Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- 1 Evolution of Lagrangian methods in oceanography
- 2 Measuring surface currents with Surface Velocity Program drifters: the instrument, its data, and some recent results
- 3 Favorite trajectories
- 4 Particle motion in a sea of eddies
- 5 Inertial particle dynamics on the rotating Earth
- 6 Predictability of Lagrangian motion in the upper ocean
- 7 Lagrangian data assimilation in ocean general circulation models
- 8 Dynamic consistency and Lagrangian data in oceanography: mapping, assimilation, and optimization schemes
- 9 Observing turbulence regimes and Lagrangian dispersal properties in the oceans
- 10 Lagrangian biophysical dynamics
- 11 Plankton: Lagrangian inhabitants of the sea
- 12 A Lagrangian stochastic model for the dynamics of a stage structured population. Application to a copepod population
- 13 Lagrangian analysis and prediction of coastal and ocean dynamics (LAPCOD)
- Index
- Plate section
- References
6 - Predictability of Lagrangian motion in the upper ocean
Published online by Cambridge University Press: 07 September 2009
Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- 1 Evolution of Lagrangian methods in oceanography
- 2 Measuring surface currents with Surface Velocity Program drifters: the instrument, its data, and some recent results
- 3 Favorite trajectories
- 4 Particle motion in a sea of eddies
- 5 Inertial particle dynamics on the rotating Earth
- 6 Predictability of Lagrangian motion in the upper ocean
- 7 Lagrangian data assimilation in ocean general circulation models
- 8 Dynamic consistency and Lagrangian data in oceanography: mapping, assimilation, and optimization schemes
- 9 Observing turbulence regimes and Lagrangian dispersal properties in the oceans
- 10 Lagrangian biophysical dynamics
- 11 Plankton: Lagrangian inhabitants of the sea
- 12 A Lagrangian stochastic model for the dynamics of a stage structured population. Application to a copepod population
- 13 Lagrangian analysis and prediction of coastal and ocean dynamics (LAPCOD)
- Index
- Plate section
- References
Summary
Introduction
The prediction particle trajectories in the ocean is of practical importance for problems such as searching for objects lost at sea, tracking floating mines, designing oceanic observing systems, and studying ecological issues such as the spreading of pollutants and fish larvae (Mariano et al., 2002). In a given year, for example, the US Coast Guard (USCG) performs over 5000 search and rescue missions (Schneider, 1998). Even though the USCG and its predecessor, the Lifesaving Service, have been performing search and rescue operations for over 200 years, it has only been in the last 30 years that Computer Assisted Search Planning has been used by the USCG. The two primary components are determining the drift caused by ocean currents and the movement caused by wind. The results presented in this review are motivated by the drift estimation problem.
A number of authors (e.g., Aref, 1984; Samelson, 1996) have shown that prediction of particle motion is an intrinsically difficult problem because Lagrangian motion often exhibits chaotic behavior, even in regular and simple Eulerian flows. In the ocean, the combined effects of complex time-dependence (Samelson, 1992; Meyers, 1994; Duan and Wiggins, 1996) and three-dimensional structure (Yang and Liu, 1996) are likely to induce chaotic transport. Chaos implies strong dependence on the initial conditions, which are usually not known with great accuracy, so that the task of predicting particle motion is often extremely difficult.
