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ACIJNXAMANAOAPAQARASATAVAWAXAYAZBABBBCBDBOBQBRBSBTBUBZCCCDCFCGCHCICKCOCP
1
DateSubmittedRecordID
CurrentFuture
ResponderName
ResponsibleOrg
UserServiceOrg
DomainECVECV_ProductPhysical_Quantity
GCOS154_ReqsID
GCOS154_ReqsComment
GCOS200_ReqsID
GCOS200_ReqsComment
GeogrCoverageExtent
GeogrCoverageDomain
HorizResolution
VertResolution
TempResolution
AccuracyStability
StartDateTCDR
EndDateTCDR
DataFormat
Dissemination
ReleaseDate
ClimateApplications
CDRUsers
int_ECV_Product
GeogrCoverageDomainComment
DataRecordNameVersion
DataCitation
DataEndDate
DataLevel
DataStartDate
InstAcronym
SatAcronym
2
2016-07-11 23:16:4910106CurrentDeborah SmithNASANASAAtmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind speed over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
0-360 degree longitude; -90-90 deg latitude
Ocean
0.25 deg x 0.25 deg
1 day (the daily file contains two layers; ascending and descending data swaths; so it actually is twice daily resolution or roughly 12 hours)
1.0 m/s
~0.05 m/s/decade
1987-07-012016-12-31netCDF
FTP;OpenDAP;WGET
2016
Climate Trend Detection; Model Uncertainty Quantification; Climate Regime Shifts; Impact Of Climate Change On Hurricanes (See http://www.remss.com/node/5096)
climate scientists; scientific researchers (See http://www.remss.com/node/5096)
SSMI and SSMIS V7 netCDF Surface Wind Data Product
Wentz;F.J.; K. Hilburn and D.K. Smith. 2012. RSS SSM/I Ocean Product Grids netCDF Collection [indicate subset used]. Data set available online [http://ghrc.nsstc.nasa.gov/] from the NASA EOSDIS Global Hydrology Resource Center Distributed Active Archive Center Huntsville; Alabama; U.S.A. doi: http://dx.doi.org/10.5067/MEASURES/SSMI-SSMIS/DATA301
1991-12-31L21987-07-09SSM/IDMSP-F08
3
2016-10-18 14:48:0910496FutureDeborah RosenCPOMCPOMOceanSea LevelRegional Sea LevelRegional Sea Level4243
50S - 88S/0-360 deg
Ocean5Monthly
2cmNot Assessed2010-10-012019-03-312019
Dynamic topography. Reference: Allard et al. (in press) Utilizing CryoSat-2 Sea Ice Thickness to Initialize a Coupled Ice-Ocean Modeling System; Advances in Space Research

National Oceanography Centre
Antarctic sea level and sea level anomaly
2019-03-31LIB and L22010-04-01SIRALCryoSat-2
4
2016-10-27 17:38:5310618FutureOlaf TuinderEUMETSAT
EUMETSAT / AC SAF
AtmosphereWater Vapour
Total Column Water Vapour
Total Column Water Vapour
78
-90..+90; -180..+180
All domains
0.5 x 0.5 degree grid; 55km x 55km at equator
1 day
Target: 10%; Optimal 5%
Not Assessed2007-01-012016-12-312019
Climate monitoring
No confirmed users. Data goes out via websites; or via the archive.
2016-12-31Level-1b2007-01-01GOME-2Metop-A
5
2016-10-31 12:44:1810656FutureSandra CoelhoEUMETSAT
IPMA (LSA SAF/EUMETSAT)
LandLAILAILAI6493
Joint reqs for modelling / adaptation
79W79E
81N81S
Land3km at SSPNA101
Not yet available.
2004-01-192012-12-312017
Vegetation state and coverage: interannual variability and trends;
Hydrology.
JRC; Copernicus Global Land
Coverage area corresponds to the MSG full disk.
2006-09-23L1.52004-01-19SEVIRIMeteosat-8
6
2016-10-31 12:49:4710658FutureSandra CoelhoEUMETSAT
IPMA (LSA SAF/EUMETSAT)
LandFAPARFAPARFAPAR6394
Joint reqs for modelling / adaptation
79W79E
81N81S
Land3km at SSPNA1015%
Not yet available.
2004-01-192012-12-312017
Vegetation state and coverage: interannual variability and trends;
Hydrology.
JRC; Copernicus Global Land
Coverage area corresponds to the MSG full disk.
2006-09-23L1.52004-01-19SEVIRIMeteosat-8
7
2016-07-11 23:23:3610115CurrentPamela RinslandNASANASAAtmosphere
Upper-air Temperature
Stratospheric Temperature Profile
Stratospheric Temperature Profile
45
Global (90 degrees S to 90 degrees N)
All domains
280 km equal area grid
Temperature profiles at 9 layers; 900 mb; 740 mb; 620 mb; 500 mb; 375 mb; 245 mb; 115 mb; 50 mb; 15 mb
Daily
https://badc.nerc.ac.uk/data/isccp/tovs.html#quality
Estimated accuracy of the temperature retrievals is ~ 2 - 3 K (McMillin and Dean 1982)
https://badc.nerc.ac.uk/data/isccp/tovs.html#quality
1983-07-012009-12-31NATIVE
Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
1984
ISCCP was established as the first project of the World Climate Research Program (WMO; 1984); to collect and analyze satellite radiance
measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis
products will be used to improve the understanding and modelling of the effects of clouds on climate. The main data set used to produce a
daily; global description of the ozone; temperature and humidity distributions is that obtained from the analysis of data from the TIROS
Operational Vertical Sounder (TOVS) system; flown on the NOAA Operational Polar Orbiting Satellite series.
International Satellite Cloud Climatology Project (ISCCP) Tiros Operational Vertical Sounder (TOVS) in native (NAT) format Version: 1
ISCCP Science Team (1999); ISCCP TOVS NAT; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ISCCP/TOVS_NAT
2001-08-01
Level 1 radiance data
1986-09-05HIRS/2NOAA-10
8
2016-07-11 23:32:2510133CurrentKara GergelyNASANSIDC DAACLandIce SheetsIce VelocityIce Velocity5758
Lat -72 to -90; Lon 180 to -180
Ice0.9kmN/A
Varies by individual sensors (record covers Winter 1997/2009)
+/- 8.5m/yearUnknown1997-09-092009-04-30
NetCDF; Binary
FTP; HTTPS2012
Ice Sheet Dynamics; Ice Sheet Modeling; Ice Sheet Mass Balance; Sea Level Contribution From Antarctica
Unknown
MEaSUREs InSAR-Based Ice Velocity Maps of Central Antarctica: 1997 and 2009
Rignot; E.; J. Mouginot; and B. Scheuchl 2012. MEaSUREs InSAR-Based Ice Velocity Maps of Central Antarctica: 1997 and 2009; Version 1. [Indicate subset used]. Boulder; Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0525.001. [Date Accessed].
1997-10-20unknown1997-09-09SARRadarSat-1
9
2016-07-11 23:23:4910116CurrentPamela RinslandNASANASAAtmosphereWater Vapour
Tropospheric and Lower-stratospheric Profiles of Water Vapour
Tropospheric and Lower-stratospheric Profiles of Water Vapour
89GlobalAll domains
280km equal area grid
Precipitable water at 5 layers: 800-1000 mb; 680 -800 m; 560 - 680 m; 440 - 560 mb; 310 - 440 mb
Daily and monthly products are available
https://badc.nerc.ac.uk/data/isccp/tovs.html#quality
Estimated error of water vapor retrievals is ~ 25-30% (Smith et al. 1979)
https://badc.nerc.ac.uk/data/isccp/tovs.html#quality
1980-12-012009-12-31Native'
Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
1984
ISCCP was established as the first project of the World Climate Research Program (WMO; 1984); to collect and analyze satellite radiance
measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis
products will be used to improve the understanding and modelling of the effects of clouds on climate. The main data set used to produce a
daily; global description of the ozone; temperature and humidity distributions is that obtained from the analysis of data from the TIROS
Operational Vertical Sounder (TOVS) system; flown on the NOAA Operational Polar Orbiting Satellite series.
International Satellite Cloud Climatology Project (ISCCP) Tiros Operational Vertical Sounder (TOVS) in native (NAT) format Version: 1
ISCCP Science Team (1999); ISCCP TOVS NAT; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ISCCP/TOVS_NAT
2001-08-01
Level 1 radiance data
1986-09-05HIRS/2NOAA-10
10
2016-07-11 23:37:0210145CurrentVicky WolfJPLNASAOceanSea IceSea Ice DriftSea Ice Drift4748
65 to 90; -180 to 180
Ocean;Ice1030.01 m/s
Not Assessed (not needed since ice motion is not dependent on sensor calibration; only on geometric fidelity)
1996-11-012008-05-08BIN; CSV; XMLFTP2010
These data will be used for development; verification of climate models; and for assimilation into models
Many publications have resulted from users using these data. Some examples are given below.
• http://dx.doi.org/10.1029/2005JC003246
• http://dx.doi.org/10.1029/2005JC003393
• http://dx.doi.org/10.1029/2008JC004783
• http://dx.doi.org/10.1016/j.coldregions.2011.04.003
• Peterson; K. and D. Sulsky; Evaluating Deformation in the Beaufort Sea Using a Kinematic Crack Algorithm with RGPS Data; In: Remote sensing of the Changing Oceans; Eds: Tang; D.; Gower; J.; Levy; G.; et al.; Science Press/Springer ISBN 978-3-642-16540-5 (2011)
• http://dx.doi.org/10.5194/tc-9-663-2015
• http://dx.doi.org/10.1002/2015JC011151
• http://dx.doi.org/10.5194/tc-10-1055-2016
Arctic Ocean
Lagrangian Sea-Ice Kinematics Dataset
Kwok; R.; RADARSAT-1 data 1997 (CSA). Dataset: Lagrangian Sea-Ice Kinematics. Retrieved from ASF DAAC <Access Date>. DOI: 10.5067/SSMPINYI15UU.
2008-05-0821996-11-01SARRadarSat-1
11
2016-07-11 23:24:0510117CurrentDeborah SmithNASANASAAtmosphere
Cloud Properties
Cloud Water Path (liquid and ice)(CWP)
Cloud Water Path (liquid)14
Reqs do not distinguish liquid / ice
15
Reqs do not distinguish liquid / ice
0-360 degree longitude; -90-90 deg latitude
Ocean
0.25 deg x 0.25 deg
1 day (the daily file contains two layers; ascending and descending data swaths; so it actually is twice daily resolution or roughly 12 hours)
0.02 mmunknown1987-07-152016-12-31netCDF
FTP; OpenDAP; WGET
2016
Analysis Of Changes In Cloud Cover (See http://www.remss.com/node/5096)
climate scientists; scientific researchers (See http://www.remss.com/node/5096)
SSMI and SSMIS V7 netCDF Cloud Liquid Water Products
Wentz;F.J.; K. Hilburn and D.K. Smith. 2012. RSS SSM/I Ocean Product Grids netCDF Collection [indicate subset used]. Data set available online [http://ghrc.nsstc.nasa.gov/] from the NASA EOSDIS Global Hydrology Resource Center Distributed Active Archive Center Huntsville; Alabama; U.S.A. doi: http://dx.doi.org/10.5067/MEASURES/SSMI-SSMIS/DATA301
1991-12-31L21987-07-09SSM/IDMSP-F08
12
2016-07-18 18:43:1110170CurrentLarry Thomason
NASA Langley Research Center
SAGE II Team
NASA Atmospheric Data Center
Atmosphere
Aerosol Properties
Aerosol-extinction Coefficient Profile
Aerosol-extinction Coefficient Profile
3434
80S to 80N; 180W to 180E; cloud top to ~40 km
All domains100 km1.0 km3 minutes5%>1%/decade1984-10-012006-03-01Binaryinternet; ASDC2012
Radiation and chemistry of the stratosphere
SAGE2 AEROSOL O3 NO2 H2O BINARY2 V7.00
SAGE II Science Team (2012); SAGE II Version 7.00 Data; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ERBS/SAGEII/SOLAR_BINARY_L2-V7.0
2005-08-011984-10-01SAGE-IIERBS
13
2016-11-25 10:18:3310702CurrentYasushi IzumikawaJMAJMAAtmosphereUpper-air Wind
Upper-air Wind Speed and Direction
Upper-air Wind Speed and Direction
67
N60-S60; E80-E200
All domains60-80km
3 levels
low [ground - 750hPa]
middle [750 - 400]
high [400 - top]
six hourly7 m/s RMSunknown1979-01-012009-09-30BUFR
postal service (DVD-ROM)
0
Climate reanalysis JRA55
JMA
Atmospheric Motion Vectors (GOES and HIMAWARI)
2005-06-152003-05-22IMAGERGOES-9
14
2016-07-11 23:24:1510118CurrentPamela RinslandNASANASAAtmosphere
Cloud Properties
Cloud Top Pressure (CTP)
Cloud Top Pressure1112
Global (90 degrees South to 90 degrees North)
All domains
280 km equal area grid
daily1980-12-012009-12-31NATIVE
Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
1984
ISCCP was established as the first project of the World Climate Research Program (WMO; 1984); to collect and analyze satellite radiance
measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis
products will be used to improve the understanding and modelling of the effects of clouds on climate. The main data set used to produce a
daily; global description of the ozone; temperature and humidity distributions is that obtained from the analysis of data from the TIROS
Operational Vertical Sounder (TOVS) system; flown on the NOAA Operational Polar Orbiting Satellite series.
International Satellite Cloud Climatology Project (ISCCP) Tiros Operational Vertical Sounder (TOVS) in native (NAT) format Version: 1
ISCCP Science Team (1999); ISCCP TOVS NAT; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ISCCP/TOVS_NAT
2001-08-01
Level 1 radiance data
1986-09-05HIRS/2NOAA-10
15
2016-07-28 11:00:0510191FutureRainer HollmannEUMETSAT (CM SAF)
FCDR EUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind speed over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 deg to 90 deg
-180 deg to 180 deg
Ocean0.5 x 0.5 degn/Amonthly
1 m/s bias (threshold);
2.8 m/s rms (threshold)
0.2 m/s per decade (threshold)
1987-07-092013-12-312017
Ocean Fluxes; Validation Of Ocean Modelling
User publications involving HOAPS data:
Alhammoud et al. 2014: http://doi.org/10.3390/atmos5020370
Brueck et al. 2015: http://doi.org/10.1175/jas-d-14-0054.1
Burdanowitz et al. 2015: http://doi.org/10.1175/jamc-d-14-0146.1
Donges et al. 2015: http://doi.org/10.1007/s00382-015-2479-3
Kent et al. 2013: http://doi.org/10.1002/joc.3606
Poli et al. 2016: http://doi.org/10.1175/jcli-d-15-0556.1
Prytherch et al. 2015: http://doi.org/10.1002/joc.4150
ice free ocean only
1991-12-18level 1c1987-07-09SSM/IDMSP-F08
16
2017-01-13 15:08:2710990FutureThierry GuinleCNSA - CNESNSOAS - CNESOceanSea StateWave HeightSignificant Wave Height4344
-/+ 180 longitude; -/+ 82 latitude
Ocean
70 km along-track; 90 km cross-track
13 days10% or 0.5 mNot Assessed2018-06-012021-06-012021
ocean-atmosphere gas transfer studies
2021-06-01Level -22018-06-01SWIMCFOSAT
17
2017-01-13 15:30:4510991FutureThierry GuinleCNSA - CNESNSOAS - CNESOceanSea StateWave HeightWave direction1002Auxiliary variables; no reqs set1002
Auxiliary variables; no reqs. Set
-/+ 180 longitude; -/+ 82 latitude
Ocean
70 km along-track; 90 km cross-track
13 days15 degNot Assessed2018-06-012021-06-012021
ocean-atmosphere gas transfer studies
2021-06-01Level -22018-06-01SWIMCFOSAT
18
2017-01-13 15:32:2110992FutureThierry GuinleCNSA - CNESNSOAS - CNESOceanSea StateWave HeightWavelength1002Auxiliary variables; no reqs set1002
Auxiliary variables; no reqs. Set
-/+ 180 longitude; -/+ 82 latitude
Ocean
70 km along-track; 90 km cross-track
13 days10 %Not Assessed2018-06-012021-06-012021
ocean-atmosphere gas transfer studies
2021-06-01Level -22018-06-01SWIMCFOSAT
19
2016-07-11 23:25:0310119CurrentPamela RinslandNASANASAAtmosphere
Cloud Properties
Cloud AmountCloud Amount1011
Global (90 degrees south to 90 degrees north) equal area grid
All domains
280 km equal area grid
daily1980-12-012009-12-31NATIVE
Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
1984
ISCCP was established as the first project of the World Climate Research Program (WMO; 1984); to collect and analyze satellite radiance
measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis
products will be used to improve the understanding and modelling of the effects of clouds on climate. The main data set used to produce a
daily; global description of the ozone; temperature and humidity distributions is that obtained from the analysis of data from the TIROS
Operational Vertical Sounder (TOVS) system; flown on the NOAA Operational Polar Orbiting Satellite series.
International Satellite Cloud Climatology Project (ISCCP) Tiros Operational Vertical Sounder (TOVS) in native (NAT) format Version: 1
ISCCP Science Team (1999); ISCCP TOVS NAT; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ISCCP/TOVS_NAT
2001-08-01
Level 1 radiance data
1986-09-05HIRS/2NOAA-10
20
2016-07-28 11:08:2210193FutureRainer HollmannEUMETSAT (CM SAF)
FCDR EUMETSAT (CM SAF)
FCDR NASA
FCDR JMA
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind speed over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 deg to 90 deg
-180 deg to 180 deg
Ocean0.5 x 0.5 degn/Amonthly
1 m/s bias (threshold);
2.8 m/s rms (threshold)
0.2 m/s per decade (threshold)
1987-07-092019-12-312021
Ocean Fluxes; Validation Of Ocean Modelling
User publications involving HOAPS data:
Alhammoud et al. 2014: http://doi.org/10.3390/atmos5020370
Brueck et al. 2015: http://doi.org/10.1175/jas-d-14-0054.1
Burdanowitz et al. 2015: http://doi.org/10.1175/jamc-d-14-0146.1
Donges et al. 2015: http://doi.org/10.1007/s00382-015-2479-3
Kent et al. 2013: http://doi.org/10.1002/joc.3606
Poli et al. 2016: http://doi.org/10.1175/jcli-d-15-0556.1
Prytherch et al. 2015: http://doi.org/10.1002/joc.4150
ice free ocean only
2011-12-13level 1c2002-01-01AMSR-EAqua
21
2017-01-17 16:17:3011011FutureDeborah RosenCPOMCPOMOceanSea IceSea Ice ThicknessSea Ice Thickness4647
50S - 88S/0-360 deg
Ice5km14 days
28% (1-sigma estimate of the error; i.e. if the thickness is 1m the 1-sigma error is 28cm)

