Cross-Calibrated Multi-Platform Ocean Surface Wind Vector L3.0 First-Look Analyses
67 Publications Cited this Dataset
Citation metrics available for years (2015-2019)
Close
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 |
| 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 | COMPLETE |
| Measurement | OCEANS > OCEAN WINDS > SURFACE WINDS |
| Processing Level | 4 |
| Coverage | Region: GLOBAL North Bounding Coordinate: 78 degrees South Bounding Coordinate: -78 degrees West Bounding Coordinate: -180 degrees East Bounding Coordinate: 180 degrees Time Span: 1987-Jul-02 to 2011-Dec-31 |
| Resolution | Spatial Resolution: 0.25 degrees (Latitude) x 0.25 degrees (Longitude) Temporal Resolution: 6 Hour |
| Projection | Projection Type: Gridded |
| Platform/Sensor | DMSP 5D-2/F10 / Platform Name: Defense Meteorological Satellite Program-F10 (DMSP 5D-2/F10) Orbit Period: 102.0 minutes Inclination Angle: 98.8 degrees SSM/I SENSOR Name: Special Sensor Microwave Imager (SSM/I) Swath Width: 1394.0 kilometers Description: Spacecraft angular distance from orbital plane relative to the Equator. ADEOS-II / Platform Name: Advanced Earth Observing Satellite-II (ADEOS-II) Orbit Period: 101.05 minutes Inclination Angle: 98.62 degrees SEAWINDS SENSOR Name: SeaWinds Scatterometer (SEAWINDS) Swath Width: 1800.0 kilometers Description: Spacecraft angular distance from orbital plane relative to the Equator. TRMM / Platform Name: Tropical Rainfall Measuring Mission (TRMM) Orbit Period: 92.4 minutes Inclination Angle: 35.0 degrees TMI SENSOR Name: TRMM Microwave Imager (TMI) Swath Width: 878.0 kilometers Description: Spacecraft angular distance from orbital plane relative to the Equator. DMSP 5D-3/F15 / Platform Name: Defense Meteorological Satellite Program-F15 (DMSP 5D-3/F15) Orbit Period: 102.0 minutes Inclination Angle: 98.8 degrees SSM/I SENSOR Name: Special Sensor Microwave Imager (SSM/I) Swath Width: 1394.0 kilometers Description: Spacecraft angular distance from orbital plane relative to the Equator. CORIOLIS / Platform Name: Coriolis (CORIOLIS) Orbit Period: 101.6 minutes Inclination Angle: 98.7 degrees WINDSAT SENSOR Name: WindSat (WINDSAT) Swath Width: 1200.0 kilometers Description: Spacecraft angular distance from orbital plane relative to the Equator. Show More |
| Project | Making Earth Science Data Records for Use in Research Environments (MEaSUREs) |
| Data Provider | Publisher: PO.DAAC 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 |
Questions related to this dataset? Contact podaac@podaac.jpl.nasa.gov
| Direct Access | |
| PO.DAAC DRIVE | https://podaac-tools.jpl.nasa.gov/drive/files/allData/ccmp/L3.0/flk PO.DAAC Drive |
| OPENDAP DATA | https://podaac-opendap.jpl.nasa.gov/opendap/allData/ccmp/L3.0/flk/ The OPeNDAP base directory location for the collection. |
| 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 |
| Format (Compression) | NETCDF |
| DATA CITATION POLICY | |
|
| 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
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 | |||||||