The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation

We present the Copernicus in situ ocean dataset of temperature and salinity (version 5.2). Ocean subsurface sampling varied widely from 1950 to 2017 as a result of changes in instrument technology and the development of in situ observational networks (in particular, tropical moorings for the Argo pr...

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Published in:Ocean Science
Main Authors: Szekely, Tanguy, Gourrion, Jérôme, Pouliquen, Sylvie, Reverdin, Gilles
Format: Text
Language:English
Published: 2019
Subjects:
Online Access:https://doi.org/10.5194/os-15-1601-2019
https://os.copernicus.org/articles/15/1601/2019/
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spelling ftcopernicus:oai:publications.copernicus.org:os73511 2023-05-15T17:29:13+02:00 The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation Szekely, Tanguy Gourrion, Jérôme Pouliquen, Sylvie Reverdin, Gilles 2019-12-04 application/pdf https://doi.org/10.5194/os-15-1601-2019 https://os.copernicus.org/articles/15/1601/2019/ eng eng doi:10.5194/os-15-1601-2019 https://os.copernicus.org/articles/15/1601/2019/ eISSN: 1812-0792 Text 2019 ftcopernicus https://doi.org/10.5194/os-15-1601-2019 2020-07-20T16:22:33Z We present the Copernicus in situ ocean dataset of temperature and salinity (version 5.2). Ocean subsurface sampling varied widely from 1950 to 2017 as a result of changes in instrument technology and the development of in situ observational networks (in particular, tropical moorings for the Argo program). Thus, global ocean temperature data coverage on an annual basis grew from 10 % in 1950 (30 % for the North Atlantic basin) to 25 % in 2000 (60 % for the North Atlantic basin) and reached 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 increased 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. Objective analyses (OAs) 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 random-error-induced variability. Text North Atlantic Copernicus Publications: E-Journals Cora ENVELOPE(-60.317,-60.317,-62.467,-62.467) Ocean Science 15 6 1601 1614
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description We present the Copernicus in situ ocean dataset of temperature and salinity (version 5.2). Ocean subsurface sampling varied widely from 1950 to 2017 as a result of changes in instrument technology and the development of in situ observational networks (in particular, tropical moorings for the Argo program). Thus, global ocean temperature data coverage on an annual basis grew from 10 % in 1950 (30 % for the North Atlantic basin) to 25 % in 2000 (60 % for the North Atlantic basin) and reached 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 increased 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. Objective analyses (OAs) 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 random-error-induced variability.
format Text
author Szekely, Tanguy
Gourrion, Jérôme
Pouliquen, Sylvie
Reverdin, Gilles
spellingShingle Szekely, Tanguy
Gourrion, Jérôme
Pouliquen, Sylvie
Reverdin, Gilles
The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation
author_facet Szekely, Tanguy
Gourrion, Jérôme
Pouliquen, Sylvie
Reverdin, Gilles
author_sort Szekely, Tanguy
title The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation
title_short The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation
title_full The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation
title_fullStr The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation
title_full_unstemmed The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation
title_sort cora 5.2 dataset for global in situ temperature and salinity measurements: data description and validation
publishDate 2019
url https://doi.org/10.5194/os-15-1601-2019
https://os.copernicus.org/articles/15/1601/2019/
long_lat ENVELOPE(-60.317,-60.317,-62.467,-62.467)
geographic Cora
geographic_facet Cora
genre North Atlantic
genre_facet North Atlantic
op_source eISSN: 1812-0792
op_relation doi:10.5194/os-15-1601-2019
https://os.copernicus.org/articles/15/1601/2019/
op_doi https://doi.org/10.5194/os-15-1601-2019
container_title Ocean Science
container_volume 15
container_issue 6
container_start_page 1601
op_container_end_page 1614
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