Bayesian Hierarchical Models to Augment the Mediterranean Forecast System Consolidating Results and Quantifying Impacts

Abstract

The long term goal of this interdisciplinary research program continues to be the demonstration of Bayesian Hierarchical Model (BHM) utility in several aspects of operational ocean forecasting. The specific goals in the current phase of the research are: 1) publications of results from ensemble ocean forecast experiments driven by the surface vector wind (SVW) BHM; b) consolidating impacts results in MFS reforecast experiments for the time-dependent error-covariance BHM; and c) running the first multi-model and multi-parameter super-ensemble BHM experiments.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2009
Accession Number
ADA531924

Entities

People

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

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Climate Change
  • Covariance
  • Data Science
  • Delphi Method
  • Demonstrations
  • Equations
  • Experimental Design
  • Gaussian Distributions
  • Information Science
  • Normal Distribution
  • North Pacific Ocean
  • Oceans
  • Pacific Ocean
  • Pressure Gradients
  • Sea Level
  • Statistics
  • Trajectories

Fields of Study

  • Environmental science

Readers

  • Acoustical Oceanography.
  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Atmospheric Science/Meteorology

Technology Areas

  • AI & ML