GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)
(MUR-JPL-L4-GLOB-v4.1)
98 Publications Cited this Dataset
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Publications citing GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)
Citation metrics available for years (2014-2021)
Year | Citation |
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2015 | ANALYSIS OF OZONE PRODUCTION AND ITS SENSITIVITY IN HOUSTON USING THE DATA COLLECTED DURING DISCOVER-AQ |
2015 | Northward migration of Cape São Tomé rings, Brazil, Continental Shelf Research,https://doi.org/10.1016/j.csr.2015.06.010 |
2015 | Observations of meandering and upwelling events in the AgulhasCurrent at 34° s |
2017 | Shelf-edge exchange in a numerical model of the Shetland shelf, PhD Thesis,http://hdl.handle.net/10044/1/52909 |
2017 | A multi-scale high-resolution analysis of global sea surface temperature, Remote Sensing of,https://doi.org/10.1016/j.rse.2017.07.029 |
2017 | GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, CA,USA. Available at http://dx.doi.org/10.5067/GHGMR-4FJ04. (Accessed 21 September 2016) High-resolution modeling of thermal thresholds and multiple environmental influences on coral bleaching for regional and local reef managements, bioRxiv,https://doi.org/10.1101/211854 |
2017 | JPL MUR MEaSUREs Project(2015).GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)In:CA, USA: PO.DAAC, DOI:https://doi.org/10.5067/GHGMR-4FJ04Ver. 4.1. Collaborations and Partnerships in NASA's Earth Science Data Systems, Data Science Journal,https://doi.org/10.5334/dsj-2017-051 |
2017 | JPL MUR MEaSUREs Project. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA.https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1. Accessed 10 Feb 2016. Remote sensing measurements of sea surface temperature as an indicator of Vibrio parahaemolyticus in oyster meat and human illnesses, Environmental,https://doi.org/10.1186/s12940-017-0301-x |
2017 | JPL MUR MEaSUREs Project. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) (PO.DAAC, 2015). Reconciling the opposing effects of warming on phytoplankton biomass in 188 large lakes, Scientific reports,https://doi.org/10.1038/s41598-017-11167-3 |
2018 | At-sea distribution and fine-scale habitat use patterns of zooplanktivorous Cassin's auklets during the chick-rearing period, Marine biology,https://doi.org/10.1007/s00227-018-3434-8 |
2018 | At-sea distribution and foraging behaviour of two North Pacific seabirds revealed through GPS tracking, N/A,N/A |
2018 | 36.Physical Oceanography Distributed Active Archive Center. JPL MUR MEaSUREs Project. 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Version 4.1.; Physical Oceanography Distributed Active Archive Center: Pasadena, CA, USA, 2015. Coastal Upwelling Front Detection off Central Chile (36.5–37° S) and Spatio-Temporal Variability of Frontal Characteristics, Remote Sensing,https://doi.org/10.3390/rs10050690 |
2018 | Movement, dive behavior, and habitat-use of common murres (Uria aalge) in the Northern California Current System under variable ocean conditions, N/A,N/A |
2018 | NASA, 2002. Jet Propulsion Laboratory, Physical Oceanography Distributed Active Archive Center (JPL PO.DAAC). GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) (GDS versions 1 and 2). National Oceanographic Data Center, NOAA. (Accessed 19 December 2017). Ocean circulation and frontal structure near the southern Kerguelen Plateau: the physical context for the Kerguelen Axis ecosystem study, Deep-Sea Research Part II: Topical Studies in Oceanography,https://doi.org/10.1016/j.dsr2.2018.07.013 |
