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Volume 13, issue 6
Ocean Sci., 13, 925–945, 2017
https://doi.org/10.5194/os-13-925-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: REP14-MED: A Glider Fleet Experiment in a Limited Marine...

Ocean Sci., 13, 925–945, 2017
https://doi.org/10.5194/os-13-925-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 Nov 2017

Research article | 20 Nov 2017

Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Reiner Onken

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Cited articles

Bell, M. J., Lefèbvre, M., Le Traon, P.-Y., Smith, N., and Wilmer-Becker, K.: GODAE: The Global Ocean Data Assimilation Experiment, Oceanography, 22, 14–21, https://doi.org/10.5670/oceanog.2009.62, 2009.
Bretherton, F. P., Davies, R. E., and Fandry, C. B.: Technique for Objective Analysis and design of oceanographic experiments applied to Mode-73, Deep-Sea Res., 23, 559–582, 1976.
Capet, X., McWilliams, J. C., Molemaker, M. J., and Shchepetkin, A. F.: Mesoscale to submesoscale transition in the California Current system, Part I: flow structure, eddy flux, and observational tests, J. Phys. Oceanogr., 38, 29–43, https://doi.org/10.1175/2007JPO3671.1, 2008.
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Chapman, D. C.: Numerical treatment of cross-shelf open boundaries in a barotropic coastal ocean model, J. Phys. Oceanogr., 15, 1060–1075, 1985.
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Short summary
An ocean prediction model was driven by observations via assimilation. The best forecast was obtained using a smoothing scale of 12.5 km and a time window of 24 h for data selection. Mostly, the forecasts were better than that of a run without assimilation, the skill score increased with increasing forecast range, and the score for temperature was higher than the score for salinity. It is shown that a vast number of data can be managed by the applied method without data reduction.
An ocean prediction model was driven by observations via assimilation. The best forecast was...
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