A particle filter for inverse Lagrangian prediction problems

T. Mike Chin, Arthur J Mariano

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The authors present a numerical method for the inverse Lagrangian prediction problem, which addresses retrospective estimation of drifter trajectories through a turbulent flow, given their final positions and some knowledge of the flow field. Of particular interest is probabilistic estimation of the origin (or launch site) of drifters for practical applications in search and rescue operations, drifting sensor array design, and biochemical source location. A typical solution involves a Monte Carlo simulation of an ensemble of Lagrangian trajectories backward in time using the known final locations, a set of velocity estimates, and a stochastic model for the unresolved flow components. Because of the exponential dispersion of the trajectories, however, the distribution of the drifter locations tends to be too diffuse to be able to reliably locate the launch site. A particle filter that constrains the drifter ensemble according to the empirical dispersion characteristics of the flow field is examined. Using the filtering method, launch-site prediction cases with and without a dispersion constraint are compared in idealized as well as realistic scenarios. It is shown that the ensemble with the dispersion constraint can locate the launch site more specifically and accurately than the unconstrained ensemble.

Original languageEnglish (US)
Pages (from-to)371-384
Number of pages14
JournalJournal of Atmospheric and Oceanic Technology
Volume27
Issue number2
DOIs
StatePublished - Feb 2010

Fingerprint

drifter
filter
trajectory
Trajectories
prediction
flow field
Flow fields
search and rescue
Sensor arrays
Stochastic models
turbulent flow
numerical method
Turbulent flow
Numerical methods
sensor
particle
simulation

ASJC Scopus subject areas

  • Atmospheric Science
  • Ocean Engineering

Cite this

A particle filter for inverse Lagrangian prediction problems. / Chin, T. Mike; Mariano, Arthur J.

In: Journal of Atmospheric and Oceanic Technology, Vol. 27, No. 2, 02.2010, p. 371-384.

Research output: Contribution to journalArticle

@article{01d3af8382b448a1837ab8b783b65935,
title = "A particle filter for inverse Lagrangian prediction problems",
abstract = "The authors present a numerical method for the inverse Lagrangian prediction problem, which addresses retrospective estimation of drifter trajectories through a turbulent flow, given their final positions and some knowledge of the flow field. Of particular interest is probabilistic estimation of the origin (or launch site) of drifters for practical applications in search and rescue operations, drifting sensor array design, and biochemical source location. A typical solution involves a Monte Carlo simulation of an ensemble of Lagrangian trajectories backward in time using the known final locations, a set of velocity estimates, and a stochastic model for the unresolved flow components. Because of the exponential dispersion of the trajectories, however, the distribution of the drifter locations tends to be too diffuse to be able to reliably locate the launch site. A particle filter that constrains the drifter ensemble according to the empirical dispersion characteristics of the flow field is examined. Using the filtering method, launch-site prediction cases with and without a dispersion constraint are compared in idealized as well as realistic scenarios. It is shown that the ensemble with the dispersion constraint can locate the launch site more specifically and accurately than the unconstrained ensemble.",
author = "Chin, {T. Mike} and Mariano, {Arthur J}",
year = "2010",
month = "2",
doi = "10.1175/2009JTECHO675.1",
language = "English (US)",
volume = "27",
pages = "371--384",
journal = "Journal of Atmospheric and Oceanic Technology",
issn = "0739-0572",
publisher = "American Meteorological Society",
number = "2",

}

TY - JOUR

T1 - A particle filter for inverse Lagrangian prediction problems

AU - Chin, T. Mike

AU - Mariano, Arthur J

PY - 2010/2

Y1 - 2010/2

N2 - The authors present a numerical method for the inverse Lagrangian prediction problem, which addresses retrospective estimation of drifter trajectories through a turbulent flow, given their final positions and some knowledge of the flow field. Of particular interest is probabilistic estimation of the origin (or launch site) of drifters for practical applications in search and rescue operations, drifting sensor array design, and biochemical source location. A typical solution involves a Monte Carlo simulation of an ensemble of Lagrangian trajectories backward in time using the known final locations, a set of velocity estimates, and a stochastic model for the unresolved flow components. Because of the exponential dispersion of the trajectories, however, the distribution of the drifter locations tends to be too diffuse to be able to reliably locate the launch site. A particle filter that constrains the drifter ensemble according to the empirical dispersion characteristics of the flow field is examined. Using the filtering method, launch-site prediction cases with and without a dispersion constraint are compared in idealized as well as realistic scenarios. It is shown that the ensemble with the dispersion constraint can locate the launch site more specifically and accurately than the unconstrained ensemble.

AB - The authors present a numerical method for the inverse Lagrangian prediction problem, which addresses retrospective estimation of drifter trajectories through a turbulent flow, given their final positions and some knowledge of the flow field. Of particular interest is probabilistic estimation of the origin (or launch site) of drifters for practical applications in search and rescue operations, drifting sensor array design, and biochemical source location. A typical solution involves a Monte Carlo simulation of an ensemble of Lagrangian trajectories backward in time using the known final locations, a set of velocity estimates, and a stochastic model for the unresolved flow components. Because of the exponential dispersion of the trajectories, however, the distribution of the drifter locations tends to be too diffuse to be able to reliably locate the launch site. A particle filter that constrains the drifter ensemble according to the empirical dispersion characteristics of the flow field is examined. Using the filtering method, launch-site prediction cases with and without a dispersion constraint are compared in idealized as well as realistic scenarios. It is shown that the ensemble with the dispersion constraint can locate the launch site more specifically and accurately than the unconstrained ensemble.

UR - http://www.scopus.com/inward/record.url?scp=77952533712&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77952533712&partnerID=8YFLogxK

U2 - 10.1175/2009JTECHO675.1

DO - 10.1175/2009JTECHO675.1

M3 - Article

AN - SCOPUS:77952533712

VL - 27

SP - 371

EP - 384

JO - Journal of Atmospheric and Oceanic Technology

JF - Journal of Atmospheric and Oceanic Technology

SN - 0739-0572

IS - 2

ER -