2018 | Does Sea Surface Temperature contribute to determining range limits and expansion of mangroves in Eastern South America (Brazil)?, Remote Sensing,https://doi.org/10.3390/rs10111787 |
2018 | MEaSUREs Project, JPL MUR, 2015. GHRSST Level 4 MUR Global Foundation Sea Surface. Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Exploring the synergy between along-track altimetry and tracer fronts to reconstruct surface ocean currents, Remote Sensing of Environment,https://doi.org/10.1016/j.rse.2018.04.036 |
2018 | JPL MUR MEaSUREs Project. 2015. GHRSST level 4 MUR global foundation sea surface temperature analysis (v4.1). (Version 4.1). PO.DAAC, CA, USA. Available at http://dx.doi.org/10.5067/GHGMR-4FJ04 (accessed 21 September 2016). High-resolution modeling of thermal thresholds and environmental influences on coral bleaching for local and regional reef management, PeerJ,https://doi.org/10.7717/peerj.4382 |
2018 | Salinity Simulation in Florida Bay with the Regional Oceanic Modeling System (ROMS), N/A,https://doi.org/10.13140/RG.2.2.24828.23683 |
2018 | The Impacts of Anthropogenic Global Change and Local Human Activities on Reef-Building Corals on the Belize Mesoamerican Barrier Reef System, N/A,N/A |
2019 | Whales in warming water: Assessing breeding habitat diversity and adaptability in Oceania's changing climate, Global Change Biology,https://doi.org/10.1111/gcb.14563 |
2019 | Crown-of-thorns starfish impede the recovery potential of coral reefs following bleaching, Marine Biology,https://doi.org/10.1007/s00227-019-3543-z |
2019 | MUR MEaSUREs Project, J.P.L., 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Ver. 4.1. PO, DAAC, CA, USA. https://doi.org/10.5067/GHGMR-4FJ04, Accessed date: 10 June 2017. Proteomic changes across a natural temperature gradient in a marine gastropod, Marine Environmental Research,https://doi.org/10.1016/j.marenvres.2019.06.002 |
2019 | Environmental representativity in marine protected area networks over large and partly unexplored seascapes, Global ecology and Conservation,https://doi.org/10.1016/j.gecco.2019.e00545 |
2019 | Extreme reduction in nutritional value of a key forage fish during the Pacific marine heatwave of 2014-2016, Marine Ecology Progress Series,https://doi.org/10.3354/meps12891 |
2019 | MUR MEaSUREs Project, J.P.L., 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis V4, vol. 1 PO.DAAC, CA, USA Last access March/2018. https://doi.org/10.5067/GHGMR-4FJ04, Ver. 4.1. https://doi.org/10.5067/GHGMR-4FJ04, Ver. 4.1. First measurements of the ocean-atmosphere CO2 fluxes at the Cabo Frio upwelling system region, Southwestern Atlantic Ocean, Continental Shelf Research,https://doi.org/10.1016/j.csr.2019.05.008 |
2019 | Fluctuating reproductive rates in Hawaii's humpback whales, Megaptera novaeangliae, reflect recent climate anomalies in the North Pacific, Royal Society Open Science,https://doi.org/10.1098/rsos.181463 |
2019 | Sensitivity of the near‐shore oceanic circulation off Central Chile to coastal wind profiles characteristics, Journal of Geophysical Research: Oceans,https://doi.org/10.1029/2018JC014051 |
2019 | Sixty years since the creation of Lake Kariba: Thermal and oxygen dynamics in the riverine and lacustrine sub-basins, PloS one,https://doi.org/10.1371/journal.pone.0224679 |
2019 | Jet Propulsion Laboratory MUR MEaSUREs Project (2015). GHRSST level 4 MUR global foundation sea surface temperature analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Dataset accessed [YYYY‐MM‐DD] at https://doi.org/10.5067/GHGMR‐4FJ04. High Rates of N2 Fixation in Temperate, Western North Atlantic Coastal Waters Expand the Realm of Marine Diazotrophy, Global Biogeochemical Cycles,https://doi.org/10.1029/2018GB006130 |
