AndreaBozzo/ceres-open-data-index huggingface dataset:
Meteosat Surface Albedo - MFG - Indian Ocean 57 E. CLIMATE APPLICATIONS: Boundary Condition For Climate Models (DOI 10.1007/s00382-005-0073-9) Direct Climate Analysis Such As Impact Of Desertification On African Climate, Impact Of Fires On Surface Reflectance (DOI 10.1029/2002JD002388) Surface Reflectance Changes As Response To Precipitation Variability And Change (DOI 10.1016/j.gloplacha.2008.04.004, DOI 10.1007/s00704-013-0880-6).

Meteosat Surface Albedo - MFG - Indian Ocean 57 E. CLIMATE APPLICATIONS: Boundary Condition For Climate Models (DOI 10.1007/s00382-005-0073-9) Direct Climate Analysis Such As Impact Of Desertification On African Climate, Impact Of Fires On Surface Reflectance (DOI 10.1029/2002JD002388) Surface Reflectance Changes As Response To Precipitation Variability And Change (DOI 10.1016/j.gloplacha.2008.04.004, DOI 10.1007/s00704-013-0880-6).. OFFICIAL CITATION: DOI: http://dx.doi.org/10.1175/BAMS-D-11-00230.1
CLIMATE APPLICATIONS: Same applications as before but with longer time series: Boundary Condition For Climate Models (DOI 10.1007/s00382-005-0073-9) Direct Climate Analysis Such As Impact Of Desertification On African Climate, Impact Of Fires On Surface Reflectance (DOI 10.1029/2002JD002388) Surface Reflectance Changes As Response To Precipitation Variability And Change (DOI 10.1016/j.gloplacha.2008.04.004, DOI 10.1007/s00704-013-0880-6).  In addition the data are being used within the EU FP7 QA4ECV project for combination with data from polar orbit for true global albedo data record and the within calculations of surface shortwave radiative flux

CLIMATE APPLICATIONS: Same applications as before but with longer time series: Boundary Condition For Climate Models (DOI 10.1007/s00382-005-0073-9) Direct Climate Analysis Such As Impact Of Desertification On African Climate, Impact Of Fires On Surface Reflectance (DOI 10.1029/2002JD002388) Surface Reflectance Changes As Response To Precipitation Variability And Change (DOI 10.1016/j.gloplacha.2008.04.004, DOI 10.1007/s00704-013-0880-6).  In addition the data are being used within the EU FP7 QA4ECV project for combination with data from polar orbit for true global albedo data record and the within calculations of surface shortwave radiative flux
algorithm_version: LMD v1.3. CLIMATE APPLICATIONS: reduction in uncertainty of the quantitative role of aerosols in climate forcing; validate and improve the capability of climate simulation models and reanalyses to represent aerosol effects;

algorithm_version: LMD v1.3. CLIMATE APPLICATIONS: reduction in uncertainty of the quantitative role of aerosols in climate forcing; validate and improve the capability of climate simulation models and reanalyses to represent aerosol effects;. OFFICIAL CITATION: dx.doi.org/10.3390/rs8050421 (Popp, et al., Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci), Remote Sensing, 8, 421, 2016)
algorithm_version:BIRA MAPIR v3.0. CLIMATE APPLICATIONS: reduction in uncertainty of the quantitative role of aerosols in climate forcing; validate and improve the capability of climate simulation models and reanalyses to represent aerosol effects;

algorithm_version:BIRA MAPIR v3.0. CLIMATE APPLICATIONS: reduction in uncertainty of the quantitative role of aerosols in climate forcing; validate and improve the capability of climate simulation models and reanalyses to represent aerosol effects;. OFFICIAL CITATION: dx.doi.org/10.3390/rs8050421 (Popp, et al., Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci), Remote Sensing, 8, 421, 2016)
algorithm_version: DLR IMARS v4.2. CLIMATE APPLICATIONS: reduction in uncertainty of the quantitative role of aerosols in climate forcing; validate and improve the capability of climate simulation models and reanalyses to represent aerosol effects;

algorithm_version: DLR IMARS v4.2. CLIMATE APPLICATIONS: reduction in uncertainty of the quantitative role of aerosols in climate forcing; validate and improve the capability of climate simulation models and reanalyses to represent aerosol effects;. OFFICIAL CITATION: dx.doi.org/10.3390/rs8050421 (Popp, et al., Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci), Remote Sensing, 8, 421, 2016)
ATSR-2 / AATSR ensemble AOD record. CLIMATE APPLICATIONS: reduction in uncertainty of the quantitative role of aerosols in climate forcing; validate and improve the capability of climate simulation models and reanalyses to represent aerosol effects;

