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dc.contributor.authorChu, Peter C.
dc.date2009-10
dc.date.accessioned2013-09-11T23:04:35Z
dc.date.available2013-09-11T23:04:35Z
dc.date.issued2009-10
dc.identifier.citationChu, P.C., 2009: Analysis of remotely sensed ocean data by the optimal spectral decomposition (OSD
dc.identifier.urihttp://hdl.handle.net/10945/36287
dc.descriptionIEEE/MTS OCEANS 2009en_US
dc.description.abstractA new data analysis/assimilation scheme, optimal spectral decomposition (OSD), has been developed to reanalyze fields from noisy and sparse data in a domain with open boundary conditions using two scalar representations for a three-dimensional incompressible flow. The reanalysis procedure is divided into two steps: (a) specification of basis functions in the spectral decomposition from knowledge of boundary geometry and velocity and (b) determination of coefficients in the spectral decomposition for the circulation solving linear or nonlinear regression equations. The basis functions are the eigenfunctions of the Laplacian operator with mixed boundary conditions. The optimization process is used to obtain unique and stable solutions on the base of an iteration procedure with special regularization (the filtration. The capability is demonstrated using various examples.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleAnalysis of remotely sensed ocean data by the optimal spectral decomposition (OSD) methoden_US
dc.typeConference Proceedingsen_US
dc.contributor.departmentDepartment of Oceanography
dc.subject.authorOptimal spectral decomposition, Argo drifter, Lagrangian data, satellite data, rotation methoden_US


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