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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Eric Jansen on behalf of the Authors (28 May 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (10 Jun 2019) by Pierre-Yves Le Traon
RR by Anonymous Referee #1 (17 Jun 2019)
RR by Salvatore Marullo (18 Jun 2019)
ED: Publish subject to technical corrections (18 Jun 2019) by Pierre-Yves Le Traon
AR by Eric Jansen on behalf of the Authors (21 Jun 2019)  Author's response    Manuscript
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.