Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts

Abstract

Quantitative uncertainty management attributes of the Bayesian Hierarchical Model (BHM) methodology are applied to the identification, characterization, and modelling of irreducible model error in ocean data assimilation and forecast systems.

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Document Details

Document Type
Technical Report
Publication Date
Sep 28, 2012
Accession Number
ADA568491

Entities

People

  • Christopher K. Wikle
  • L. M. Berliner
  • Radu Herbei
  • Ralph F. Milliff

Organizations

  • Northwest Research Associates

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Assimilation
  • Boltzmann Equation
  • Computers
  • Data Science
  • Differential Equations
  • Diffusion
  • Diffusion Coefficient
  • Equations
  • Grids
  • Information Science
  • Monte Carlo Method
  • Ocean Currents
  • Oceanography
  • Oceans
  • Statistics
  • Three Dimensional
  • Uncertainty

Fields of Study

  • Environmental science

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference