Land Surface Temperature from GOES-East and GOES-West

  • Wen Chen
    a Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland
  • Rachel T. Pinker
    a Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland
  • Yingtao Ma
    a Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland
  • Glynn Hulley
    b Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • Eva Borbas
    c Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin
  • Tanvir Islam
    b Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • Kerry-A. Cawse-Nicholson
    b Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • Simon Hook
    b Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • Chris Hain
    d NASA Marshall Space Flight Center, Huntsville, Alabama
  • Jeff Basara
    e School of Meteorology, University of Oklahoma, Norman, Oklahoma

Abstract

<jats:title>ABSTRACT</jats:title><jats:p>Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).</jats:p>

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