoil moisture for three soil layersnSnow water equivalentnnIt 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 versionnuses 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).nnVariables 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 moisturennVariables in the dataset/application are:nOrography, Upstream area |
CEMS |
CEMS |
ECMWF,CEMS,EFAS,historical,river,discharge |
ATMOSPHERIC |
proprietary |
River discharge and related historical data from the European Flood Awareness System |
1992-01-02T00:00:00Z |
EFAS_HISTORICAL |
available |
available |
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EFAS_REFORECAST |
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:nnRiver dischargenSoil moisture for three soil layersnSnow water equivalentnnIt 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).nnVariables in the dataset/application are:nRiver discharge, Snow depth water equivalent, Soil depth, Volumetric soil moisturennVariables in the dataset/application are:nOrography, Upstream area |
CEMS |
CEMS |
ECMWF,CEMS,EFAS,reforecast,river,discharge |
ATMOSPHERIC |
proprietary |
Reforecasts of river discharge and related data by the European Flood Awareness System |
2003-03-27T00:00:00Z |
EFAS_REFORECAST |
available |
available |
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EFAS_SEASONAL |
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:nnRiver dischargenSoil moisture for three soil layersnSnow water equivalentnnIt 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).nnVariables in the dataset/application are:nRiver discharge in the last 24 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisturennVariables in the dataset/application are:nOrography, Upstream area |
CEMS |
CEMS |
ECMWF,CEMS,EFAS,seasonal,forecast,river,discharge |
ATMOSPHERIC |
proprietary |
Seasonal forecasts of river discharge and related data by the European Flood Awareness System |
2020-11-01T00:00:00Z |
EFAS_SEASONAL |
available |
available |
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EFAS_SEASONAL_REFORECAST |
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:nnRiver dischargenSoil moisture for three soil layersnSnow water equivalentnnIt 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).nnVariables in the dataset/application are:nRiver discharge in the last 24 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisturennVariables in the dataset/application are:nOrography, Upstream area” |
CEMS |
CEMS |
ECMWF,CEMS,EFAS,seasonal,reforecast,river,discharge |
ATMOSPHERIC |
proprietary |
Seasonal reforecasts of river discharge and related data by the European Flood Awareness System |
1991-01-01T00:00:00Z |
EFAS_SEASONAL_REFORECAST |
available |
available |
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ERA5_LAND |
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 |
ERA5 |
ERA5 |
ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,hourly,evolution |
ATMOSPHERIC |
proprietary |
ERA5-Land hourly data from 1950 to present |
1950-01-01T00:00:00Z |
ERA5_LAND |
available |
available |
||||||||||||||||||||||
ERA5_LAND_MONTHLY |
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 |
ERA5 |
ERA5 |
ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,monthly,evolution |
ATMOSPHERIC |
proprietary |
ERA5-Land monthly averaged data from 1950 to present |
1950-01-01T00:00:00Z |
ERA5_LAND_MONTHLY |
available |
available |
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ERA5_PL |
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) |
ERA5 |
ERA5 |
ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,sea,hourly,pressure,levels |
ATMOSPHERIC |
proprietary |
ERA5 hourly data on pressure levels from 1940 to present |
1940-01-01T00:00:00Z |
ERA5_PL |
available |
available |
||||||||||||||||||||||
ERA5_PL_MONTHLY |
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). |
ERA5 |
ERA5 |
Climate,ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,sea,monthly,pressure,levels |
ATMOSPHERIC |
proprietary |
ERA5 monthly averaged data on pressure levels from 1940 to present |
1940-01-01T00:00:00Z |
ERA5_PL_MONTHLY |
available |
available |
||||||||||||||||||||||
ERA5_SL |
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). |
ERA5 |
ERA5 |
ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,sea,hourly,single,levels |
ATMOSPHERIC |
proprietary |
ERA5 hourly data on single levels from 1940 to present |
1940-01-01T00:00:00Z |
ERA5_SL |
available |
available |
||||||||||||||||||||||
ERA5_SL_MONTHLY |
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). |
ERA5 |
ERA5 |
Climate,ECMWF,Reanalysis,ERA5,CDS,Atmospheric,land,sea,monthly,single,levels |
ATMOSPHERIC |
proprietary |
ERA5 monthly averaged data on single levels from 1940 to present |
1940-01-01T00:00:00Z |
ERA5_SL_MONTHLY |
available |
available |
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FIRE_HISTORICAL |
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 |
CEMS |
CEMS |
ECMWF,EFFIS,fire,historical,ERA5,european,sustainability,CEMS,system |
ATMOSPHERIC |
proprietary |
Fire danger indices historical data from the Copernicus Emergency Management Service |
1940-01-03T00:00:00Z |
FIRE_HISTORICAL |
available |
available |
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GLACIERS_DIST_RANDOLPH |
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 fromnmaps, aerial photographs and satellite images mostly acquired in the period 2000-2010. |
INSITU |
ECMWF,WGMS,INSITU,CDS,C3S,glacier,randolph,distribution,inventory |
ATMOSPHERIC |
proprietary |
Glaciers distribution data from the Randolph Glacier Inventory for year 2000 |
