Quantifying Uncertainties in Ocean Predictions

Abstract

A multitude of physical and biological processes occur in the ocean over a wide range of temporal and spatial scales. Many of these processes are nonlinear and highly variable, and involve interactions across several scales and oceanic disciplines. For example, sound propagation is infl uenced by physical and biological properties of the water column and by the seabed. From observations and conservation laws, ocean scientists formulate models that aim to explain and predict dynamics of the sea. This formulation is intricate because it is challenging to observe the ocean on a sustained basis and to transform basic laws into generic but usable models. There are imperfections in both data and model estimates. It is important to quantify such uncertainties to understand limitations and identify the research needed to increase accuracies, which will lead to fundamental progress.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2006
Accession Number
ADA496346

Entities

People

  • Alex Pang
  • Allan Richard Robinson
  • Ching-sang Chiu
  • Francois Lekien
  • Glen G. Gawarkiewicz
  • Patrick J. Haley
  • Phil Abbot
  • Pierre F.j. Lermusiaux
  • Robert N. Miller
  • Sharanya J. Majumdar
  • Wayne G. Leslie

Organizations

  • Harvard University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Acoustics
  • Applied Mathematics
  • Computational Fluid Dynamics
  • Computational Science
  • Computer Science
  • Data Science
  • Equations
  • Fluid Dynamics
  • Mathematical Filters
  • Measurement
  • New York
  • Oceanography
  • Ridges
  • Simulations
  • Standards
  • Statistical Estimation
  • Uncertainty

Fields of Study

  • Environmental science

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Systems Analysis and Design