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Improved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles Rixen, M.; Book, J.W.; Carta, A.; Grandi, V.; Gualdesi, L.; Stoner, R.; Ranelli, P.; Cavanna, A.; Zanasca, P.; Baldasserini, G.; Trangeled, A.; Lewis, C.; Trees, C.; Grasso, R.; Giannechini, S.; Fabiani, A.; Merani, D.; Berni, A.; Leonard, M.; Martin, P.; Rowley, C.; Hulbert, M.; Quaid, A.; Goode, W.; Preller, R.; Pinardi, N.; Oddo, P.; Guarnieri, A.; Chiggiato, J.; Carniel, S.; Russo, A.; Tudor, M.; Lenartz, F.; Vandenbulcke, L. (2009). Improved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles. J. Mar. Syst. 78: S282-S289. dx.doi.org/10.1016/j.jmarsys.2009.01.014 In: Journal of Marine Systems. Elsevier: Tokyo; Oxford; New York; Amsterdam. ISSN 0924-7963; e-ISSN 1879-1573

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Beschikbaar in	Auteurs
VLIZ: Open Marine Archive 231095 [ download pdf ]

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Trefwoord

Marien/Kust

Author keywords

Ocean prediction skill; Uncertainties; Multi model super-ensembles;

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Auteurs		Top
Rixen, M. Book, J.W. Carta, A. Grandi, V. Gualdesi, L. Stoner, R. Ranelli, P. Cavanna, A. Zanasca, P. Baldasserini, G. Trangeled, A. Lewis, C.	Trees, C. Grasso, R. Giannechini, S. Fabiani, A. Merani, D. Berni, A. Leonard, M. Martin, P. Rowley, C. Hulbert, M. Quaid, A.	Goode, W. Preller, R. Pinardi, N. Oddo, P. Guarnieri, A. Chiggiato, J. Carniel, S. Russo, A. Tudor, M. Lenartz, F. Vandenbulcke, L.

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Abstract

The use of Multi-model Super-Ensembles (SE) which optimally combine different models, has been shown to significantly improve atmospheric weather and climate predictions. In the highly dynamic coastal ocean, the presence of small-scales processes. the lack of real-time data, and the limited skill of operational models at the meso-scale have so far limited the application of SE methods. Here, we report results from state-of-the-art super-ensemble techniques in which SEPTR (a trawl-resistant bottom mounted instrument platform transmitting data in near real-time) temperature profile data are combined with outputs from eight ocean models run in a coastal area during the Dynamics of the Adriatic in Real-Time (DART) experiment in 2006. New Kalman filter and particle filter based SE methods, which allow for dynamic evolution of weights and associated uncertainty, are compared to standard SE techniques and numerical models. Results show that dynamic SE are able to significantly improve prediction skill. In particular, the particle fitter SE copes with non-Gaussian error statistics and provides robust and reduced uncertainty estimates.

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