LONG-TERM GOALSThe long range scientific goals of the proposed research comprise: (1) developing rigorous approaches to optimal combining different kinds of data (images, CTD, HFR, glider, drifters , and possibly output of regional circulation models ) for accurate estimating the upper ocean velocity field, subsurface thermohaline structure, and mixing characteristics (2) constructing computationally efficient and robust estimation algorithms based on alternative parameterizations of uncertainty and comprehensive testing them on synthetic data (3) processing real data collected in coastal zones via new techniques
OBJECTIVESThe objectives for the last year of research were: (1) Testing an earlier developed method for fusing ADCP data with CTD profiles on real data . (2) Developing and testing an algorithm of combining two tracer observations with HFR data for estimating surface velocities. (3) Theoretical studing finite time Lyapunov exponent (FTLE) to lay a ground for estimating it from observations.
APPROACHWe develop theoretical approaches to the data fusion problem in context of the possibility theory (fuzzy logic) and in the framework of the classical theory of random processes and fields covered by stochastic partial differential equations. We also design computational algorithms derived from the theoretical findings. A significant part of the algorithm validation is their testing via Monte Carlo simulations. Such an approach provides us with an accurate error analysis. Together with my collaborators from Rosenstiel School of Marine and Atmospheric Research (RSMAS), Consiglio Nazionale delle Ricerche (ISMAR, LaSpezia, Italy), University of Toulon (France), Observatoire Oceanologique de Villefranche sur Mer (France), and Naval Postgraduate School (Monterrey, CA) we implement the algorithms in concrete ocean models such as HYCOM, NCOM, MFS, and NEMO as well as carry out statistical analysis of real data sets by means of new methods