Accession Number:

ADA512888

Title:

Improved Ocean Prediction Skill and Reduced Uncertainty in the Coastal From Multi-Model Super-Ensembles

Descriptive Note:

Journal article

Corporate Author:

NAVAL RESEARCH LAB STENNIS SPACE CENTER MS OCEANOGRAPHY DIV

Report Date:

2009-01-01

Pagination or Media Count:

11.0

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.

Subject Categories:

  • Physical and Dynamic Oceanography

Distribution Statement:

APPROVED FOR PUBLIC RELEASE