Not Assessed2010-10-012019-03-312023
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
British Antarctic Survey
Antarctic sea ice thickness
2019-03-31LIB and L22010-04-01SIRALCryoSat-2
22
2017-05-08 17:28:3111155FutureThierry GuinleCNESCNESOceanSea LevelRegional Sea LevelRegional Sea Level4243
-/+ 180 longitude; 0 to +/- 66-82 latitude (depending on sensor)
Ocean
7 km along-track
10 days0.03 m
0.6 mm/year (TBC)
1992-09-012023-02-1320232012-04-01Level-22002-09-01RA-2Envisat
23
2016-07-11 23:25:2910121CurrentDeborah SmithNASANASAAtmospherePrecipitationPrecipitationLiquid precipitation2
Reqs do not distinguish liquid / solid
2
Reqs do not distinguish liquid / solid
0-360 degree longitude; -90-90 deg latitude
Ocean
0.25 deg x 0.25 deg
1 day (the daily file contains two layers; ascending and descending data swaths; so it actually is twice daily resolution or roughly 12 hours)
500 mm/yr (Rain is validated in by comparing to buoy rain gauges on an annual basis due to the high variability of rain.)
4.8 (mm/year)/decade
1987-07-012016-12-31netCDF
FTP; OpenDAP; WGET
2016
Study Of Global Changes In Rain Patterns; Analysis Of Water Cycle (See http://www.remss.com/node/5096)
climate scientists; scientific researchers (See http://www.remss.com/node/5096)
SSMI and SSMIS netCDF Data Rain Rate Ocean Products
Wentz;F.J.; K. Hilburn and D.K. Smith. 2012. RSS SSM/I Ocean Product Grids netCDF Collection [indicate subset used]. Data set available online [http://ghrc.nsstc.nasa.gov/] from the NASA EOSDIS Global Hydrology Resource Center Distributed Active Archive Center Huntsville; Alabama; U.S.A. doi: http://dx.doi.org/10.5067/MEASURES/SSMI-SSMIS/DATA301
1991-12-31L21987-07-09SSM/IDMSP-F08
24
2016-08-07 23:00:0410220CurrentRoy Grainger ESA
University of Oxford / Science Technology Facility Council - Rutherford Appleton Laboratory
Atmosphere
Aerosol Properties
Aerosol Optical Depth
Aerosol Optical Depth3131globalAll domains10 km3 day0.1unknown1995-06-012003-04-01NetCDF
internet; Obs4MIPS
2016
Huang; H.; G.E. Thomas and R.G. Grainger; Relationship between wind speed and aerosol optical depth over the remote ocean; Atmospheric Chemistry and Physics; 10; 5943—5950; doi: 10.5194/acp-10-5943-2010; 2010.

Bulgin; C. E.; P. I. Palmer G. E. Thomas; C. P.G. Arnold; E. Campmany; E. Carboni; R. G.Grainger; C. Poulsen; R. Siddans; B. N. Lawrence; Regional and seasonal variations of the Twomey indirect effect as observed by the ATSR-2 satellite instrument; Geophysical Research Letters; 35; L02811; doi: 10.1029/2007GL031394; 2008.
University of Oxford
Rutherford Appleton Laboratory
University of Edinburgh
ESA_CCI_ORAC_AEROSOL V 3.0.2 (ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from ATSR2 (ORAC algorithm); Version 3.02)
ESA Aerosols CCI project team; Popp; T.; de Leeuw; G. (2016): ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from ATSR2 (ORAC algorithm); Version 3.02. Centre for Environmental Data Analysis; date of citation. http://catalogue.ceda.ac.uk/uuid/b6751acb59b242bfa33af689c4778abd
2003-04-0111995-06-01ATSR-2ERS-2
25
2016-07-11 23:29:3810125CurrentPamela RinslandNASANASAAtmosphereOzoneTotal OzoneTotal Ozone2727
Global: Latitude 90 (N) to -90 (S); Longitude 180 (E) to -180 (W)
All domains
280km equal area grid
N/A
Daily and monthly
10% to 15% error
1981-06-012009-12-31NATIVE
Earth Data Search portal at https://search.earthdata.nasa.gov/; ASDC order tool and DATA.GOV
1984
ISCCP was established as the first project of the World Climate Research Program (WMO; 1984); to collect and analyze satellite radiance
measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis
products will be used to improve the understanding and modelling of the effects of clouds on climate. The main data set used to produce a
daily; global description of the ozone; temperature and humidity distributions is that obtained from the analysis of data from the TIROS
Operational Vertical Sounder (TOVS) system; flown on the NOAA Operational Polar Orbiting Satellite series.
International Satellite Cloud Climatology Project (ISCCP) Tiros Operational Vertical Sounder (TOVS) in native (NAT) format Version: 1
ISCCP Science Team (1999); ISCCP TOVS NAT; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ISCCP/TOVS_NAT
2001-08-01
Level 1 radiance data
1986-09-05HIRS/2NOAA-10
26
2016-08-09 11:56:3910224FutureAnton VerhoefEUMETSATOSI SAFAtmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind vector over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 to 90 and 0 to 360 degrees.
Ocean
25 km along-track and cross-track sampling.
N/A
Approximately 0.5 days revisit interval.
Error in zonal and meridional wind components is lower than 1.0 m/s as obtained from triple collocation analysis.
Better than 0.1 m/s per decade.
2009-12-152014-02-202017
- Re-analyses of atmospheric and oceanographic modeling
- Study of air-sea interaction
- (Wind) climate research
- Environmental monitoring
- Climate scientists
- Oceanographers
- Meteorological services
Winds are available over ice-free oceans.
2014-02-20Level 1B2009-12-15OSCATOceanSat-2
27
2017-05-08 18:15:4411158FutureThierry Guinle
NASA - NOAA - EUMETSAT - CNES
NASA - CNESOceanSea StateWave HeightSignificant Wave Height4344
-/+ 180 longitude; -/+ 66 latidude
Ocean
7 km along-track; 315 km cross-track
10 days0.12 mNot Assessed2016-01-172021-12-312021
ocean-atmosphere gas transfer studies
2021-12-31Level-22016-01-17Poseidon-3BJASON-3
28
2017-05-08 18:25:0211159FutureThierry Guinle
NASA - NOAA - EUMETSAT - CNES
NASA - CNESOceanSea StateWave HeightSignificant Wave Height4344
-/+ 180 longitude; -/+ 66 latidude
Ocean
7 km along-track; 315 km cross-track
10 days0.12 mNot Assessed2008-06-202021-12-312021
ocean-atmosphere gas transfer studies
2016-12-31Level-22008-06-20Poseidon-3JASON-2
29
2017-05-08 18:29:2911160FutureThierry GuinleISRO - CNESCNESOceanSea StateWave HeightSignificant Wave Height4344
-/+ 180 longitude; -/+ 81.5 latidude
Ocean
7 km along-track; 80 km cross-track
35 days0.04 mNot Assessed2013-02-252019-12-312019
ocean-atmosphere gas transfer studies
2019-12-31Level-22013-02-25AltiKaSARAL
30
2017-05-28 12:55:2211177FutureRoselyne LacazeECECLand
Fire Disturbance
Burnt AreaBurnt Area6667
180W to 180E
75N to 60S
Land1 km1 dayNot AssessedNot Asessed1998-12-102018-12-212018
Land Surface - Carbon Cycle
European and national institutions reporting on GHG emissions
2018-12-31
Daily Synthesis (S1) TOC reflectances
2014-01-01Vegetation-PPROBA-V
31
2016-07-11 23:32:1110132CurrentKara GergelyNASANSIDC DAACLand
Ice Sheets and Ice Shelves
Ice ShelvesGrounding Line Location1001New to GCOS-20085NULL
Lat -60 to -90; Lon 180 to -180
Ice
.12km (ALOS PALSAR; 2007-09-08 to 2009-01-22) 0.05km (ERS-1; ERS-2; Sentinel-1A; 1999-02-15 to 2000-03-05; 2011-04-01 to 2011-05-30; 2014-01-01 to 2014-12-17) 0.035km (RADARSAT-1; 2000-09-03 to 2000-11-14 ); 0.046km (RADARSAT-2; 2009-02-17 to 2009-04-12); 0.025km (COSMO Skymed; 2013-01-01 to 2013-12-31)
N/A
Varies by individual sensors. Record covers 22 years with coverage for 1992; 1994-1996; 1999-2000; 2007-2009; 2013-2014
+/- 6m/yearUnknown1992-02-152014-12-17ShapefileHTTPS2016
Ice Sheet Dynamics; Ice Sheet Modeling; Ice Sheet Mass Balance; Sea Level Contribution From Antarctica
Pope; Allen; W. Gareth Rees; Adrian J. Fox; and Andrew Fleming. 'Open access data in polar and cryospheric remote sensing.' Remote Sensing 6; no. 7 (2014): 6183-6220. http://dx.doi.org/10.3390/rs6076183

Balco; Greg; Claire Todd; Kathleen Huybers; Seth Campbell; Michael Vermeulen; Matthew Hegland; Brent M. Goehring; and Trevor R. Hillebrand. 'Cosmogenic-nuclide exposure ages from the Pensacola Mountains adjacent to the Foundation Ice Stream; Antarctica.' American Journal of Science 316; no. 6 (2016): 542-577.
http://dx.doi.org/10.2475/06.2016.02
The data cover roughly 75 percent of the Antarctic grounding line (the transition from grounded ice to floating ice sheet) and partial coverage of the grounding line of ice-covered offshore islands. Lines are discontinuous; and in some areas multiple picks from different SAR missions and dates are shown. Most of the fast-flowing; large-flux outlet glaciers and ice streams are mapped.
MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry Version 2
Rignot; E.; J. Mouginot; and B. Scheuchl 2016. MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry; Version 2. [Indicate subset used]. Boulder; Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: hhttp://dx.doi.org/10.5067/IKBWW4RYHF1Q. [Date Accessed].
2009-01-22unknown2007-09-08PALSARALOS
32
2016-07-11 23:32:3810134CurrentPamela RinslandNASANASALand
Land-Surface Temperature
Land-Surface Temperature
Land-Surface Temperature
7071
Global (90 degrees south to 90 degrees N)
All domains
280 km equal area grid
N/A
daily and monthly
2 to 3 degrees Kelvin; somewhat larger errors over land
1980-12-012009-12-31NATIVE
Earth Data Search client at https://search.earthdata.nasa.gov/ and DATA.GOV
1984
ISCCP was established as the first project of the World Climate Research Program (WMO; 1984); to collect and analyze satellite radiance
measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis
products will be used to improve the understanding and modelling of the effects of clouds on climate. The main data set used to produce a
daily; global description of the ozone; temperature and humidity distributions is that obtained from the analysis of data from the TIROS
Operational Vertical Sounder (TOVS) system; flown on the NOAA Operational Polar Orbiting Satellite series.
International Satellite Cloud Climatology Project (ISCCP) Tiros Operational Vertical Sounder (TOVS) in native (NAT) format Version: 1
ISCCP Science Team (1999); ISCCP TOVS NAT; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ISCCP/TOVS_NAT
2001-08-01
Level 1 radiance data
1986-09-05HIRS/2NOAA-10
33
2016-08-09 12:09:2610225FutureAnton VerhoefEUMETSATOSI SAFAtmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind vector over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 to 90 and 0 to 360 degrees.
Ocean
50 km along-track and cross-track sampling.
N/A
Approximately 0.5 days revisit interval.
Error in zonal and meridional wind components is lower than 1.0 m/s as obtained from triple collocation analysis.
Better than 0.1 m/s per decade.
2009-12-152014-02-202017
- Re-analyses of atmospheric and oceanographic modeling
- Study of air-sea interaction
- (Wind) climate research
- Environmental monitoring
- Climate scientists
- Oceanographers
- Meteorological services
Winds are available over ice-free oceans.
2014-02-20Level 1B2009-12-15OSCATOceanSat-2
34
2016-07-11 23:32:5910136CurrentKara GergelyNASANSIDC DAACLandIce SheetsIce VelocityIce Velocity5758
Lat 83 to 60; Lon -14 to -75
Ice0.5kmN/A
Varies by individual sensors (record covers years) Seasonal winter coverage 2000-2010
Accurate to less than 5m/yr
Unknown2000-09-032010-02-28GeoTIFFFTP; HTTPS2015
Ice Sheet Dynamics; Ice Sheet Modeling; Ice Sheet Mass Balance; Sea Level Contribution From Greenland
Colgan; William; et al. 2016. The abandoned ice sheet base at Camp Century; Greenland; in a warming climate. Geophysical Research Letters 43(15): 8091â??8096. doi: 10.1002/2016GL069688.

