Optimal spectral decomposition (OSD) for remotely sensed ocean data assimilation
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Authors
Chu, Peter C.
Subjects
—Optimal spectral decomposition (OSD),
Argo profiling and trajectory data, OSCAR, CODAR,
GTSPP
Advisors
Date of Issue
2008
Date
2008
Publisher
Language
Abstract
Assimilation of remotely sensed ocean data (velocity,
temperature, and salinity) into numerical model is of great
importance in oceanic and climatic research. However, the
data should be reconstructed (onto grids) before assimilation
since the original datasets are usually noisy and sparse. This
paper describes a recently developed optimal spectral
decomposition (OSD) method for mapping and noise
filtration with examples of reconstructing the data from the Argo profiling and trajectories, Ocean Surface Current Analyses – Real time (OSCAR), shore-based high-frequency (HF) Doppler radar (CODAR) and Global Temperature-Salinity Profile Program (GTSPP).
Type
Conference Proceedings
Description
Proceedings on IEEE International Geoscience & Remote Sensing Symposium, DVD-ROM,
Series/Report No
Department
Department of Oceanography
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Chu, P.C., 2008: Optimal spectral decomposition (OSD) for remotely sensed ocean data assimilation. Proceedings on IEEE International Geoscience & Remote Sensing Symposium, DVD-ROM
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.