Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L3.0 First-Look Analyses

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Publications citing Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L3.0 First-Look Analyses

Citation metrics available for years (2015-2019)

Year	Citation
2015	Seasonal SSH variability of the northern South China Sea, Journal of Physical Oceanography,https://doi.org/10.1175/JPO-D-14-0193.1
2015	Evaluation of the simulation capability of the Wavewatch III model for Pacific Ocean wave, Acta Oceanologica Sinica,https://doi.org/10.1007/s13131-015-0737-1
2015	Surface boundary layer evolution and near‐inertial wind power input, Journal of Geophysical Research: Oceans,https://doi.org/10.1002/2015JC011213
2015	Features of near-inertial motions observed on the northern South China Sea shelf during the passage of two typhoons, Acta Oceanologica Sinica,https://doi.org/10.1007/s13131-015-0594-y
2015	Wavelet analysis of coastal-trapped waves along the China coast generated by winter storms in 2008, Acta Oceanologica Sinica,https://doi.org/10.1007/s13131-015-0701-0
2015	Atlas, R., Ardizzone, J. V., Hoffman, R., Jusem, J. C., and Leidner, S. M.: Cross-calibrated, multi-platform ocean surface wind velocity product (MEaSUREs Project), Guide Document, Physical Oceanography Distributed Active Archive Center (PODAAC), JPL, Pasadena, Cali5 fornia, 18 May 2009, Version 1.0., 26 pp., 2009. Long-term variability of the South Adriatic circulation and phytoplankton biomass in relation to large-scale climatic pattern., Ocean Science Discussions,https://doi.org/10.5194/osd-12-203-2015
2015	Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation, Chinese journal of oceanology and limnology,https://doi.org/10.1007/s00343-014-4252-6
2015	NASA/JPL. Web page: Physical Oceanography Distributed Active Archive Center. 2015. http://podaac.jpl.nasa.gov/. Online; accessed 01-June-2015. An improvement on the gas transfer velocity model with application to scatterometer data,https://doi.org/10.11606/T.21.2015.tde-07102015-143819
2015	Low‐salinity water off West Luzon Island in summer, Journal of Geophysical Research: Oceans,https://doi.org/10.1002/2014JC010465
2015	Natural variability of surface oceanographic conditions in the offshore Gulf of Mexico, Progress in Oceanography,https://doi.org/10.1016/j.pocean.2014.12.007
2015	Characterization of the seascape used by juvenile and wintering adult Southern Giant Petrels from Patagonia Argentina, Estuarine, Coastal and Shelf Science,https://doi.org/10.1016/j.ecss.2014.12.007
2015	CO2 flux variability in the Galician and Californian upwelling systems
2015	Phytoplankton phenology in the coastal upwelling region off central‐southern C hile (35° S–38° S): Time‐space variability, coupling to environmental factors, and …, Journal of Geophysical Research: Oceans,https://doi.org/10.1002/2014JC010330
2015	JPL (CCMP_MEASURES_ATLAS_L4_OW_L3_0_WIND_VECTORS_FLK, DOI 10.5067/CCF30-01XXX downloaded from https://podaac.jpl.nasa.gov/dataset/CCMP_MEASURES_ATLAS_L4_OW_L3_0_WIND_VECTORS_FLK ), July 2012 * Process to identify and classify oil seep areas at the seabed through inverse modeling
2015	Coastal circulation driven by short-period upwelling-favorable winds in the northern Baja California region, Deep Sea Research Part I: Oceanographic Research Papers,https://doi.org/10.1016/j.dsr.2014.12.003
2015	Quantitative characterization of spurious numerical oscillations in 48 CMIP5 models, Geophysical Research Letters,https://doi.org/10.1002/2015GL063931
2015	Detection of mesoscale thermal fronts from 4 km data using smoothing techniques: Gradient-based fronts classification and basin scale application, Remote sensing of environment,https://doi.org/10.1016/j.rse.2015.03.030
