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GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis
(JPL_OUROCEAN-L4UHfnd-GLOB-G1SST)
31 Publications Cited this Dataset
Citation metrics available for years (2014-2021)
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Publications citing GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis
Citation metrics available for years (2014-2021)
| Year | Citation |
|---|---|
| 2014 | Seasonal migrations of North Atlantic minke whales: novel insights from large-scale passive acoustic monitoring networks, Movement Ecology,https://doi.org/10.1186/s40462-014-0024-3 |
| 2015 | Impact of multichannel river network on the plume dynamics in the P earl R iver estuary, Journal of Geophysical Research: Oceans,https://doi.org/10.1002/2014JC010490 |
| 2015 | A case study of large phytoplankton blooms off the New Jersey coast with multi-sensor observations, Continental Shelf Research,https://doi.org/10.1016/j.csr.2015.07.006 |
| 2015 | Microplastics in Arctic polar waters: the first reported values of particles in surface and sub-surface samples, Scientific reports,https://doi.org/10.1038/srep14947 |
| 2015 | Omura's whales (Balaenoptera omurai) off northwest Madagascar: ecology, behaviour and conservation needs, Royal Society Open Science,https://doi.org/10.1098/rsos.150301 |
| 2017 | JPLOurOceanProject. (2010). GHRSST Level 4 G1SST global foundation sea surface temperature analysis. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [2015-09-23] at Retrieved from https://doi.org/10.5067/ghg1s-4fp01 Does upwelling intensity determine larval fish habitats in upwelling ecosystems? The case of Senegal and Mauritania, Fisheries,https://doi.org/10.1111/fog.12224 |
| 2017 | JPLOurOceanProject, 2010. GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [2015-09-23] at. http://dx.doi.org/10.5067/GHG1S-4FP01.I13 Larval fish assemblages across an upwelling front: Indication for active and passive retention, Estuarine, Coastal and Shelf Science,https://doi.org/10.1016/j.ecss.2016.12.015 |
| 2017 | JPL Our Ocean.GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis; JPL OurOcean Project; NASA PO.DAAC: Pasadena, CA, USA, 2010. Available online:http://dx.doi.org/10.5067/GHG1S-4FP01(accessed on 31 March 2017). Synergistic Use of Remote Sensing and Modeling to Assess an Anomalously High Chlorophyll-a Event during Summer 2015 in the South Central Red Sea, Remote Sensing,https://doi.org/10.3390/rs9080778 |
| 2018 | Sensitivity of offshore surface fluxes and sea breezes to the spatial distribution of sea-surface temperature, Boundary-Layer Metereology,https://doi.org/10.1007/s10546-017-0313-7 |
| 2018 | ESR. 2009. OSCAR third degree resolution ocean surface currents. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [2018-03-20] at http://dx.doi.org/10.5067/OSCAR-03D01. Spatial Ecology and Movement Patterns of Deep-Diving Odontocetes in the Western North Atlantic, N/A,N/A |
| 2018 | Spatiotemporal patterns of overlap between short-finned pilot whales and the US pelagic longline fishery in the Mid-Atlantic Bight: An assessment to inform the …, Fisheries Research,https://doi.org/10.1016/j.fishres.2018.07.008 |
| 2018 | JPL, 2010. GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis. Ver. 1. PO.DAAC, CA, USA Dataset accessed [2016-11-22] at. https://doi.org/10.5067/GHG1S-4FP01. Water masses and oceanic eddy regulation of larval fish assemblages along the Cape Verde Frontal Zone, Journal of Marine Systems,https://doi.org/10.1016/j.jmarsys.2018.03.004 |
| 2018 | A 3D unstructured-grid model for Chesapeake Bay: Importance of bathymetry, Ocean Modelling,https://doi.org/10.1016/j.ocemod.2018.05.002 |
| 2019 | Predicted changes in the potential distribution of seerfish (Scomberomorus sierra) under multiple climate change scenarios in the Colombian Pacific Ocean, Ecological Informatics,https://doi.org/10.1016/j.ecoinf.2019.100985 |
| 2019 | Predicting fisheries bycatch: A case study and field test for pilot whales in a pelagic longline fishery, Diversity and Distributions,https://doi.org/10.1111/ddi.12912 |
| 2019 | Stratification has strengthened in the Baltic Sea–an analysis of 35 years of observational data, Frontiers in Earth Science,https://doi.org/10.3389/feart.2019.00174 |
| 2019 | Third-order WENO transport scheme for simulating the baroclinic eddying ocean on an unstructured grid, Ocean Modelling,https://doi.org/10.1016/j.ocemod.2019.101466 |
| 2020 | JPL OurOcean Project: GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis. Ver. 1. PO.DAAC, CA, USA, https://doi.org/10.5067/ghg1s-4fp01, 2010. Fine-scale vertical structure of sound-scattering layers over an east border upwelling system and its relationship to pelagic habitat characteristics, Ocean Science,https://doi.org/10.5194/os-16-65-2020 |
