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https://doi.org/10.5194/os-2018-132
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2018-132
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 29 Nov 2018

Research article | 29 Nov 2018

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

A multiscale ocean data assimilation approach combining spatial and spectral localisation

Ann-Sophie Tissier1, Jean-Michel Brankart1, Charles-Emmanuel Testut2, Giovanni Ruggiero2, Emmanuel Cosme1, and Pierre Brasseur1 Ann-Sophie Tissier et al.
  • 1Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
  • 2Mercator Océan, Toulouse, France

Abstract. Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e.g. using a localization algorithm in ensemble data assimilation systems), so that the large scale patterns are removed from the error statistics.

To better exploit the observational information available for all scales in CMEMS assimilation systems, we investigate a new method to introduce scale separation in the assimilation scheme.

The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The target is to improve the observational update of the large scale components of the signal by an explicit observational constraint applied directly on the large scales, and to restrict the use of spatial localisation to the small scale components of the signal.

To evaluate our method, twin experiments are carried out with synthetic altimetry observations (simulating the JASON tracks), assimilated in a 1/4° model configuration of the North Atlantic and the Nordic Seas.

Results show that the transformation to the spectral space and the spectral localization provides consistent ensemble estimates of the state of the system (in the spectral space,or after backward transformation to the spatial space). Combined with spatial localisation for the residual scales, the new scheme is able to provide a reliable ensemble update for all scales, with improved accuracy for the large scale; and the performance of the system can be checked explicitly and separately for all scales in the assimilation system.

Ann-Sophie Tissier et al.
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To better exploit the observational information available for all scales in data assimilation systems, we investigate a new method to introduce scale separation in the algorithm. It consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The performance is then checked explicitly and separately for all scales. Results show that accuracy can be improved for the large scales while preserving reliability at all scales.
To better exploit the observational information available for all scales in data assimilation...
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