ATSR-2 / AATSR ensemble AOD record. CLIMATE APPLICATIONS: reduction in uncertainty of the quantitative role of aerosols in climate forcing; validate and improve the capability of climate simulation models and reanalyses to represent aerosol effects;. OFFICIAL CITATION: Popp, et al., Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci), Remote Sensing, 8, 421, 2016; dataset freely available at http://www.icare.univ-lille1.fr/archive/?dir=CCI-Aerosols
CLIMATE APPLICATIONS: Climate change detection (Ringer and Healy, Geophys. Res. Lett., 2008; Lackner et al., J. Clim., 2011; Steiner et al. Radio Sci., 2011; Ao and Hajj, J. Geophys. Res., 2013),  General climate science (Anthes, Atmos. Meas. Tech., 2011),  Climate model validation (Leroy et al., JGR, 2006; Ao et al., J. Geophys. Res, 2015),  Atmospheric physics and processes (Schmidt et al., J. Geophys. Res., 2004; Schmidt et al., J. Geophys. Res., 2016)

CLIMATE APPLICATIONS: Climate change detection (Ringer and Healy, Geophys. Res. Lett., 2008; Lackner et al., J. Clim., 2011; Steiner et al. Radio Sci., 2011; Ao and Hajj, J. Geophys. Res., 2013),  General climate science (Anthes, Atmos. Meas. Tech., 2011),  Climate model validation (Leroy et al., JGR, 2006; Ao et al., J. Geophys. Res, 2015),  Atmospheric physics and processes (Schmidt et al., J. Geophys. Res., 2004; Schmidt et al., J. Geophys. Res., 2016)
CryoSat-2 monthly Antarctic sea ice thickness. CLIMATE APPLICATIONS: Model initialisation and evaluation, global warming.  Reference: Allard et al. (in press) Utilizing CryoSat-2 Sea Ice Thickness to Initialize a Coupled Ice-Ocean Modeling System, Advances in Space Research

CryoSat-2 monthly Antarctic sea ice thickness. CLIMATE APPLICATIONS: Model initialisation and evaluation, global warming.  Reference: Allard et al. (in press) Utilizing CryoSat-2 Sea Ice Thickness to Initialize a Coupled Ice-Ocean Modeling System, Advances in Space Research. OFFICIAL CITATION: Tilling, R.L., A.L. Ridout, A. Shepherd, D.J. Wingham (2015) Increased Arctic sea ice volume after anomalously low melting in 2013, Nature Geoscience 8, 643-646 doi:10.1038/ngeo2489
CLIMATE APPLICATIONS: Global sea-level budget, Greenland mass balance interpretation e.g. for Greenland and Antarctica: - A Reconciled Estimate of Ice-Sheet Mass Balance, Andrew Shepherd et al, Science  30 Nov 2012: Vol. 338, Issue 6111, pp. 1183-1189 DOI: 10.1126/science.1228102   For Antarctica only: Increased Ice Losses from Antarctica Detected by CryoSat-2, Malcolm McMillan et al, GRL, Volume 41, Issue 11, 16 June 2014, Pages 3899�3905, DOI: 10.1002/2014GL060111

