Enhanced Ocean Predictability Through Optimal Observing Strategies

Abstract

The long-term goals of this research are to develop the requisite technology to design effective observation strategies that will maximize the capacity to predict mesoscale and submesoscale conditions so as to understand the physical processes responsible for these conditions and to provide the best possible now-casts and forecasts of oceanic conditions. There are three tightly integrated objectives. The first is to focus both oceanographic and dynamical systems approaches on developing optimal observing strategies. The common thread linking both approaches is Lagrangian analysis, and so this phase of the work addresses the question of how best to utilize Eulerian current maps constructed from disparate data and how to use the information contained therein to design optimal observing systems. The second objective will be to design an optimal observing strategy from a synthetic database. Here we will use primitive equation model simulations as the control. The last objective will be to apply this technology to the Gulf of Mexico where both high-resolution numerical model results and drifter data are available.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2000
Accession Number
ADA610071

Entities

People

  • Albert D. Kirwan
  • Michael S. Toner

Organizations

  • University of Delaware

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Altimetry
  • Black Sea
  • Boundaries
  • Crisis Management
  • Dispersions
  • Eigenvalues
  • Environmental Assessment
  • Equations
  • Geometry
  • High Resolution
  • Observation
  • Oceans
  • Simulations
  • Stagnation Point
  • Stratified Fluids
  • Systems Approach
  • Universities

Fields of Study

  • Environmental science

Readers

  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Operations Research