An Algorithm for Classifying Unknown Expendable Bathythermograph (XBT) Instruments Based on Existing Metadata

Palmer, Matthew D.; Boyer, Tim; Cowley, Rebecca; Kizu, Shoichi; Reseghetti, Franco; Suzuki, Toru; Thresher, Ann

doi:10.1175/jtech-d-17-0129.1

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An Algorithm for Classifying Unknown Expendable Bathythermograph (XBT) Instruments Based on Existing Metadata

2018
By Palmer, Matthew D. ; Boyer, Tim ; Cowley, Rebecca ; ...
Source: Journal of Atmospheric and Oceanic Technology, 35(3), 429-440

[PDF-1.67 MB]

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Journal Title:

Journal of Atmospheric and Oceanic Technology
Personal Author:

Palmer, Matthew D. ; Boyer, Tim ; Cowley, Rebecca ; Kizu, Shoichi ; Reseghetti, Franco ; ... More +

Palmer, Matthew D. ; Boyer, Tim ; Cowley, Rebecca ; Kizu, Shoichi ; Reseghetti, Franco ; Suzuki, Toru ; Thresher, Ann Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information)

NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information) Less -
Description:

Time-varying biases in expendable bathythermograph (XBT) instruments have emerged as a key uncertainty 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, which is also a period when metadata information is particularly poor. The results suggest that substantive improvements could be made through further data analysis and that future algorithms may benefit from including a larger number of predictor variables.
Keywords:

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Atmospheric Science Ocean Engineering
Source:

Journal of Atmospheric and Oceanic Technology, 35(3), 429-440
DOI:

https://doi.org/10.1175/jtech-d-17-0129.1
ISSN:

0739-0572;1520-0426;
Format:

PDF
Publisher:

American Meteorological Society
Document Type:

Journal Article
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https://creativecommons.org/licenses/by/4.0/
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CC BY
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urn:sha256:7c5b42b18c34b93a51a080bf0e14101756ec95d303595134585f3b8334760172
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https://repository.library.noaa.gov/view/noaa/59098/noaa_59098_DS1.pdf
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An Algorithm for Classifying Unknown Expendable Bathythermograph (XBT) Instruments Based on Existing Metadata

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