2000-01-01T00:00:00Z |
GLACIERS_DIST_RANDOLPH |
available |
available |
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GLOFAS_FORECAST |
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.nnVariables in the dataset/application are:nRiver discharge in the last 24 hoursnnVariables in the dataset/application are:nUpstream area |
CEMS |
CEMS |
ECMWF,CEMS,GloFAS,forecast,river,discharge |
ATMOSPHERIC |
proprietary |
River discharge and related forecasted data by the Global Flood Awareness System |
2021-05-26T00:00:00Z |
GLOFAS_FORECAST |
available |
available |
||||||||||||||||||||||
GLOFAS_HISTORICAL |
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.nnVariables in the dataset/application are:nRiver discharge in the last 24 hoursnnVariables in the dataset/application are:nUpstream area |
CEMS |
CEMS |
ECMWF,CEMS,GloFAS,historical,river,discharge |
ATMOSPHERIC |
proprietary |
River discharge and related historical data from the Global Flood Awareness System |
1979-01-01T00:00:00Z |
GLOFAS_HISTORICAL |
available |
available |
||||||||||||||||||||||
GLOFAS_REFORECAST |
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).nnVariables in the dataset/application are:nRiver discharge in the last 24 hoursnnVariables in the dataset/application are:nUpstream area |
CEMS |
CEMS |
ECMWF,CEMS,GloFAS,reforecast,river,discharge |
ATMOSPHERIC |
proprietary |
Reforecasts of river discharge and related data by the Global Flood Awareness System |
2003-03-27T00:00:00Z |
GLOFAS_REFORECAST |
available |
available |
||||||||||||||||||||||
GLOFAS_SEASONAL |
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).nnVariables in the dataset/application are:nRiver discharge in the last 24 hoursnnVariables in the dataset/application are:nUpstream area |
CEMS |
CEMS |
ECMWF,CEMS,GloFAS,seasonal,forecast,river,discharge |
ATMOSPHERIC |
proprietary |
Seasonal forecasts of river discharge and related data by the Global Flood Awareness System |
2021-06-01T00:00:00Z |
GLOFAS_SEASONAL |
available |
available |
||||||||||||||||||||||
GLOFAS_SEASONAL_REFORECAST |
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).nnVariables in the dataset/application are:nRiver discharge in the last 24 hoursnnVariables in the dataset/application are:nUpstream area” |
CEMS |
CEMS |
ECMWF,CEMS,GloFAS,seasonal,forecast,river,discharge |
ATMOSPHERIC |
proprietary |
Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System |
1981-01-01T00:00:00Z |
GLOFAS_SEASONAL_REFORECAST |
available |
available |
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GRIDDED_GLACIERS_MASS_CHANGE |
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 |
ECMWF,WGMS,INSITU,CDS,C3S,glacier,randolph,mass,gridded |
ATMOSPHERIC |
proprietary |
Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database |
1975-01-01T00:00:00Z |
GRIDDED_GLACIERS_MASS_CHANGE |
available |
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L57_REFLECTANCE |
Landsat 5,7,8 L2A data (old format) distributed by Theia (2014 to 2017-03-20) using MUSCATE prototype, Lamber 93 projection. |
OLI,TIRS |
LANDSAT |
L5,L7,L8 |
L2A |
OLI,TIRS,LANDSAT,L5,L7,L8,L2,L2A,MUSCATE |
OPTICAL |
proprietary |
Landsat 5,7,8 Level-2A |
2014-01-01T00:00:00Z |
L57_REFLECTANCE |
available |
|||||||||||||||||||||
L8_OLI_TIRS_C1L1 |
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 |
OLI,TIRS |
LANDSAT8 |
L8 |
L1 |
OLI,TIRS,LANDSAT,LANDSAT8,L8,L1,C1,COLLECTION1 |
OPTICAL |
proprietary |
Landsat 8 Level-1 |
2013-02-11T00:00:00Z |
L8_OLI_TIRS_C1L1 |
available |
available |
available |
|||||||||||||||||||
L8_REFLECTANCE |
Landsat 8 L2A data distributed by Theia since 2017-03-20 using operational version of MUSCATE, UTM projection, and tiled using Sentinel-2 tiles. |
OLI,TIRS |
LANDSAT8 |
L8 |
L2A |
OLI,TIRS,LANDSAT,LANDSAT8,L8,L2,L2A,MUSCATE |
OPTICAL |
proprietary |
Landsat 8 Level-2A |
2013-02-11T00:00:00Z |
L8_REFLECTANCE |
available |
|||||||||||||||||||||
LANDSAT_C2L1 |
The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers. |
OLI,TIRS |
LANDSAT |
L1,L2,L3,L4,L5,L6,L7,L8 |
L1 |
OLI,TIRS,LANDSAT,L1,L2,L3,L4,L5,L6,L7,L8,C2,COLLECTION2 |
OPTICAL |
proprietary |
Landsat Collection 2 Level-1 Product |
1972-07-25T00:00:00Z |
LANDSAT_C2L1 |
available |
available |
available |
available |
||||||||||||||||||
LANDSAT_C2L2 |
Collection 2 Landsat OLI/TIRS Level-2 Science Products (L2SP) include Surface Reflectance and Surface Temperature scene-based products. |
OLI,TIRS |
LANDSAT |
L8,L9 |
L1 |
OLI,TIRS,LANDSAT,L8,L9,L2,C2,COLLECTION2 |
OPTICAL |
proprietary |
Landsat OLI and TIRS Collection 2 Level-2 Science Products 30-meter multispectral data. |
2013-02-11T00:00:00Z |
LANDSAT_C2L2 |
available |
available |
available |
|||||||||||||||||||
LANDSAT_C2L2ALB_BT |
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. |
OLI,TIRS |
LANDSAT |
L4,L5,L7,L8 |
L2 |
OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,BT,Brightness,Temperature,C2,COLLECTION2 |
OPTICAL |
proprietary |
Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product |
1982-08-22T00:00:00Z |
LANDSAT_C2L2ALB_BT |
available |
|||||||||||||||||||||
LANDSAT_C2L2ALB_SR |
The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth’s surface to the Landsat sensor. |
OLI,TIRS |
LANDSAT |
L4,L5,L7,L8 |
L2 |
OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,L2ALB,SR,Surface,Reflectance,C2,COLLECTION2 |
OPTICAL |
proprietary |
Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product |
1982-08-22T00:00:00Z |
LANDSAT_C2L2ALB_SR |
available |
|||||||||||||||||||||
LANDSAT_C2L2ALB_ST |
The Landsat Surface Temperature (ST) product represents the temperature of the Earth’s surface in Kelvin (K). |
