An Algorithm for Classifying Unknown Expendable Bathythermograph (XBT) Instruments Based on Existing Metadata.
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Date
2018Author
Palmer, Matthew D.
Boyer, Tim
Cowley, Rebecca
Kizu, Shoichi
Reseghetti, Franco
Suzuki, Toru
Thresher, Ann
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Show full item recordAbstract
Time-varying biases in expendable bathythermograph (XBT) instruments have emerged as a key un-
certainty in estimates of historical ocean heat content variability and change. One of the challenges in the
development of XBT bias corrections is the lack of metadata in ocean profile databases. Approximately 50%
of XBT profiles in the World Ocean database (WOD) have no information about manufacturer or probe type.
Building on previous research efforts, this paper presents a deterministic algorithm for assigning missing XBT
manufacturer and probe type for individual temperature profiles based on 1) the reporting country, 2) the
maximum reported depth, and 3) the record date. The criteria used are based on bulk analysis of known XBT
profiles in the WOD for the period 1966–2015. A basic skill assessment demonstrates a 77% success rate at
correctly assigning manufacturer and probe type for profiles where this information is available. The skill rate
is lowest during the early 1990s, w.....
Journal
Journal of Atmospheric and Oceanic TechnologyVolume
35Page Range
pp.429-440Document Language
enSustainable Development Goals (SDG)
14.AEssential Ocean Variables (EOV)
Subsurface temperatureBest Practice Type
Best PracticeGuide
DOI Original
10.1175/JTECH-D-17-0129.1Citation
Palmer, M.D.; Boyer, T.; Cowley, R.; Kizu, S.; Reseghetti, F.; Suzuki, T. and Threshe, A.r (2018) An Algorithm for Classifying Unknown Expendable Bathythermograph (XBT) Instruments Based on Existing Metadata. Journal of Atmospheric and Oceanic Technology, 35, pp.429-440. DOI: 10.1175/JTECH-D-17-0129.1Collections
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