Articles | Volume 17, issue 5
https://doi.org/10.5194/os-17-1273-2021
https://doi.org/10.5194/os-17-1273-2021
Research article
 | 
17 Sep 2021
Research article |  | 17 Sep 2021

Filtering method based on cluster analysis to avoid salinity drifts and recover Argo data in less time

Emmanuel Romero, Leonardo Tenorio-Fernandez, Iliana Castro, and Marco Castro

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Cited articles

Argo: Argo [data set], available at: https://argo.ufcsd.edu/, last access: January 2020a. a, b
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) – Snapshot of Argo GDAC of December 10st 2020, [data set], https://doi.org/10.17882/42182#79118, 2020b. a, b, c
Argo Data Management Team: Argo user's manual V3.3, Report, https://doi.org/10.13155/29825, 2019. a, b
Argo Data Management: Argo Data Management, available at: http://www.argodatamgt.org/, last access: 2020. a, b
Coriolis: Coriolis: In situ data for operational oceanography, available at: http://www.coriolis.eu.org/, last access: 2020. a
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Short summary
Due to the need to obtain a greater amount of data in less time in areas with little in situ hydrographic data, an algorithm based on cluster analysis is proposed. This algorithm allows real-time quality control of Argo data which has patterns similar to data in delayed mode. To test this, a study area of high scientific interest but with little concentration of in situ data was chosen. In this area 80 % of the data normally discarded because of salinity drifts was recovered.