OLI,TIRS |
LANDSAT |
L4,L5,L7,L8 |
L2 |
OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,L2ALB,Surface,Temperature,ST,C2,COLLECTION2 |
OPTICAL |
proprietary |
Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product |
1982-08-22T00:00:00Z |
LANDSAT_C2L2ALB_ST |
available |
|||||||||||||||||||||
LANDSAT_C2L2ALB_TA |
The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product. |
OLI,TIRS |
LANDSAT |
L4,L5,L7,L8 |
L2 |
OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,L2ALB,TA,Top,Atmosphere,Reflectance,C2,COLLECTION2 |
OPTICAL |
proprietary |
Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product |
1982-08-22T00:00:00Z |
LANDSAT_C2L2ALB_TA |
available |
|||||||||||||||||||||
LANDSAT_C2L2_SR |
The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth’s surface to the Landsat sensor. |
OLI,TIRS |
LANDSAT |
L4,L5,L7,L8 |
L2 |
OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,SR,surface,reflectance,C2,COLLECTION2 |
OPTICAL |
proprietary |
Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product |
1982-08-22T00:00:00Z |
LANDSAT_C2L2_SR |
available |
|||||||||||||||||||||
LANDSAT_C2L2_ST |
The Landsat Surface Temperature (ST) product represents the temperature of the Earth’s surface in Kelvin (K). |
OLI,TIRS |
LANDSAT |
L4,L5,L7,L8 |
L2 |
OLI,TIRS,LANDSAT,L4,L5,L7,L8,L2,ST,surface,temperature,C2,COLLECTION2 |
OPTICAL |
proprietary |
Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product |
1982-08-22T00:00:00Z |
LANDSAT_C2L2_ST |
available |
|||||||||||||||||||||
MODIS_MCD43A4 |
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. |
MODIS |
Terra+Aqua |
EOS AM-1+PM-1 |
L3 |
MODIS,Terra,Aqua,EOS,AM-1+PM-1,L3,MCD43A4 |
OPTICAL |
proprietary |
MODIS MCD43A4 |
2000-03-05T00:00:00Z |
MODIS_MCD43A4 |
available |
available |
available |
|||||||||||||||||||
NAIP |
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. |
film and digital cameras |
National Agriculture Imagery Program |
NAIP |
N/A |
film,digital,cameras,Agriculture,NAIP |
OPTICAL |
proprietary |
National Agriculture Imagery Program |
2003-01-01T00:00:00Z |
NAIP |
available |
available |
available |
available |
||||||||||||||||||
NEMSAUTO_TCDC |
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. |
NEMSAUTO |
NEMSAUTO |
meteoblue,NEMS,NEMSAUTO,CLOUD,COVER,TOTAL,TCDC,DAILY,MEAN |
ATMOSPHERIC |
proprietary |
NEMSAUTO Total Cloud Cover daily mean |
1984-01-01T00:00:00Z |
NEMSAUTO_TCDC |
available |
|||||||||||||||||||||||
NEMSGLOBAL_TCDC |
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. |
NEMSGLOBAL |
NEMSGLOBAL |
meteoblue,NEMS,NEMSGLOBAL,CLOUD,COVER,TOTAL,TCDC,DAILY,MEAN |
ATMOSPHERIC |
proprietary |
NEMSGLOBAL Total Cloud Cover daily mean |
1984-01-01T00:00:00Z |
NEMSGLOBAL_TCDC |
available |
|||||||||||||||||||||||
OSO |
An overview of OSO Land Cover data is given on https://www.theia-land.fr/en/ceslist/land-cover-sec/ and the specific description of OSO products is available on https://www.theia-land.fr/product/carte-doccupation-des-sols-de-la-france-metropolitaine/ |
L3B |
L3B,OSO,land,cover |
proprietary |
OSO Land Cover |
2016-01-01T00:00:00Z |
OSO |
available |
|||||||||||||||||||||||||
PLD_BUNDLE |
Pleiades Bundle (Pan, XS) |
PHR |
PLEIADES |
P1A,P1B |
PRIMARY |
PHR,PLEIADES,P1A,P1B,PRIMARY,PLD,BUNDLE,Pan,Xs |
OPTICAL |
proprietary |
Pleiades Bundle |
2011-12-17T00:00:00Z |
PLD_BUNDLE |
available |
|||||||||||||||||||||
PLD_PAN |
Pleiades Panchromatic (Pan) |
PHR |
PLEIADES |
P1A,P1B |
PRIMARY |
PHR,PLEIADES,P1A,P1B,PRIMARY,PLD,PAN,Panchromatic |
OPTICAL |
proprietary |
Pleiades Panchromatic |
2011-12-17T00:00:00Z |
PLD_PAN |
available |
|||||||||||||||||||||
PLD_PANSHARPENED |
Pleiades Pansharpened (Pan+XS) |
PHR |
PLEIADES |
P1A,P1B |
PRIMARY |
PHR,PLEIADES,P1A,P1B,PRIMARY,PLD,PANSHARPENED,Pan,Xs |
OPTICAL |
proprietary |
Pleiades Pansharpened |
2011-12-17T00:00:00Z |
PLD_PANSHARPENED |
available |
|||||||||||||||||||||
PLD_XS |
Pleiades Multispectral (XS) |
PHR |
PLEIADES |
P1A,P1B |
PRIMARY |
PHR,PLEIADES,P1A,P1B,PRIMARY,PLD,XS,Multispectral |
OPTICAL |
proprietary |
Pleiades Multispectral |
2011-12-17T00:00:00Z |
PLD_XS |
available |
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S1_SAR_GRD |
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 |
SAR |
SENTINEL1 |
S1A,S1B |
L1 |
SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L1,GRD,SAFE |
RADAR |
proprietary |
SENTINEL1 Level-1 Ground Range Detected |
2014-04-03T00:00:00Z |
S1_SAR_GRD |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
|||||||||||
S1_SAR_OCN |
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 |
SAR |
SENTINEL1 |
S1A,S1B |
L2 |
SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L2,OCN,SAFE |
RADAR |
proprietary |
SENTINEL1 Level-2 OCN |
2014-04-03T00:00:00Z |
S1_SAR_OCN |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||
S1_SAR_RAW |
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 |
SAR |
SENTINEL1 |
S1A,S1B |
L0 |
SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L0,RAW,SAFE |
RADAR |
proprietary |
SENTINEL1 SAR Level-0 |
2014-04-03T00:00:00Z |
S1_SAR_RAW |
available |
available |
available |
available |
available |
|||||||||||||||||
S1_SAR_SLC |
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 |
SAR |
SENTINEL1 |
S1A,S1B |
L1 |
SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L1,SLC,SAFE |
RADAR |
proprietary |
SENTINEL1 Level-1 Single Look Complex |
2014-04-03T00:00:00Z |
S1_SAR_SLC |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||||
S2_MSI_L1C |
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 |
MSI |
SENTINEL2 |
S2A,S2B |
L1 |
MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L1,L1C,SAFE |
OPTICAL |
proprietary |
SENTINEL2 Level-1C |
2015-06-23T00:00:00Z |
S2_MSI_L1C |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
available |
||||||||||
S2_MSI_L2A |
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 |
MSI |
SENTINEL2 |
S2A,S2B |
L2 |
MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,SAFE |
OPTICAL |
proprietary |
SENTINEL2 Level-2A |
2018-03-26T00:00:00Z |
S2_MSI_L2A |
available |
available |
available |
available |
available |
available |
available |
available |
available |
|||||||||||||
S2_MSI_L2AP |
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. Level-2AP are the pilot products of Level-2A product generated by ESA until March 2018. After March, they are operational products |
MSI |
SENTINEL2 |
S2A,S2B |
L2 |
MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,SAFE, pilot |
OPTICAL |
proprietary |
SENTINEL2 Level-2A pilot |
2017-05-23T00:00:00Z |
S2_MSI_L2AP |
available |
|||||||||||||||||||||
S2_MSI_L2A_COG |
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. |
MSI |
SENTINEL2 |
S2A,S2B |
L2 |
MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,COG |
OPTICAL |
proprietary |
SENTINEL2 Level-2A |
2015-06-23T00:00:00Z |
S2_MSI_L2A_COG |
available |
|||||||||||||||||||||
S2_MSI_L2A_MAJA |
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 |
MSI |
SENTINEL2 |
S2A,S2B |
L2 |
MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,MAJA |
OPTICAL |
proprietary |
SENTINEL2 Level-2A |
2015-06-23T00:00:00Z |
S2_MSI_L2A_MAJA |
available |
|||||||||||||||||||||
S2_MSI_L2B_MAJA_SNOW |
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 |
MSI |
SENTINEL2 |
S2A,S2B |
L2 |
MSI,MAJA,SENTINEL,sentinel2,S2,S2A,S2B,L2,L2B,SNOW |
OPTICAL |
proprietary |
SENTINEL2 snow product |
2015-06-23T00:00:00Z |
S2_MSI_L2B_MAJA_SNOW |
available |
|||||||||||||||||||||
S2_MSI_L2B_MAJA_WATER |
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 |
MSI |
SENTINEL2 |
S2A,S2B |
L2 |
MSI,MAJA,SENTINEL,sentinel2,S2,S2A,S2B,L2,L2B,WATER |
OPTICAL |
proprietary |
SENTINEL2 L2B-WATER |
2015-06-23T00:00:00Z |
S2_MSI_L2B_MAJA_WATER |
available |
|||||||||||||||||||||
S2_MSI_L3A_WASP |
The Level-3A product provides a monthly synthesis of surface reflectances from Theia’s L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor), by MUSCATE data center at CNES, in the framework of THEIA data center. The full description of the product format is available at https://theia.cnes.fr/atdistrib/documents/THEIA-ST-411-0419-CNES_01-04_Format_Specification_of_MUSCATE_Level-3A_Products-signed.pdf |
MSI |
SENTINEL2 |
S2A,S2B |
L3 |
MSI,SENTINEL,sentinel2,S2,S2A,S2B,L3,L3A,WASP |
OPTICAL |
proprietary |
SENTINEL2 Level-3A |
2015-06-23T00:00:00Z |
S2_MSI_L3A_WASP |
available |
|||||||||||||||||||||
S3_EFR |
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. |
OLCI |
SENTINEL3 |
S3A,S3B |
L1 |
OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,EFR |
OPTICAL |
proprietary |
SENTINEL3 EFR |
2016-02-16T00:00:00Z |
S3_EFR |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_ERR |
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. |
OLCI |
SENTINEL3 |
S3A,S3B |
L1 |
OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,ERR |
OPTICAL |
proprietary |
SENTINEL3 ERR |
2016-02-16T00:00:00Z |
S3_ERR |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_LAN |
LAN or SR_2_LAN___ (peps) |
SRAL |
SENTINEL3 |
S3A,S3B |
L2 |
SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,LAN |
RADAR |
proprietary |
SENTINEL3 SRAL Level-2 LAN |
2016-02-16T00:00:00Z |
S3_LAN |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_LAN_HY |
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. |
SRAL |
SENTINEL3 |
S3A,S3B |
L2 |
SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,LAN,HYDROLOGY |
RADAR |
proprietary |
SENTINEL3 SRAL Level-2 LAN HYDRO |
2016-02-16T00:00:00Z |
S3_LAN_HY |
available |
|||||||||||||||||||||
S3_LAN_LI |
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. |
SRAL |
SENTINEL3 |
S3A,S3B |
L2 |
SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,LAN,LAND,ICE |
RADAR |
proprietary |
SENTINEL3 SRAL Level-2 LAN LAND ICE |
2016-02-16T00:00:00Z |
S3_LAN_LI |
available |
|||||||||||||||||||||
S3_LAN_SI |
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. |
SRAL |
SENTINEL3 |
S3A,S3B |
L2 |
SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,LAN,SEA,ICE |
RADAR |
proprietary |
SENTINEL3 SRAL Level-2 LAN SEA ICE |
2016-02-16T00:00:00Z |
S3_LAN_SI |
available |
|||||||||||||||||||||
S3_OLCI_L2LFR |
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 |
OLCI |
SENTINEL3 |
S3A,S3B |
L2 |
OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2LFR,LFR |
OPTICAL |
proprietary |
SENTINEL3 OLCI Level-2 Land Full Resolution |
2016-02-16T00:00:00Z |
S3_OLCI_L2LFR |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_OLCI_L2LRR |
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 |
OLCI |
SENTINEL3 |
S3A,S3B |
L2 |
OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2LRR,LRR |
OPTICAL |
proprietary |
SENTINEL3 OLCI Level-2 Land Reduced Resolution |
2016-02-16T00:00:00Z |
S3_OLCI_L2LRR |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_OLCI_L2WFR |
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 |
OLCI |
SENTINEL3 |
S3A,S3B |
L2 |
OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WFR,WFR |
OPTICAL |
proprietary |
SENTINEL3 OLCI Level-2 Water Full Resolution |
2016-02-16T00:00:00Z |
S3_OLCI_L2WFR |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_OLCI_L2WRR |
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 |