Stokes; C. R.; M. Margold; C. D. Clark; and L. Tarasov. 'Ice stream activity scaled to ice sheet volume during Laurentide Ice Sheet deglaciation.' Nature 530; no. 7590 (2016): 322-326.

MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data; Version 2
Joughin; I.; B. Smith; I. Howat; and T. Scambos. 2015. MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data; Version 2. [Indicate subset used]. Boulder; Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/OC7B04ZM9G6Q. [Date Accessed].
2010-02-2802008-12-01PALSARALOS
35
2016-08-09 12:13:0710226FutureAnton VerhoefEUMETSATOSI SAFAtmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind vector over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 to 90 and 0 to 360 degrees.
Ocean
25 km along-track and cross-track sampling.
N/A
Approximately 0.5 days revisit interval.
Error in zonal and meridional wind components is lower than 1.0 m/s as obtained from triple collocation analysis.
Better than 0.1 m/s per decade.
1992-03-022001-01-152017
- Re-analyses of atmospheric and oceanographic modeling
- Study of air-sea interaction
- (Wind) climate research
- Environmental monitoring
- Climate scientists
- Oceanographers
- Meteorological services
Winds are available over ice-free oceans.
1996-06-03Level 21992-03-02AMI-SCATERS-1
36
2016-08-20 0:56:3310258CurrentVicky WolfJPLNASAOceanSea IceSea Ice DriftSea Ice Drift4748
65 to 90; -180 to 180
Ocean;Ice1030.01 m/s
Not Assessed (not needed since ice motion is not dependent on sensor calibration; only on geometric fidelity)
1996-11-012008-05-08
BIN; GEOTIFF; XML; NETCDF
FTP2010
These data will be used for development; verification of climate models; and for assimilation into models
RAMA20170501: Many publications have resulted from users using these data. Some examples are given below.
• http://dx.doi.org/10.1029/2005JC003246
• http://dx.doi.org/10.1029/2005JC003393
• http://dx.doi.org/10.1029/2008JC004783
• http://dx.doi.org/10.1016/j.coldregions.2011.04.003
• Peterson; K. and D. Sulsky; Evaluating Deformation in the Beaufort Sea Using a Kinematic Crack Algorithm with RGPS Data; In: Remote sensing of the Changing Oceans; Eds: Tang; D.; Gower; J.; Levy; G.; et al.; Science Press/Springer ISBN 978-3-642-16540-5 (2011)
• http://dx.doi.org/10.5194/tc-9-663-2015
• http://dx.doi.org/10.1002/2015JC011151
• http://dx.doi.org/10.5194/tc-10-1055-2016
Arctic Ocean
3-Day Gridded Sea Ice Kinematics Dataset
Kwok; R.; RADARSAT-1 data 1997 (CSA). Dataset: 3-Day Gridded Sea-Ice Kinematics. Retrieved from ASF DAAC <Access Date>. DOI: 10.5067/GWQU7WKQZBO4.
2008-05-0821996-11-01SARRadarSat-1
37
2016-07-11 23:33:0910137CurrentKara GergelyNASANSIDC DAACLandIce SheetsIce VelocityIce Velocity5758
Lat 82 to 60; Lon -20 to -70
Ice0.1kmN/A
Varies by region. (record covers 6 years; 2009-2015)
Accurate to less than 5m/yr
Unknown2009-01-012015-01-06GeoTIFFFTP; HTTPS2015
Ice Sheet Dynamics; Ice Sheet Modeling; Ice Sheet Mass Balance; Sea Level Contribution From Greenland
Scheuchl; Bernd; and Croul Hall. 'SAR Science Requirements for Ice Sheets.' Polar Space Task Group (2013).
MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR; Version 1
Joughin; I.; I. Howat; B. Smith; and T. Scambos. 2011; updated 2016. MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR; Version 1. [Indicate subset used]. Boulder; Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0481.001. [Date Accessed].
2015-01-06unknown2009-01-01SAR-XTerraSAR-X
38
2016-08-20 1:05:1710259CurrentVicky WolfJPLNASAOceanSea IceSea Ice DriftSea Ice Drift4748
65 to 90; -180 to 180
Ocean;Ice1030.01 m/s
Not Assessed (not needed since ice motion is not dependent on sensor calibration; only on geometric fidelity)
2007-04-012011-03-30
CSV; HDF4; HDF5; SHP; XML
FTP2011
These data will be used for development; verification of climate models; and for assimilation into models
Many publications have resulted from users using these data. Some examples are given below.
• http://dx.doi.org/10.1029/2005JC003246
• http://dx.doi.org/10.1029/2005JC003393
• http://dx.doi.org/10.1029/2008JC004783
• http://dx.doi.org/10.1016/j.coldregions.2011.04.003
• Peterson; K. and D. Sulsky; Evaluating Deformation in the Beaufort Sea Using a Kinematic Crack Algorithm with RGPS Data; In: Remote sensing of the Changing Oceans; Eds: Tang; D.; Gower; J.; Levy; G.; et al.; Science Press/Springer ISBN 978-3-642-16540-5 (2011)
• http://dx.doi.org/10.5194/tc-9-663-2015
• http://dx.doi.org/10.1002/2015JC011151
• http://dx.doi.org/10.5194/tc-10-1055-2016
Arctic Ocean
Eulerian Sea Ice Kinematics Dataset
Kwok; R.; ENVISAT data 2007 (CSA). Dataset: Eulerian Sea-Ice Kinematics. Retrieved from ASF DAAC <Access Date>. DOI: 10.5067/JQHSWB5Y45AY.
2011-03-3022007-04-01ASAREnvisat
39
2016-07-11 23:33:1710138CurrentPamela RinslandNASANASAOceanSea Ice
Sea Ice Extent/Edge
Sea Ice Extent/Edge4546
90 degrees S - 90 degrees N
All domains
150 km Equal area grid (1 degree in latitude and longitude at the equator)
N/A
5-day averaged data
1983-07-012009-12-31NATIVE
Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
1984
The ISCCP version of snow/ice data are created to help discriminate between ice/snow cover and clouds in performing cloud analysis of operational satellite imagery.

Section 11.2 of ISCCP SNOW/ICE Data at
https://badc.nerc.ac.uk/data/isccp_d2/snow.html

The publications listed in the link below represent research analyses using ISCCP datasets; studies of topics of direct concern to the validity of the ISCCP data products; or related research of interest. There is no possibility that this is a complete list of work done that is related to ISCCP nor does it represent all the research that has been done on clouds. Rather; this list is meant to point to interesting things that are being done with ISCCP datasets or to other interesting research topics.
http://isccp.giss.nasa.gov/research.html
International Satellite Cloud Climatology Project (ISCCP) ICE/SNOW in native (NAT) format Version: 1
ISCCP Science Team (1999); ISCCP IceSnow NAT; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ISCCP/ICESNOW_NAT
2001-08-01
Level 1 radiance data
1986-09-05HIRS/2NOAA-10
40
2016-09-08 14:10:2010266Future
Jean-Francois Legeais
ESA: European Space Agency
CLS; Collecte Localisation Satellites
OceanSea LevelRegional Sea LevelRegional Sea Level4243GlobalOcean25kmN/A30 days1 to 2 cm
2 to 5 mm/yr for the regional mean sea level trend according to the regions and 0;4 mm/yr for the associated global mean sea level trend derived from the sea level maps.
1993-01-152015-12-152017
Ananlyses of the Global mean sea level change; ocean circulation; sea level rise; ocean dynamics and processes; model development and validation.
Scientific research; technical applications; services; insurance
the polar regions are not totally covered by the satellites. The quality of the altimeter measurements is deteriorated close to the coast (<20km).
2015-12-31Level 22010-07-01SIRALCryoSat-2
41
2016-07-11 23:33:5210140CurrentPamela RinslandNASANASALandSnow CoverSnow Areal ExtentSnow Areal Extent5253
Global (90 degrees S - 90 degrees N)
All domains
One degree in latitude and longitude at the equator; 150 km equal area grid
N/A
5 days (average)
Because the analysis of snow cover is to a large extent a subjective process; some systematic changes in the analyses have been noted between the earliest charts and contemporary analyses. Part of the differences can be attributed to iproved satellites and sensors and increased experience and attention to detail; especially outside of North America. Early in the record; the Himalayan region was particularly susceptible to such inconsistencies. (Matson and Wiesnet; 1981).
1983-07-012009-12-31NATIVE
Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
1984
The ISCCP version of snow/ice data are created to help discriminate between ice/snow cover and clouds in performing cloud analysis of operational satellite imagery.

Section 11.2 of ISCCP SNOW/ICE Data at
https://badc.nerc.ac.uk/data/isccp_d2/snow.html

Section 11.2 of ISCCP SNOW/ICE Data at
https://badc.nerc.ac.uk/data/isccp_d2/snow.html and
http://isccp.giss.nasa.gov/research.html
International Satellite Cloud Climatology Project (ISCCP) ICE/SNOW in native (NAT) format Version: 1
ISCCP Science Team (1999); ISCCP IceSnow NAT; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/ISCCP/ICESNOW_NAT
2001-08-01
Level 1 radiance data
1986-09-05HIRS/2NOAA-10
42
2016-09-08 15:35:5010267Future
Jean-Francois Legeais
ECMWF: European Centre for Medium-range Weather Forecast within the Copernicus Climate Change Service (C3S)
CLS (Collecte Localisation Satellites) within the Copernicus Climate Change Service (C3S)
OceanSea LevelRegional Sea LevelRegional Sea Level4243GlobalOcean25km7 days1 to 2 cm
2 to 5 mm/yr for the regional mean sea level trend according to the regions and 0;4 mm/yr for the global mean sea level trend derived from the sea level maps.
1993-01-012015-12-312017
Ananlyses of the Global mean sea level change; ocean circulation; sea level rise; ocean dynamics and processes; model development and validation.
Scientific research; technical applications; services; insurance
the polar regions are not totally covered by the satellites. The quality of the altimeter measurements is deteriorated close to the coast (<20km)
2013-02-28Level 22012-01-01SIRALCryoSat-2
43
2016-07-11 23:34:4110142CurrentDeborah SmithNASANASAAtmosphereWater Vapour
Total Column Water Vapour
Total Column Water Vapour
78
0-360 degree longitude; -90-90 deg latitude
Ocean
0.25 deg x 0.25 deg
1 day (the daily file contains two layers; ascending and descending data swaths; so it actually is twice daily resolution or roughly 12 hours)
1 mm
0.05 mm/decade
1987-07-012016-12-31netCDF
FTP; OpenDAP; WGET
2016
Understanding Water Cycle Changes; Climate Model Validation; Study Of Long Term Trends (See http://www.remss.com/node/5096)
climate scientists; scientific researchers (See http://www.remss.com/node/5096)
SSMI and SSMIS V7 netCDF Water Vapor Data Product
Wentz;F.J.; K. Hilburn and D.K. Smith. 2012. RSS SSM/I Ocean Product Grids netCDF Collection [indicate subset used]. Data set available online [http://ghrc.nsstc.nasa.gov/] from the NASA EOSDIS Global Hydrology Resource Center Distributed Active Archive Center Huntsville; Alabama; U.S.A. doi: http://dx.doi.org/10.5067/MEASURES/SSMI-SSMIS/DATA301
1991-12-31L21987-07-09SSM/IDMSP-F08
44
2016-09-10 2:13:5410281CurrentEdward Armstrong
NASA (National Aeronautics and Space Administration)
NASA EOSDIS PO.DAAC
LandGroundwater
Groundwater Volume Change
Groundwater Volume Change
1001New to GCOS-20078NULLGlobalLand1 degreeMonthlyNot AssessedNot Assessed2002-04-012016-12-31netCDF4FTP/OPeNDAP/2016
Ocean basin mass changes
Climate scientists; ocean modelers; hydrologists
(Dataset also covers ocean)
JPL GRACE Mascon Ocean; Ice; and Hydrology Equivalent Water Height RL05M.1 CRI Filtered Version 2
D. N. Wiese; D.-N. Yuan; C. Boening; F. W. Landerer; M. M. Watkins. 2016. JPL GRACE Mascon Ocean; Ice; and Hydrology Equivalent Water Height RL05M.1 CRI Filtered Version 2. Ver. 2. PO.DAAC; CA; USA. Dataset accessed [YYYY-MM-DD] at http://dx.doi.org/10.5067/TEMSC-2LCR5.
2016-12-312002-04-01BlackJackGRACE (2 sats)
45
2016-07-11 23:35:1210143CurrentPamela RinslandNASANASAAtmosphereWater Vapour
Total Column Water Vapour
Total Column Water Vapour
78
Global - ocean only
Ocean
Low resolution: 1 degree by 1 degree
N/A1 per day
resolution dependent; The uncertainties of the retrieval algorithms for AIRS; SSM/I and HIRS are documented in their reference publications and in the ATBD. Further discussion was presented in the journal paper published in 2012 in Geophysical Research Letters. Additional validation was performed by the project with intercomparisons against each other and against radiosondes and GPS surface observations. Since NVAP-M uses a changing mixture of inputs through time as the number of satellites change; sampling in time and space can vary. This effect requires further study.
Estimated to be ~2 mm over ocean; as this record is from intercalibrated SSM/I radiances with the same algorithm.
1988-01-012009-06-01
netCDF 4.0; http://www.unidata.ucar.edu/software/netcdf/conventions.html
FTP; Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
2013
Quantifying Precipitation With Uncertainty Estimates To Monitor Global And Regional Water And Energy Budgets - used for studies of climate change; interannual variability; and independent comparison to other ocean-only datasets. Ideal for comparison to similar ocean-only microwave water vapor data sets
Ideal for comparison to similar ocean-only microwave water vapor data sets
NVAP_OCEAN_TPW Version: 1
NVAP Science Team; NVAP-M Data; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/NVAP-M/NVAP_OCEAN_Total-Precipitable-Water_L3.001
1991-12-31
Level 1 Radiances
1987-06-01SSM/IDMSP-F08
46
2016-09-13 18:59:2510287CurrentDaniel WunderNOAA/NESDIS/NCEI
NOAA/NESDIS/NCEI
Atmosphere
Cloud Properties
Cloud AmountCloud Amount1011GlobalAll domains
0.1 x 0.1 degree equal-angle grid
N/ADaily10%
There is no established stability index in the context of the GCOS metrics.
1979-01-012016-10-07
NetCDF-4 (level-2b) and hdf4 (level-2)
NCEI HDSS Access