2019 | JPL MUR MEaSUREs Project. 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Dataset accessed [2018-05] at http://dx.doi.org/10.5067/GHGMR-4FJ04. Spatial and temporal visualizations of satellite-derived sea surface temperatures for Alaska fishery management areas, Pacific States E-Journal of Scientific Visualizations,https://doi.org/10.28966/PSESV.2019.003 |
2019 | Saildrone. Saildrone Baja Field Campaign Surface and ADCP Measurements. Ver. 1.0. PO.DAAC, CA, USA, 2018. Available online: http://dx.doi.org/10.5067/SDRON-SURF0 (accessed on 19 December 2018). Using saildrones to validate satellite-derived sea surface salinity and sea surface temperature along the California/Baja Coast, Remote Sensing,https://doi.org/10.3390/rs11171964 |
2020 | B. Beckley, R. Ray, S. Holmes, N. Zelensky, F. Lemoine, X. Yang, S. Brown, S. Desai, G. Mitchum, and J. Hausman, “Integrated multi- mission ocean altimeter data for climate research topex/poseidon, jason- 1 and ostm/jason-2 user’s handbook version 3.0, 61 pp., california institute of technology,” California Institute of Technology, ftp://podaac.jpl.nasa.gov/allData/merged alt L, vol. 2, p. 61, 2018. Super-Resolution of Sea Surface Temperature Satellite Images, Global Oceans 2020: Singapore – U.S. Gulf Coast,https://doi.org/10.1109/IEEECONF38699.2020.9389030 |
2020 | Horizontal and vertical movements of humpback whales inform the use of critical pelagic habitats in the western South Pacific, Scientific Reports,https://doi.org/10.1038/s41598-020-61771-z |
2020 | JPL MUR MEaSUREs Project (2015). GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Version 4.1, https://doi.org/10.5067/GHGMR-4FJ04. PO.DAAC, CA, USA. Variability of physicochemical and biological parameters in the Sado Estuary: integration of in situ observations and satellite data, N/A,http://hdl.handle.net/10451/45253 |
2020 | Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager, Remote Sensing,https://doi.org/10.3390/rs12193142 |
2020 | 9. JPL MUR MEaSUREs Project. 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analy‐ sis (v4.1). Ver. 4.1. PO.DAAC, CA, USA Available at: https://podaac.jpl.nasa.gov/dataset/MUR‐JPL‐L4‐GLOB‐ v4.1 (accessed 10.02.2020). DOI 10.5067/GHGMR‐4FJ04 Выявление температурных аномалий на западном Каспии за 2017 г. по данным дистанционного зондирования, Юг России: экология, развитие,https://doi.org/10.18470/1992‐1098‐2020‐4‐63‐74 |
2020 | JPL MUR MEaSUREs Project. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis Ver. 4.1, PO.DAAC, CA, USA, 2015. doi 10.5067/GHGMR-4FJ04. Стандартизация уловов на усилие минтая в северной части Охотского моря с учетом некоторых факторов среды, Известия ТИНРО …,https://doi.org/10.26428/1606-9919-2020-200-819-836 |
2020 | Modelling the effects of large dams on water quality in tropical rivers, N/A,https://doi.org/10.3929/ethz-b-000476521 |
2020 | Accuracy of empirical satellite algorithms for mapping phytoplankton diagnostic pigments in the open ocean: A supervised learning perspective, Frontiers in Marine Science,https://doi.org/10.3389/fmars.2020.00599 |
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 | MUR MEaSUREs Project, J.P.L. (2015) GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis V4, vol. 1 PO.DAAC, CA, USA. Last access March 2018. Ver. 4.1. Available at: https://doi.org/10.5067/GHGMR-4FJ04. Observations of air–sea heat fluxes in the southwestern Atlantic under high‐frequency ocean and atmospheric perturbations, Quarterly Journal of the Royal Metereological Society,https://doi.org/10.1002/qj.3905 |
2020 | Open Data, Collaborative Working Platforms, and Interdisciplinary Collaboration: Building an Early Career Scientist Community of Practice to Leverage Ocean …, Frontiers in Marine ,https://doi.org/10.3389/fmars.2020.593512 |