OLCI |
SENTINEL3 |
S3A,S3B |
L2 |
OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WRR,WRR |
OPTICAL |
proprietary |
SENTINEL3 OLCI Level-2 Water Reduced Resolution |
2016-02-16T00:00:00Z |
S3_OLCI_L2WRR |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_RAC |
Sentinel 3 OLCI products output during Radiometric Calibration mode |
OLCI |
SENTINEL3 |
S3A,S3B |
L1 |
OLCI,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,L2,RAC |
OPTICAL |
proprietary |
SENTINEL3 RAC |
2016-02-16T00:00:00Z |
S3_RAC |
available |
|||||||||||||||||||||
S3_SLSTR_L1RBT |
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 |
SLSTR |
SENTINEL3 |
S3A,S3B |
L1 |
SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L1,L1RBT,RBT |
ATMOSPHERIC |
proprietary |
SENTINEL3 SLSTR Level-1 |
2016-02-16T00:00:00Z |
S3_SLSTR_L1RBT |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_SLSTR_L2 |
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. |
SLSTR |
SENTINEL3 |
S3A,S3B |
L2 |
SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2FRP,FRP,L2WCT,WCT,L2WST,WST,L2AOD,AOD |
ATMOSPHERIC |
proprietary |
SENTINEL3 SLSTR Level-2 |
2017-07-05T00:00:00Z |
S3_SLSTR_L2 |
available |
|||||||||||||||||||||
S3_SLSTR_L2AOD |
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. |
SLSTR |
SENTINEL3 |
S3A,S3B |
L2 |
SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2AOD,AOD |
ATMOSPHERIC |
proprietary |
SENTINEL3 SLSTR Level-2 AOD |
2016-02-16T00:00:00Z |
S3_SLSTR_L2AOD |
available |
available |
available |
available |
||||||||||||||||||
S3_SLSTR_L2FRP |
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. |
SLSTR |
SENTINEL3 |
S3A,S3B |
L2 |
SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2FRP,FRP |
ATMOSPHERIC |
proprietary |
SENTINEL3 SLSTR Level-2 FRP |
2016-02-16T00:00:00Z |
S3_SLSTR_L2FRP |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_SLSTR_L2LST |
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) |
SLSTR |
SENTINEL3 |
S3A,S3B |
L2 |
SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2LST,LST |
ATMOSPHERIC |
proprietary |
SENTINEL3 SLSTR Level-2 LST |
2016-02-16T00:00:00Z |
S3_SLSTR_L2LST |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_SLSTR_L2WST |
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) |
SLSTR |
SENTINEL3 |
S3A,S3B |
L2 |
SLSTR,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,L2WST,WST |
ATMOSPHERIC |
proprietary |
SENTINEL3 SLSTR Level-2 WST |
2016-02-16T00:00:00Z |
S3_SLSTR_L2WST |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_SRA |
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. |
SRAL |
SENTINEL3 |
S3A,S3B |
L1 |
SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1 |
RADAR |
proprietary |
SENTINEL3 SRAL Level-1 |
2016-02-16T00:00:00Z |
S3_SRA |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_SRA_A |
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) |
SRAL |
SENTINEL3 |
S3A,S3B |
L1 |
SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1 |
RADAR |
proprietary |
SENTINEL3 SRAL Level-1 SRA_A |
2016-02-16T00:00:00Z |
S3_SRA_A |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_SRA_BS |
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 |
SRAL |
SENTINEL3 |
S3A,S3B |
L1 |
SRA,SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L1 |
RADAR |
proprietary |
SENTINEL3 SRAL Level-1 SRA_BS |
2016-02-16T00:00:00Z |
S3_SRA_BS |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S3_SY_AOD |
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 |
SYNERGY |
SENTINEL3 |
S3A,S3B |
L2 |
SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,AOD |
OPTICAL,RADAR |
proprietary |
SENTINEL3 SYNERGY Level-2 AOD |
2016-02-16T00:00:00Z |
S3_SY_AOD |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_SY_SYN |
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. |
SYNERGY |
SENTINEL3 |
S3A,S3B |
L2 |
SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,SYN |
OPTICAL,RADAR |
proprietary |
SENTINEL3 SYNERGY Level-2 SYN |
2016-02-16T00:00:00Z |
S3_SY_SYN |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_SY_V10 |
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. |
SYNERGY |
SENTINEL3 |
S3A,S3B |
LEVEL-2W |
SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,V10 |
OPTICAL,RADAR |
proprietary |
SENTINEL3 SYNERGY Level-2 V10 |
2016-02-16T00:00:00Z |
S3_SY_V10 |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_SY_VG1 |
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. |
SYNERGY |
SENTINEL3 |
S3A,S3B |
LEVEL-2 |
SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,VG1 |
OPTICAL,RADAR |
proprietary |
SENTINEL3 SYNERGY Level-2 VG1 |
2016-02-16T00:00:00Z |
S3_SY_VG1 |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_SY_VGP |
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. |
SYNERGY |
SENTINEL3 |
S3A,S3B |
LEVEL-2 |
SYNERGY,SY,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,VGP |
OPTICAL,RADAR |
proprietary |
SENTINEL3 SYNERGY Level-2 VGP |
2016-02-16T00:00:00Z |
S3_SY_VGP |
available |
available |
available |
available |
available |
|||||||||||||||||
S3_WAT |
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. |
SRAL |
SENTINEL3 |
S3A,S3B |
L2 |
SRAL,SENTINEL,SENTINEL3,S3,S3A,S3B,L2,WAT |
RADAR |
proprietary |
SENTINEL3 SRAL Level-2 WAT |
2016-02-16T00:00:00Z |
S3_WAT |
available |
available |
available |
available |
available |
available |
||||||||||||||||
S5P_L1B_IR_ALL |
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). |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,IR,SIR,SWIR,Irradiances,UVN |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Irradiances for the SWIR and UNV bands |
2017-10-13T00:00:00Z |
