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  • Meteorology and Climatology  (2)
  • Microwave  (2)
  • Sea Surface Temperature  (2)
  • 1
    Unknown
    Basel, Beijing, Wuhan, Barcelona, Belgrade : MDPI
    Keywords: Remote Sensing ; Sea Surface Temperature ; Infrared ; Microwave ; Accuracy
    Description / Table of Contents: Does Sea Surface Temperature Contribute to Determining Range Limits and Expansion of Mangroves in Eastern South America (Brazil)? / by Arimatéa C. Ximenes, Leandro Ponsoni, Catarina F. Lira, Nico Koedam and Farid Dahdouh-Guebas / Remote Sens. 2018, 10(11), 1787; https://doi.org/10.3390/rs10111787 --- Sea Surface Temperature (SST) Variability of the Eastern Coastal Zone of the Gulf of California / by Carlos Manuel Robles-Tamayo, José Eduardo Valdez-Holguín, Ricardo García-Morales, Gudelia Figueroa-Preciado, Hugo Herrera-Cervantes, Juana López-Martínez and Luis Fernando Enríquez-Ocaña / Remote Sens. 2018, 10(9), 1434; https://doi.org/10.3390/rs10091434 --- Quality Assessment of Sea Surface Temperature from ATSRs of the Climate Change Initiative (Phase 1) / by Christoforos Tsamalis and Roger Saunders / Remote Sens. 2018, 10(4), 497; https://doi.org/10.3390/rs10040497 --- Confirmation of ENSO-Southern Ocean Teleconnections Using Satellite-Derived SST / by Brady S. Ferster, Bulusu Subrahmanyam and Alison M. Macdonald / Remote Sens. 2018, 10(2), 331; https://doi.org/10.3390/rs10020331 --- Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation / by Ronan Fablet, Phi Huynh Viet, Redouane Lguensat, Pierre-Henri Horrein and Bertrand Chapron / Remote Sens. 2018, 10(2), 310; https://doi.org/10.3390/rs10020310 --- Optimal Estimation of Sea Surface Temperature from AMSR-E / by Pia Nielsen-Englyst, Jacob L. Høyer, Leif Toudal Pedersen, Chelle L. Gentemann, Emy Alerskans, Tom Block and Craig Donlon / Remote Sens. 2018, 10(2), 229; https://doi.org/10.3390/rs10020229 --- Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures / by Stéphane Saux Picart, Pierre Tandeo, Emmanuelle Autret and Blandine Gausset / Remote Sens. 2018, 10(2), 224; https://doi.org/10.3390/rs10020224 --- The Accuracies of Himawari-8 and MTSAT-2 Sea-Surface Temperatures in the Tropical Western Pacific Ocean / by Angela L. Ditri, Peter J. Minnett, Yang Liu, Katherine Kilpatrick and Ajoy Kumar / Remote Sens. 2018, 10(2), 212; https://doi.org/10.3390/rs10020212 --- Role of El Niño Southern Oscillation (ENSO) Events on Temperature and Salinity Variability in the Agulhas Leakage Region / by Morgan L. Paris and Bulusu Subrahmanyam / Remote Sens. 2018, 10(1), 127; https://doi.org/10.3390/rs10010127 --- Stability Assessment of the (A)ATSR Sea Surface Temperature Climate Dataset from the European Space Agency Climate Change Initiative / by David I. Berry, Gary K. Corlett, Owen Embury and Christopher J. Merchant / Remote Sens. 2018, 10(1), 126; https://doi.org/10.3390/rs10010126 --- Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data / by Claire E. Bulgin, Jonathan P. D. Mittaz, Owen Embury, Steinar Eastwood and Christopher J. Merchant / Remote Sens. 2018, 10(1), 97; https://doi.org/10.3390/rs10010097 --- The Role of Advanced Microwave Scanning Radiometer 2 Channels within an Optimal Estimation Scheme for Sea Surface Temperature / by Kevin Pearson, Christopher Merchant, Owen Embury and Craig Donlon / Remote Sens. 2018, 10(1), 90; https://doi.org/10.3390/rs10010090 --- Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm / by William Skirving, Susana Enríquez, John D. Hedley, Sophie Dove, C. Mark Eakin, Robert A. B. Mason, Jacqueline L. De La Cour, Gang Liu, Ove Hoegh-Guldberg, Alan E. Strong, Peter J. Mumby and Roberto