NCEI Public FTP

NCEP public HTTP
2016
PATMOS-x AVHRR supports: NCEI AVHRR Aerosol TCDR; Climate analysis; Climate model verification
http://dx.doi.org/10.1038/nature18273;
http://dx.doi.org/10.1175/JCLI-D-14-00805.1;
http://dx.doi.org/10.1175/JCLI-D-14-00734.1;
http://dx.doi.org/10.1002/met.235;

Performance degraded in polar regions
NOAA Climate Data Record (CDR) of Cloud Properties from AVHRR Pathfinder Atmospheres - Extended (PATMOS-x); Version 5.3
Andrew K. Heidinger; Michael J. Foster; Andi Walther; Xuepeng Zhao; and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Cloud Properties from AVHRR Pathfinder Atmospheres - Extended (PATMOS-x); Version 5.3. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V5348HCK [access date]
2016-10-07Level-2b2007-06-28AVHRR/3Metop-A
47
2016-07-11 23:35:5310144CurrentPamela RinslandNASANASAAtmosphereWater Vapour
Total Column Water Vapour
Total Column Water Vapour
78
Global: Latitude 90 (N) to -90 (S); Longitude 180 (E) to -180 (W)
All domains
1 degree resolution
N/A1 time per day
resolution dependent; The uncertainties of the retrieval algorithms for AIRS; SSM/I and HIRS are documented in their reference publications and in the ATBD. Further discussion was presented in the journal paper published in 2012 in Geophysical Research Letters. Additional validation was performed by the project with intercomparisons against each other and against radiosondes and GPS surface observations. Since NVAP-M uses a changing mixture of inputs through time as the number of satellites change; sampling in time and space can vary. This effect requires further study.
Greater stability over ocean than land; but less stability than the NVAP-M Ocean product stability. This is because the HIRS instrument channelization and sampling changes in the period 1988-2009.
Over the land; the stability varies regionally. North Africa and S. America are less stable (see Schröder et al. 2016)
1998-01-012009-06-01
NetCDF 4.0; http://www.unidata.ucar.edu/software/netcdf/conventions.html
FTP; Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
2013
Quantifying Precipitation With Uncertainty Estimates To Monitor Global And Regional Water And Energy Budgets. Typical types of uses of NVAP-M include model intercomparison; process studies such as of the MJO; and hydrological moisture budget and transport studies.Comparison against regional climate models over Canada.Analysis of breakpoints and comparison to reanalysis and other satellite datasets.
Schröder; M.; Lockhoff; M.; Forsythe; J.M.; Cronk; H.Q.; Vonder Haar; T.H. and Bennartz; R.; 2016. The GEWEX water vapor assessment: Results from intercomparison; trend and homogeneity analysis of total column water vapour. Journal of Applied Meteorology and Climatology.

D. Paquin; A. Frigon & K. E. Kunkel (2016): Evaluation of Total Precipitable Water from CRCM4 using the NVAP-MEaSUREs Dataset and ERA-Interim Reanalysis Data;
Atmosphere-Ocean; DOI: 10.1080/07055900.2016.1230043

NVAP_CLIMATE_TPW Version: 1
NVAP Science Team; NVAP-M Data; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/NVAP-M/NVAP_CLIMATE_Total-Precipitable-Water_L3.001
1991-12-31
Level 1 Radiances
1987-06-01SSM/IDMSP-F08
48
2016-07-11 23:37:3410147CurrentKara GergelyNASANSIDC DAACLandSnow CoverSnow Areal ExtentSnow Areal Extent5253
Lat 90 to 0; Lon 180 to -180
Land25kmN/ADailyUnknownUnknown1999-01-012012-12-31NetCDFFTP; HTTPS2014
Assessments of climate variability and change; and in investigations regarding the role of snow cover in the climate system.
English; Jason M.; Andrew Gettelman; and Gina R. Henderson. 2015. Arctic Radiative Fluxes: Present-Day Biases and Future Projections in CMIP5 Models. Journal of Climate 28(15): 6019-6038. doi: 10.1175/JCLI-D-14-00801.1
MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0; Version 1
Robinson; D.; D. K. Hall; and T. L. Mote. 2014. MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0; Version 1. [Indicate subset used]. Boulder; Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0530.001. [Date Accessed]
2011-12-04unknown2002-05-02AMSR-EAqua
49
2016-07-11 23:37:4610148CurrentKara GergelyNASANSIDC DAACLandSnow CoverSnow Areal ExtentSnow Areal Extent5253
Lat 90 to 0; Lon 180 to -180
Land100kmN/AWeeklyUnknownUnknown1966-10-042012-12-31NetCDFFTP; HTTPS2014
Assessments of climate variability and change; and in investigations regarding the role of snow cover in the climate system.
Unknown
MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Weekly 100km EASE-Grid 2.0; Version 1
Robinson; D. and T. L. Mote. 2014. MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Weekly 100km EASE-Grid 2.0; Version 1. [Indicate subset used]. Boulder; Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0531.001. [Date Accessed]
1991-12-16unknown1987-07-07SSM/IDMSP-F08
50
2016-09-13 19:43:2410288CurrentGary EllingsonNOAA/NESDIS/NCEI
NOAA/NESDIS/NCEI
Atmosphere
Earth Radiation Budget
Top-of-Atmosphere ERB Shortwave (reflected)
Upward Shortwave Radiation at TOA
1718GlobalAll domains
0.1 x 0.1 degree equal-angle grid
N/ADaily10%
There is no established stability index in the context of the GCOS metrics.
1979-01-012016-10-07
NetCDF-4 (level-2b) and hdf4 (level-2)
NCEI HDSS Access

NCEI Public FTP

NCEP public HTTP
2016
PATMOS-x AVHRR supports: NCEI AVHRR Aerosol TCDR; Climate analysis; Climate model verification
http://dx.doi.org/10.1038/nature18273;
http://dx.doi.org/10.1175/JCLI-D-14-00805.1;
http://dx.doi.org/10.1175/JCLI-D-14-00734.1;
http://dx.doi.org/10.1002/met.235;

Performance degraded in polar regions.
NOAA Climate Data Record (CDR) of Cloud Properties from AVHRR Pathfinder Atmospheres - Extended (PATMOS-x); Version 5.3
Andrew K. Heidinger; Michael J. Foster; Andi Walther; Xuepeng Zhao; and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Cloud Properties from AVHRR Pathfinder Atmospheres - Extended (PATMOS-x); Version 5.3. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V5348HCK [access date]
2016-10-07Level-2b2007-06-28AVHRR/3Metop-A
51
2016-07-11 23:37:5510149CurrentKara GergelyNASANSIDC DAACOceanSea Ice
Sea Ice Extent/Edge
Sea Ice Extent/Edge4546
Lat 90 to 0; Lon 180 to -180
Ocean;Ice100kmN/AWeeklyUnknownUnknown1987-07-072012-12-31NetCDFFTP; HTTPS2015
Assessments of climate variability and change; and in investigations regarding the role of snow cover and sea ice in the climate system.
Unknown
MEaSUREs Northern Hemisphere State of Cryosphere Weekly 100km EASE-Grid 2.0; Version 1
Robinson; D.; M. Anderson; T. W. Estilow; and T. L. Mote. 2015. MEaSUREs Northern Hemisphere State of Cryosphere Weekly 100km EASE-Grid 2.0; Version 1. [Indicate subset used]. Boulder; Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0535.001. [Date Accessed].
1991-12-16unknown1987-07-07SSM/IDMSP-F08
52
2016-09-13 19:55:5510289CurrentGary EllingsonNOAA/NESDIS/NCEI
NOAA/NESDIS/NCEI
Atmosphere
Cloud Properties
Cloud Top Pressure (CTP)
Cloud Top Pressure1112GlobalAll domains
0.1 x 0.1 degree equal-angle grid
N/ADaily100 hpa
No established stability indes in the context of the GCOS metrics.
1979-01-012016-10-07
NetCDF-4 (level-2b) and hdf4 (level-2)
NCEI HDSS Access; HTTPS


NCEI Public FTP

NCEP public HTTP
2016
PATMOS-x AVHRR supports: NCEI AVHRR Aerosol TCDR; Climate analysis; Climate model verification.
http://dx.doi.org/10.1038/nature18273

http://dx.doi.org/10.1175/JCLI-D-14-00805.1

http://dx.doi.org/10.1175/JCLI-D-14-00734.1

http://dx.doi.org/10.1002/met.235
Performance degraded in polar regions.
NOAA Climate Data Record (CDR) of Cloud Properties from AVHRR Pathfinder Atmospheres - Extended (PATMOS-x); Version 5.3
Andrew K. Heidinger; Michael J. Foster; Andi Walther; Xuepeng Zhao; and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Cloud Properties from AVHRR Pathfinder Atmospheres - Extended (PATMOS-x); Version 5.3. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V5348HCK [access date]
2016-10-07Level-2b2007-06-28AVHRR/3Metop-A
53
2016-07-12 4:59:0010151CurrentBruce VollmerNASANASAAtmosphereWater Vapour
Tropospheric and Lower-stratospheric Profiles of Water Vapour
Tropospheric and Lower-stratospheric Profiles of Water Vapour
89GlobalAll domains100033010%
<0.5%/yr (but still being studied)
1991-09-012012-12-31netCDF-4http; Opendap2013
Impact On Radiative Forcing And Ozone
Wang; Y.; H. Su; J. H. Jiang; N. J. Livesey; M. L. Santee; L. Froidevaux; W. G. Read; and J. Anderson; The linkage between stratospheric water vapor and surface temperature in an observation constrained coupled general circulation model; Clim. Dyn.; doi:10.1007/s00382-016-3231-3; 2016.
Zonal Averages on a Vertical Pressure Grid
GOZCARDS Merged Water Vapor 1 month L3 10 degree Zonal Means on a Vertical Pressure Grid V1
To cite the data in publications:
Anderson; J.; L. Froidevaux; R. A. Fuller; P. F. Bernath; N. J. Livesey; H. C. Pumphrey; W. G. Read; J. M. Russell III; and K. A. Walker (2013); GOZCARDS Merged Water Vapor 1 month L3 10 degree Zonal Means on a Vertical Pressure Grid V1; version 1; Greenbelt; MD; USA; Goddard Earth Sciences Data and Information Services Center (GES DISC);Accessed [Enter User Data Access Date at] 10.5067/MEASURES/GOZCARDS/DATA3004
2012-12-3122004-08-01MLSAura
54
2016-09-13 21:16:2410293CurrentGary EllingsonNOAA; NCEINOAA; NCEILandFAPARFAPARFAPAR6394
Joint reqs for modelling / adaptation
GlobalLand0.05 DegreeN/ADaily0.05
No established stability index in the context of the GCOS metrics.
1981-06-242016-09-22NetCDF-4
ftp; https; Geoportal
2016
- Evaluating vegetation stress
- Forecasting agricultural yields
- Forestry and crop management
- Carbon cycle modeling
Not available
NOAA Climate Data Record (CDR) of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR); Version 4
Martin Claverie; Eric Vermote; Chris Justice; Ivan Csiszar; Jeff Eidenshink; Ranga Myneni; Frederic Baret; Ed Masuoka; Robert Wolfe and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation (LAI/FAPAR); Version 4. [indicate subset used]. NOAA National Climatic Data Center. doi:10.7289/V5M043BX [access date]
1994-12-31Level-1b1988-11-08AVHRR/2NOAA-11
55
2016-07-12 5:00:1710152CurrentBruce VollmerNASANASAAtmosphere
Earth Radiation Budget
Surface ERB Shortwave
Upward Shortwave Radiation at Surface
1003Partial component; no reqs set1003
Partial component; no reqs set
GlobalAll domains2 x 5 degrees
N/A (Total Column)
102%2%1980-01-012012-12-31HDF-EOShttp2013
See 1. Herman; J. R.; G. Labow; N. C. Hsu; and D. Larko (2009); Changes in cloud and aerosol cover (1980-2006) from reflectivity time series using SeaWiFS; N7-TOMS; EP-TOMS; SBUV-2; and OMI radiance data; J. Geophys. Res.; 114; D01201; doi:10.1029/2007JD009508.:
2. Labow; G. J.; J. R. Herman; L.-K. Huang; S. A. Lloyd; M. T. DeLand; W. Qin; J. Mao; and D. E. Larko (2011); Diurnal variation of 340 nm Lambertian equivalent reflectivity due to clouds and aerosols over land and oceans; J. Geophys. Res.; 116; D11202; doi:10.1029/2010JD014980.
The following publications illustrate a sample of users of this dataset:
Bais; A. F.; et al. 'Ozone depletion and climate change: impacts on UV radiation.' Photochemical & Photobiological Sciences 14.1 (2015): 19-52.

Krotkov; Nickolay A.; et al. 'Aura OMI observations of regional SO 2 and NO 2 pollution changes from 2005 to 2015.' Atmospheric Chemistry and Physics16.7 (2016): 4605-4629.