- Type
- Chapter
- Information
- Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics , pp. 136 - 171Publisher: Cambridge University PressPrint publication year: 2007
References
, , , , and , 1998. Eddy-mean flow decomposition and eddy-diffusivity estimates in the tropical Pacific Ocean. J. Geophys. Res., 103, 30855–71.CrossRefGoogle Scholar
, , and , 2002. Eddy-mean flow and eddy diffusivity estimates in the tropical Pacific Ocean. 2: Results. J. Geophys. Res., 107, (c10), 3154–71.CrossRefGoogle Scholar
and , 2002. Material transport in oceanic gyres. Part II: Hierarchy of stochastic models. J. Phys. Oceanogr., 32, 797–830.2.0.CO;2>CrossRefGoogle Scholar
, , and , 2002. Material transport in oceanic gyres. Part I: Phenomenology. J. Phys. Oceanogr., 32, 764–96.2.0.CO;2>CrossRefGoogle Scholar
and , 2003. Material transport in oceanic gyres. Part III: Randomized stochastic models. J. Phys. Oceanogr., 33, 1416–45.2.0.CO;2>CrossRefGoogle Scholar
and , 1994. Stochastic equations with multifractal random increments for modeling turbulent dispersion. Phys. Fluids, 6, 618.CrossRefGoogle Scholar
, , , and , 2001. Prediction of particle trajectories in the Adriatic Sea using Lagrangian data assimilation. J. Mar. Sys., 29, 33–50.CrossRefGoogle Scholar
, 1994. Introduction to Geophysical Fluid Dynamics. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
and , 1996. Fluid exchange across a meandering jet with quasiperiodic variability. J. Phys. Oceanogr., 26, 1176–88.2.0.CO;2>CrossRefGoogle Scholar
, , , and , 2000. Transport properties in the Adriatic Sea deduced from drifter data. J. Phys. Oceanogr., 30, 2055–71.2.0.CO;2>CrossRefGoogle Scholar
and , 2004. The Lyapunov exponent for inertial particles and an explosive ergodic diffusion. Submitted to Comm. Math. Phys.Google Scholar
, , , , and , 1996. The three-dimensional structure of an upper ocean vortex in the tropical Pacific Ocean. Nature, 383, 610–13.CrossRefGoogle Scholar
Griffa, A., 1996. Applications of stochastic particle models to oceanographic problems. In Stochastic Modelling in Physical Oceanography, ed. , , and . Cambridge, MA: Birkhauser Boston, 113–28.CrossRefGoogle Scholar
, , , and , 1995. Estimates of turbulence parameters from Lagrangian data using a stochastic particle model. J. Mar. Res., 53, 212–34.CrossRefGoogle Scholar
, , and , 2004. Predictability of Lagrangian particles: effects of smoothing of the underlying Eulerian flow. J. Mar. Res., 62/1, 1–35.CrossRefGoogle Scholar
and , 1996. Quality control and interpolations of WOCE/TOGA drifter data. J. Atmos. Oceanic Tec., 13, 900–9.2.0.CO;2>CrossRefGoogle Scholar
, , , and , 2002. Lagrangian analysis and predictability of coastal and ocean dynamics 2000. J. Atmos. Ocean. Tech., 19, 1114–26.2.0.CO;2>CrossRefGoogle Scholar
and , 1975. Statistical Fluid Mechanics: Mechanics of Turbulence. Cambridge, MA: MIT Press.Google Scholar
, 1994. Cross-frontal mixing in a meandering jet. J. Phys. Oceanogr., 24, 1641–6.2.0.CO;2>CrossRefGoogle Scholar
, , , , and , 1995. Measurements of the water-following capability of holey-sock and TRISTART drifters. Deep Sea Res., 42, 1951–64.CrossRefGoogle Scholar
, , , and , 2000. On the predictability of the Lagrangian trajectories in the ocean. J. Atmos. Ocean. Tech., 17/3, 366–83.2.0.CO;2>CrossRefGoogle Scholar
, , , and , 2001. Predictability of drifter trajectories in the tropical Pacific Ocean. J. Phys. Oceanogr., 31, 2691–720.2.0.CO;2>CrossRefGoogle Scholar
, , , , and , 2004. A practical hybrid model for predicting the trajectories of near-surface drifters in the Pacific Ocean. J. Ocean. Atmos. Sci., 21, 1246–58.Google Scholar
, 2001a. The top Lyapunov exponent for a stochastic flow modeling the upper ocean turbulence. SIAM J. Appl. Math., 62, 777–800.CrossRefGoogle Scholar
, 2001b. Short term prediction of Lagrangian trajectories. J. Atmos. Ocean. Tech., 18, 1398–410.2.0.CO;2>CrossRefGoogle Scholar
and , 2002. A simple prediction algorithm for the Lagrangian motion in two-dimensional turbulent flows. SIAM J. Appl. Math., 63, 116–48.CrossRefGoogle Scholar
Piterbarg, L. I., 2004a. On predictability of particle clusters in a stochastic flow, to appear in Stochastics and Dynamics.
