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Exploring image data assimilation in the prospect of high-resolution satellite oceanic observations

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Abstract

Satellite sensors increasingly provide high-resolution (HR) observations of the ocean. They supply observations of sea surface height (SSH) and of tracers of the dynamics such as sea surface salinity (SSS) and sea surface temperature (SST). In particular, the Surface Water Ocean Topography (SWOT) mission will provide measurements of the surface ocean topography at very high-resolution (HR) delivering unprecedented information on the meso-scale and submeso-scale dynamics. This study investigates the feasibility to use these measurements to reconstruct meso-scale features simulated by numerical models, in particular on the vertical dimension. A methodology to reconstruct three-dimensional (3D) multivariate meso-scale scenes is developed by using a HR numerical model of the Solomon Sea region. An inverse problem is defined in the framework of a twin experiment where synthetic observations are used. A true state is chosen among the 3D multivariate states which is considered as a reference state. In order to correct a first guess of this true state, a two-step analysis is carried out. A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a reduced-order Kalman filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis. Experiments show that the proposed technique succeeds in correcting a multivariate state using meso-scale and submeso-scale information contained in HR SSH and image structure observations. It also demonstrates how the surface information can be used to reconstruct the ocean state below the surface.

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Acknowledgements

The research presented in this paper has been supported by the CNES (French Space Agency) and the CNRS (French National Research center). Funding has been received from the European Community’s Seventh Framework Programme FP7/2007–2013 under grant agreements 283367 (MyOcean2), H2020 633085 (MyOcean-FO) and 283580 (SANGOMA). The calculations were performed using HPC resources from GENCI-IDRIS (grant 2015011279).

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Authors and Affiliations

  1. Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, F-38000, Grenoble, France

    Marina Durán Moro, Jean-Michel Brankart, Pierre Brasseur & Jacques Verron

Authors
  1. Marina Durán Moro
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  2. Jean-Michel Brankart
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  3. Pierre Brasseur
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  4. Jacques Verron
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Corresponding author

Correspondence to Marina Durán Moro.

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Responsible Editor: Alexander Barth

This article is part of the Topical Collection on the 48th International Liège Colloquium on Ocean Dynamics, Liège, Belgium, 23–27 May 2016

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Durán Moro, M., Brankart, JM., Brasseur, P. et al. Exploring image data assimilation in the prospect of high-resolution satellite oceanic observations. Ocean Dynamics 67, 875–895 (2017). https://doi.org/10.1007/s10236-017-1062-3

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