Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services
See the referenced specification
DE/DLR
Climatology Meteorology Atmosphere
The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational BrO (Bromine monoxide) total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. For more details please refer to https://atmos.eoc.dlr.de/app/missions/gome2
METOP GOME-2 - Bromine Monoxide (BrO) - Global
Nutzungsbedingungen: Lizenz, / Terms of use: License,
Please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages for more details on bromine monoxide retrieval: https://atmos.eoc.dlr.de/app/docs/
EARTH SCIENCE>ATMOSPHERE>ATMOSPHERIC CHEMISTRY>HALOCARBONS AND HALOGENS>BROMINE MONOXIDE
METOP-A
METOP-B
METOP-C
Metop-A
Metop-B
Metop-C
Nutzungsbedingungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Terms of use: DLR not liable for damage resulting from use.
MetOp-A
MetOp-B
MetOp-C
BrO
Bromine Monoxide
AC-SAF
GOME.TC.AGG
urn:eop:DLR:EOWEB:GOME.TC.AGG
FEDEO
Atmospheric conditions

Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services
See the referenced specification
DE/DLR
Climatology Meteorology Atmosphere
The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region (425-450 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
METOP GOME-2 - Nitrogen Dioxide (NO2) - Global
Nutzungsbedingungen: Lizenz, / Terms of use: License,
Please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages for more details on nitrogen dioxide retrieval: https://atmos.eoc.dlr.de/app/docs/
EARTH SCIENCE>ATMOSPHERE>ATMOSPHERIC CHEMISTRY>NITROGEN COMPOUNDS>NITROGEN DIOXIDE
METOP-A
METOP-B
METOP-C
Metop-A
Metop-B
Metop-C
Nutzungsbedingungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Terms of use: DLR not liable for damage resulting from use.
MetOp-A
MetOp-B
MetOp-C
Nitrogen Dioxide
AC-SAF
GOME.TC.AGG
urn:eop:DLR:EOWEB:GOME.TC.AGG
FEDEO
Atmospheric conditions

Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services
See the referenced specification
DE/DLR
Climatology Meteorology Atmosphere
The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
METOP GOME-2 - Cloud Optical Thickness (COT) - Global
Nutzungsbedingungen: Lizenz, / Terms of use: License,
Please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages for more details on cloud optical thickness retrieval: https://atmos.eoc.dlr.de/app/docs/
EARTH SCIENCE>ATMOSPHERE>CLOUDS>CLOUD MICROPHYSICS>CLOUD OPTICAL DEPTH/THICKNESS
METOP-A
METOP-B
METOP-C
Metop-A
Metop-B
Metop-C
Nutzungsbedingungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Terms of use: DLR not liable for damage resulting from use.
MetOp-A
MetOp-B
MetOp-C
COT
Cloud Optical Thickness
AC-SAF
GOME.TC.AGG
urn:eop:DLR:EOWEB:GOME.TC.AGG
FEDEO
Atmospheric conditions

The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational HCHO total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
METOP GOME-2 - Formaldehyde (HCHO) - Global
Please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages for more details on formaldehyde retrieval: https://atmos.eoc.dlr.de/app/docs/
EARTH SCIENCE>ATMOSPHERE>ATMOSPHERIC CHEMISTRY>CARBON AND HYDROCARBON COMPOUNDS>FORMALDEHYDE
HCHO
Formaldehyde

RAL Space, STFC Rutherford Appleton Laboratory, Harwell Campus
United Kingdom
CEDA
This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1979-2023. The product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by MetsÃ¤mÃ¤ki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 Âµm and 1.61 Âµm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 Âµm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. RMSE is retrieved from a statistical model and added as pixel-wise information.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises a few data gaps in 1979 â 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March â 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2023), version 4.0
Under the following licence appropriate use of these data may fall under any use. This message is intended as guidance, always read the full licence. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
The snow_cci SCFV product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017). The final product is quality checked.
Wu, Xiaodan; Naegeli, Kathrin; Premier, Valentina; Marin, Carlo; Ma, Dujuan; Wang, Jingping; Wunderle, Stefan (2021). Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982â2018) in the Hindu Kush Himalayas. The Cryosphere, 15(9), pp. 4261-4279. Copernicus Publications
Devasthale, A. et al. PyGac: An open-source, community-driven Python interface to preprocess nearly 40-year AVHRR Global Area Coverage (GAC) data record. Quarterly 11, 3â5 (2017).
MetsÃ¤mÃ¤ki, S., Pulliainen, J., Salminen, M., Luojus, K., Wiesmann, A., Solberg R. and Ripper, E. 2015. Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment. Remote Sensing of Environment, 156, 96â108.
EUMETSAT, 2023b. PyGAC Fundamental Data Record Algorithm Theoretical Basis Document.
Karlsson, K.-G., Stengel, M., Meirink, J.F., RiihelÃ¤, A., Trentmann, J., Akkermans, T., Stein, D., Devasthale, A., Eliasson, S., Johansson, E., HÃ¥kansson, N., Solodovnik, I., Benas, N., Clerbaux, N., Selbach, N., SchrÃ¶der, M., Hollmann, R., 2023. CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023. Earth Syst. Sci. Data 15, 4901â4926.
EARTH SCIENCE>CLIMATE INDICATORS>CRYOSPHERIC INDICATORS>SNOW COVER
EARTH SCIENCE>SPECTRAL/ENGINEERING>INFRARED WAVELENGTHS
TIROS-N
AVHRR
Level 3C
snow
Public data: access to these data is available to both registered and non-registered users.
ESA
CCI
Snow
Snow Cover Fraction
orthoimagery
Unavailable

EUMETSAT
Geoscientific Information
CLIMATE APPLICATIONS: water vapour feedback; A long-term UTH data record will be used in a wide range of process studies. Upper-tropospheric moisture is central to tracking, understanding, modelling and predicting convection and advection at low latitudes. UTH data can be combined synergistically with a wide range of other datasets such as cloud, precipitation and flow, whether derived from satellites, reanalyses or other sources, for model evaluation, variability analysis, predictability research, and perhaps most of all, physical process studies (e.g., stratospheric-tropospheric exchange and the variations in lower stratospheric water vapour.).
EARTH SCIENCE>ATMOSPHERE>ATMOSPHERIC WATER VAPOR
EARTH SCIENCE>SPECTRAL/ENGINEERING>MICROWAVE
DMSP 5D-2/F11
DMSP 5D-2/F12
DMSP 5D-2/F14
MWHS
MHS
AMSU-B
Water Vapour
ECV
ATMS
SNPP

DOC/NOAA/NESDIS/NCEI
Geoscientific Information
CLIMATE APPLICATIONS: PATMOS-x AVHRR supports: NCEI AVHRR Aerosol TCDR, Climate analysis, Climate model verification
EARTH SCIENCE>ATMOSPHERE>CLOUDS>CLOUD PROPERTIES
TIROS-N
AVHRR
Cloud Properties
ECV

EUMETSAT
Geoscientific Information
CLIMATE APPLICATIONS: earth radiation budget studies climate research (monitoring and modelling), NMHSs & government agencies, private and public sector
EARTH SCIENCE>ATMOSPHERE>ATMOSPHERIC RADIATION>EARTH RADIATION BUDGET
TIROS-N
AVHRR
Earth Radiation Budget
ECV
SNPP
VIIRS