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

Abstract

We seek to focus quantitative uncertainty management attributes of the Bayesian Hierarchical Model (BHM) methodology on the identification, characterization, and evolution of irreducible model error in ocean data assimilation and forecast systems.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2010
Accession Number
ADA542614

Entities

People

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

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Applied Mathematics
  • Assimilation
  • Bayesian Networks
  • Climate Change
  • Computational Science
  • Data Science
  • Differential Equations
  • Fokker Planck Equations
  • Information Science
  • Monte Carlo Method
  • Oceans
  • Operations Research
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Sampling
  • Statistics

Fields of Study

  • Environmental science

Readers

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

Technology Areas

  • AI & ML