Iglesias-Prieto / Remote Sens. 2018, 10(1), 18; https://doi.org/10.3390/rs10010018 --- Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks / by Zhihong Liao, Qing Dong, Cunjin Xue, Jingwu Bi and Guangtong Wan / Remote Sens. 2017, 9(11), 1204; https://doi.org/10.3390/rs9111204 --- Submesoscale Sea Surface Temperature Variability from UAV and Satellite Measurements / by Sandra L. Castro, William J. Emery, Gary A. Wick and William Tandy / Remote Sens. 2017, 9(11), 1089; https://doi.org/10.3390/rs9111089 --- Environmental Variability and Oceanographic Dynamics of the Central and Southern Coastal Zone of Sonora in the Gulf of California by Ricardo García-Morales, Juana López-Martínez, Jose Eduardo Valdez-Holguin, Hugo Herrera-Cervantes and Luis Daniel Espinosa-Chaurand Remote Sens. 2017, 9(9), 925; https://doi.org/10.3390/rs9090925 --- Determining the Pixel-to-Pixel Uncertainty in Satellite-Derived SST Fields / by Fan Wu, Peter Cornillon, Brahim Boussidi and Lei Guan / Remote Sens. 2017, 9(9), 877; https://doi.org/10.3390/rs9090877 --- Evaluation of the Multi-Scale Ultra-High Resolution (MUR) Analysis of Lake Surface Temperature / by Erik Crosman, Jorge Vazquez-Cuervo and Toshio Michael Chin / Remote Sens. 2017, 9(7), 723; https://doi.org/10.3390/rs9070723
    Pages: Online-Ressource (XI, 326 Seiten) , Illustrationen, Diagramme, Karten
    Edition: Printed Edition of the Special Issue Published in Remote Sensing
    ISBN: 9783038974802
    Language: English
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  • 2
    Unknown
    Basel, Beijing, Wuhan, Barcelona, Belgrade : MDPI
    Keywords: Remote Sensing ; Sea Surface Temperature ; Infrared ; Microwave ; Accuracy
    Description / Table of Contents: Does Sea Surface Temperature Contribute to Determining Range Limits and Expansion of Mangroves in Eastern South America (Brazil)? / by Arimatéa C. Ximenes, Leandro Ponsoni, Catarina F. Lira, Nico Koedam and Farid Dahdouh-Guebas. Remote Sensing 2018, 10(11), 1787; https://doi.org/10.3390/rs10111787 --- Sea Surface Temperature (SST) Variability of the Eastern Coastal Zone of the Gulf of California / by Carlos Manuel Robles-Tamayo, José Eduardo Valdez-Holguín, Ricardo García-Morales, Gudelia Figueroa-Preciado, Hugo Herrera-Cervantes, Juana López-Martínez and Luis Fernando Enríquez-Ocaña. Remote Sensing 2018, 10(9), 1434; https://doi.org/10.3390/rs10091434 --- Quality Assessment of Sea Surface Temperature from ATSRs of the Climate Change Initiative (Phase 1) / by Christoforos Tsamalis and Roger Saunders. Remote Sensing 2018, 10(4), 497; https://doi.org/10.3390/rs10040497 --- Confirmation of ENSO-Southern Ocean Teleconnections Using Satellite-Derived SST / by Brady S. Ferster, Bulusu Subrahmanyam and Alison M. Macdonald. Remote Sensing 2018, 10(2), 331; https://doi.org/10.3390/rs10020331 --- Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation / by Ronan Fablet, Phi Huynh Viet, Redouane Lguensat, Pierre-Henri Horrein and Bertrand Chapron. Remote Sensing 2018, 10(2), 310; https://doi.org/10.3390/rs10020310 --- Optimal Estimation of Sea Surface Temperature from AMSR-E / by Pia Nielsen-Englyst, Jacob L. Høyer, Leif Toudal Pedersen, Chelle L. Gentemann, Emy Alerskans, Tom Block and Craig Donlon. Remote Sensing 2018, 10(2), 229; https://doi.org/10.3390/rs10020229 --- Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures / by Stéphane Saux Picart, Pierre Tandeo, Emmanuelle Autret and Blandine Gausset. Remote Sensing 2018, 10(2), 224; https://doi.org/10.3390/rs10020224 --- The Accuracies of Himawari-8 and MTSAT-2 Sea-Surface Temperatures in the Tropical Western Pacific Ocean / by Angela L. Ditri, Peter