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Discussion papers
https://doi.org/10.5194/os-2018-144
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2018-144
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 21 Jan 2019

Research article | 21 Jan 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Ocean Science (OS).

The CORA 5.2 dataset: global in-situ Temperature and Salinity measurements dataset. Data description and validation

Tanguy Szekely1, Jérôme Gourrion1, Sylvie Pouliquen2, and Gilles Reverdin3 Tanguy Szekely et al.
  • 1Societe Coopérative OceanScope, 115 rue Claude Chape, 29290, Plouzané, Brest, France
  • 2IFREMER, BP 70, Plouzané, 29280, France
  • 3Sorbonne – Université, CNRS/IRD/MNHN (LOCEAN), Paris, France

Abstract. We present the Copernicus in-situ ocean dataset of temperature and salinity (version V5.2). The ocean subsurface sampling varied widely from 1950 to 2017, as a result of changes in the instrument technology and development of in-situ observational networks (in particular, tropical moorings, ARGO program). The global ocean temperature data coverage on an annual basis grows thus from 10 % in 1950 (30 % for the North Atlantic basin) to 25 % in 2000 (60 % for the North Atlantic basin) and reaches a plateau exceeding 80 % (95 % for the North Atlantic Ocean) after the deployment of the ARGO program. The average depth reached by the profiles also increases from 1950 to 2017. The validation framework is presented, and an objective analysis-based method is developed to assess the quality of the dataset validation process. Analyses of the ocean variability are calculated without taking into account the data quality flags (raw dataset OA), with the near real time quality flags (NRT dataset OA) and with the delayed time mode quality flags (CORA dataset OA). The comparison of the objective analysis variability shows that the near real time dataset managed to detect and to flag most of the large measurement errors, reducing the analysis error bar compared to the raw dataset error bar. It also shows that the ocean variability of the delayed time mode validated dataset is almost exempt from the random error induced variability.

Tanguy Szekely et al.
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Tanguy Szekely et al.
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