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Ocean Science An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/os-2018-166
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2018-166
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 23 Jan 2019

Research article | 23 Jan 2019

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

Using Canonical Correlation Analysis to produce dynamically-based highly-efficient statistical observation operators

Eric Jansen1, Sam Pimentel2, Wang-Hung Tse2, Dimitra Denaxa3, Gerasimos Korres3, Isabelle Mirouze1, and Andrea Storto1 Eric Jansen et al.
  • 1Euro-Mediterranean Center on Climate Change (CMCC), Italy
  • 2Trinity Western University (TWU), Langley, BC, Canada
  • 3Hellenic Centre for Marine Research (HCMR), Athens, Greece

Abstract. Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are not or not sufficiently described by the model. In such cases a dynamical OO, an OO that interfaces to a secondary and more specialised model, often provides the best results. However, given the large number of observations to be assimilated in a typical atmospheric or oceanographic model, the computational resources needed for using a fully dynamical OO mean that this option is usually not feasible. This paper presents a method, based on canonical correlation analysis (CCA), that can be used to generate highly-efficient statistical OOs that are based on a dynamical model. These OOs can provide an approximation to the dynamical model at a fraction of the computational cost.

One possible application of such an OO is the modelling of the diurnal cycle of sea surface temperature (SST) in ocean general circulation models (OGCMs). Satellites that measure SST measure the temperature of the thin uppermost layer of the ocean. This layer is strongly affected by the atmospheric conditions and its temperature can differ significantly from the water below. This causes a discrepancy between the SST measurements and the upper layer of the OGCM, which typically has a thickness of around 1 m. The CCA OO method is used to parametrise the diurnal cycle of SST. The CCA OO is based on an input dataset from the General Ocean Turbulence Model (GOTM), a high-resolution water column model that has been specifically tuned for this purpose. The parameterisations of the CCA OO are found to be in good agreement with the results from GOTM, showing the potential of this method for use in data assimilation systems.

Eric Jansen et al.
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Eric Jansen et al.
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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.
The assimilation of satellite SST data into ocean models is complex. The temperature of the thin...
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