Ocean Science
Ocean Science
OS
Articles | Volume 15, issue 4
Articles | Volume 15, issue 4
https://doi.org/10.5194/os-15-1023-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-15-1023-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
Research article
 | 
02 Aug 2019
Research article |  | 02 Aug 2019

Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators

Eric Jansen, Sam Pimentel, Wang-Hung Tse, Dimitra Denaxa, Gerasimos Korres, Isabelle Mirouze, and Andrea Storto

Viewed

Total article views: 2,597 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,726 779 92 2,597 109 93
  • HTML: 1,726
  • PDF: 779
  • XML: 92
  • Total: 2,597
  • BibTeX: 109
  • EndNote: 93
Views and downloads (calculated since 23 Jan 2019)
Cumulative views and downloads (calculated since 23 Jan 2019)

Viewed (geographical distribution)

Total article views: 2,597 (including HTML, PDF, and XML) Thereof 2,090 with geography defined and 507 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
The assimilation of satellite SST data into ocean models is complex. The temperature of the thin uppermost layer that is measured by satellites may differ from the much thicker upper layer used in numerical models, leading to biased results. This paper shows how canonical correlation analysis can be used to generate observation operators from existing datasets of model states and corresponding observation values. This type of operator can correct for near-surface effects when assimilating SST.
Read more
Special issue
The Copernicus Marine Environment Monitoring Service (CMEMS):...