Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

Abstract

Year 2 of Phase 1 Bayesian Hierarchical Models (BHM) to Augment the Mediterranean Forecast System ( MFS) ended in May 2007. Long-term goals for Phase I included: a) development of an ensemble ocean forecast methodology based on a surface wind BHM (MFS-Wind-BHM) in data assimilation and forecast steps of the MFS; and b) development of a BHM for time-dependent background error covariance evolution (MFS-Error-BHM) in the MFS data assimilation system . Phase II of the project was initiated in June 2007. Long term goals for the second phase include the development of a BHM to guide ocean model super-ensemble experiments, in both multi-model and the multi-parameter experimental designs. The MFS ocean forecast model will be modified for multiparameter super-ensemble experiments, and MFS will be joined by a Mediterranean Sea implementation of the Regional Ocean Modeling System (MedROMS: http://www.med-roms.org) in multi-model super-ensemble experiments.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2007
Accession Number
ADA573246

Entities

People

  • Christopher K. Wikle
  • Emanuele Di Lorenzo
  • Mark Berliner
  • Ralph F. Milliff

Tags

Communities of Interest

  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Assimilation
  • Atmospheric Sciences
  • Case Studies
  • Coefficients
  • Covariance
  • Data Science
  • Experimental Design
  • Information Science
  • Kinetic Energy
  • Mediterranean Sea
  • Momentum
  • Oceans
  • Sea Surface Temperature
  • Statistics
  • Surface Temperature
  • Time Dependence
  • Weather Forecasting

Fields of Study

  • Environmental science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Coastal Oceanography

Technology Areas

  • AI & ML