2015	Reconstruction of super-resolution fields of ocean pCO2 and air–sea fluxes of CO2 from satellite imagery in the Southeastern Atlantic, Biogeosciences,https://doi.org/10.5194/bgd-12-1405-2015
2015	Establishment and tests of EnOI assimilation module for WAVEWATCH III, Chinese journal of oceanology and limnology,http://dx.doi.org/10.1007/s00343-015-4282-8
2016	Causes for intraseasonal sea surface salinity variability in the western tropical P acific O cean and its seasonality, Journal of Geophysical Research: Oceans,https://doi.org/10.1002/2015JC011413
2016	Atlas, R., Ardizzone, J. V., Hoffman, R., Jusem, J. C. and Leidner, S. M.: Cross-calibrated, multiplatform ocean surface wind velocity product (MEaSUREs Project). Guide Document. Physical Oceanography Distributed Active Archive Center (PO.DAAC). JPL, Pasadena, California, 18 May 2009, Version 1.0., 26p, 2009. Mediterranean thermohaline properties and large-scale climatic patterns,https://arts.units.it/handle/11368/2908028
2016	Seasonal cycle of nearbottom transport and currents in the northern Gulf of California, Journal of Geophysical,https://doi.org/10.1002/2016JC012063
2016	Common characteristics of directional spreadingsteepness joint distribution in freak wave events, Ocean Science,https://doi.org/10.5194/os-12-781-2024
2016	Anomalous Java cooling at the initiation of positive Indian Ocean Dipole events, Journal of Geophysical,https://doi.org/10.1002/2016JC011635
2016	Impacts of reprocessed altimetry on the surface circulation and variability of the Western Alboran Gyre, Advances in Space Research,https://doi.org/10.1016/j.asr.2016.05.026
2016	Global oceanic wind speed trends, Ocean & Coastal Management,https://doi.org/10.1016/j.ocecoaman.2016.05.001
2016	Evaluation of Different Wind Fields for Storm surge Modeling in the Persian Gulf, core.ac.uk
2016	Spatial and temporal variations in high turbidity surface water off the Thule region, northwestern Greenland, Polar Science,https://doi.org/10.1016/j.polar.2016.07.003
2016	Comments from the editors and reviewers, researchgate.net,https://doi.org/10.1016/j.csr.2017.05.011
2016	On the role of atmospheric forcing on upper ocean physics in the Southern Ocean and biological impacts,https://escholarship.org/uc/item/5qs1x85p
2016	Phenology of size-partitioned phytoplankton carbon-biomass from ocean color remote sensing and cmip5 models,https://doi.org/10.3389/fmars.2016.00039
2016	Coupled patterns between the surface chlorophyll-a and the physical factors in the Pacific Ocean, International conference on Geoscience and Remote Sensig,https://doi.org/10.1109/IGARSS.2016.7730195
2016	O Estudo da interao oceano-atmosfera em um ciclone extratropical no Atlntico Sudoeste: uma abordagem numrica em altssima resoluo
2016	Atlas, R., Ardizzone, J. V., Hoffman, R., Jusem, J. C., and Leidner, S. M.: Cross-calibrated, multi-platform ocean surface wind velocity product (MEaSUREs Project), Guide Document, Physical Oceanography Distributed Active Archive Center (PO.DAAC), JPL, Pasadena, California, 18 May 2009, Version 1.0., 26 pp., 2009. Long-term variability of the southern Adriatic circulation in relation to North Atlantic Oscillation, Ocean,https://doi.org/10.5194/os-12-233-2016
2016	Surface suspended particulate matter concentration in the Taiwan Strait during summer and winter monsoons, Ocean Dynamics
2016	NASA/GSFC/NOAA (2009), Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L3.0 First-Look Analyses Ver. 1. PO.DAAC, Calif.Dataset accessed [2016-02-01] at. [Available at 10.5067/CCF30-01XXX.] Impact of ocean resolution on coupled airsea fluxes and largescale climate, Geophysical,https://doi.org/10.1002/2016GL070559