2020 | JPL MUR MEaSUREs Project, 2015. GHRSST level 4 MUR global foundation sea surface temperature analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Available at: https://doi.org/10.5067/GHGMR-4FJ04. Phytoplankton community dynamics in a coastal bay under upwelling influence (Central Chile), Estuarine, Coastal and Shelf Science,https://doi.org/10.1016/j.ecss.2020.106968 |
2020 | Characterizing phytoplankton biomass seasonal cycles in two NE Atlantic coastal bays, Continental Shelf Research,https://doi.org/10.1016/j.csr.2020.104200 |
2020 | JPL MUR MEaSUREs Project (2015). GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). NASA PO.DAAC. Available online at: http://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1 (accessed October 29, 2019). Climate extreme seeds a new domoic acid hotspot on the US west coast, Frontiers in Climate,https://doi.org/10.3389/fclim.2020.571836 |
2020 | Remote Sensing of Phytoplankton Size Classes on the Northeast US Continental Shelf, N/A,https://digitalcommons.uri.edu/theses/1913 |
2020 | Response of near-inertial energy to a supercritical tropical cyclone and jet in the South China Sea: modelling study, Ocean Science,https://doi.org/10.5194/os-16-1095-2020 |
2020 | Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments, Remote Sensing,https://doi.org/10.3390/rs12111839 |
2020 | JPL MUR MEaSUREs Project (2015). GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, Caifornia. doi: 10.5067/GHGMR-4FJ04. Complementary tools for aquaculture management: remote sensing and in situ approaches for Sines, N/A,http://hdl.handle.net/10451/45301 |
2020 | JPL Mur MEaSUREs Project (2015). JPL MUR MEaSUREs Project, GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis. , Pasadena, CA: PO.DAAC. Contemporary climate change hinders hybrid performance of ecologically dominant marine invertebrates, Journal of evolutionary …,https://doi.org/10.1111/jeb.13609 |
2020 | JPL MUR MEaSUREs Project, 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Dataset accessed [2018-09-10]. http://dx.doi.org/10.5067/GHGMR-4FJ04 Sea surface temperature trends and the influence of ENSO on the southwest sea of Vietnam using remote sensing data and GIS, Vietnam Journal of Marine Sciene and Technology,https://doi.org/10.15625/1859-3097/20/2/14173 |
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 | SMART drumlines at Réunion Island do not attract bull sharks Carcharhinus leucas into nearshore waters: Evidence from acoustic monitoring, Fisheries Research,https://doi.org/10.1016/j.fishres.2019.105480 |
2020 | JPL MUR MEaSUREs Project. GHRSST Level 4 MUR global foundation sea surface temperature analysis (v4.1).PO.DAAC. https://doi.org/10.5067/GHGMR-4FJ04 (2015). Effects of intense storm events on dolphin occurrence and foraging behavior, Scientific Reports,https://doi.org/10.1038/s41598-020-76077-3 |
2021 | Ocean front detection with glider and satellite-derived sst data in the southern california current system, Journal,10.3390/rs13245032 |
2021 | Jpl Mur MEaSUREs Project, 2015. GHRSST level 4 MUR global foundation Sea Surface temperature analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Dataset accessed [2019- 03-25] at. https://doi.org/10.5067/GHGMR-4FJ04. Variability of mackerel fish catch and remotely-sensed biophysical controls in the eastern Pemba Channel, Journal,10.1016/j.ocecoaman.2021.105593 |
2021 | JPL MUR MEaSUREs Project. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). PO.DAAC, CA, USA; 2015 [2019/04/13]. Available from: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4 -GLOB-v4.1 Oceanic primary production trend patterns along coast of Ecuador, Journal,10.1080/23766808.2021.1964915 |