CLIMATE APPLICATIONS: Global sea-level budget, Greenland mass balance interpretation e.g. for Greenland and Antarctica: - A Reconciled Estimate of Ice-Sheet Mass Balance, Andrew Shepherd et al, Science  30 Nov 2012: Vol. 338, Issue 6111, pp. 1183-1189 DOI: 10.1126/science.1228102   For Antarctica only: Increased Ice Losses from Antarctica Detected by CryoSat-2, Malcolm McMillan et al, GRL, Volume 41, Issue 11, 16 June 2014, Pages 3899�3905, DOI: 10.1002/2014GL060111
CLIMATE APPLICATIONS: Users who have downloaded our products mentioned the following climate applications: emissions estimations, land-climate interactions, carbon cycle, climate change, land-atmosphere pollution emission modelling, emissions and fire management. Some examples of applications are: http://www.atmos-chem-phys.net/10/11707/2010/acp-10-11707-2010.html, http://onlinelibrary.wiley.com/doi/10.1111/geb.12440/epdf, http://www.biogeosciences.net/13/3359/2016/, http://www.geosci-model-dev-discuss.net/gmd-2016-237/, http://link.springer.com/article/10.1007/s10584-011-0154-1.

CLIMATE APPLICATIONS: Users who have downloaded our products mentioned the following climate applications: emissions estimations, land-climate interactions, carbon cycle, climate change, land-atmosphere pollution emission modelling, emissions and fire management. Some examples of applications are: http://www.atmos-chem-phys.net/10/11707/2010/acp-10-11707-2010.html, http://onlinelibrary.wiley.com/doi/10.1111/geb.12440/epdf, http://www.biogeosciences.net/13/3359/2016/, http://www.geosci-model-dev-discuss.net/gmd-2016-237/, http://link.springer.com/article/10.1007/s10584-011-0154-1.
CLIMATE APPLICATIONS: Global sea-level budget, Antarctic mass balance interpretation, Greenland mass balance interpretation.  Products from CCI are not publicly released yet (due Apr 2017). Similar data was used in McMillan et al, 2014. GRL, Increased ice losses from Antarctica detected by CryoSat-2 doi:10.1002/2014GL060111

CLIMATE APPLICATIONS: Global sea-level budget, Antarctic mass balance interpretation, Greenland mass balance interpretation.  Products from CCI are not publicly released yet (due Apr 2017). Similar data was used in McMillan et al, 2014. GRL, Increased ice losses from Antarctica detected by CryoSat-2 doi:10.1002/2014GL060111
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.).

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.).
CLIMATE APPLICATIONS: NMHS: Automation, extension, homogenization and quality check of cloud observations (MeteoSwiss, DWD, ...) Validation of regional climate models (e.g. COSMOCLM), especially sub grid-scale parameterization, diurnal cycle climatology Climate Monitoring; GCOS ECV climate impact analysis

CLIMATE APPLICATIONS: NMHS: Automation, extension, homogenization and quality check of cloud observations (MeteoSwiss, DWD, ...) Validation of regional climate models (e.g. COSMOCLM), especially sub grid-scale parameterization, diurnal cycle climatology Climate Monitoring; GCOS ECV climate impact analysis
GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance(GEOP) (8-Days,1/24 deg)

GCOM-C/SGLI L3 Binned Atmospheric corrected reflectance (GEOP) (8-Days,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.The Geometry data of VNR-PL sensor is stored. This dataset includes absolute relative azimuth angle and sensor zenith angle. The data unit is degree. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag).The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.
CLIMATE APPLICATIONS: The Landsat land surface temperature products are being developed in response to the needs of scientific research and applications for high quality thermal measurements for use in applications such as energy balance modeling for estimating evapotranspiration and water use consumption by irrigated agriculture.  This need for such information has also been identified through the US National Earth Observation Assessments (EOA) and recognized by GCOS.

CLIMATE APPLICATIONS: The Landsat land surface temperature products are being developed in response to the needs of scientific research and applications for high quality thermal measurements for use in applications such as energy balance modeling for estimating evapotranspiration and water use consumption by irrigated agriculture.  This need for such information has also been identified through the US National Earth Observation Assessments (EOA) and recognized by GCOS.
GCOM-C/SGLI L3 Binned Atmospheric Correction Parameter (ACP T865) (1-Day,1/24 deg)