Damiani; A.; et al. 'Cloud cover and UV index estimates in Chile from satellite-derived and ground-based data.' Atmospheric Research 138 (2014): 139-151.

McLean; John. 'Late Twentieth-Century Warming and Variations in Cloud Cover.' Atmospheric and Climate Sciences 4.04 (2014): 727.

Weaver; Clark; et al. 'Shortwave TOA cloud radiative forcing derived from a long-term (1980–present) record of satellite UV reflectivity and CERES measurements.' Journal of Climate 28.23 (2015): 9473-9488.

Sánchez-Lugo; A.; et al. 'REGIONAL CLIMATES.' Bulletin of the American Meteorological Society 95.7 (2014): S157.

Damiani; A.; et al. 'Changes in the UV Lambertian equivalent reflectivity in the Southern Ocean: Influence of sea ice and cloudiness.' Remote Sensing of Environment 169 (2015): 75-92.

Thejll; P.; H. Gleisner; and C. Flynn. 'Influence of celestial light on lunar surface brightness determinations: Application to earthshine studies.'Astronomy & Astrophysics 573 (2015): A131.

Song; Jinjie; Yuan Wang; and Jianping Tang. 'A Hiatus of the Greenhouse Effect.' Scientific Reports 6 (2016).
Multi-Satellite Lambertian Equivalent Reflectivity (Noon Normalized) 10-Day L3 Global 2.0x5.0deg Lat/Lon Grid V1
To cite the data in publications:
J.R. Herman; et al. (2013); Multi-Satellite Lambertian Equivalent Reflectivity (Noon Normalized) 10-Day L3 Global 2.0x5.0deg Lat/Lon Grid V1; version 1; Greenbelt; MD; USA; Goddard Earth Sciences Data and Information Services Center (GES DISC);Accessed [Enter User Data Access Date at] 10.5067/MEASURES/MSLER/DATA304
1990-06-2131978-11-01SBUVNimbus-7
56
2016-07-12 5:01:2310153CurrentKara GergelyNASANSIDC DAACLandSoil MoistureFreeze/ThawFreeze/Thaw1001New to GCOS-20083NULL
Lat 87 to -87; Lon 180 to -180
Land;Ice25kmN/ADaily
>80% mean annual spatial classification accuracy
Unknown1979-01-012016-12-31
GeoTIFF; HDF; GIF
FTP; HTTPS2016
The F/T-ESDR will provide a consistent and well calibrated record of the spatial pattern; temporal variability and long-term changes in terrestrial F/T state dynamics; enabling accurate assessment of associated changes in terrestrial growing seasons and vegetation productivity; seasonal snow cover; permafrost and land-atmosphere energy; water and carbon exchanges. The enhanced precision and consistent temporal record provided by the F/T-ESDR will improve measurement and diagnosis of climate change trajectories and impacts to the global biosphere. Anticipated applications of the F/T-ESDR include global change assessment and monitoring; numerical weather forecasting; hydrological and biospheric assessment and forecasting. https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects/esdr-for-land-surface-freeze-thaw-state
Høgda; Stein Rune Karlsen; Victor Brovkin; Ramakrishna R. Nemani; and B. Ranga. 'Changes in Growing Season Duration and Productivity of Northern 3 Vegetation Inferred from Long-term Remote Sensing Data 4.'
MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status; Version 3
Kim; Y.; J. S. Kimball; J. Glassy; and K. C. McDonald. 2014. MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status; Version 3. [Indicate subset used]. Boulder; Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0477.003. [Date Accessed]
2011-09-27unknown2002-06-19AMSR-EAqua
57
2016-09-13 21:25:1610294CurrentGary EllingsonNOAA; NCEI

NOAA; NCEI

LandLAILAILAI6493
Joint reqs for modelling / adaptation
GlobalLand0.05 DegreeN/ADaily
Effective LAI 0.08 True LAI 0.25
No established stability index in the context of the GCOS metrics.
1981-06-242016-09-22NetCDF-4
ftp; https; Geoportal
2016
- Evaluating vegetation stress
- Forecasting agricultural yields
- Forestry and crop management
- Carbon cycle modeling
Not available
NOAA Climate Data Record (CDR) of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR); Version 4
Martin Claverie; Eric Vermote; Chris Justice; Ivan Csiszar; Jeff Eidenshink; Ranga Myneni; Frederic Baret; Ed Masuoka; Robert Wolfe and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation (LAI/FAPAR); Version 4. [indicate subset used]. NOAA National Climatic Data Center. doi:10.7289/V5M043BX [access date]
1994-12-31Level-1b1988-11-08AVHRR/2NOAA-11
58
2016-07-12 5:01:5710154CurrentBruce VollmerNASANASAAtmosphereOzone
Ozone Profile in Upper Troposphere and Lower Stratosphere
Ozone Profile in Upper Troposphere and Lower Stratosphere
2929GlobalAll domains100033010%
<0.5%/yr (but still being studied)
1979-02-012012-12-31netCDF-4http2013
Ozone Radiative Heating And Impact On Dynamics
1.Tummon; F.; et al.; Intercomparison of vertically resolved merged satellite ozone datasets: interannual variability and long-term trends; Atmos. Chem. Phys.; 15; 3021-3043; doi:10.5194/acp-15-3021-2015; 2015.

2.Nair; P. J.; et al.; Subtropical and midlatitude ozone trends in the stratosphere: Implications for recovery; J. Geophys. Res. Atmos.; 120; 7247-7257; doi:10.1002/2014JD022371; 2015.

3. Harris; N. R. P.; et al.; Past changes in the vertical distribution of ozone-Part 3: Analysis and interpretation of trends; Atmos. Chem. Phys.; 15; 9965-9982; doi:10.5194/acp-15-9965-2015; 2015.

4. WMO-World Meteorological Organization: Scientific Assessment of Ozone Depletion: 2014; Global Ozone Research and Monitoring Project Report No. 55; 416 pp.; Geneva; Switzerland; 2014.
GOZCARDS Merged Ozone 1 month L3 10 degree Zonal Means on a Vertical Pressure Grid V1
To cite the data in publications:
Wang; R.; L. Froidevaux; J. Anderson; R. A. Fuller; P. F. Bernath; M. P. McCormick; N. J. Livesey; J. M. Russell III; K. A. Walker; and J. M. Zawodny (2013); GOZCARDS Merged Ozone 1 month L3 10 degree Zonal Means on a Vertical Pressure Grid V1; version 1; Greenbelt; MD; USA; Goddard Earth Sciences Data and Information Services Center (GES DISC);Accessed [Enter User Data Access Date at] 10.5067/MEASURES/GOZCARDS/DATA3006
1981-11-1921979-02-18SAGE-IAEM-2
59
2016-07-18 19:12:3210173CurrentNorman LoebNASA
NASA Langley Atmospheric Science Data Center
Atmosphere
Earth Radiation Budget
Top-of-Atmosphere ERB Shortwave (reflected)
Upward Shortwave Radiation at TOA
1718180/360All domains
1 degree by 1 degree
monthly
monthly regional: 4 W/m2 at 1 sigma
0.3 W/m2 per Decade at 2 sigma
2000-03-012016-05-31NetCDF
Internet and Obs4MIPs
2016
Earth Radiation Energy budget time series; Climate model assessment

Norman Loeb; Natividad Manalo-Smith; Wenying Su; Mohan Shankar; Susan Thomas; CERES Top-of-Atmosphere Earth Radiation Budget Climate Data Record: Accounting for in-Orbit Changes in Instrument Calibration; Remote Sens.; 2016; 8; 182; doi: 10.3390/rs8030182.

Hailan Wang and Wenying Su; The ENSO Effects on Tropical Clouds and Top-of-Atmosphere Cloud Radiative Effects in CMIP5 Models; J. Geophys. Res.; 120; 4443-4465; doi:10.1002/2014JD022337; 2015.

Hailan Wang and Wenying Su; Evaluating and Understanding Top-of-Atmosphere Cloud Radiative Effects in IPCC-AR5/CMIP5 Models using Satellite Observations; J. Geophys. Res.; 118; doi:10.1029/2012JD018619; 2013.

Climate modeling centers; climate scientists; weather forecast research
global coverage
2016-05-31Level 12002-07-01CERESAqua
60
2016-09-20 12:22:2210320FutureRainer HollmannEUMETSAT (CM SAF)
FCDR EUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
AtmospherePrecipitationPrecipitation
Precipitation (liquid and solid)
22
-90 deg to 90 deg
-180 deg to 180 deg
Ocean0.5 x 0.5 degn/Amonthly
1.6 mm/d mean bias (threshold);
2.25 mm/d rms (threshold)
0.03 mm/d per decade (threshold)
1987-07-092013-12-312017
Ocean Fluxes; Validation Of Ocean Modelling
User publications involving HOAPS data:
Alhammoud et al. 2014: http://doi.org/10.3390/atmos5020370
Brueck et al. 2015: http://doi.org/10.1175/jas-d-14-0054.1
Burdanowitz et al. 2015: http://doi.org/10.1175/jamc-d-14-0146.1
Donges et al. 2015: http://doi.org/10.1007/s00382-015-2479-3
Kent et al. 2013: http://doi.org/10.1002/joc.3606
Poli et al. 2016: http://doi.org/10.1175/jcli-d-15-0556.1
Prytherch et al. 2015: http://doi.org/10.1002/joc.4150
ice free ocean only
1991-12-18level 1c1987-07-09SSM/IDMSP-F08
61
2016-07-19 14:23:0010177Current
Jean-Francois Legeais
ESA: European Space Agency
CLS; Collecte Localisation Satellites
OceanSea LevelRegional Sea LevelRegional Sea Level4243GlobalOcean25kmN/A30 days1 to 2 cm
0;5 mm/yr for the global mean sea level trend and 2 to 6 mm/yr for the regional mean sea level trend according to the regions.
1993-01-152014-12-15netcdf CF
ftp
currently submitted to Obs4MIPs
2015
Ananlyses of the Global mean sea level change; ocean circulation; sea level rise; ocean dynamics and processes; model development and validation.
- Cazenave A.; Dieng H.B.; Meyssignac B.; von Schuckmann K. Decharme B. and Berthier E.; The rate of sea level rise. Nature Climate Change; vol 4; 358-361; DOI: 10.1038/NCLIMATE2159; 2014.
- Dieng; H. B.; Palanisamy; H; Cazenave; A.; Meyssignac; B.; Von Schuckmann; K. (2015) The Sea Level Budget Since 2003: Inference on the Deep Ocean Heat Content; Survey in Geophysics. DOI 10.1007/s10712-015-9314-6.
- Dieng H.; Cazenave A.; von Schuckmann K.; Ablain M. and Meyssignac B.; Sea level budget over 2005-2013: missing contributions and data errors; Ocean Science Discussions; 11; 1-33; doi:10.5194/osd-11-1-2015; 2015.
- Henry O.; Ablain M.; Meyssignac B.; Cazenave A.; Masters D.; Nerem S.; Leuliette E. and Garric G.; Effect of the processing methodology on satellite altimetry-based global mean sea level rise over the Jason-1 operating period; J. of Geodesy; 88:351–361; doi: 10.1007/s00190-013-0687-3; 2014.
- Legeais; J.-F.; Prandi; P.; and Guinehut; S. (2016) Analyses of altimetry errors using Argo and GRACE data; Ocean Sci.; 12; 647-662; doi:10.5194/os-12-647-2016.
- Meyssignac; B.; Piecuch; C.G.; Merchant; C.J.; Racault; M-F.; Palanisamy; H.; McIntosh; C.; Sathyendranath; S.; Brewin; R. (2016) Causes of the regional variability in observed sea level; sea surface temperature and ocean colour; Surveys in Geophysics; Manuscript Number: GEOP-D-16-00027.
- Palanisamy; H. Cazenave A.; Delcroix T. and Meyssignac B.; Spatial trend patterns in Pacific Ocean sea level during the altimetry era : the contribution of thermocline depth change and internal climate variability; Ocean Dynamics; DOI 10.1007/s10236-014-0805-7.
- Zuo; H.; Balmaseda; M. A. and Mogensen; K. (2015) The new eddy-permitting ORAP5 ocean reanalysis: description; evaluation and uncertainties in climate signals. Climate Dynamics; 10.1007/s00382-015-2675-1