S5P_L1B_IR_ALL |
available |
|||||||||||||||||||||
S5P_L1B_IR_SIR |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,IR,SIR,SWIR,Irradiances |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Irradiances for the SWIR bands |
2017-10-13T00:00:00Z |
S5P_L1B_IR_SIR |
available |
available |
available |
|||||||||||||||||||
S5P_L1B_IR_UVN |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,IR,UVN,Irradiances |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Irradiances for the UVN bands |
2017-10-13T00:00:00Z |
S5P_L1B_IR_UVN |
available |
available |
available |
|||||||||||||||||||
S5P_L1B_RA_BD1 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD1,BAND1,B01 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Radiances for spectral band 1 |
2017-10-13T00:00:00Z |
S5P_L1B_RA_BD1 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L1B_RA_BD2 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD2,BAND2,B02 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Radiances for spectral band 2 |
2017-10-13T00:00:00Z |
S5P_L1B_RA_BD2 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L1B_RA_BD3 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD3,BAND3,B03 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Radiances for spectral band 3 |
2017-10-13T00:00:00Z |
S5P_L1B_RA_BD3 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L1B_RA_BD4 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD4,BAND4,B04 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Radiances for spectral band 4 |
2017-10-13T00:00:00Z |
S5P_L1B_RA_BD4 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L1B_RA_BD5 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD5,BAND5,B05 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Radiances for spectral band 5 |
2017-10-13T00:00:00Z |
S5P_L1B_RA_BD5 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L1B_RA_BD6 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD6,BAND6,B06 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Radiances for spectral band 6 |
2017-10-13T00:00:00Z |
S5P_L1B_RA_BD6 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L1B_RA_BD7 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD7,BAND7,B07 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Radiances for spectral band 7 |
2017-10-13T00:00:00Z |
S5P_L1B_RA_BD7 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L1B_RA_BD8 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L1B |
SENTINEL,SENTINEL5P,S5P,L1,L1B,TROPOMI,RA,Radiances,BD8,BAND8,B08 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 1B Radiances for spectral band 8 |
2017-10-13T00:00:00Z |
S5P_L1B_RA_BD8 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_AER_AI |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,AER,AI,Ultraviolet,Aerosol,Index |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Ultraviolet Aerosol Index |
2017-10-13T00:00:00Z |
S5P_L2_AER_AI |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_AER_LH |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,AER,LH,Aerosol,Layer,Height |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Aerosol Layer Height |
2017-10-13T00:00:00Z |
S5P_L2_AER_LH |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_CH4 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,CH4,Methane |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Methane |
2017-10-13T00:00:00Z |
S5P_L2_CH4 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_CLOUD |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,CLOUD |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Cloud |
2017-10-13T00:00:00Z |
S5P_L2_CLOUD |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_CO |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,CO,Carbon,Monoxide |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Carbon Monoxide |
2017-10-13T00:00:00Z |
S5P_L2_CO |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_HCHO |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,HCHO,Formaldehyde |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Formaldehyde |
2017-10-13T00:00:00Z |
S5P_L2_HCHO |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_IR_ALL |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Data |
2018-04-01T00:00:00Z |
S5P_L2_IR_ALL |
available |
|||||||||||||||||||||
S5P_L2_NO2 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,NO2,Nitrogen,Dioxide |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Nitrogen Dioxide |
2017-10-13T00:00:00Z |
S5P_L2_NO2 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_NP_BD3 |
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). |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,NP,NPP,Cloud,BD3,B03,BAND3 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 NPP Cloud for band 3 |
2017-10-13T00:00:00Z |
S5P_L2_NP_BD3 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_NP_BD6 |
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). |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,NP,NPP,Cloud,BD6,B06,BAND6 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 NPP Cloud for band 6 |
2017-10-13T00:00:00Z |
S5P_L2_NP_BD6 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_NP_BD7 |
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). |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,NP,NPP,Cloud,BD7,B07,BAND7 |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 NPP Cloud for band 7 |
2017-10-13T00:00:00Z |
S5P_L2_NP_BD7 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_O3 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,O3,Ozone |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Ozone |
2017-10-13T00:00:00Z |