, 2005. Relative dispersion in 2D stochastic flows. J. of Turbulence, 6(4), doi:10.1080/14685240500103168.CrossRefGoogle Scholar
, 1987. Consistency conditions for random walk models of turbulent dispersion. Phys. Fluids, 30, 2374–9.CrossRefGoogle Scholar
, 1999. Drifter observations of surface circulation in the Adriatic Sea between December 1994 and March 1996. J. Mar. Sys., 20, 231–53.CrossRefGoogle Scholar
, , and , 1994. Seasonal variability in the surface currents of the Equatorial Pacific. J. Geophys. Res., 99(10), 20323–44.CrossRefGoogle Scholar
, 1998. On the formulation of Lagrangian stochastic models of scalar dispersion within plant canopies. Boundary-Layer Meteor., 86, 333–44.CrossRefGoogle Scholar
, 1996. Stochastic Lagrangian models of turbulent diffusion, Meteorological Monographs, 26, n.48. Boston: AMS.CrossRefGoogle Scholar
, 1992. Fluid exchange across a meandering jet. J. Phys. Oceanogr., 22, 431–40.2.0.CO;2>CrossRefGoogle Scholar
Samelson, R. M., 1996. Chaotic transport by mesoscale motions. In Stochastic Modelling in Physical Oceanography, ed. , , and . Cambridge, MA: Birkhäuser Boston, 423–38.CrossRefGoogle Scholar
, 1993. Recent developments in the Lagrangian stochastic theory of turbulent dispersion. Boundary-Layer Meteor., 62, 197–215.CrossRefGoogle Scholar
Schneider, T., 1998. Lagrangian drifter models as search and rescue tools. M. S. Thesis, Dept. of Meteorology and Physical Oceanography, University of Miami.
, 1986. A random walk model of dispersion in turbulent flows and its application to dispersion in a valley. Quat. J. R. Met. Soc., 112, 511–29.CrossRefGoogle Scholar
, 1987. Criteria for the selection of stochastic models of particle trajectories in turbulent flows. J. Fluid Mech., 180, 529–56.CrossRefGoogle Scholar
, 1990. A stochastic model for the motion of particle pairs in isotropic high-Reynolds-number turbulence, and its application to the problem of concentration variance. J. Fluid Mech., 210, 113–53.CrossRefGoogle Scholar
, , , , , and , 2001a. Reconstructing basin-scale Eulerian velocity fields from simulated drifter data. J. Phys. Oceanogr., 31, 1361–76.2.0.CO;2>CrossRefGoogle Scholar
, , , and , 2001b. Can general circulation models be assessed and their output enhanced with drifter data?J. Geophys. Res. Oceans, 106, 19563–79.CrossRefGoogle Scholar
, , , and , 2004. Oceanic turbulence and stochastic models from subsurface Lagrangian data for the North-West Atlantic Ocean, J. Phys. Oceanogr., 34, 1884–1906.2.0.CO;2>CrossRefGoogle Scholar
and , 1996. The three-dimensional chaotic transport and the great ocean barrier. J. Phys. Oceanogr., 27, 1258–73.2.0.CO;2>CrossRefGoogle Scholar
, , and , 2001. Detection of vortices and saddle points in SST data. Geophys. Res. Lett., 28, 331–4.CrossRefGoogle Scholar
and , 1994. Effects of finite scales of turbulence on dispersion estimates. J. Mar. Res., 52, 129–48.CrossRefGoogle Scholar
- 3
- Cited by