the polar regions are not totally covered by the satellites. The quality of the altimeter measurements is deteriorated close to the coast (<20km).
ESACCI-SEALEVEL-L4-MSLA-MERGED-19930115000000-fv01
Ablain; M.; Cazenave; A.; Larnicol; G.; Balmaseda; M.; Cipollini; P.; Faug�?�¨re; Y.; Fernandes; M. J.; Henry; O.; Johannessen; J. A.; Knudsen; P.; Andersen; O.; Legeais; J.; Meyssignac; B.; Picot; N.; Roca; M.; Rudenko; S.; Scharffenberg; M. G.; Stammer; D.; Timms; G.; and Benveniste; J.: Improved sea level record over the satellite altimetry era (1993-2010) from the Climate Change Initiative project; Ocean Sci.; 11; 67-82; doi:10.5194/os-11-67-2015; 2015.
2012-04-01Level 22002-09-01RA-2Envisat
62
2016-07-22 10:11:4610182CurrentRainer HollmannEUMETSAT (CM SAF)
FCDR EUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind speed over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 deg to 90 deg
-180 deg to 180 deg
Ocean0.5 x 0.5 degn/Amonthly
0.24 m/s bias;
0.15 m/s rms
0.09 m/s per decade
1987-07-092008-12-31netcdfftp; disk2012
Ocean Fluxes; Validation Of Ocean Modelling
User publications involving HOAPS data:
Alhammoud et al. 2014: http://doi.org/10.3390/atmos5020370
Brueck et al. 2015: http://doi.org/10.1175/jas-d-14-0054.1
Burdanowitz et al. 2015: http://doi.org/10.1175/jamc-d-14-0146.1
Donges et al. 2015: http://doi.org/10.1007/s00382-015-2479-3
Kent et al. 2013: http://doi.org/10.1002/joc.3606
Poli et al. 2016: http://doi.org/10.1175/jcli-d-15-0556.1
Prytherch et al. 2015: http://doi.org/10.1002/joc.4150
ice free oceanHOAPS 3.2
Fennig et al. 2012: Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data - HOAPS 3.2 - Monthly Means / 6-Hourly Composites. Satellite Application Facility on Climate Monitoring. http://doi.org/10.5676/EUM_SAF_CM/HOAPS/V001.
publication reference: Andersson et al. 2010: http://doi.org/10.5194/essd-2-215-2010
1991-12-18level 1c1987-07-09SSM/IDMSP-F08
63
2016-07-22 23:04:5410183CurrentChristina HsuNASANASAAtmosphere
Aerosol Properties
Aerosol Optical Depth
Aerosol Optical Depth3131globalAll domains
13.5 km x 13.5 km
Total columndaily
0.03+10% over water and 0.05+15% over land
0.05 over the 13 years of mission
1997-09-012010-12-31HDF5internet2012
environmental and climate-monitoring products and assessments; such as IPCC assessments
air quality: van Donkelaar; A.; R.V Martin; M.Brauer; N. C. Hsu; R. A. Kahn; R. C Levy; A. Lyapustin; A. M. Sayer; and D. M Winker (2016); Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites; Models; and Monitors; Environ. Sci. Technol.; doi: 10.1021/acs.est.5b05833;
climate study community: Hsu; N. C.; Gautam; R.; Sayer; A. M.; Bettenhausen; C.; Li; C.; Jeong; M. J.; Tsay; S.-C.; and Holben; B. N. (2012); Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010; Atmos. Chem. Phys.; 12; 8037-8053; doi:10.5194/acp-12-8037-2012. Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis. Atmos. Meas. Tech.; 7; 2531-2549; doi:10.5194/amt-7-2531-2014.
SWDB_L2: SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Level 2 Data V004
N. Christina Hsu; Andrew M. Sayer; M.-J. Jeong; and Corey Bettenhausen (2013); SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Daily Level 3 Data Gridded at 1.0 Degrees V004; Greenbelt; MD; USA; Goddard Earth N. Christina Hsu; Andrew M. Sayer; M.-J. Jeong; and Corey Bettenhausen (2013); SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Level 2 Data V004; Greenbelt; MD; USA; Goddard Earth Sciences Data and Information Services Center (GES DISC); Accessed [Data Access Date] DOI:10.5067/MEASURES/SWDB/DATA201
2010-12-31Level 21997-09-01SeaWiFS
OrbView-2/SeaStar
64
2016-09-20 12:27:0710321FutureRainer HollmannEUMETSAT (CM SAF)
FCDR EUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
AtmospherePrecipitationPrecipitation
Precipitation (liquid and solid)
22
-90 deg to 90 deg
-180 deg to 180 deg
Ocean0.5 x 0.5 degn/Amonthly
1.6 mm/d mean bias (threshold);
2.25 mm/d rms (threshold)
0.03 mm/d per decade (threshold)
1987-07-092019-12-312021
Ocean Fluxes; Validation Of Ocean Modelling
User publications involving HOAPS data:
Alhammoud et al. 2014: http://doi.org/10.3390/atmos5020370
Brueck et al. 2015: http://doi.org/10.1175/jas-d-14-0054.1
Burdanowitz et al. 2015: http://doi.org/10.1175/jamc-d-14-0146.1
Donges et al. 2015: http://doi.org/10.1007/s00382-015-2479-3
Kent et al. 2013: http://doi.org/10.1002/joc.3606
Poli et al. 2016: http://doi.org/10.1175/jcli-d-15-0556.1
Prytherch et al. 2015: http://doi.org/10.1002/joc.4150
ice free ocean only
2011-12-13level 1c2002-01-01AMSR-EAqua
65
2016-07-26 0:00:0010185CurrentPamela RinslandNASANASAAtmosphereWater Vapour
Tropospheric and Lower-stratospheric Profiles of Water Vapour
Tropospheric and Lower-stratospheric Profiles of Water Vapour
89
Global coverage
All domains
1 degree by 1 degree
Variable (based on 4 pressure levels) surface to 700 hPa; 700 to 500 hPa; 500 to 300 hPa; >300 hPa
1 time per day
varies with time due to satellite sampling; The uncertainties of the retrieval algorithms for AIRS; SSM/I and HIRS are documented in their reference publications and in the ATBD. Further discussion was presented in the journal paper published in 2012 in Geophysical Research Letters. Additional validation was performed by the project with intercomparisons against each other and against radiosondes and GPS surface observations. Since NVAP-M uses a changing mixture of inputs through time as the number of satellites change; sampling in time and space can vary. This effect requires further study.
varies with time due to satellite sampling - the stability of the Layered-Precipitable Water product has not been quantified.
1988-01-012009-06-01
netCDF 4.0; http://www.unidata.ucar.edu/software/netcdf/conventions.html
FTP; Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
2013
Monitor The Global And Regional Distribution Of Atmospheric Water Vapor; used for studies of climate change and interannual variability. Typical types of uses of NVAP-M include model intercomparison; process studies such as of the MJO; and hydrological moisture budget and transport studies. Also observational layered precipitable water in data sparse regions for radiative transfer calculations.
Rondanelli; Roberto; Alejandra Molina; and Mark Falvey; 2015:. 'The Atacama surface solar maximum.' Bulletin of the American Meteorological Society (96) 405-418.
NVAP_CLIMATE_LPW Version: 1
NVAP Science Team; NVAP-M Data; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/NVAP-M/NVAP_CLIMATE_Layered-Precipitable-Water_L3.001
1991-12-31
Level 1 Radiances
1987-06-01SSM/IDMSP-F08
66
2016-09-21 11:06:4210339FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Cloud Properties
Cloud AmountCloud Amount1011
-90 deg to 90 deg
-180 deg to 180 deg
All domains0.25 x 0.25 degn/Amonthly
10% (threshold)
40% bc-rms (threshold)
5 % per decade (threshold)
1982-01-012015-12-312017
global cloud climatologies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
67
2016-07-26 21:53:2510187CurrentChristina HsuNASANASAAtmosphere
Aerosol Properties
Aerosol Optical Depth
Aerosol Optical Depth3131GlobalAll domains
0.5 deg latitude x 0.5 deg longitude
total columndaily
0.03+10% over water and 0.05+15% over land
0.05 over the 13 years of mission
1997-09-012010-12-31HDF5internet2012
environmental and climate-monitoring products and assessments; such as IPCC assessments
air quality: van Donkelaar; A.; R.V Martin; M.Brauer; N. C. Hsu; R. A. Kahn; R. C Levy; A. Lyapustin; A. M. Sayer; and D. M Winker (2016); Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites; Models; and Monitors; Environ. Sci. Technol.; doi: 10.1021/acs.est.5b05833;
climate study community: Hsu; N. C.; Gautam; R.; Sayer; A. M.; Bettenhausen; C.; Li; C.; Jeong; M. J.; Tsay; S.-C.; and Holben; B. N. (2012); Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010; Atmos. Chem. Phys.; 12; 8037-8053; doi:10.5194/acp-12-8037-2012. Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis. Atmos. Meas. Tech.; 7; 2531-2549; doi:10.5194/amt-7-2531-2014.
SWDB_L305: SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Daily Level 3 Data Gridded at 0.5 Degrees V004
N. Christina Hsu; Andrew M. Sayer; M.-J. Jeong; and Corey Bettenhausen (2013); SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Daily Level 3 Data Gridded at 0.5 Degrees V004; Greenbelt; MD; USA; Goddard Earth Sciences Data and Information Services Center (GES DISC); Accessed [Data Access Date] DOI:10.5067/MEASURES/SWDB/DATA301
2010-12-31Level 31997-09-01SeaWiFS
OrbView-2/SeaStar
68
2016-09-21 11:15:1310341FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Cloud Properties
Cloud Top Pressure (CTP)
Cloud Top Pressure1112
-90 deg to 90 deg
-180 deg to 180 deg
All domains
0.25 x 0.25 deg (25 km equal area polar grid also available)
n/Adaily
80 hPa bias (threshold)
120 hPa bc-rms (threshold)
30 hPa per decade (threshold)
1982-01-012015-12-312017
global cloud climatologies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
69
2016-07-26 22:02:0110188CurrentChristina HsuNASANASAAtmosphere
Aerosol Properties
Aerosol Optical Depth
Aerosol Optical Depth3131GlobalAll domains
1 deg latitude x 1 deg longitude
Total columndaily
0.03+10% over water and 0.05+15% over land
0.05 over the 13 years of mission
1997-09-012010-12-31HDF5internet2012
environmental and climate-monitoring products and assessments; such as IPCC assessments
air quality: van Donkelaar; A.; R.V Martin; M.Brauer; N. C. Hsu; R. A. Kahn; R. C Levy; A. Lyapustin; A. M. Sayer; and D. M Winker (2016); Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites; Models; and Monitors; Environ. Sci. Technol.; doi: 10.1021/acs.est.5b05833;
climate study community: Hsu; N. C.; Gautam; R.; Sayer; A. M.; Bettenhausen; C.; Li; C.; Jeong; M. J.; Tsay; S.-C.; and Holben; B. N. (2012); Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010; Atmos. Chem. Phys.; 12; 8037-8053; doi:10.5194/acp-12-8037-2012. Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis. Atmos. Meas. Tech.; 7; 2531-2549; doi:10.5194/amt-7-2531-2014.
SWDB_L310: SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Daily Level 3 Data Gridded at 1.0 Degrees V004
N. Christina Hsu; Andrew M. Sayer; M.-J. Jeong; and Corey Bettenhausen (2013); SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Daily Level 3 Data Gridded at 1.0 Degrees V004; Greenbelt; MD; USA; Goddard Earth Sciences Data and Information Services Center (GES DISC); Accessed [Data Access Date] DOI:10.5067/MEASURES/SWDB/DATA302
2010-12-31Level 31997-09-01SeaWiFS
OrbView-2/SeaStar
70
2016-09-21 11:20:3110342FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Cloud Properties
Cloud Top Temperature (CTT)
Cloud Top Temperature1213
-90 deg to 90 deg
-180 deg to 180 deg
All domains
0.25 x 0.25 deg (25 km equal area polar grid also available)
n/Adaily
bias: 2 K / 3 K / 5 K for low / mid / high clouds bc-rms: 5 K/ 7 K / 9 K for low / mid / high clouds
0.2 K / dec1982-01-012015-12-312017
global cloud climatologies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
71
2016-07-28 13:40:2010196CurrentRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
AtmosphereWater Vapour
Total Column Water Vapour
Total Column Water Vapour
78
-80 deg to 80 deg
-180 deg to 180 deg
All domains
90km x 90km sinusoidal equal area grid
5 level profile: surface-850; 850-700; 700-500; 500-300; 300-200 hPa
monthly
-0.16 kg/m2 bias;
3.25 kg/m2 rms
1.9 % decadal stab
1999-01-012011-12-31netcdfftp; disk2013
Climate Research
NMHSs
WCRP GEWEX
WMO RCC-CM for RA-VI
see 'climate applications'
User publications involving ATOVS data:
Obregon et al. 2014: http://doi.org/10.5194/asr-11-25-2014
WVT_ATOVS 1
Courcoux and Schroeder 2013: Vertically integrated water vapour; humidity and temperature at pressures levels and layers from ATOVS - Daily Means / Monthly Means. Satellite Application Facility on Climate Monitoring. http://dx.doi.org/10.5676/EUM_SAF_CM/WVT_ATOVS/V001.
publication reference: Courcoux and Schroeder 2015: http://doi.org/10.5194/essd-7-397-2015
2011-12-31level 1c2007-06-01HIRS/4Metop-A
72
2016-09-21 11:24:3210344FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Cloud Properties
Cloud Water Path (liquid and ice)(CWP)
Cloud Water Path (liquid)14
Reqs do not distinguish liquid / ice
15
Reqs do not distinguish liquid / ice
-90 deg to 90 deg
-180 deg to 180 deg
All domains
0.25 x 0.25 deg (25 km equal area polar grid also available)
n/Amonthly
20 g/m2 bias (threshold)
40 g/m2 rms (threshold)
5 g/m2 decadal stability (threshold)
1982-01-012015-12-312017
global cloud climatologies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
73
2016-07-29 23:04:5810198CurrentChristina HsuNASANASAAtmosphere
Aerosol Properties
Aerosol Optical Depth
Aerosol Optical Depth3131GlobalAll domains
1.0 deg latitude x 1.0 deg longitude
total columnmonthly
0.03+10% over water and 0.05+15% over land
0.02 over the 13 years of mission
1997-09-012010-12-31HDF5http2012
environmental and climate-monitoring products and assessments; such as IPCC assessments
air quality and climate study community
SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Monthly Level 3 Data Gridded at 1.0 Degrees V004
To cite the data in publications: N. Christina Hsu; Andrew M. Sayer; M.-J. Jeong; and Corey Bettenhausen (2013); SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Monthly Level 3 Data Gridded at 1.0 Degrees V004; version 004; Greenbelt; MD; USA; Goddard Earth Sciences Data and Information Services Center (GES DISC);Accessed [Enter User Data Access Date at] 10.5067/MEASURES/SWDB/DATA304
2010-12-31Level 31997-09-01SeaWiFS
OrbView-2/SeaStar
74
2016-09-21 11:26:1610345FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Cloud Properties
Cloud Water Path (liquid and ice)(CWP)
Cloud Water Path (ice)14
Reqs do not distinguish liquid / ice
15
Reqs do not distinguish liquid / ice
-90 deg to 90 deg
-180 deg to 180 deg
All domains
0.25 x 0.25 deg (25 km equal area polar grid also available)
n/Amonthly
40 g/m2 bias (threshold)
80 g/m2 rms (threshold)
10 g/m2 decadal stability (threshold)
1982-01-012015-12-312017
global cloud climatologies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
75
2016-07-29 23:08:4210199CurrentChristina HsuNASANASAAtmosphere
Aerosol Properties
Aerosol Optical Depth
Aerosol Optical Depth3131GlobalAll domains
0.5 deg latitude x 0.5 deg longitude
total columnmonthly
0.03+10% over water and 0.05+15% over land
0.02 over the 13 years of mission
1997-09-012010-12-31HDF5http2012
environmental and climate-monitoring products and assessments; such as IPCC assessments
air quality and climate study community
SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Monthly Level 3 Data Gridded at 0.5 Degrees V004
To cite the data in publications: N. Christina Hsu; Andrew M. Sayer; M.-J. Jeong; and Corey Bettenhausen (2013); SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Monthly Level 3 Data Gridded at 0.5 Degrees V004; version 004; Greenbelt; MD; USA; Goddard Earth Sciences Data and Information Services Center (GES DISC);Accessed [Enter User Data Access Date at] 10.5067/MEASURES/SWDB/DATA303
2010-12-31Level 31997-09-01SeaWiFS
OrbView-2/SeaStar
76
2016-09-21 21:40:4510353FutureJohn Dwyer
U.S. Geological Survey (USGS)
U.S. Geological Survey (USGS)
Land
Land-Surface Temperature
Land-Surface Temperature
Land-Surface Temperature
7071
6 - 76 degrees north; 73 - 157 degrees west
Land
120m resampled to 30m
N/A16 day
Yet to be determined
Yet to be determined
1984-03-012011-11-302017
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.
Government and university research and applications scientists
North America2011-11-301b1984-03-01TMLandsat-5
77
2016-08-01 22:44:2810202CurrentChristina HsuNASANASAAtmosphere
Aerosol Properties
Aerosol Optical Depth
Aerosol Optical Depth3131GlobalLand10 Kmtotal columndaily
Retrieval-level uncertainty estimates provided; global average uncertainty of order 0.03+20%
0.052000-03-012016-12-31EOS HDF4internet2016
environmental and climate-monitoring products and assessments; such as IPCC assessments; assimilation; air-quality.
Assimilation: the US Naval Research Laboratory and UK Met Office both assimilate the MODIS Deep Blue aerosol data set

