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
8 - Dynamic consistency and Lagrangian data in oceanography: mapping, assimilation, and optimization schemes
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
As illustrated throughout this book, Lagrangian data can provide us with a unique perspective on the study of geophysical fluid dynamics, particle dispersion, and general circulation. Drifting buoys, floats, and even a crate-full of rubber ducks or athletic shoes lost in mid-ocean (Christopherson, 2000) may be used to gain insights into ocean circulation. All Lagrangian instruments will be referred to as “drifters” hereafter for simplicity. Because movement of a drifter tends to follow that of a water parcel, the primary attributes of Lagrangian measurements are (i) horizontal coverage due to dispersion in time, (ii) that many of the observed variables obey conservation laws approximately over some lengths of time, and (iii) their ability to trace circulation features such as meanders and vortices at a wide range of spatial scales. Due mainly to inherently irregular spatial distributions, the Lagrangian measurements must first be interpolated for most applications. As we will see, the design of interpolation and mapping schemes that can preserve the Lagrangian attributes is often non-trivial.
To observe finer dynamical details of oceanic and coastal phenomena and to forecast drifter trajectories more accurately (for search-and-rescue operation, spill containment, and so on), Lagrangian data afford a particularly informative and novel perspective if they are combined with a dynamical model, rather than mapped by a standard synoptic-scale interpolation procedure which can smear some details at smaller and faster scales. Data assimilation can be viewed as a methodology for imposing dynamical consistency upon observed data for the purpose of space-time interpolation.
- Type
- Chapter
- Information
- Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics , pp. 204 - 230Publisher: 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 , 1999. Open boundary conditions for Lagrangian geophysical fluid dynamics. J. Comp. Phys., 153, 418–36.CrossRefGoogle Scholar
, , and , 1976. Technique for objective analysis and design of oceanographic experiments applied to MODE-73. Deep-Sea Res., 23 (7), 559–82.Google Scholar
, 1989. Assimilation of Lagrangian data into a numerical-model. Dyn. Atmos. Oceans., 13, 335–48.CrossRefGoogle Scholar
and , 1987. Analysis models for the estimation of oceanic fields. J. Atm. Ocean. Tech., 4 (1), 49–74.2.0.CO;2>CrossRefGoogle Scholar
, , and , 1999. Spatial regression and multi-scale approximations for sequential data assimilation in ocean models. J. Geophys. Res., 104 (C4), 7991–8014.CrossRefGoogle Scholar
, 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10143–62.CrossRefGoogle Scholar
and , 1976. Objective analysis of meso-scale ocean circulation features. Deep-Sea Res., 23, 915.Google Scholar
and , 1991. Data assimilation in meteorology and oceanography. Adv. Geophys., 33, 141–266.CrossRefGoogle Scholar
and , 1985. The structure and transport of the Gulf-Stream at 73-degrees-W. J. Phys. Oceanogr., 15, 1439–52.2.0.CO;2>CrossRefGoogle Scholar
, , and , 1986. An objective analysis of the POLYMODE local dynamics experiment. Part II: Streamfunction and potential vorticity fields during the intensive period. J. Phys. Oceanogr., 15, 506–22.2.0.CO;2>CrossRefGoogle Scholar
and , 1997a. Extended Kalman filtering for vortex systems. Part I: Methodology and point vortices. Dynamics of Atmospheres and Oceans, 27, 301–32.CrossRefGoogle Scholar
and , 1997b. Extended Kalman filtering for vortex systems. Part II: Rankine vortices and observing-system design. Dynamics of Atmospheres and Oceans, 27, 333–50.CrossRefGoogle Scholar
, , , and , 1997. A unified notation for data assimilation. J. Metorolor. Soc. Japan, 75(1B), 181–9.CrossRefGoogle Scholar
, , , and , 1996. Successive correction of the mean sea surface height by the simultaneous assimilation of drifting buoy and altimetric data. J. of Phys. Oceanogr., 26, 2381–97.2.0.CO;2>CrossRefGoogle Scholar
, 1960. A new approach to linear filtering and prediction problems. J. Basic Eng., 82, 35–45.CrossRefGoogle Scholar
and , 1995. Continuous data assimilation of drifting buoy trajectory into an equatorial Pacific Ocean model. J. Mar. Sys. 6, 159–78.CrossRefGoogle Scholar
, , and , 1988. Snakes: Active contour models. Int. J. Comput. Vision, 1, 321–31.CrossRefGoogle Scholar
, 1989. An inverse model for near-surface velocity from infrared images. J. Phys. Oceanogr., 19, 1845–64.2.0.CO;2>CrossRefGoogle Scholar
, , and , 2003. A method for assimilation of Lagrangian data. Mon. Wea. Rev., 131, 2247–60.2.0.CO;2>CrossRefGoogle Scholar
, 1982. Adaptation of P. D. Thompson's scheme to the constraint of potential vorticity conservation. Mon. Wea. Rev., 110, 1618–34.2.0.CO;2>CrossRefGoogle Scholar
Mariano, A. J. and T. M. Chin, 1996. Feature and contour based data analysis and assimilation in physical oceanography. In Stochastic Modelling in Physical Oceanography, ed. et al., Cambridge, MA: Birkhäuser Boston, 311–42.CrossRefGoogle Scholar
and , 1989. The Lagrangian potential vorticity balance during POLYMODE. J. Phys. Oceanogr., 19, 927–39.2.0.CO;2>CrossRefGoogle Scholar
McWilliams, J. C. (1991). Geostrophic vortices. In Nonlinear Topics in Geophysical Processes: Proceedings of International School of Physics “Enrico Fermi” Course 109, ed. . Amsterdam: Elsevier, 5–50.Google Scholar
and , 2001. Towards regional assimilation of Lagrangian data: the Lagrangian form of the shallow water model and its inverseJ. Mar. Sys., 29, 365–84.CrossRefGoogle Scholar
, , , , and , 2003. Assimilation of drifter positions for the reconstruction of the Eulerian circulation field. J. Geophys. Res. Oceans, 108, 3056, doi:10.1029/2001JC001240.CrossRefGoogle Scholar
, , , , and , 2003. Assimilation of drifter observations in primitive equation models of midlatitude ocean circulation. J. Geophys. Res. (Oceans), 108, 3238, doi:10.1029/2002JC001719.CrossRefGoogle Scholar
and , 1987. Dynamics of deep thermocline jets in the POLYMODE region. J. Phys. Oceanogr., 17, 1163–88.2.0.CO;2>CrossRefGoogle Scholar
, , and , 1986. The spatial and temporal evolution of a cluster of SOFAR floats in the POLYMODE local dynamics experiment (LDE). J. Phys. Oceanogr., 16, 428–42.2.0.CO;2>CrossRefGoogle Scholar
, 1970. Least-squares estimation: from Gauss to Kalman. IEEE Spectrum, 7, 63–8.CrossRefGoogle Scholar
, 1994. A Bayesian approach to dynamic contours through stochastic sampling and simulated annealing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 976–86.CrossRefGoogle Scholar
, 1969. Reduction of analysis error through constraints of dynamical consistency. J. Appl. Meteorol., 8, 738–42.2.0.CO;2>CrossRefGoogle Scholar