2021 | JPL MUR MEaSUREs Project, 2010: GHRSST level 4 MUR global foundation sea surface temperature analysis, version 2. PO.DAAC, accessed 24 January 2019, https://doi.org/10.5067/ GHGMR-4FJ01 On the hyperbolicity of the bulk air-sea heat flux functions: insights into the efficiency of air-sea moisture disequilibrium for tropical cyclone intensification, Journal,10.1175/MWR-D-20-0324.1 |
2021 | Optimization and assessment of phytoplankton size class algorithms for ocean color data on the Northeast US continental shelf, Journal,10.1016/j.rse.2021.112729 |
2021 | JPL MUR MEaSUREs Project: GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1), Ver. 4.1. PO.DAAC, CA, USA, https://doi.org/10.5067/GHGMR-4FJ04, 2015 Performance of the Adriatic Sea and Coast (AdriSC) climate component–a COAWST V3. 3-based one-way coupled atmosphere–ocean modelling suite …, Journal,10.5194/gmd-14-5927-2021 |
2021 | Effects of competitive pressure and habitat heterogeneity on niche partitioning between Arctic and boreal congeners, Journal,10.1038/s41598-021-01506-w |
2021 | Persistent meanders and eddies lead to quasi-steady Lagrangian transport patterns in a weak western boundary current, Journal,10.1038/s41598-020-79386-9 |
2021 | (Acknowledgements) The Group for High Resolution Sea Surface Temperature(GHRSST) Multi-scale Ultra-high Resolution (MUR) SST data were ob-tained from the NASA EOSDIS Physical Oceanography DistributedActive Archive Center (PO.DAAC) at the Jet Propulsion Laboratory,Pasadena, CA (https://doi.org/10.5067/GHGMR-4FJ04). Phytoplankton communities in two wide-open bays in the Iberian upwelling system, Journal,10.1016/j.seares.2020.101982 |
2021 | JPL (2015). JPL Mur Measures Project. Ghrsst Level 4 MUR Global Foundation Sea Surface Temperature Analysis. Ver. 4.1. PO.DAAC, CA, USA. Available online at: https://doi.org/10.5067/GHGMR-4FJ04. (Accessed October, 1, 2020). Environmental Factors Affecting Spatial Dinoflagellate Cyst Distribution in Surface Sediments Off Aveiro-Figueira da Foz (Atlantic Iberian Margin), Journal,10.3389/fmars.2021.699483 |
2021 | NASA/JPL (2015) GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). https://podaac. jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1 Environmental forcing on zooplankton distribution in the coastal waters of the Galápagos Islands: spatial and seasonal patterns in the copepod community structure, Journal,10.3354/meps13617 |
2021 | GHRSST, 2019: The Group for High Resolution Sea Surface Temperature (GHRSST) Multiscale Ultrahigh Resolution (MUR) SST data. JPL MUR MEaSUREs Project, accessed 5 June 2019, https://podaac-opendap.jpl.nasa.gov/opendap/ allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/ Equatorial waves triggering extreme rainfall and floods in southwest Sulawesi, Indonesia, Journal,10.1175/MWR-D-20-0262.1 |
2021 | Rapid Warming Events in a Small Coastal Upwelling Embayment, Dissertation,N/A |
2021 | JPL MUR MEaSUREs Project: GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analy- sis, Version 4.1, PO.DAAC [data set], CA, USA, at https://doi.org/10.5067/GHGMR-4FJ04 (last access: 10 Febru- ary 2020), 2015. Evaluating high-frequency radar data assimilation impact in coastal ocean operational modelling, Journal,10.5194/os-17-1157-2021 |
2021 | Research and Development of a Supporting Information System for Optimization of Salmon Release Operations and Monitoring the Coastal Environment on the …, Report,N/A |
2021 | Experimental assessment of vulnerability to warming in tropical shallow-water marine organisms, Journal,10.3389/fmars.2021.767628 |