S5P_L2_O3 |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_O3_PR |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,O3,PR,Ozone,Profile |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Ozone Profile |
2017-10-13T00:00:00Z |
S5P_L2_O3_PR |
available |
available |
available |
available |
||||||||||||||||||
S5P_L2_O3_TCL |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,O3,TCL,Tropospheric,Ozone |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Tropospheric Ozone |
2017-10-13T00:00:00Z |
S5P_L2_O3_TCL |
available |
available |
available |
|||||||||||||||||||
S5P_L2_SO2 |
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. |
TROPOMI |
SENTINEL5P |
S5P |
L2 |
SENTINEL,SENTINEL5P,S5P,L2,TROPOMI,SO2,Sulphur,Dioxide |
ATMOSPHERIC |
proprietary |
Sentinel-5 Precursor Level 2 Sulphur Dioxide |
2017-10-13T00:00:00Z |
S5P_L2_SO2 |
available |
available |
available |
available |
||||||||||||||||||
SATELLITE_CARBON_DIOXIDE |
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).nnVariables in the dataset/application are:nColumn-average dry-air mole fraction of atmospheric carbon dioxide (XCO2), Mid-tropospheric columns of atmospheric carbon dioxide (CO2) |
ECMWF,CDS,C3S,carbon-dioxide |
ATMOSPHERIC |
proprietary |
Carbon dioxide data from 2002 to present derived from satellite observations |
2002-10-01T00:00:00Z |
SATELLITE_CARBON_DIOXIDE |
available |
available |
||||||||||||||||||||||||
SATELLITE_METHANE |
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).nnVariables in the dataset/application are:nColumn-average dry-air mole fraction of atmospheric methane (XCH4), Mid-tropospheric columns of atmospheric methane (CH4) |
ECMWF,CDS,C3S,methane |
ATMOSPHERIC |
proprietary |
Methane data from 2002 to present derived from satellite observations |
2002-10-01T00:00:00Z |
SATELLITE_METHANE |
available |
available |
||||||||||||||||||||||||
SATELLITE_SEA_ICE_EDGE_TYPE |
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 platform: |
ECMWF,CDS,C3S,sea,ice |
ATMOSPHERIC |
proprietary |
Sea ice edge and type daily gridded data from 1978 to present derived from satellite observations |
1979-01-01T00:00:00Z |
SATELLITE_SEA_ICE_EDGE_TYPE |
available |
|||||||||||||||||||||||||
SATELLITE_SEA_LEVEL_BLACK_SEA |
Sea level anomaly is the height of water over the mean sea surface in a given time and region. Up-to-date altimeter standards are used to estimate the sea level anomalies with a mapping algorithm dedicated to the Black sea region. Anomalies are computed with respect to a twenty-year mean reference period (1993-2012). The steady number of reference satellite used in the production of this dataset contributes to the long-term stability of the sea level record. Improvements of the accuracy, sampling of meso-scale processes and of the high-latitude coverage were achieved by using a few additional satellite missions. New data are provided with a delay of about 4-5 months relatively to near-real time or interim sea level products. This delay is mainly due to the timeliness of the input data, the centred processing temporal window and the validation process. However, this processing and validation adds stability and accuracy to the sea level variables and make them adapted to climate applications. This dataset includes uncertainties for each grid cell. More details about the sea level retrieval, additional filters, optimisation procedures, and the error estimation are given in the Documentation section. 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 |
Climate,ECMWF,CDS,C3S,sea,level,Black Sea |
HYDROLOGICAL |
proprietary |
Sea level daily gridded data from satellite observations for the Black Sea from 1993 to 2020 |
1993-01-01T00:00:00Z |
SATELLITE_SEA_LEVEL_BLACK_SEA |
available |
available |
||||||||||||||||||||||||
SATELLITE_SEA_LEVEL_GLOBAL |
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 |
Climate,ECMWF,CDS,C3S,sea,level,global |
HYDROLOGICAL |
proprietary |
Sea level gridded data from satellite observations for the global ocean |
1993-01-01T00:00:00Z |
SATELLITE_SEA_LEVEL_GLOBAL |
available |
|||||||||||||||||||||||||
SATELLITE_SEA_LEVEL_MEDITERRANEAN |
Sea level anomaly is 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). Up-to-date altimeter standards are used to estimate the sea level anomalies with a mapping algorithm specifically dedicated to the Mediterranean Sea. The steady number of reference satellite used in the production of this dataset contributes to the long-term stability of the sea level record. Improvements of the accuracy, sampling of meso-scale processes and of the high-latitude coverage were achieved by using a few additional satellite missions. New data are provided with a delay of about 4-5 months relatively to near-real time or interim sea level products. This delay is mainly due to the timeliness of the input data, the centred processing temporal window and the validation process. However, this processing and validation adds stability and accuracy to the sea level variables and make them adapted to climate applications. This dataset includes uncertainties for each grid cell. More details about the sea level retrieval, additional filters, optimisation procedures, and the error estimation are given in the Documentation section. 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 |
Climate,ECMWF,CDS,C3S,sea,level,mediterranean |
HYDROLOGICAL |
proprietary |
Sea level daily gridded data from satellite observations for the Mediterranean Sea |
1993-01-01T00:00:00Z |
SATELLITE_SEA_LEVEL_MEDITERRANEAN |
available |
|||||||||||||||||||||||||
