Titolo | An algorithm for classifying unknown expendable bathythermograph (XBT) instruments based on existing metadata |
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Tipo di pubblicazione | Articolo su Rivista peer-reviewed |
Anno di Pubblicazione | 2018 |
Autori | Palmer, M.D., Boyer T., Cowley R., Kizu S., Reseghetti Franco, Suzuki T., and Thresher A. |
Rivista | Journal of Atmospheric and Oceanic Technology |
Volume | 35 |
Paginazione | 429-440 |
ISSN | 07390572 |
Parole chiave | algorithm, Climate record, Data handling, Data processing, database, Database systems, Deterministic algorithms, Expendable bathythermographs, heating, In situ processing, instrumentation, Metadata, Metadata information, observational method, ocean, Oceanographic instruments, Oceanography, Probes, Ship observations, Situ oceanic observations, Temperature measuring instruments, Temperature profiles, Uncertainty analysis |
Abstract | 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. © 2018 American Meteorological Society. |
Note | cited By 3 |
URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044647617&doi=10.1175%2fJTECH-D-17-0129.1&partnerID=40&md5=8a6f402d6d46166621d99dfd65742bff |
DOI | 10.1175/JTECH-D-17-0129.1 |
Citation Key | Palmer2018429 |