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dc.contributor.authorPaduan, Jeffrey D.
dc.contributor.authorFrolov, Sergey
dc.contributor.authorCook, Michael
dc.contributor.authorBellingham, James
dc.dateMay 17-18, 2012
dc.date.accessioned2014-05-15T22:01:36Z
dc.date.available2014-05-15T22:01:36Z
dc.date.issued2012-05
dc.identifier.urihttp://hdl.handle.net/10945/41227
dc.descriptionThe 1st ORCA, S3-1en_US
dc.description.abstractAccurate short-term prediction of surface currents can improve efficiency of search-and-rescue operations, oil-spill response, and marine operations. We developed a linear statistical model for predicting surface currents (up to 48 hours in the future) based on a short time-history of past HF-radar observations (past 48 hours) and an optional forecast of surface winds. Our model used empirical orthogonal functions (EOFs) to capture spatial correlations in the HF-radar data and used a linear autoregression model to predict the temporal dynamics of the EOF coefficients. We tested the developed statistical model using historical observations of surface currents in Monterey Bay, California. The predicted particle trajectories separated from particles advected with HF-radar data at a rate of 4.4 km/day, which represents an improvement over the existing statistical model (drifter separation of 5.5 km/day). We found that the minimal length of the HF-radar data required to train an accurate statistical model was between one and two years, depending on the accuracy desired.en_US
dc.rightsThis 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.en_US
dc.titleImproved statistical prediction of surface currents based on historic HF-radar observationsen_US
dc.typeArticleen_US
dc.contributor.departmentOceanographyen_US
dc.subject.authorSurface Currentsen_US
dc.subject.authorHF Radaren_US
dc.subject.authorVelocity Forecasten_US
dc.subject.authorSearch and Rescueen_US


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