SEASONAL_MONTHLY_PL |
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.nnVariables in the dataset/application are:nGeopotential, Specific humidity, Temperature, U-component of wind, V-component of wind |
ECMWF,CDS,C3S,seasonal,forecast,monthly,pressure,levels |
ATMOSPHERIC |
proprietary |
Seasonal forecast monthly statistics on pressure levels |
1981-01-01T00:00:00Z |
SEASONAL_MONTHLY_PL |
available |
available |
||||||||||||||||||||||||
SEASONAL_MONTHLY_SL |
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.nnVariables 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 |
ECMWF,CDS,C3S,seasonal,forecast,monthly,single,levels |
ATMOSPHERIC |
proprietary |
Seasonal forecast monthly statistics on single levels |
1981-01-01T00:00:00Z |
SEASONAL_MONTHLY_SL |
available |
available |
||||||||||||||||||||||||
SEASONAL_ORIGINAL_PL |
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.nnVariables in the dataset/application are:nGeopotential, Specific humidity, Temperature, U-component of wind, V-component of wind |
ECMWF,CDS,C3S,seasonal,forecast,subdaily,pressure,levels |
ATMOSPHERIC |
proprietary |
Seasonal forecast subdaily data on pressure levels |
1981-01-01T00:00:00Z |
SEASONAL_ORIGINAL_PL |
available |
available |
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SEASONAL_ORIGINAL_SL |
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.nnVariables 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 |
ECMWF,CDS,C3S,seasonal,forecast,daily,single,levels |
ATMOSPHERIC |
proprietary |
Seasonal forecast daily and subdaily data on single levels |
1981-01-01T00:00:00Z |
SEASONAL_ORIGINAL_SL |
available |
available |
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SEASONAL_POSTPROCESSED_PL |
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.nnVariables in the dataset/application are:nGeopotential anomaly, Specific humidity anomaly, Temperature anomaly, U-component of wind anomaly, V-component of wind anomaly |
ECMWF,CDS,C3S,seasonal,forecast,anomalies,pressure,levels |
ATMOSPHERIC |
proprietary |
Seasonal forecast anomalies on pressure levels |
2017-09-01T00:00:00Z |
SEASONAL_POSTPROCESSED_PL |
available |
available |
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SEASONAL_POSTPROCESSED_SL |
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.nnVariables 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 |
ECMWF,CDS,C3S,seasonal,forecast,anomalies,single,levels |
ATMOSPHERIC |
proprietary |
Seasonal forecast anomalies on single levels |
2017-09-01T00:00:00Z |
SEASONAL_POSTPROCESSED_SL |
available |
available |
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SIS_HYDRO_MET_PROJ |
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. nnVariables in the dataset/application are:n2m air temperature, Highest 5-day precipitation amount, Longest dry spells, Number of dry spells, Precipitation |
ECMWF,CDS,C3S,hydrology,meterology,water,precipitation,temperature |
ATMOSPHERIC |
proprietary |
Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections |
1970-01-01T00:00:00Z |
SIS_HYDRO_MET_PROJ |
available |
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SPOT5_SPIRIT |
SPOT 5 stereoscopic survey of Polar Ice. |
SPOT5 |
SPOT5 |
L1A |
SPOT,SPOT5,L1A |
OPTICAL |
proprietary |
Spot 5 SPIRIT |
2002-05-04T00:00:00Z |
SPOT5_SPIRIT |
available |
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SPOT_SWH |
The Spot World Heritage (SWH) programme objective is the free availability for non-commercial use of orthorectified products derived from multispectral images of more than 5 years old from the Spot 1-5 satellites family. More informations on https://www.theia-land.fr/en/product/spot-world-heritage/ |
SPOT1-5 |
SPOT1-5 |
L1C |
SPOT,SPOT1,SPOT2,SPOT3,SPOT4,SPOT5,L1C |
OPTICAL |
proprietary |
Spot World Heritage |
1986-02-22T00:00:00Z |
SPOT_SWH |
available |
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SPOT_SWH_OLD |
Spot world heritage Old format. |
SPOT1-5 |
SPOT1-5 |
L1C |
SPOT,SPOT1,SPOT2,SPOT3,SPOT4,SPOT5,L1C |
OPTICAL |
proprietary |
Spot World Heritage |
1986-02-22T00:00:00Z |
SPOT_SWH_OLD |
available |
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TIGGE_CF_SFC |
TIGGE (THORPEX Interactive Grand Global Ensemble) Surface Control forecast from ECMWF |
TIGGE |
TIGGE |
THORPEX,TIGGE,CF,SFC,ECMWF |
ATMOSPHERIC |
proprietary |
TIGGE ECMWF Surface Control forecast |
2003-01-01T00:00:00Z |
TIGGE_CF_SFC |
available |
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UERRA_EUROPE_SL |
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 |
SURFEX |
SURFEX |
Climate,ECMWF,Reanalysis,Regional,Europe,UERRA,UERRA-HARMONIE,SURFEX,MESCAN-SURFEX,CDS,Atmospheric,single,levels |
ATMOSPHERIC |
proprietary |
UERRA regional reanalysis for Europe on single levels from 1961 to 2019 |
1961-01-01T00:00:00Z |
UERRA_EUROPE_SL |
available |
available |
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VENUS_L1C |
A light description of Venus L1 data is available at http://www.cesbio.ups-tlse.fr/multitemp/?page_id=12984 |
VENUS |
VENUS |
L1C |
VENUS,L1,L1C |
OPTICAL |
proprietary |
Venus Level1-C |
2017-08-02T00:00:00Z |
VENUS_L1C |
available |
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VENUS_L2A_MAJA |
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. |
VENUS |
VENUS |
L2A |
VENUS,L2,L2A |
OPTICAL |
proprietary |
Venus Level2-A |
2017-08-02T00:00:00Z |
VENUS_L2A_MAJA |
available |
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VENUS_L3A_MAJA |
VENUS |
VENUS |
L3A |
VENUS,L3,L3A |
OPTICAL |
proprietary |
Venus Level3-A |
2017-08-02T00:00:00Z |
VENUS_L3A_MAJA |
available |