Theory and Practice of Data Assimilation in Ocean Modeling

Abstract

The long range goal of this project is to combine computational models with observational data to form the best picture of the ocean as an evolving system, and use that picture to understand the physical influences which 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 data assimilation. In particular, we hope 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, 2002
Accession Number
ADA626934

Entities

People

  • Robert N. Miller

Organizations

  • Oregon State University

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Accuracy
  • Assimilation
  • Atmospheres
  • Atmospheric Sciences
  • Channel Models
  • Confidence Limits
  • Data Science
  • Demographic Cohorts
  • Differential Equations
  • Errors
  • Gaussian Noise
  • Information Science
  • Models
  • Monte Carlo Method
  • Oceans
  • Universities
  • Weather Forecasting

Fields of Study

  • Environmental science

Readers

  • Computational Modeling and Simulation
  • Oceanography.
  • Systems Analysis and Design