2017	Environmental effects on the spatiotemporal patterns of abundance and distribution of Sardina pilchardus and sardinella off the Mauritanian coast (NorthWest Africa, Fisheries
2017	Factors influencing the skill of synthesized satellite wind products in the tropical Pacific, Journal of Geophysical Research: Oceans,https://doi.org/10.1002/2016JC012340
2017	Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometerbased wind products over the Nordic Seas and the northern North, Journal of Geophysical Research: Oceans,https://doi.org/10.1002/2016JC012453
2017	NASA/GSFC/NOAA. Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L3.0 First-Look Analyses. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [2015-11-01] at http://dx.doi.org/10.5067/CCF30-01XXX (2009). Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean, Applied Energy,https://doi.org/10.1016/j.apenergy.2017.09.030
2017	OPeNDAP link wrong Distribution of sea-air CO2 fluxes in the Patagonian Sea: seasonal, biological and thermal effects, Continental Shelf,https://doi.org/10.1016/j.csr.2017.05.011
2017	Recent Decadal Trend in the North Atlantic Wind Energy Resources, Advances in Meteorology,https://doi.org/10.1155/2017/7257492
2017	Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L3.0 First-Look Analyses. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [2015-09-02] at http://dx.doi.org/10.5067/CCF30-01XXX. Assessment of the Offshore Wind Speed Distributions at Selected Stations in the South-West Coast, Nigeria, International Journal of Renewable Energy Research
2017	Trends in significant wave height and surface wind speed in the China Seas between 1988 and 2011, Journal of Ocean University of,https://doi.org/10.1007/s11802-017-3213-z
2018	NASA/GSFC/NOAA, 2009. Cross-calibrated multi-platform ocean surface wind vector L3.0 first-look analyses. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [2016-09-30] at http://dx.doi.org/10.5067/CCF30-01XXX. Anchovy (Engraulis encrasicolus) early life stages in the Central Mediterranean Sea: connectivity issues emerging among adjacent sub-areas across the Strait of …, Hydrobiologia,https://doi.org/10.1007/s10750-017-3253-9
2018	Anisotropic larval connectivity and metapopulation structure driven by directional oceanic currents in a marine fish targeted by small-scale fisheries, Marine biology,https://doi.org/10.1007/s00227-017-3267-x
2018	Comparison of the accuracy of various global wind speed datasets obtained from satellites and reanalyses, Journal of Advanced Marine Science and Technology Society ,https://doi.org/10.14928/amstec.24.2_31
2018	NASA/GSFC/NOAA: Cross-Calibrated Multi-Platform Ocean Sur- face Wind Vector L3.0 First-Look Analyses, Ver. 1, PO.DAAC, CA, USA, https://doi.org/10.5067/CCF30-01XXX,2009. Physical modulation to the biological productivity in the summer Vietnam upwelling system, Ocean Science,https://doi.org/10.5194/os-14-1303-2018
2018	[54] a Atlas R, Hoffman RN, Ardizzone J, Leidner SM, Jusem JC, Smith DK, Gombos D. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull Am Meteorol Soc 2011;92:157e74. b NASA/GSFC/NOAA. Cross-calibrated multi-platform ocean surface wind vector L3.0 first-look analyses. CA, USA: PO.DAAC; 2009., Version 1. https:// doi.org/10.5067/CCF30-01XXX. [Accessed 5 November 2016]. https://doi.org/10.1175/2010BAMS2946.1. Review of energy systems deployment and development of offshore wind energy resource map at the coastal regions of Africa, Energy,https://doi.org/10.1016/j.energy.2018.07.185
2018	Integrated kinetic energy of Atlantic tropical cyclones in a global ocean surface wind analysis, International Journal of Climatology,https://doi.org/10.1002/joc.5450