2021 | FISH larvae distribution and transport on the thermal fronts in the Midriff Archipelago region, Gulf of California, Journal,10.1016/j.csr.2021.104384 |
2021 | JPL MUR MEaSUREs Project. (2015). GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) . Ver. 4.1. (Publication no. https://doi.org/10.5067/GHGMR-4FJ04). Retrieved 2020-01-17, from PO.DAAC, CA, USA https://doi. org/10.5067/GHGMR-4FJ04 Genotype-Environment mismatch of kelp forests under climate change, Journal,10.1111/mec.15993 |
2021 | Available online: Https://Podaac.Jpl.Nasa.Gov/Dataset/MUR-JPL-L4-GLOB-v4.1 (accessed on 7 October 2019). Satellite sea surface temperature product comparison for the Southern African marine region, Journal,10.3390/rs13071244 |
2021 | JPL MUR MEaSUREs Project. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis. Ver. 4.1. PO.DAAC, CA, USA, 2015. Available online: https://doi.org/10.5067/GHGMR-4FJ04 (accessed on 25 October 2021) High Chlorophyll-a Areas along the Western Coast of South Sulawesi-Indonesia during the Rainy Season Revealed by Satellite Data, Journal,10.3390/rs13234833 |
2021 | Seasonal instability of the Western Equatorial Indian Ocean, Journal,10.1029/2021JC017875 |
2021 | JPL MUR MEaSUREs Project. 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Dataset accessed [2016-10-30] at https://doi.org/10.5067/ GHGMR-4FJ04 Hydroclimatic changes in Alaska portrayed by a high-resolution regional climate simulation, Journal,10.1007/s10584-021-02956-x |
2021 | Seasonal Variability of SST Fronts in the Inner Sea of Chiloé and Its Adjacent Coastal Ocean, Northern Patagonia, Journal,10.3390/rs13020181 |
2021 | Sensitivity of photosynthesis to warming in two similar species of the aquatic angiosperm Ruppia from tropical and temperate habitats, Journal,10.3390/su13169433 |
2021 | Impacts of the Kuroshio intrusion through the luzon strait on the local precipitation anomaly, Journal,10.3390/rs13061113 |
2021 | Surface Current Variations and Oceanic Fronts in the Southern East China Sea: Drifter Experiments, Coastal Radar Applications, and Satellite Observations, Journal,10.1029/2021JC017373 |
2021 | JPL MUR MEaSUREs Project (2015). GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). Ver. 4.1. PO.DAAC, CA, USA. Available online at: https://doi.org/10.5067/GHGMR-4FJ04 (accessed August, 2020) Temperature and Patterns of Occurrence and Abundance of Key Copepod Taxa in the Northeast Pacific, Journal,10.3389/fmars.2021.670795 |
2021 | JPL MUR Measures Project (2015). GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis.Ver. 4.1. Pasadena, CA: PO.DAAC. Temporal Variability of Thermohaline Fine-Structure Associated With the Subtropical Front Off the Southeast Coast of New Zealand in High-Frequency Short-Streamer …, Journal,10.3389/fmars.2021.751385 |
2021 | NASA/JPL (2015) GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1). NASA PO. DAAC. https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4- GLOB-v4.1 Influence of land-derived stressors and environmental variability on compositional turnover and diversity of estuarine benthic communities, Journal,10.3354/meps13714 |
2021 | PL MUR MEaSUREs Project. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) [Internet]. PO.DAAC, CA, USA: NASA; 2015 [cited 2018 Dec 11]. http://podaac.jpl.nasa.gov/dataset/ MUR-JPL-L4-GLOB-v4.1 Influence of thermal stratification and storms on acoustic telemetry detection efficiency: a year-long test in the US Southern Mid-Atlantic Bight, Journal,10.1186/s40317-021-00233-3 |
