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


Rixen, Michel; Book, Jeffrey W.; Carta, Alessandro; Grandi, Vittorio; Gualdesi, Lavinio; Stoner, Richard; Ranelli, Peter; Cavanna, Andrea; Zanasca, Pietro; Baldasserini, Gisella et al.
Improved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles // Journal of Marine Systems, 78 (2009), Suppl. 1; 282-289 doi:10.1016/j.jmarsys.2009.01.014 (međunarodna recenzija, članak, znanstveni)


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

Autori
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

Izvornik
Journal of Marine Systems (0924-7963) 78 (2009), Suppl. 1; 282-289

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Ocean prediction skill; Uncertainties; Multi model super-ensembles; Coastal environments; Kalman Filter; Particle Filter

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Fizika

Napomena
Coastal Processes: Challenges for Monitoring and Prediction Edited by Michel Rixen, Jeffrey W. Book and Mirko Orlic



POVEZANOST RADA


Projekt / tema
004-1193086-3036 - Oluje i prirodne katastrofe u Hrvatskoj (Branka Ivančan-Picek, )

Ustanove
Državni hidrometeorološki zavod

Autor s matičnim brojem:
Martina Tudor, (300826)

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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