2018	Interannual variability of winter precipitation linked to upper ocean heat content off the east coast of Korea, International Journal of Climatology,https://doi.org/10.1002/joc.5354
2018	NASA/GSFC/NOAA, 2009. Cross-calibrated multi-platform ocean surface wind vector L3.0 first-look analyses. Ver. 1. PO.DAAC, CA, USA. Linking surface hydrodynamics to planktonic ecosystem: the case study of the ichthyoplanktonic assemblages in the Central Mediterranean Sea, Hydrobiologia,N/A
2018	NASA/GSFC/NOAA: Cross-Calibrated Multi-Platform Ocean Sur- face Wind Vector L3.0 First-Look Analyses, Ver. 1. PO.DAAC, CA, USA, https://doi.org/10.5067/CCF30-01XXX, 2009. Uncertainty in the global oceanic CO uptake induced by wind forcing: quantification and spatial analysis, Biogeosciences,https://doi.org/10.5194/bg-15-1701-2018
2019	Multidecadal wind variability drives temperature shifts on the Agulhas Bank, Journal of Geophysical Research: Oceans,https://doi.org/10.1029/2018JC014614
2019	Wind and Wave Energy in the Important Waters of the South China Sea, 21st Century Maritime Silk Road: Construction of Remote Islands and Reefs,https://doi.org/10.1007/978-981-10-8114-9_5
2019	CMIP5-based wave energy projection: case studies of the South China Sea and the East China Sea, IEEE Access,https://doi.org/10.1109/ACCESS.2019.2924197
2019	a Atlas R, Hoffman RN, Ardizzone J, Leidner SM, Jusem JC, Smith DK, Gombos D. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull Am Meteorol Soc 2011;92:157e74. https://doi.org/10.1175/2010BAMS2946.1.b NASA/ GSFC/NOAA. Cross-calibrated Multi-Platform ocean surface wind Vector L3.0 First-look analyses. 2009. PO.DAAC, CA, USA, Version 1. https://dx.doi.org/10. 5067/CCF30-01XXX. [Accessed 5 November 2016]. at. Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based), Energy,https://doi.org/10.1016/j.energy.2019.116232
2019	Roles of different physical processes in upper ocean responses to Typhoon Rammasun (2008)-induced wind forcing, Science China Earth Sciences,https://doi.org/10.1007/s11430-018-9313-8
2019	Sea surface temperature, ocean color and wind forcing patterns in the Bay of La Paz, Gulf of California: Seasonal variability, Atmósfera,http://doi.org/10.20937/atm.2019.32.01.03
2019	Global pattern of phytoplankton diversity driven by temperature and environmental variability, Science Advances,10.1126/sciadv.aau6253
2019	SST dynamics at different scales: evaluating the oceanographic model resolution skill to represent SST processes in the Southern Ocean, Journal of Geophysical Research: Oceans,https://doi.org/10.1029/2018JC014791
2019	Stokes 漂流近似公式对海洋表层流场估算的影响, 海洋与湖沼,http://dx.doi.org/10.11693/hyhz20180500110
2019	21st Century Maritime Silk Road: Construction of Remote Islands and Reefs, N/A,https://doi.org/10.1007/978-981-10-8114-9
2019	Study of upper ocean parameters during passage of tropical cyclones over Indian seas, International Journal of Remote Sensing,https://doi.org/10.1080/01431161.2019.1573336
2019	https://podaac.jpl.nasa.gov/Cross_Calibrated_Multi_ Platform_OceanSurfaceWindVectorAnalyses. Diakses bulan Januari 2016. KAJIAN INDEKS VARIABILITAS TINGGI GELOMBANG SIGNIFIKAN DI INDONESIA, Jurnal Segara,https://doi.org/10.15578/segara.v14i3.6650
2019	Micro-scale classification of offshore wind energy resource——A case study of the New Zealand, Journal of Cleaner Production,https://doi.org/10.1016/j.jclepro.2019.04.082
2019	Assessment of Significant Wave Height in the Taiwan Strait Measured by a Single HF Radar System, Journal of Atmospheric and Oceanic Technologies,https://doi.org/10.1175/JTECH-D-18-0146.1

