ESTIMATES OF TURBULENCE PARAMETERS FROM LAGRANGIAN DATA USING A STOCHASTIC PARTICLE MODEL

A new parametric approach for the study of Lagrangian data is presente d. It provides parameter estimates for velocity and transport componen ts and is based on a stochastic model for single particle motion. The main advantage of this approach is that it provides more accurate para meter estimates than existing methods by using the a-priori knowledge of the model. Also, it provides a complete error analysis of the estim ates and is valid in presence of observation errors. Unlike nonparamet ric methods (e.g. Davis, 1991b), our technique depends on a-priori ass umptions which require that the model validity be checked in order to obtain reliable estimates. The model used here is the simplest one in a hierarchy of ''random flight'' models (e.g. Thomson, 1987), and it d escribes the turbulent velocity as a linear Markov process, characteri zed by an exponential autocorrelation. Experimental and numerical esti mates show that the model is appropriate for mesoscale turbulent flows in homogeneous regions of the upper ocean. More complex models, valid under more general conditions, are presently under study. Estimates o f the mean flow, variance, turbulent time scale and diffusivity are ob tained. The properties of the estimates are discussed in terms of bias es and sampling errors, both analytically and using numerical experime nts. Optimal sampling for the measurements is studied and an example a pplication to drifter data from the Brazil/Malvinas extension is prese nted.