Optimal Combining Data for Improving Ocean Modeling
Abstract
The long range scientific goals of the proposed research comprise: (1) developing rigorous approaches to optimal combining different kinds of observations (images, ADCP, HFR, glider, drifters etc) with output of regional circulation models for accurate estimating the upper ocean velocity field 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 in the Adriatic and Ligurian Sea (MREA coastal experiments) via new techniques. The objectives for the second year of research were: Enhancing the fusion method for computing surface velocities [1] by accounting for uncertainties in tracer generation and dissipation; Constructing and testing fusion algorithms for data coming from different sources at different resolution and Developing fusion methods based on the fuzzy logic [2-4] for estimating oceanic parameters from small biased samples. We 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2010
- Accession Number
- ADA542480
Entities
People
- L. I. Piterbarg
Organizations
- University of Southern California