Evaluation of in situ sea surface temperatures for use in the calibration and validation of satellite retrievals

Authors

  • Feng Xu,

    1. Center for Satellite Applications and Research, National Environmental Satellite, Data and Information Service, NOAA, Camp Springs, Maryland, USA
    2. CIRA, Colorado State University, Fort Collins, Colorado, USA
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  • Alexander Ignatov

    1. Center for Satellite Applications and Research, National Environmental Satellite, Data and Information Service, NOAA, Camp Springs, Maryland, USA
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Abstract

[1] In situ sea surface temperatures (SSTs) are used for calibration and validation of satellite retrievals. This study analyzes three in situ data sets from the National Centers for Environmental Prediction (NCEP) Global Telecommunication System (GTS), the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) release 2.4, and the U.S. Global Ocean Data Assimilation Experiment/Fleet Numerical Meteorology and Oceanography Center (FNMOC). Comparisons show that most reports in the ICOADS and FNMOC are of the same origin as NCEP GTS. Quality control (QC) information is either unavailable (NCEP), not well documented (FNMOC), or nonuniform (ICOADS, FNMOC). Preliminary QC was implemented in this study and uniformly applied to all data sets. All analyses are stratified by major types of in situ platforms including ships, drifters, and moored buoys, the latter being further subdivided into tropical and coastal. Ships overwhelmingly prevailed before 1990 but then declined, whereas the number of drifters significantly increased, as did their reporting density. Although both platforms sample the full SST range well, drifters cover the global ocean much more uniformly than ships. Statistical analyses are performed on the in situ SST anomalies with respect to daily Reynolds and daily Pathfinder. Different global mean biases are observed for different platform types (e.g., ∼+0.03 K for drifters and tropical moorings and ∼+0.15 K for ships, with respect to Reynolds SST), suggesting existence of cross-platform biases that need to be reconciled. Root mean square (RMS) errors of the four types of in situ data have been estimated via three-way analyses proposed in O'Carroll et al. (2008). The geographical distributions of RMS errors in Pathfinder, Reynolds, and in situ SSTs show distinct spatial patterns, which require further understanding and remediation.

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