2021 | PL MUR MEaSUREs Project. 2010. GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis. Ver. 1. PO.DAAC, CA, USA. Dataset accessed [2020-02-03] at https://doi.org/10.5067/GHGMR-4FJ04 Assessing spatial segregation of beluga whales (Delphinapterus leucas) in Western Hudson Bay estuaries, Preprint,10.1101/2021.08.05.455325 |
2021 | JPL MUR MEaSUREs Project: GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analy- sis (v4.1), Ver. 4.1. PO.DAAC [data set], CA, USA, https://doi.org/10.5067/GHGMR-4FJ04, 2015 The COTUR project: remote sensing of offshore turbulence for wind energy application, Journal,10.5194/amt-14-6137-2021 |
2021 | Cascading weather events amplify the coastal thermal conditions prior to the shelf transit of Hurricane Sally (2020), Journal,10.1029/2021JC017957 |
2021 | Catastrophic loss of tropical seagrass habitats at the Cocos (Keeling) Islands due to multiple stressors, Journal,10.1016/j.marpolbul.2021.112602 |
2021 | The South Atlantic Meridional Overturning Circulation and Mesoscale Eddies in the First GOâ€SHIP Section at 34.5° S, Journal,10.1029/2020JC016962 |
2021 | Cephalopod paralarval species richness, abundance and size structure during the 2014–2017 anomalous warm period in the southern Gulf of California, Journal,10.1093/plankt/fbab010 |
2021 | MUR MEaSUREs Project, J.P.L., 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis. PO.DAAC, CA, USA. https://doi.org/10.5067/ GHGMR-4FJ04 (v4.1). Ver. 4.1 Changes in rocky intertidal communities after the 2015 and 2017 El Niño events along the Peruvian coast, Journal,10.1016/j.ecss.2020.107142 |
2021 | MUR MEaSUREs Project, JPL (2015). GHRSST level 4 MUR global foundation sea surface temperature analysis (v4.1). Ver. 4.1 (Vol. 1). CA, USA: PO.DAAC. Dataset accessed [2020-03-31] at https://doi.org/10.5067/GHGMR-4FJ04 Circulation over the Southâ€East Greenland Shelf and potential for liquid freshwater export: a drifter study, Journal,10.1029/2020GL091948 |
2021 | JPL MUR MEaSUREs Project: GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis. Ver. 4.1. PO.DAAC, JPL NASA, https://doi.org/10.5067/GHGMR- 4FJ04, 2015 Coupled and decoupled stratocumulus-topped boundary layers: turbulence properties, Journal,10.5194/acp-21-10965-2021 |
2021 | JPL MUR MEaSUREs Project: GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis. Ver. 4.1. PO.DAAC, CA, USA, http://doi.org/10.5067/GHGMR-4FJ04, 2015. Nutrients attenuate the negative effect of ocean acidification on reef coral calcification in the Arabian Sea upwelling zone (Masirah Island, Oman), Preprint,10.5194/bg-2021-213 |
2021 | JPL MUR MEaSUREs Project 2015. GHRSST level 4 MUR global foundation sea surface temperature analysis (4.1). doi: 10.5067/GHGMR-4FJ04 |
2021 | JPL MUR MEaSUREs Project, “GHRSST Level 4 MUR Global Foun- dation Sea Surface Temperature Analysis,” Ver. 4.1. PO.DAAC, CA, USA; https://doi.org/10.5067/GHGMR-4FJ04, 2015, online; accessed March, 2021 Decentralized nested Gaussian processes for multi-robot systems, Conference Paper,10.1109/ICRA48506.2021.9561566 |
2021 | JPL MUR MEaSUREs Project, 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis, vol. 1. https://doi.org/10.5067/GHGMR-4FJ04. Ver. 4.1. PO. DAAC, CA, USA. Dataset accessed [2020-04-21] at. Observations of northeastward flow on a narrow shelf dominated by the Agulhas Current, Journal,10.1016/j.ecss.2021.107197 |
2021 | Ocean and Sea Ice Retrievals From an Endâ€Toâ€End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements, Journal,10.1029/2021JC017610 |
Version | 4.1 |
Processing Level | 4 |
Start/Stop Date | 2002-May-31 to Present |
Short Name | MUR-JPL-L4-GLOB-v4.1 |
Description | A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata "history:" attribute to determine if a granule is near-realtime or retrospective. |
DOI | 10.5067/GHGMR-4FJ04 |