J. Minnett, Yang Liu, Katherine Kilpatrick and Ajoy Kumar. Remote Sensing 2018, 10(2), 212; https://doi.org/10.3390/rs10020212 --- Role of El Niño Southern Oscillation (ENSO) Events on Temperature and Salinity Variability in the Agulhas Leakage Region / by Morgan L. Paris and Bulusu Subrahmanyam. Remote Sensing 2018, 10(1), 127; https://doi.org/10.3390/rs10010127 --- Stability Assessment of the (A)ATSR Sea Surface Temperature Climate Dataset from the European Space Agency Climate Change Initiative / by David I. Berry, Gary K. Corlett, Owen Embury and Christopher J. Merchant. Remote Sensing 2018, 10(1), 126; https://doi.org/10.3390/rs10010126 --- Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data / by Claire E. Bulgin, Jonathan P. D. Mittaz, Owen Embury, Steinar Eastwood and Christopher J. Merchant. Remote Sensing 2018, 10(1), 97; https://doi.org/10.3390/rs10010097 --- The Role of Advanced Microwave Scanning Radiometer 2 Channels within an Optimal Estimation Scheme for Sea Surface Temperature / by Kevin Pearson, Christopher Merchant, Owen Embury and Craig Donlon. Remote Sensing 2018, 10(1), 90; https://doi.org/10.3390/rs10010090 --- Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm / by William Skirving, Susana Enríquez, John D. Hedley, Sophie Dove, C. Mark Eakin, Robert A. B. Mason, Jacqueline L. De La Cour, Gang Liu, Ove Hoegh-Guldberg, Alan E. Strong, Peter J. Mumby and Roberto Iglesias-Prieto. Remote Sensing 2018, 10(1), 18; https://doi.org/10.3390/rs10010018 --- Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks / by Zhihong Liao, Qing Dong, Cunjin Xue, Jingwu Bi and Guangtong Wan. Remote Sensing 2017, 9(11), 1204; https://doi.org/10.3390/rs9111204 --- Submesoscale Sea Surface Temperature Variability from UAV and Satellite Measurements / by Sandra L. Castro, William J. Emery, Gary A. Wick and William Tandy. Remote Sensing 2017, 9(11), 1089; https://doi.org/10.3390/rs9111089 --- Environmental Variability and Oceanographic Dynamics of the Central and Southern Coastal Zone of Sonora in the Gulf of California / by Ricardo García-Morales, Juana López-Martínez, Jose Eduardo Valdez-Holguin, Hugo Herrera-Cervantes and Luis Daniel Espinosa-Chaurand. Remote Sensing 2017, 9(9), 925; https://doi.org/10.3390/rs9090925 --- Determining the Pixel-to-Pixel Uncertainty in Satellite-Derived SST Fields / by Fan Wu, Peter Cornillon, Brahim Boussidi and Lei Guan. Remote Sensing 2017, 9(9), 877; https://doi.org/10.3390/rs9090877 --- Evaluation of the Multi-Scale Ultra-High Resolution (MUR) Analysis of Lake Surface Temperature / by Erik Crosman, Jorge Vazquez-Cuervo and Toshio Michael Chin. Remote Sensing 2017, 9(7), 723; https://doi.org/10.3390/rs9070723
    Pages: Online-Ressource (XI, 326 Seiten) , Illustrationen, Diagramme, Karten
    Edition: Printed Edition of the Special Issue Published in Remote Sensing
    ISBN: 9783038974802
    Language: English
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  • 3
    Publication Date: 2019-07-13
    Description: Past studies have shown that the use of coarse resolution SST products such as from the real-time global (RTG) SST analysis[1] or other coarse resolution once-a-day products do not properly portray the diurnal variability of fluxes of heat and moisture from the ocean that drive the formation of low level clouds and precipitation over the ocean. For example, the use of high resolution MODIS SST composite [2] to initialize the Advanced Research Weather Research and Forecast (WRF) (ARW) [3] has been shown to improve the prediction of sensible weather parameters in coastal regions [4][5}. In an extend study, [6] compared the MODIS SST composite product to the RTG