Theory and Practice of Data Assimilation in Ocean Modeling

Abstract

The long-range goal of this project is to form the best picture of the ocean as an evolving system based on data assimilation, i.e, the construction of a composite estimate of the state of the ocean based on a combination of observed data with computational model output, and to use that picture to understand the physical processes that govern the ocean's behavior. Oceanic observations are sparse and models are limited in accuracy, but taken together, one can form a quantitative description of the state of the ocean that is superior to any based on either models or data alone. Along with the goals of analysis and prediction, we seek reliable estimates of the errors in our results. We expect our results to have implications beyond the technical challenges of data assimilation. In particular, we believe this research will lead to enhanced understanding of the implications of nonlinearity and randomness for predictability of the ocean and atmosphere.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2008
Accession Number
ADA534245

Entities

People

  • Robert N. Miller

Organizations

  • Oregon State University

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Assimilation
  • Atmospheres
  • Atmospheric Sciences
  • Complex Systems
  • Composite Materials
  • Confidence Limits
  • Construction
  • Data Science
  • Data Sets
  • Demographic Cohorts
  • Differential Equations
  • Equations
  • Errors
  • Monte Carlo Method
  • Quality Control
  • Weather Forecasting

Fields of Study

  • Environmental science

Readers

  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Statistical inference.