Air quality: Ma; Z.; X. Hu; A. M. Sayer; R. Levy; Q. Zhang; Y. Xue; S. Tong; J. Bi; L. Huang; and Y. Liu (2016); Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China; 2004–2013; Environ Health Perspect; DOI:10.1289/ehp.1409481; van Donkelaar; A.; R.V Martin; M.Brauer; N. C. Hsu; R. A. Kahn; R. C Levy; A. Lyapustin; A. M. Sayer; and D. M Winker (2016); Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites; Models; and Monitors; Environ. Sci. Technol.; doi: 10.1021/acs.est.5b05833

Climate study community: Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part II: Using maximum covariance analysis to effectively compare spatio-temporal variability of satellite and AERONET measured aerosol optical depth. J. Geophys. Res. Atmos.; 119; no. 1; 153-166; doi:10.1002/2013JD020537; Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part III: Using combined PCA to compare spatio-temporal variability of MODIS; MISR and OMI aerosol optical depth. J. Geophys. Res. Atmos.; 119; no. 7; 4017-4042; doi:10.1002/2013JD020538; Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis. Atmos. Meas. Tech.; 7; 2531-2549; doi:10.5194/amt-7-2531-2014; Ginoux; P.; J. M. Prospero; T. E. Gill; N. C. Hsu; and M. Zhao (2012); Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products; Rev. Geophys.; 50; RG3005; doi:10.1029/2012RG000388.
MOD04_L2: MODIS/Terra Aerosol 5-Min L2 Swath 10km V006
Levy; R.; Hsu; C.; et al.; 2015. MODIS Atmosphere L2 Aerosol Product. NASA MODIS Adaptive Processing System; Goddard Space Flight Center; USA: http://dx.doi.org/10.5067/MODIS/MOD04_L2.006
2016-12-31Level 22000-03-01MODISTerra
78
2016-09-22 12:32:4010354FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Earth Radiation Budget
Surface ERB Shortwave
Downward Shortwave Radiation at Surface
1003Partial component; no reqs set1003
Partial component; no reqs set
-90 deg to 90 deg
-180 deg to 180 deg
All domains0.25 x 0.25 degn/Amonthly
15 W/m2 (threshold)
4 W/m2 per decade (threshold)
1982-01-012015-12-312017
earth radiation budget studies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
79
2016-08-02 22:27:1610208CurrentChristina HsuNASANASAAtmosphere
Aerosol Properties
Aerosol Optical Depth
Aerosol Optical Depth3131GlobalLand10 Kmtotal columndaily
Retrieval-level uncertainty estimates provided; global average uncertainty of order 0.03+20%
0.052002-06-252016-12-31EOS HDF4internet2016
environmental and climate-monitoring products and assessments; such as IPCC assessments; assimilation; air-quality.
Assimilation: the US Naval Research Laboratory and UK Met Office both assimilate the MODIS Deep Blue aerosol data set

Air quality: Ma; Z.; X. Hu; A. M. Sayer; R. Levy; Q. Zhang; Y. Xue; S. Tong; J. Bi; L. Huang; and Y. Liu (2016); Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China; 2004–2013; Environ Health Perspect; DOI:10.1289/ehp.1409481; van Donkelaar; A.; R.V Martin; M.Brauer; N. C. Hsu; R. A. Kahn; R. C Levy; A. Lyapustin; A. M. Sayer; and D. M Winker (2016); Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites; Models; and Monitors; Environ. Sci. Technol.; doi: 10.1021/acs.est.5b05833

Climate study community: Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part II: Using maximum covariance analysis to effectively compare spatio-temporal variability of satellite and AERONET measured aerosol optical depth. J. Geophys. Res. Atmos.; 119; no. 1; 153-166; doi:10.1002/2013JD020537; Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part III: Using combined PCA to compare spatio-temporal variability of MODIS; MISR and OMI aerosol optical depth. J. Geophys. Res. Atmos.; 119; no. 7; 4017-4042; doi:10.1002/2013JD020538; Li; J.; B.E. Carlson; and A.A. Lacis (2014); Application of spectral analysis techniques in the inter-comparison of aerosol data; Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis. Atmos. Meas. Tech.; 7; 2531-2549; doi:10.5194/amt-7-2531-2014; Ginoux; P.; J. M. Prospero; T. E. Gill; N. C. Hsu; and M. Zhao (2012); Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products; Rev. Geophys.; 50; RG3005; doi:10.1029/2012RG000388.
MYD04_L2: MODIS/Aqua Aerosol 5-Min L2 Swath 10km V006
Levy; R.; Hsu; C.; et al.; 2015. MODIS Atmosphere L2 Aerosol Product. NASA MODIS Adaptive Processing System; Goddard Space Flight Center; USA: http://dx.doi.org/10.5067/MODIS/MYD04_L2.006
2016-12-31Level 22002-06-25MODISAqua
80
2016-09-22 12:39:4410356FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Earth Radiation Budget
Surface ERB Longwave
Downward Longwave Radiation at Surface
1003Partial component; no reqs set1003
Partial component; no reqs set
-90 deg to 90 deg
-180 deg to 180 deg
All domains0.25 x 0.25 degn/Amonthly
15 W/m2 (threshold)
5 W/m2 per decade (threshold)
1982-01-012015-12-312017
earth radiation budget studies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
81
2016-08-04 16:08:1310217CurrentAnton VerhoefEUMETSATOSI SAFAtmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind vector over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 to 90 and 0 to 360 degrees.
Ocean
25 km along-track and cross-track sampling.
N/A
Approximately 0.5 days revisit interval.
Error in zonal and meridional wind components is lower than 0.7m/s as obtained from triple collocation analysis. See validation report.
Better than 0.1 m/s per decade.
1999-07-192009-11-22BUFR; NetCDF
Through internet and media
2015
- Re-analyses of atmospheric and oceanographic modeling
- Study of air-sea interaction
- (Wind) climate research
- Environmental monitoring
- Climate scientists
- Oceanographers
- Meteorological services
Winds are available over ice-free oceans.
OSI SAF Reprocessed SeaWinds L2 25 km winds
2009-11-22Level 2A1999-07-19SeaWindsQuikSCAT
82
2016-09-22 12:41:1610357FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Earth Radiation Budget
Surface ERB Longwave
Upward Longwave Radiation at Surface
1003Partial component; no reqs set1003
Partial component; no reqs set
-90 deg to 90 deg
-180 deg to 180 deg
All domains0.25 x 0.25 degn/Amonthly
15 W/m2 (threshold)
5 W/m2 per decade (threshold)
1982-01-012015-12-312017
earth radiation budget studies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
83
2016-08-05 15:19:4810218CurrentPamela RinslandNASANASAAtmosphereWater Vapour
Tropospheric and Lower-stratospheric Profiles of Water Vapour
Tropospheric and Lower-stratospheric Profiles of Water Vapour
89GlobalAll domains
1/2 degree by 1/2 degree
Variable (based on 4 pressure levels) surface to 700 hPa; 700 to 500 hPa; 500 to 300 hPa; >300 hPa
4 times per day
varies with time due to satellite sampling; The uncertainties of the retrieval algorithms for AIRS; SSM/I and HIRS are documented in their reference publications and in the ATBD. Further discussion was presented in the journal paper published in 2012 in Geophysical Research Letters. Additional validation was performed by the project with intercomparisons against each other and against radiosondes and GPS surface observations. Since NVAP-M uses a changing mixture of inputs through time as the number of satellites change; sampling in time and space can vary. This effect requires further study.
varies with time due to satellite sampling; Tis product is intended for short case studies; not long term climate; so stability is not defined.
1988-01-012009-06-01
netCDF 4.0; http://www.unidata.ucar.edu/software/netcdf/conventions.html
FTP; Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
2013
Monitor The Global And Regional Distribution Of Atmospheric Water Vapor; NVAP-M weather data sets are used for weather case studies on timescales of days to weeks. Typical types of uses of NVAP-M include model
intercomparison; process studies such as of the MJO; and hydrological moisture budget and transport
studies.
NVAP_WEATHER_LPW Version: 1
NVAP Science Team; NVAP-M Data; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/NVAP-M/NVAP_WEATHER_Layered-Precipitable-Water_L3.001
1991-12-31
Level 1 Radiances
1987-06-01SSM/IDMSP-F08
84
2016-09-22 12:43:5810359FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
LandAlbedoBlack-sky Albedo
Broadband land-surface black-sky albedo
5995
Joint reqs for modelling / adaptation
-90 deg to 90 deg
-180 deg to 180 deg
All domains0.25 x 0.25 degn/Amonthly
25 % (relative; threshold)
15 % (relative; threshold)
1982-01-012015-12-312017
- Cryospheric applications involving the monitoring of snow and sea ice albedo (e.g. validation
of snow/ice albedo parameterizations in climate models).
- Global and regional climate change studies.
- Monitoring and analysis of the earths radiation budget.
- Validation of regional and global climate models (e.g. ECHAM5).
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
85
2016-08-05 15:23:1310219CurrentPamela RinslandNASANASAAtmosphereWater Vapour
Total Column Water Vapour
Total Column Water Vapour
78
Global: Latitude 90 (N) to -90 (S); Longitude 180 (E) to -180 (W)
All domains
1/2 degree by 1/2 degree
N/A4 times per day
varies with time due to satellite sampling; The uncertainties of the retrieval algorithms for AIRS; SSM/I and HIRS are documented in their reference publications and in the ATBD. Further discussion was presented in the journal paper published in 2012 in Geophysical Research Letters. Additional validation was performed by the project with intercomparisons against each other and against radiosondes and GPS surface observations. Since NVAP-M uses a changing mixture of inputs through time as the number of satellites change; sampling in time and space can vary. This effect requires further study.
varies with time due to satellite sampling
1988-01-012009-06-01
netCDF 4.0; http://www.unidata.ucar.edu/software/netcdf/conventions.html
FTP; Earth Data Search portal at https://search.earthdata.nasa.gov/ and DATA.GOV
2013
Monitor The Global And Regional Distribution Of Atmospheric Water Vapor; NVAP-M weather data sets are used for weather case studies on timescales of days to weeks. Typical types of uses of NVAP-M include model intercomparison; process studies such as of the MJO; and hydrological moisture budget and transport studies. Serves for comparison to a new satellite retrieval from AMSR2.
Du; J.; Kimball; J.S. and Jones; L.A.; 2015. Satellite microwave retrieval of total precipitable water vapor and surface air temperature over land from AMSR2. IEEE Transactions on Geoscience and Remote Sensing; 53(5); pp.2520-2531.
NVAP_WEATHER_TPW Version: 1
NVAP Science Team; NVAP-M Data; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/NVAP-M/NVAP_WEATHER_Total-Precipitable-Water_L3.001
1991-12-31
Level 1 Radiances
1987-06-01SSM/IDMSP-F08
86
2016-09-22 15:52:3110361FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Cloud Properties
Cloud AmountCloud Amount1011
-90 deg to 90 deg
-180 deg to 180 deg
All domains
0.25 x 0.25 deg (25 km equal area polar grid also available)
n/Amonthly
20% (threshold)
40% bc-rms (threshold)
5 % per decade (threshold)
1978-10-132018-12-312021
global cloud climatologies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
87
2016-08-08 14:35:5810221CurrentAnton VerhoefEUMETSATOSI SAFAtmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind vector over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 to 90 and 0 to 360 degrees.
Ocean
50 km along-track and cross-track sampling.
N/A
Approximately 0.5 days revisit interval.
Error in zonal and meridional wind components is lower than 0.5m/s as obtained from triple collocation analysis. See validation report.
Better than 0.1 m/s per decade.
1999-07-192009-11-22BUFR; NetCDF
Through internet and media
2015
- Re-analyses of atmospheric and oceanographic modeling
- Study of air-sea interaction
- (Wind) climate research
- Environmental monitoring
- Climate scientists
- Oceanographers
- Meteorological services
Winds are available over ice-free oceans.
OSI SAF Reprocessed SeaWinds L2 50 km winds
2009-11-22Level 2A1999-07-19SeaWindsQuikSCAT
88
2016-09-22 20:35:4610363CurrentCharles TrepteNASA
NASA Langley Atmospheric Science Data Center
Atmosphere
Aerosol Properties
Aerosol-layer Height
Aerosol-layer Height333382N-82S/360All domains5 km
60 m (surface to 8.2 km); 120 m (8.2 km to 20 km)
1 sec30 mNot Assessed2006-06-122016-12-31HDFInternet2016
climate modelling; air quality forecast; weather forecast
climate modeling centers; climate scientist; air quality forecast research; weather forecast research
CAL_LID_L2_05kmALay-Standard-V4-10
CALIPSO Science Team (2016); CALIPSO/CALIOP Level 2; Lidar Aerosol Layer Data; version 4.10; Hampton; VA; USA: NASA Atmospheric Science Data Center (ASDC); Accessed <author citing data inserts date here> at doi: 10.5067/CALIOP/CALIPSO/LID_L2_05kmALay-Standard-V4-10
2016-12-31Level 22006-06-12CALIOPCALIPSO
89
2016-08-08 14:41:0010222CurrentAnton VerhoefEUMETSATOSI SAFAtmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind vector over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 to 90 and 0 to 360 degrees.
Ocean
25 km along-track and cross-track sampling.
N/A
Approximately 0.5 days revisit interval.
Error in zonal and meridional wind components is lower than 0.6 m/s as obtained from triple collocation analysis. See validation report.
Better than 0.1 m/s per decade.
2007-01-012014-03-31BUFR; NetCDF
Through internet and media
2016
- Re-analyses of atmospheric and oceanographic modeling
- Study of air-sea interaction
- (Wind) climate research
- Environmental monitoring
- Climate scientists
- Oceanographers
- Meteorological services
Winds are available over ice-free oceans.
OSI SAF Metop-A ASCAT L2 25 km winds Data Record
2014-03-31Level 1b2007-01-01ASCATMetop-A
90
2016-09-23 16:44:3110365FutureJohn Dwyer
U.S. Geological Survey
U.S. Geological Survey
Land
Land-Surface Temperature
Land-Surface Temperature
Land-Surface Temperature
7071
56 - 76 degrees north; 73 - 157 degree west
Land
60m resampled to 30m
N/A16 days
Not yet determiend
not yet determined
1999-04-192017-06-302017
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.
Government; commercial and academic research and applications scientists.
North America2017-06-301b1999-04-19ETM+Landsat-7
91
2016-08-09 11:49:5210223CurrentAnton VerhoefEUMETSATOSI SAFAtmosphere
Surface Wind Speed and Direction
Surface Wind Speed and Direction
Wind vector over ocean surface (horizontal)
1
Reqs are set for wind speed only (no direction)
1
Reqs are set for wind speed only (no direction)
-90 to 90 and 0 to 360 degrees.
Ocean
12.5 km along-track and cross-track sampling.
N/A
Approximately 0.5 days revisit interval.
Error in zonal and meridional wind components is lower than 0.8 m/s as obtained from triple collocation analysis. See validation report.
Better than 0.1 m/s per decade.
2007-01-012014-03-31BUFR; NetCDF
Through internet and media
2016
- Re-analyses of atmospheric and oceanographic modeling
- Study of air-sea interaction
- (Wind) climate research
- Environmental monitoring
- Climate scientists
- Oceanographers
- Meteorological services
Winds are available over ice-free oceans.
OSI SAF Metop-A ASCAT L2 12.5 km winds Data Record
2014-03-31Level 1b2007-01-01ASCATMetop-A
92
2016-09-23 19:39:5810366FutureJohn Dwyer
U.S. Geological Survey
U.S. Geological Survey
Land
Land-Surface Temperature
Land-Surface Temperature
Land-Surface Temperature
7071
56 - 76 degrees north; 73 - 157 degrees west
Land
100m resampled to 30m
N/A16 daysNot yet definedNot yet defined2013-02-132017-06-302017
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.
Government; commercial and academic research and applications scientists
North America2017-06-301b2013-02-13OLILandsat-8
93
2016-08-11 7:50:1110229CurrentMichael BuchwitzESA
Institute of Environmental Physics (IUP); Univ. Bremen; Germany; supported by GHG-CCI project team (http://www.esa-ghg-cci.org).
Atmosphere
Carbon Dioxide; Methane and other Greenhouse Gases
Tropospheric CO2 Column
Tropospheric CO2 Column
2222Quasi-globalLand30 km x 60 kmN/A6 days
0.4 - 0.8 ppm (depending on assessment method; see PVIRv4 (http://www.esa-ghg-cci.org/?q=webfm_send/300))
Better than 0.2 ppm/year (see PVIRv4 (http://www.esa-ghg-cci.org/?q=webfm_send/300))
2003-01-012012-03-30
NetCDF (see Product Specification Document: http://www.esa-ghg-cci.org/index.php?q=webfm_send/160 ).
Data products are available from ESA / GHG-CCI website (http://www.esa-ghg-cci.org/sites/default/files/documents/public/documents/GHG-CCI_DATA.html (http://www.esa-ghg-cci.org -> CRDP (Data) -> GHG-CCI Data Products Main Website)) but also from ESA CCI Open Data Portal (http://cci.esa.int/data).
2016
Primarily to obtain information on the natural and anthropogenic sources and sinks of CO2 (details see; e.g.; GHG-CCI Climate Assessment Report (CAR): http://www.esa-ghg-cci.org/?q=webfm_send/318). Comparison with climate models.
Primarily the CO2 inverse modelling community interested in global assessments. Represented for Europe primarily by the members of the GHG-CCI Climate Research Group (CRG). Details: see http://www.esa-ghg-cci.org/?q=node/83 (http://www.esa-ghg-cci.org -> Project Team). Furthermore; users interested in regional / local applications to obtain information on atmospheric concentrations and surface fluxes. Climate modellers.
Coverage (e.g.; latitudinal) depends on solar zenith angle; cloud cover; etc.
CO2_SCI_BESD; version 02.01.01
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.
2012-03-30Level 1B2002-10-01
SCIAMACHY-nadir
Envisat
94
2016-08-11 9:08:2910230CurrentMichael BuchwitzESA
Institute of Environmental Physics (IUP); Univ. Bremen; Germany; supported by GHG-CCI project team (http://www.esa-ghg-cci.org).
Atmosphere
Carbon Dioxide; Methane and other Greenhouse Gases
Tropospheric CH4 Column
Tropospheric CH4 Column
2424Quasi-globalLand30 km x 60 kmN/A6 days
7 - 17 ppb (depending on assessment method; see PVIRv4 (http://www.esa-ghg-cci.org/?q=webfm_send/300))
Better than 4 ppb/year (see PVIRv4 (http://www.esa-ghg-cci.org/?q=webfm_send/300))
2002-10-012011-12-30
NetCDF (see Product Specification Document: http://www.esa-ghg-cci.org/index.php?q=webfm_send/160 ).
Data products are available from ESA / GHG-CCI website (http://www.esa-ghg-cci.org/sites/default/files/documents/public/documents/GHG-CCI_DATA.html (http://www.esa-ghg-cci.org -> CRDP (Data) -> GHG-CCI Data Products Main Website)) but also from ESA CCI Open Data Portal (http://cci.esa.int/data).
2016
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.
Primarily the CH4 inverse modelling community interested in global assessments. Represented for Europe primarily by the members of the GHG-CCI Climate Research Group (CRG). Details: see http://www.esa-ghg-cci.org/?q=node/83 (http://www.esa-ghg-cci.org -> Project Team). Furthermore; users interested in regional / local applications to obtain information on atmospheric concentrations and surface fluxes. Climate modellers.
Coverage (e.g.; latitudinal) depends on solar zenith angle; cloud cover; etc. Some ocean coverage.
CH4_SCI_WFMD; version 3.9
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.
2012-03-30Level 1B2002-10-01
SCIAMACHY-nadir
Envisat
95
2016-08-11 9:18:2910231CurrentMichael BuchwitzESA
Institute of Environmental Physics (IUP); Univ. Bremen; Germany; supported by GHG-CCI project team (http://www.esa-ghg-cci.org).
Atmosphere
Carbon Dioxide; Methane and other Greenhouse Gases
Tropospheric CO2 Column
Tropospheric CO2 Column
2222Quasi-globalLand
5 deg x 5 deg (approx. 500 km x 500 km)
N/Amonthly
1.2 ppm (including representativity error; see http://www.esa-ghg-cci.org/?q=webfm_send/330)
No significant trend detected. Underlying Level 2 data: Better than 0.2 ppm/year (see PVIRv4 (http://www.esa-ghg-cci.org/?q=webfm_send/300))
2003-01-012014-12-31
Obs4MIPs NetCDF format (http://www.esa-ghg-cci.org/?q=webfm_send/330).
Data products are available from ESA / GHG-CCI website (http://www.esa-ghg-cci.org/sites/default/files/documents/public/documents/GHG-CCI_DATA.html (http://www.esa-ghg-cci.org -> CRDP (Data) -> GHG-CCI Data Products Main Website)) but also from ESA CCI Open Data Portal (http://cci.esa.int/data).
2016
Comparison with climate models.
Climate modellers.
Coverage (e.g.; latitudinal) depends on solar zenith angle; cloud cover; etc. Some ocean coverage.
XCO2_Obs4MIPs; version 1.0
Buchwitz; M.; and M. Reuter;
Merged SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT
atmospheric column-average dry-air mole fraction of CO2 (XCO2)
(XCO2_CRDP3_001); Technical Note; Version: 1 (rev 2); 2-June-2016;
http://www.esa-ghg-cci.org/?q=webfm_send/330; 2016.
2012-03-30Level 1B2002-10-01
SCIAMACHY-nadir
Envisat
96
2016-08-11 9:55:1910232CurrentMichael BuchwitzESA
Institute of Environmental Physics (IUP); Univ. Bremen; Germany; supported by GHG-CCI project team (http://www.esa-ghg-cci.org).
Atmosphere
Carbon Dioxide; Methane and other Greenhouse Gases
Tropospheric CH4 Column
Tropospheric CH4 Column
2424Quasi-globalLand
5 deg x 5 deg (approx. 500 km x 500 km)
N/Amonthly
11 ppb (including representativity error; see http://www.esa-ghg-cci.org/?q=webfm_send/331)
No significant trend detected. Underlying Level 2 data: Better than 4 ppb/year (see PVIRv4 (http://www.esa-ghg-cci.org/?q=webfm_send/301))
2003-01-012014-12-31
Obs4MIPs NetCDF format (http://www.esa-ghg-cci.org/?q=webfm_send/331).
Data products are available from ESA / GHG-CCI website (http://www.esa-ghg-cci.org/sites/default/files/documents/public/documents/GHG-CCI_DATA.html (http://www.esa-ghg-cci.org -> CRDP (Data) -> GHG-CCI Data Products Main Website)) but also from ESA CCI Open Data Portal (http://cci.esa.int/data).
2016
Comparison with climate models.
Climate modellers.
Coverage (e.g.; latitudinal) depends on solar zenith angle; cloud cover; etc. Some ocean coverage.
XCH4_Obs4MIPs; version 1.0
Buchwitz; M.; and M. Reuter;
Merged SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT
atmospheric column-average dry-air mole fraction of CH4 (XCH4)
(XCH4_CRDP3_001); Technical Note; Version: 1 (rev 2); 2-June-2016;
http://www.esa-ghg-cci.org/?q=webfm_send/331; 2016.
2012-03-30Level 1B2002-10-01
SCIAMACHY-nadir
Envisat
97
2016-08-12 21:02:0210236CurrentNorman LoebNASA
NASA Langley Atmospheric Science Data Center
Atmosphere
Earth Radiation Budget
Top-of-Atmosphere ERB Longwave
Upward Longwave Radiation at TOA
1617180/360All domains
1 degree by 1 degree
monthly
monthly regional: 2 W/m2 at 1 sigma
0.2 W/m2 per Decade at 2 sigma
2000-03-012016-05-31NetCDF
Internet and Obs4MIPs
2016
Earth Radiation Energy budget time series; Climate model assessment

