Separation of the Mean Gulf Stream Topography from an Altimeter-Derived Reference Surface Using Genetic Algorithms

Abstract

The mean dynamic topography in the reference surface used to calculate altimetric sea-surface height (SSH) residuals leads to significant difficulty in interpretation. When the geoid is subtracted from an individual pass the mean dynamic topography is also subtracted, leading to counterflow as strong as the Gulf Stream itself. Several synthetic geoid methods have been developed to address this problem. A simple approach involves a mathematical representation of mean and instantaneous Gulf Stream profiles. The method employs a least squares fit to SSH residuals to determine model parameters. The modeled mean Gulf Stream is then added to the SSH residual profile to allow a better depiction of the instantaneous Gulf Stream. This method works well, but convergence is often not achieved unless the initial parameter estimates are close to the correct values. Genetic algorithms (GAs) were used in an effort to find a more robust approach. GAs are search techniques based on the mechanics of natural selection. GAs apply the 'generate and test' search procedure iteratively over a large set of candidate solutions. They search large numbers of candidate solutions simultaneously, and use random search and/or selection rather than deterministic methods. (mm)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA229821

Entities

People

  • Kenneth C. Messa
  • Matthew Lybanon

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Altimeters
  • Classification
  • Genetic Algorithms
  • Gulf Stream
  • Information Operations
  • Mechanics
  • Monitoring
  • Optimization
  • Remote Sensing
  • Residuals
  • Security
  • Topography

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval
  • AI & ML - Machine Learning Algorithms
  • Biotechnology