Ocean Science
Ocean Science
OS
Articles | Volume 17, issue 5
Articles | Volume 17, issue 5
https://doi.org/10.5194/os-17-1273-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-17-1273-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 5
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

Viewed

Total article views: 3,099 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,108 923 68 3,099 75 88
  • HTML: 2,108
  • PDF: 923
  • XML: 68
  • Total: 3,099
  • BibTeX: 75
  • EndNote: 88
Views and downloads (calculated since 06 Apr 2021)
Cumulative views and downloads (calculated since 06 Apr 2021)

Viewed (geographical distribution)

Total article views: 3,099 (including HTML, PDF, and XML) Thereof 3,010 with geography defined and 89 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 Nov 2025
Download
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.
Read more
Share