Measurement | OCEANS > OCEAN TEMPERATURE > SEA SURFACE TEMPERATURE |
Platform/Sensor | Aqua / Platform Name: Earth Observing System, Aqua (Aqua) Orbit Period: 98.4 minutes Inclination Angle: 98.1 degrees MODIS SENSOR Name: Moderate-Resolution Imaging Spectroradiometer (MODIS) Swath Width: 2330.0 kilometers Description: Spacecraft angular distance from orbital plane relative to the Equator. Aqua / Platform Name: Earth Observing System, Aqua (Aqua) Orbit Period: 98.4 minutes Inclination Angle: 98.1 degrees AMSR-E SENSOR Name: Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Swath Width: 1450.0 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. Terra / Platform Name: Earth Observing System, Terra (AM-1) (Terra) Orbit Period: 98.8 minutes Inclination Angle: 98.2 degrees MODIS SENSOR Name: Moderate-Resolution Imaging Spectroradiometer (MODIS) Swath Width: 2330.0 kilometers Description: Spacecraft angular distance from orbital plane relative to the Equator. NOAA-19 / Platform Name: National Oceanic & Atmospheric Administration-19 (NOAA-19) Orbit Period: 102.12 minutes Inclination Angle: 98.74 degrees AVHRR-3 SENSOR Name: Advanced Very High Resolution Radiometer-3 (AVHRR-3) Swath Width: 2400.0 kilometers Description: Spacecraft angular distance from orbital plane relative to the Equator. Show More |
Project | Group for High Resolution Sea Surface Temperature (GHRSST) |
Data Provider | Publisher: JPL NASA Creator: JPL MUR MEaSUREs Project Release Place: Jet Propulsion Laboratory Release Date: 2015-Mar-11 Resource: https://podaac.jpl.nasa.gov/MEaSUREs-MUR |
Format | netCDF-4 |
Keyword(s) | GHRSST, sea surface temperature, Level 4, SST, surface temperature, MUR, foundation SST, SST anomaly, anomaly |
Questions related to this dataset? Contact podaac@podaac.jpl.nasa.gov
Resolution Spatial Resolution: 0.01 Decimal Degrees x 0.01 Decimal Degrees Temporal Resolution: Hourly - < Daily Coverage Region: GLOBAL North Bounding Coordinate: 90 degrees South Bounding Coordinate: -90 degrees West Bounding Coordinate: -180 degrees East Bounding Coordinate: 180 degrees Time Span: 2002-May-31 to Present Granule Time Span: 2002-Jun-01 to 2023-May-31 Projection Projection Type: Cylindrical Lat-Lon Projection Detail: Geolocation information included for each pixel Ellipsoid: WGS 84 |
DIRECT ACCESS | |
HTTPS endpoint for data browse and download | |
Search Granules | |
DIRECT S3-ACCESS | |
Available for access in-region with AWS Cloud | |
Region | |
us-west-2 | |
podaac-ops-cumulus-public/MUR-JPL-L4-GLOB-v4.1/ | |
podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/ | |
AWS S3 Credentials | |
Get AWS S3 Credentials | Documentation | |
TOOLS AND SERVICES | |
MUR formatted in ZARR for Amazon Web Services |
GENERAL DOCUMENTATION | |
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USER'S GUIDE | |
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DATA RECIPE | |
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DATA CITATION POLICY | |
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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 MUR MEaSUREs Project. 2015. GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis. Ver. 4.1. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at https://doi.org/10.5067/GHGMR-4FJ04
For more information see Data Citations and Acknowledgments.
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Journal Reference | Chin, T.M, J. Vazquez-Cuervo, and E.M. Armstrong. 2017. A multi-scale high-resolution analysis of global sea surface temperature, Remote Sensing of Environment , 200 . https://doi.org/10.1016/j.rse.2017.07.029
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