Ocean Science
Ocean Science
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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

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Latest update: 19 Apr 2024
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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.
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