SST analysis and evaluated forecast differences for a 6 month period from March through August 2007 over the Florida coastal regions. In a comparison to buoy data, they found that that the MODIS SST composites reduced the bias and standard deviation over that of the RTG data. These improvements led to significant changes in the initial and forecasted heat fluxes and the resulting surface temperature fields, wind patterns, and cloud distributions. They also showed that the MODIS composite SST product, produced for the Terra and Aqua satellite overpass times, captured a component of the diurnal cycle in SSTs not represented in the RTG or other one-a-day SST analyses. Failure to properly incorporate these effects in the WRF initialization cycle led to temperature biases in the resulting short term forecasts. The forecast impact was limited in some situations however, due to composite product inaccuracies brought about by data latency during periods of long-term cloud cover. This paper focuses on the forecast impact of an enhanced MODIS/AMSR-E composite SST product designed to reduce inaccuracies due data latency in the MODIS only composite product.
    Keywords: Meteorology and Climatology
    Type: M10-0189 , 2010 IEEE International Geoscience and Remote Sensing Symposium; Jul 25, 2010 - Jul 30, 2010; Honolulu, HI; United States
    Format: application/pdf
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  • 4
    Publication Date: 2019-07-19
    Description: Recent applications of a high resolution MODlS composite SST product have clearly shown the importance of developing high-resolution SST data sets for coastal applications and modeling. In general, coupling between the oceans and atmospheres has been closely linked to SST gradients and fronts, indicating a need for high resolution SSTs, specifically in the areas of large gradients associated with coastal regions. Thus an accurate determination of SST gradients has become critical for determining the appropriate air-sea coupling and the influence on ocean modeling. Recent research is focused on improving the accuracy and spatial coverage of the current operational MODIS SST composite product provided by the Short-term Prediction Research and Transition (SPORT) project and distributed to the community. GHRSST-PP MODlS data and microwave AMSR-E data are being combined to produce composite data sets for both the West Coast and East Coast of the United States, including the Gulf of Mexico. The use of 1 km MODIS data has explicit advantages over other SST products including its global coverage and high resolution. The AMSR-E data will reduce the latency of the composites. A strategy for utilizing the error characteristics contained in the GHRSST data has been developed. This strategy will include using the error characteristics directly to calculate weights in the SST composites, uncertainty maps based on the composite biases and RMS errors, and latency products calculated in the compositing process. Recent accomplishments include the development of an enhanced compositing approach based on the error-weighted combination of recent clear MODIS SST values, where the error contributions come from measurement error, potential cloud contamination, and data latency sources. Future plans call for the inclusion of AMSR-E SST values with appropriate weights based upon measurement accuracy, MODIS-AMSR-E SST bias, and latency.
    Keywords: Meteorology and Climatology
    Type: M09-0234 , 89th American Meteorological Society Annual Meeting; Jan 11, 2009 - Jan 15, 2009; Phoenix, AZ; United States
    Format: text
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