Filtering Drifter Trajectories Sampled at Submesoscale Resolution

Abstract

In this paper, a variational method for removing positioning errors (PEs) from drifter trajectories is proposed. The technique is based on the assumption of statistical independence of the PEs and drifter accelerations. The method provides a realistic approximation to the probability density function of the accelerations while keeping the difference between the filtered and observed trajectories within the error bars of the positioning noise. Performance of the method is demonstrated in application to real data acquired during the Grand Lagrangian Deployment (GLAD) experiment in the Northern Gulf of Mexico in 2012.

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Document Details

Document Type
Technical Report
Publication Date
May 11, 2015
Accession Number
ADA626002

Entities

People

  • Emanuel F. Coelho
  • Max Yaremchuk

Organizations

  • University of Miami

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Satellites
  • Central Processing Units
  • Computational Science
  • Data Processing
  • Databases
  • Engineering
  • Experimental Data
  • Gaussian Processes
  • Global Positioning Systems
  • High Acceleration
  • Information Processing
  • Information Science
  • Probability
  • Statistics
  • Time Intervals
  • Trajectories

Readers

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