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Volume 23 Issue 3

May  2006

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2006 >  23(3): 474-486

Derivation of Regression Coefficients for Sea Surface Temperature Retrieval over East Asia

  • 1.

    Remote Sensing Research Laboratory/Meteorological Research Institute, Seoul, Korea,Remote Sensing Research Laboratory/Meteorological Research Institute, Seoul, Korea,Remote Sensing Research Laboratory/Meteorological Research Institute, Seoul, Korea,Remote Sensing Research Laboratory/Meteorological Research Institute, Seoul, Korea,Office of Research and Applications, National Environmental Satellite, Data, and Information Service, National Oceanic and Atmospheric Administration


doi: 10.1007/s00376-006-0474-7

  • Among the regression-based algorithms for deriving SST from satellite measurements, regionally optimized algorithms normally perform better than the corresponding global algorithm. In this paper, three algorithms are considered for SST retrieval over the East Asia region (15–55N, 105–170E), including the multi-channel algorithm (MCSST), the quadratic algorithm (QSST), and the Pathfinder algorithm (PFSST). All algorithms are derived and validated using collocated buoy and Geostationary Meteorological Satellite (GMS-5) observations from 1997 to 2001. An important part of the derivation and validation of the algorithms is the quality control procedure for the buoy SST data and an improved cloud screening method for the satellite brightness temperature measurements. The regionally optimized MCSST algorithm shows an overall improvement over the global algorithm, removing the bias of about ?0.13C and reducing the root-mean-square difference (rmsd) from 1.36C to 1.26C. The QSST is only slightly better than the MCSST. For both algorithms, a seasonal dependence of the remaining error statistics is still evident. The Pathfinder approach for deriving a season-specific set of coefficients, one for August to October and one for the rest of the year, provides the smallest rmsd overall that is also stable over time.
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    • Manuscript History

    Manuscript received: 10 May 2006
    Manuscript revised: 10 May 2006
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Derivation of Regression Coefficients for Sea Surface Temperature Retrieval over East Asia

    • 1. Remote Sensing Research Laboratory/Meteorological Research Institute, Seoul, Korea,Remote Sensing Research Laboratory/Meteorological Research Institute, Seoul, Korea,Remote Sensing Research Laboratory/Meteorological Research Institute, Seoul, Korea,Remote Sensing Research Laboratory/Meteorological Research Institute, Seoul, Korea,Office of Research and Applications, National Environmental Satellite, Data, and Information Service, National Oceanic and Atmospheric Administration
    • Manuscript received: 2006-05-10
    • Manuscript revised: 2006-05-10

    Abstract: Among the regression-based algorithms for deriving SST from satellite measurements, regionally optimized algorithms normally perform better than the corresponding global algorithm. In this paper, three algorithms are considered for SST retrieval over the East Asia region (15–55N, 105–170E), including the multi-channel algorithm (MCSST), the quadratic algorithm (QSST), and the Pathfinder algorithm (PFSST). All algorithms are derived and validated using collocated buoy and Geostationary Meteorological Satellite (GMS-5) observations from 1997 to 2001. An important part of the derivation and validation of the algorithms is the quality control procedure for the buoy SST data and an improved cloud screening method for the satellite brightness temperature measurements. The regionally optimized MCSST algorithm shows an overall improvement over the global algorithm, removing the bias of about ?0.13C and reducing the root-mean-square difference (rmsd) from 1.36C to 1.26C. The QSST is only slightly better than the MCSST. For both algorithms, a seasonal dependence of the remaining error statistics is still evident. The Pathfinder approach for deriving a season-specific set of coefficients, one for August to October and one for the rest of the year, provides the smallest rmsd overall that is also stable over time.

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