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DOI	10.5067/CCF30-01XXX
Short Name	CCMP_MEASURES_ATLAS_L4_OW_L3_0_WIND_VECTORS_FLK
Description	This dataset is derived under the Cross-Calibrated Multi-Platform (CCMP) project and contains a value-added 6-hourly gridded analysis of ocean surface winds. The CCMP datasets combine cross-calibrated satellite winds obtained from Remote Sensing Systems (REMSS) using a Variational Analysis Method (VAM) to produce a high-resolution (0.25 degree) gridded analysis. The CCMP data set includes cross-calibrated satellite winds derived from SSM/I, SSMIS, AMSR-E, TRMM TMI, QuikSCAT, SeaWinds, WindSat and other satellite instruments as they become available from REMSS. REMSS uses a cross-calibrated sea-surface emissivity model function which improves the consistency between wind speed retrievals from microwave radiometers (i.e., SSM/I, SSMIS, AMSR, TMI, and WindSat) and those from scatterometers (i.e., QuikSCAT and SeaWinds). The VAM combines these data with in situ measurements and a starting estimate (first guess) of the wind field. The European Center for Medium-Range Weather Forecasts (ECMWF) ERA-40 Reanalysis is used as the first-guess from 1987 to 1998. The ECMWF Operational analysis is used from January 1999 onward. All wind observations and analysis fields are referenced to a height of 10 meters. The ERA-40 can be obtained from the Computation and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR): http://rda.ucar.edu/datasets/ds117.0/. The ECMWF Operational analysis can also be obtained from CISL at NCAR: http://rda.ucar.edu/datasets/ds111.1/. Three products are distributed to complete the CCMP dataset series. L3.0 product contains high-resolution analyses every 6-hours. These data are then time averaged over monthly and 5-day periods to derive the L3.5 product. Directions from the L3.0 product are then assigned to the time and location of the passive microwave satellite wind speed observations to derive the L2.5 product. All datasets are distributed on a 0.25 degree cylindrical coordinate grid. This dataset is one in a series of First-Look (FLK) CCMP datasets and is a continuation and expansion of the SSM/I surface wind velocity project that began under the NASA Pathfinder Program. Refinements and upgrades to the FLK version will be incorporated under a new release (date to be determined) known as Late-look (LLK) and may include additional satellite datasets. All satellite surface wind data are obtained from REMSS under the DISCOVER project: Distributed Information Services: Climate/Ocean Products and Visualizations for Earth Research (http://www.discover-earth.org/index.html). The CCMP project is the result of an investigation funded by the NASA Making Earth Science data records for Use in Research Environments (MEaSUREs) program (http://community.eosdis.nasa.gov/measures/). In accordance with the MEaSUREs program, the CCMP datasets are also known as Earth System Data Records (ESDRs). In collaboration with private and government institutions, a team led by Dr. Robert Atlas (PI; proposal originally solicited by REASoN, and currently funded by MEaSURES) has created the CCMP project to provide multi-instrument ocean surface wind velocity ESDRs, with wide ranging research applications in meteorology and oceanography.
Version	1
Dataset Type	OPEN
Measurement	OCEANS > OCEAN WINDS > SURFACE WINDS > Ocean Surface Wind Vectors
Processing Level	4
Coverage	Region: Global Northernmost Latitude: 78 degrees Southernmost Latitude: -78 degrees Westernmost Longitude: 0 degrees Easternmost Longitude: 360 degrees Time Span: 1987-Jul-02 to 2011-Dec-31 Granule Time Span: 1987-Jul-01 to Present
Resolution	Spatial Resolution: 0.25 degrees (Latitude) x 0.25 degrees (Longitude) Temporal Resolution: 6 Hour
Projection	Type: Gridded Detail: Ellipsoid:
Latency	hours