Norman Loeb; Natividad Manalo-Smith; Wenying Su; Mohan Shankar; Susan Thomas; CERES Top-of-Atmosphere Earth Radiation Budget Climate Data Record: Accounting for in-Orbit Changes in Instrument Calibration; Remote Sens.; 2016; 8; 182; doi: 10.3390/rs8030182.

Hailan Wang and Wenying Su; The ENSO Effects on Tropical Clouds and Top-of-Atmosphere Cloud Radiative Effects in CMIP5 Models; J. Geophys. Res.; 120; 4443-4465; doi:10.1002/2014JD022337; 2015.

Hailan Wang and Wenying Su; Evaluating and Understanding Top-of-Atmosphere Cloud Radiative Effects in IPCC-AR5/CMIP5 Models using Satellite Observations; J. Geophys. Res.; 118; doi:10.1029/2012JD018619; 2013.
Climate modeling centers; climate scientists; weather forecast research
global coverage
2016-05-31Level 12002-07-01CERESAqua
98
2016-09-26 15:30:4110368FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Cloud Properties
Cloud Top Pressure (CTP)
Cloud Top Pressure1112
-90 deg to 90 deg
-180 deg to 180 deg
All domains0.25 x 0.25 degn/Amonthly;
150 hPa bias (threshold)
160 hPa bc-rms (threshold)
30 hPa per dcade
1978-10-132018-12-312021
global cloud climatologies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
99
2016-08-12 21:02:3110237CurrentNorman LoebNASA
NASA Langley Atmospheric Science Data Center
Atmosphere
Earth Radiation Budget
Surface ERB Shortwave
Downward Shortwave Radiation at Surface
1003Partial component; no reqs set1003
Partial component; no reqs set
180/360All domains
1 degree by 1 degree
monthly
monthly regional: 10 W/m2 at 1 sigma
0.8 W/m2 per Decade at 1 sigma
2000-03-012016-05-31NetCDF
Internet and Obs4MIPs
2016
Earth Radiation Energy budget time series; Climate model assessment

Wild; M. and D. Folini and C. Schar and N. G. Loeb and E. G. Dutton and G. Konig-Langlo; 2013: The global energy balance from a surface perspective; Clim. Dyn.; 40; 3107-3134; DOI:10.1007/s00382-012-1569-8. http://link.springer.com/article/10.1007%2Fs00382-012-1569-8
Climate modeling centers; climate scientists; applied sciences (energy and agriculture sectors)
global coverage
2016-05-31Level 12002-07-01CERESAqua
100
2016-09-26 15:33:1210369FutureRainer HollmannEUMETSAT (CM SAF)
TCDR EUMETSAT (CM SAF)
Documents EUMETSAT (CM SAF)
Ancillary: EUMETSAT (CM SAF)
Atmosphere
Cloud Properties
Cloud Top Temperature (CTT)
Cloud Top Temperature1213
-90 deg to 90 deg
-180 deg to 180 deg
All domains0.25 x 0.25 degn/Amonthly
indirectly defined via cloud top pressure
indirectly defined via cloud top pressure
1978-10-132018-12-312021
global cloud climatologies
climate research (monitoring and modelling); NMHSs & government agencies; private and public sector
see 'climate applications'
The CM SAF CLARA data records have been widely cited in the peer-reviewed literature.
User publications involving CLARA data:
Abera et al. 2016: http://dx.doi.org/10.5194/hess-2016-290
Blunden and Arndt 2015: http://dx.doi.org/10.1175/2015BAMSStateoftheClimate.1
Calbo et al. 2016: http://dx.doi.org/10.1002/joc.4435
Calbo et al. 2016: http://dx.doi.org/10.1007/s00704-016-1829-3
Cao et al. 2015: http://dx.doi.org/10.1175/jcli-d-14-00389.1
Duan and Bastiaanssen 2015: http://dx.doi.org/10.1016/j.rse.2014.09.009
Enriquez-Alonso et al. 2015: http://dx.doi.org/10.1007/s00382-015-2834-4
He et al. 2014: http://dx.doi.org/10.1002/2014JD021667
Karlsson and Svensson 2013: http://dx.doi.org/10.1002/grl.50768
Koenigk et al. 2015: http://dx.doi.org/10.3402/polar.v34.24603
Light et al. 2015: http://dx.doi.org/10.1002/2014jc010149
Loew et al. 2016: http://dx.doi.org/10.1175/jcli-d-14-00503.1
Obregon et al. 2014: http://dx.doi.org/10.5194/asr-11-25-2014
Qu et al. 2015: http://dx.doi.org/10.3390/rs70100990
Quante et al. 2016: http://dx.doi.org/10.1007/978-3-319-39745-0_1
Yao et al. 2016: http://dx.doi.org/10.5194/gmd-9-2239-2016
2015-12-31L1b2006-10-25AVHRR/3Metop-A
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ECV_public_approved_records.csv