8 - Dynamic consistency and Lagrangian data in oceanography: mapping, assimilation, and optimization schemes

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.