Platform/Sensor	Aqua Platform Name: Earth Observing System, AQUA (Aqua) Orbit Period: 98.4 minutes Inclination Angle: 98.1 degrees Ascending Node: 1970-Jan-01 00:00:00 / AMSR-E SENSOR Name: Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Swath Width: 1450 km Description: The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. TRMM Platform Name: Tropical Rainfall Measuring Mission (TRMM) Orbit Period: 92.4 minutes Inclination Angle: 35 degrees Ascending Node: 1970-Jan-01 00:00:00 / TMI SENSOR Name: TRMM Microwave Imager (TMI) Swath Width: 878 km Description: The Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) is a passive microwave sensor designed to provide quantitative rainfall information over a wide swath under the TRMM satellite. QUIKSCAT Platform Name: QUIKSCAT (QUIKSCAT) Orbit Period: 100.93 minutes Inclination Angle: 98.6 degrees Ascending Node: 1970-Jan-01 00:00:00 / SEAWINDS SENSOR Name: SeaWinds Scatterometer (SEAWINDS) Swath Width: 1800 km Description: SeaWinds is a Ku-band (13.4 GHz) Scatterometer which features a single circular scanning dish antenna to provide pencil-beam measurements, as opposed to the common fan beam design as seen with other spaceborne scatterometers. The pencil-beam design has several key advantages over the more common fan beam design: higher signal-to-noise ratio, smaller size, and superior coverage. The SeaWinds single antenna utilizes twin offset feeds, thus providing two pencil-beam footprint paths with an elliptical sampling footprint of approximately 25 km (in azimuth) by 37 km (in look/range direction); these pencil beams have opposing polarizations (i.e., vertical and horizontal), thus achieving dual-polarization. The SeaWinds instrument provides a full 1800 km-wide swath coverage (including the nadir region), which allows for a nominal daily measurement coverage of 90% over the ice-free oceans. ADEOS-II Platform Name: Advanced Earth Observing Satellite-II (ADEOS-II) Orbit Period: 101.05 minutes Inclination Angle: 98.62 degrees Ascending Node: 1970-Jan-01 00:00:00 / SEAWINDS SENSOR Name: SeaWinds Scatterometer (SEAWINDS) Swath Width: 1800 km Description: SeaWinds is a Ku-band (13.4 GHz) Scatterometer which features a single circular scanning dish antenna to provide pencil-beam measurements, as opposed to the common fan beam design as seen with other spaceborne scatterometers. The pencil-beam design has several key advantages over the more common fan beam design: higher signal-to-noise ratio, smaller size, and superior coverage. The SeaWinds single antenna utilizes twin offset feeds, thus providing two pencil-beam footprint paths with an elliptical sampling footprint of approximately 25 km (in azimuth) by 37 km (in look/range direction); these pencil beams have opposing polarizations (i.e., vertical and horizontal), thus achieving dual-polarization. The SeaWinds instrument provides a full 1800 km-wide swath coverage (including the nadir region), which allows for a nominal daily measurement coverage of 90% over the ice-free oceans. DMSP 5D-2/F8 Platform Name: Defense Meteorological Satellite Program-F08 (DMSP 5D-2/F8) Orbit Period: 102 minutes Inclination Angle: 98.8 degrees Ascending Node: 1970-Jan-01 00:00:00 / SSM/I SENSOR Name: Special Sensor Microwave Imager (SSM/I) Swath Width: 1394 km Description: The Special Sensor Microwave Imager (SSM/I) flown on board the DMSP F8, F10, F11, F13, F14, and F15 platforms is designed to measure ocean surface wind speed, ice coverage and age, cloud water content, rainfall and atmospheric vertical temperature and humidity profiles. DMSP 5D-2/F10 Platform Name: Defense Meteorological Satellite Program-F10 (DMSP 5D-2/F10) Orbit Period: 102 minutes Inclination Angle: 98.8 degrees Ascending Node: 1970-Jan-01 00:00:00 / SSM/I SENSOR Name: Special Sensor Microwave Imager (SSM/I) Swath Width: 1394 km Description: The Special Sensor Microwave Imager (SSM/I) flown on board the DMSP F8, F10, F11, F13, F14, and F15 platforms is designed to measure ocean surface wind speed, ice coverage and age, cloud water content, rainfall and atmospheric vertical temperature and humidity profiles. DMSP 5D-2/F13 Platform Name: Defense Meteorological Satellite Program-F13 (DMSP 5D-2/F13) Orbit Period: 102 minutes Inclination Angle: 98.8 degrees Ascending Node: 1970-Jan-01 00:00:00 / SSM/I SENSOR Name: Special Sensor Microwave Imager (SSM/I) Swath Width: 1394 km Description: The Special Sensor Microwave Imager (SSM/I) flown on board the DMSP F8, F10, F11, F13, F14, and F15 platforms is designed to measure ocean surface wind speed, ice coverage and age, cloud water content, rainfall and atmospheric vertical temperature and humidity profiles. DMSP 5D-2/F11 Platform Name: Defense Meteorological Satellite Program-F11 (DMSP 5D-2/F11) Orbit Period: 102 minutes Inclination Angle: 98.8 degrees Ascending Node: 1970-Jan-01 00:00:00 / SSM/I SENSOR Name: Special Sensor Microwave Imager (SSM/I) Swath Width: 1394 