Optimal Combining Data for Improving Ocean Modeling
UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES CENTER FOR APPLIED MATHEMATICAL SCIENCES
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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, 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 in the Adriatic and Ligurian Sea MREA coastal experiments via new techniques The objectives for the first year of research were - Developing and testing methods for fusing HF radar data with tracer SST, color andor drifter observations to improve surface velocity estimates - Constructing and testing fusion algorithms for combining glider observations with output of high resolution circulation model - Incorporating subgrid Lagrangian models identified via drifter data into circulation models for improving estimates of FSLE finite size Lyapunov exponent - Developing theoretical approaches based on fuzzy logic to estimating oceanic parameters from small biased samples.
- Physical and Dynamic Oceanography