| 2020 | JPL Our Ocean Project, 2010. GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis. Ver. 1. NASA PO.DAAC. https://doi.org/10.5067/GHG1S-4FP01. Hydrographic Variability and Estuarine Classification of Inhambane Bay (Mozambique), Journal of Coastal Research,https://doi.org/10.2112/SI95-126.1 |
| 2020 | Monitoring pearl farming lagoon temperature with global high resolution satellite-derived products: An evaluation using Raroia Atoll, French Polynesia, Marine Pollution Bulletin,https://doi.org/10.1016/j.marpolbul.2020.111576 |
| 2020 | Capturing a Mode of Intermediate Water Formation in the Red Sea, Journal of Geophysical Research: Oceans,https://doi.org/10.1029/2019JC015803 |
| 2020 | Prospective modelling of operational offshore windfarms on the distribution of marine megafauna in the southern North Sea, bioRxiv,https://doi.org/10.1101/2020.12.16.423009 |
| 2020 | Discrepancies between satellite-derived and in situ SST data in the Cape Frio Upwelling System, Southeastern Brazil (23˚ S), Remote Sensing Letters,https://doi.org/10.1080/2150704X.2020.1742941 |
| 2020 | Shifts in Phytoplankton Community Structure Across an Anticyclonic Eddy Revealed From High Spectral Resolution Lidar Scattering Measurements, Frontiers in Marine Science,https://doi.org/10.3389/fmars.2020.00493 |
| 2020 | Simulating storm surge and compound flooding events with a creek-to-ocean model: Importance of baroclinic effects, Ocean Modelling,https://doi.org/10.1016/j.ocemod.2019.101526 |
| 2021 | JPL Our Ocean Project, 2010. GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis. Ver. 1. PO.DAAC, CA, USA. https://doi.org/10.5067/GHG1S- 4FP01. (Accessed 1 November 2020). Paralytic shellfish toxins in Peruvian scallops associated with blooms of Alexandrium ostenfeldii (Paulsen) Balech & Tangen in Paracas Bay, Peru, Journal,10.1016/j.marpolbul.2021.112988 |
| 2021 | Available online: Https://Podaac.Jpl.Nasa.Gov/Dataset/JPL_OUROCEAN-L4UHfnd-GLOB-G1SST (accessed on 8 July 2019). Satellite sea surface temperature product comparison for the Southern African marine region, Journal,10.3390/rs13071244 |
| 2021 | JPL OurOcean Project. (2010). GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis. Ver. 1. PO.DAAC, CA, USA. Retrieved February 18, 2021, from https://doi.org/10.5067/GHG1S-4FP01 Hydrographic variability in the Gulf of Papagayo, Costa Rica during 2017-2019, Journal,10.15517/rbt.v69iSuppl.2.48308 |
| 2021 | Assessment of SCATSat-1 Scatterometer Winds on the Upper Ocean Simulations in the North Indian Ocean, Journal,10.1029/2020JC016677 |
| 2021 | JPL OurOcean Project (2010). G1SST L4 SST Analysis. Ver. 1. PO.DAAC, CA, USA. Available online at: https://doi.org/10.5067/GHG1S-4FP01 (accessed December 01, 2020) New insights into the seasonal movement patterns of Shortfin Mako sharks in the Gulf of Mexico, Journal,10.3389/fmars.2021.623104 |
| 2021 | JPL Our Ocean Project (2013). GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis. Pasadena, CA: PO.DAAC. doi: 10.5067/GHG1S-4FP01 Novel Method for the Estimation of Vertical Temperature Profiles Using a Coastal Acoustic Tomography System, Journal,10.3389/fmars.2021.675456 |
| DOI | 10.5067/GHG1S-4FP01 |
| Short Name | JPL_OUROCEAN-L4UHfnd-GLOB-G1SST |
| Description | A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the JPL OurOcean group using a multi-scale two-dimensional variational (MS-2DVAR) blending algorithm on a global 0.009 degree grid. This Global 1 km SST (G1SST) analysis uses satellite data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Geostationary Operational Environmental Satellite (GOES) Imager, the Multi-Functional Transport Satellite 1R (MTSAT-1R) radiometer, and in situ data from drifting and moored buoys. |
| Version | 1 |
| Dataset Type | DEPRECATED |
Questions related to this dataset? Contact podaac@podaac.jpl.nasa.gov
Please contact podaac@podaac.jpl.nasa.gov for more info.
Citation is critically important for dataset documentation and discovery. Please cite the data as follows, and cite the reference papers when it is appropriate.
| Citation | JPL OurOcean Project. 2010. G1SST L4 SST Analysis. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at https://doi.org/10.5067/GHG1S-4FP01
For more information see Data Citations and Acknowledgments.
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| Journal Reference | Chao, Y., Z. Li, J. D. Farrara, and P. Huang: Blended sea surface temperatures from multiple satellites and in-situ observations for coastal oceans, 2009: Journal of Atmospheric and Oceanic Technology, 26 (7), 1435-1446, 10.1175/2009JTECHO592.1 . |
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