km Description: The Special Sensor Microwave Imager (SSM/I) flown on board the DMSP F8, F10, F11, F13, F14, and F15 platforms is designed to measure ocean surface wind speed, ice coverage and age, cloud water content, rainfall and atmospheric vertical temperature and humidity profiles. DMSP 5D-2/F14 Platform Name: Defense Meteorological Satellite Program-F14 (DMSP 5D-2/F14) Orbit Period: 102 minutes Inclination Angle: 98.8 degrees Ascending Node: 1970-Jan-01 00:00:00 / SSM/I SENSOR Name: Special Sensor Microwave Imager (SSM/I) Swath Width: 1394 km Description: The Special Sensor Microwave Imager (SSM/I) flown on board the DMSP F8, F10, F11, F13, F14, and F15 platforms is designed to measure ocean surface wind speed, ice coverage and age, cloud water content, rainfall and atmospheric vertical temperature and humidity profiles. DMSP 5D-3/F15 Platform Name: Defense Meteorological Satellite Program-F15 (DMSP 5D-3/F15) Orbit Period: 102 minutes Inclination Angle: 98.8 degrees Ascending Node: 1970-Jan-01 00:00:00 / SSM/I SENSOR Name: Special Sensor Microwave Imager (SSM/I) Swath Width: 1394 km Description: The Special Sensor Microwave Imager (SSM/I) flown on board the DMSP F8, F10, F11, F13, F14, and F15 platforms is designed to measure ocean surface wind speed, ice coverage and age, cloud water content, rainfall and atmospheric vertical temperature and humidity profiles. CORIOLIS Platform Name: Coriolis (CORIOLIS) Orbit Period: 101.6 minutes Inclination Angle: 98.7 degrees Ascending Node: 1970-Jan-01 00:00:00 / WINDSAT SENSOR Name: WindSat (WINDSAT) Swath Width: 1200 km Description: WindSat is a microwave polarimetric radiometer designed with the capability of retrieving ocean surface wind vectors using three fully polarimetric feed horns at 10.7, 18.7, and 37.0 GHz. With 2 additional dual-polarized (i.e., not fully polarimetric) radiometer bands and a total of 22 descrete micrwave channels, WindSat can also retrieve sea surface temperature, rain rate, water vaper, and cloud liquid water. DMSP 5D-3/F17 Platform Name: Defense Meteorological Satellite Program-F17 (DMSP 5D-3/F17) Orbit Period: 102 minutes Inclination Angle: 98.8 degrees Ascending Node: 1970-Jan-01 00:00:00 / SSMIS SENSOR Name: Special Sensor Microwave Imager/Sounder (SSMIS) Swath Width: 1394 km Description: The Special Sensor Microwave Imager Sounder, flown on board the DMSP F16 and F17 platforms, is designed to measure ocean surface wind speed, ice coverage and age, cloud water content, rainfall and atmospheric vertical temperature and humidity profiles.
Project	Making Earth Science Data Records for Use in Research Environments (MEaSUREs)
Data Provider	Creator: NASA/GSFC/NOAA Release Place: PO.DAAC Release Date: 2009-May-10 Resource: https://podaac-tools.jpl.nasa.gov/drive/files/allData/ccmp/L3.0/docs/ccmp_users_guide.pdf
Keyword(s)	wind data, wind, ocean wind, wind speed, vector, vectors, ocean wind vector, ocean wind vectors, cross-calibrated, multi-platform, ccmp, analysis, analyses, gridded, assimilated, l3.0, ecmwf, measures
Persistent ID	PODAAC-CCF30-01XXX

Questions related to this dataset? Contact podaac@podaac.jpl.nasa.gov

PO.DAAC DRIVE	https://podaac-tools.jpl.nasa.gov/drive/files/allData/ccmp/L3.0/flk
OPENDAP	https://podaac-opendap.jpl.nasa.gov/opendap/allData/ccmp/L3.0/flk/
Format (Compression)	NETCDF (GZIP)
Web Service	https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-CCF30-01XXX&apidoc (Search Granule)
THREDDS	http://thredds.jpl.nasa.gov/thredds/catalog/ncml_aggregation/OceanWinds/ccmp/catalog.html?dataset=ncml_aggregation/OceanWinds/ccmp/aggregate__CCMP_MEASURES_ATLAS_L4_OW_L3_0_WIND_VECTORS_FLK.ncml THREDDS Data Server access for this dataset

Citation

NASA/GSFC/NOAA. 2009. Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L3.0 First-Look Analyses. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at https://doi.org/10.5067/CCF30-01XXX.

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For more information see Data Citations and Acknowledgments.

Journal Reference

Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, D. Gombos, 2011: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Amer. Meteor. Soc., 92, 157-174. doi: 10.1175/2010BAMS2946.1

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Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L3.0 First-Look Analyses

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