GCOM-C/SGLI L3 Binned Atmospheric Correction Parameter (ACP T865) (1-Day,1/24 deg) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is daily spatial binning statistics product by reducing the spatial resolution to 1/24 deg with EQA projection.This dataset includes TAUA_865: Aerosol Optical Thickness (TauA) at 865. The physical quantity unit is nm. The stored statistics values are average (AVE), root-mean-square (RMS), maximum value (MAX), minimum value (MIN), number of input data (Ninput), number of used data (Nused), date of observation (Date), and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/24 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.
NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 2. CLIMATE APPLICATIONS: Cryosphere, climate modeling, ocean, atmosphere, biology, fisheries, native communities, shipping, natural resources, policymakers, national defense, educators, students, media, blogosphere, general public

NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 2. CLIMATE APPLICATIONS: Cryosphere, climate modeling, ocean, atmosphere, biology, fisheries, native communities, shipping, natural resources, policymakers, national defense, educators, students, media, blogosphere, general public. OFFICIAL CITATION: Meier, W., F. Fetterer, M. Savoie, S. Mallory, R. Duerr, and J. Stroeve. 2013. NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration. Version 2. [indicate subset used]. Boulder, Colorado USA: National Snow and Ice Data Center. http://dx.doi.org/10.7265/N55M63M1. [date accessed].
NOAA Climate Data Record (CDR) of Ocean Near Surface Atmospheric Properties, Version 2. CLIMATE APPLICATIONS: This data supports the WCRP Core Project GEWEX program (i.e. Kummerow et al. 2015). Global analyses of the water and energy cycles. Regional scale analyses of air-sea interaction.  Storm scale analyses of air-sea interaction.

NOAA Climate Data Record (CDR) of Ocean Near Surface Atmospheric Properties, Version 2. CLIMATE APPLICATIONS: This data supports the WCRP Core Project GEWEX program (i.e. Kummerow et al. 2015). Global analyses of the water and energy cycles. Regional scale analyses of air-sea interaction.  Storm scale analyses of air-sea interaction.. OFFICIAL CITATION: Clayson, Carol Anne, Brown, Jeremiah, and NOAA CDR Program (2016). NOAA Climate Data Record Ocean Surface Bundle (OSB) Climate Data Record (CDR) of Ocean Near Surface Atmospheric Properties, Version 2. [indicate subset used]. NOAA National Center for Environmental Information. doi:doi:10.7289/V55T3HH0 [access date].
CLARA-A1. CLIMATE APPLICATIONS: earth radiation budget studies climate research (monitoring and modelling), NMHSs & government agencies

CLARA-A1. CLIMATE APPLICATIONS: earth radiation budget studies climate research (monitoring and modelling), NMHSs & government agencies. OFFICIAL CITATION: Karlsson et al. 2012: CM SAF cLouds, Albedo and Radiation dataset from AVHRR data - Edition 1 - Monthly Means / Daily Means / Pentad Means / Monthly Histograms. Satellite Application Facility on Climate Monitoring. DOI:10.5676/EUM_SAF_CM/CLARA_AVHRR/V001. http://dx.doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V001 publication reference: Karlsson et al. 2013: http://doi.org/10.5194/acpd-13-935-2013
ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method

ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed "Midori II" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is "Version 2".
CH4_GOS_SRFP v2.3.7. CLIMATE APPLICATIONS: Primarily to obtain information on the natural and anthropogenic sources and sinks of CH4 (details see, e.g., GHG-CCI Climate Assessment Report (CAR): http://www.esa-ghg-cci.org/?q=webfm_send/318). Comparison with climate models.

CH4_GOS_SRFP v2.3.7. CLIMATE APPLICATIONS: Primarily to obtain information on the natural and anthropogenic sources and sinks of CH4 (details see, e.g., GHG-CCI Climate Assessment Report (CAR): http://www.esa-ghg-cci.org/?q=webfm_send/318). Comparison with climate models.. OFFICIAL CITATION: Buchwitz, M., M. Reuter, O. Schneising, et al., Global satellite observations of column-averaged carbon dioxide and methane:  The GHG-CCI XCO2 and XCH4 CRDP3 data set, Remote Sensing of Environment (in review), 2016.
ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.2

The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v06.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717â739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001