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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 Jan 2019

Research article | 15 Jan 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Ocean Science (OS).

Assimilation of SST data in the POSEIDON system using the SOSSTA statistical-dynamical observation operator

Gerasimos Korres1, Dimitra Denaxa1, Eric Jansen2, Isabelle Mirouze2, Sam Pimentel3, Wang-Hung Tse3, and Andrea Storto2 Gerasimos Korres et al.
  • 1Hellenic Centre for Marine Research (HCMR), Athens, Greece
  • 2Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Italy
  • 3Department of Mathematical Sciences, Trinity Western University, Langley, BC, Canada

Abstract. In spite of their long-standing availability, the optimal assimilation of sea surface temperature (SST) observations retrieved from infrared and microwave space-borne sensors is still challenging in oceanographic forecast systems. One prominent problem stems from the fact that ocean general circulation models do not resolve the diurnal variability of SST data as measured by satellites. In order to improve SST data assimilation schemes and enhance the exploitation of swath SST data, an observation operator capable of representing the SST diurnal cycle is introduced and called SOSSTA. Firstly, a one-dimensional turbulence model is used to produce a data set of upper ocean temperature profiles with corresponding skin and subskin SSTs. A canonical correlation analysis is then used to extract the maximally correlated modes of variability between temperatures at depth and skin/subskin SST, conditioned to atmospheric state (insolation and wind speed). These canonical correlations form the novel observation operator, which is implemented in the POSEIDON model forecasting system (Aegean Sea) to test the assimilation of daytime SST retrievals from the SEVIRI infrared radiometer. Comparison of misfits (off-line assessment) suggests that the new operator outperforms the mere use of the first model level to calculate SST innovations. Real-world data assimilation experiments indicate that the use of the SOSSTA operator is beneficial to the skill scores and in particular improves the sea surface height analysis and forecast skill scores, whose improvement is maintained throughout a one year long experiment.

Gerasimos Korres et al.
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Gerasimos Korres et al.
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Short summary
A statistical-dynamical observation operator (SOSSTA) for satellite SST data assimilation able to account for SST diurnal variability, is formulated and implemented into the POSEIDON forecasting system (Aegean Sea). Model experiments where daytime SST retrievals from the SEVIRI infrared radiometer are introduced into the data assimilation procedure through the application of the observation operator, showed an improvement of the POSEIDON modelling system performance.
A statistical-dynamical observation operator (SOSSTA) for satellite SST data assimilation able...