Improved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles

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...

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Bibliographic Details
Published in:Journal of marine systems 2009-11, Vol.78, p.S282-S289
Main Authors: Rixen, Michel, Book, Jeffrey W., Carta, Alessandro, Grandi, Vittorio, Gualdesi, Lavinio, Stoner, Richard, Ranelli, Peter, Cavanna, Andrea, Zanasca, Pietro, Baldasserini, Gisella, Trangeled, Alex, Lewis, Craig, Trees, Chuck, Grasso, Rafaelle, Giannechini, Simone, Fabiani, Alessio, Merani, Diego, Berni, Alessandro, Leonard, Michel, Martin, Paul, Rowley, Clark, Hulbert, Mark, Quaid, Andrew, Goode, Wesley, Preller, Ruth, Pinardi, Nadia, Oddo, Paolo, Guarnieri, Antonio, Chiggiato, Jacopo, Carniel, Sandro, Russo, Aniello, Tudor, Martina, Lenartz, Fabian, Vandenbulcke, Luc
Format: Article
Language:English
Subjects:
Coastal
Coastal environments
Dynamics
Kalman Filter
Marine
Mathematical models
Multi model super-ensembles
Ocean models
Ocean prediction skill
Oceans
Particle Filter
Real time
Skills
Uncertainties
Uncertainty
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Summary: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 filter SE copes with non-Gaussian error statistics and provides robust and reduced uncertainty estimates.
ISSN:0924-7963
1879-1573
DOI:10.1016/j.jmarsys.2009.01.014