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OS | Articles | Volume 15, issue 2
Ocean Sci., 15, 443–457, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: The Copernicus Marine Environment Monitoring Service (CMEMS):...

Ocean Sci., 15, 443–457, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Apr 2019

Research article | 26 Apr 2019

A multiscale ocean data assimilation approach combining spatial and spectral localisation

Ann-Sophie Tissier et al.

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Cited articles

Anderson, J. L.: A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations, J. Climate, 9, 1518–1530,<1518:AMFPAE>2.0.CO;2, 1996. a, b
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436,<0420:ASWTET>2.0.CO;2, 2001. a, b
Brankart, J.-M.: Impact of uncertainties in the horizontal density gradient upon low resolution global ocean modelling, Ocean Model., 66, 64–76,, 2013. a
Brankart, J.-M., Cosme, E., Testut, C.-E., Brasseur, P., and Verron, J.: Efficient Local Error Parameterizations for Square Root or Ensemble Kalman Filters: Application to a Basin-Scale Ocean Turbulent Flow, Mon. Weather Rev., 139, 474–493,, 2011. a, b
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297,, 2015. a
Publications Copernicus
Short summary
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...