Improved Ocean Prediction Skill and Reduced Uncertainty in the Coastal From Multi-Model Super-Ensembles
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 arc 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2009
- Accession Number
- ADA512888
Entities
People
- Alessandro Berni
- Alessandro Carta
- Alessio Fabiani
- Alex Trangeled
- Andrea Cavanna
- Chuck Trees
- Craig Lewis
- Diego Merani
- Gisella Baldasserini
- Jeffrey W. Book
- Lavinio Gualdesi
- Michel Leonard
- Michel Rixen
- Paul J. Martin
- Peter Ranelli
- Pietro Zanasca
- Rafaelle Grasso
- Richard Stoner
- Simone Giannechini
- Vittorio Grandi
Organizations
- United States Naval Research Laboratory