The application of a quantile regression metamodel for salinity event detection confirmation within New York Harbour oceanographic data

Authors: Kerman, M.C.; Biang, W.; Blumberg, A.F.; Buttrey, S.E.

Source: Journal of Operational Oceanography, Volume 2, Number 1, February 2009 , pp. 49-70(22)

Publisher: IMarEST

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Abstract:

This paper presents a continuation of research regarding the utilisation of robust metamodels for uncertainty quantification and event detection within a geophysical system. Using salinity data supplied by the New York Harbour Observing and Prediction System (NYHOPS) for two test datasets and three actual sensors, event detection results from a static threshold method are compared against those of a dynamic uncertainty quantification-based technique and a composite technique that combines both the static and dynamic methods. The results clearly show an appreciable reduction in the number of false positive detections when using the composite event detection method; in test data void of salinity events, false detection rates for low salinity conditions decreased by as much as 80%.

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Keywords: QUANTILE REGRESSION; METAMODELS; SALINITY; NEW YORK HARBOUR; EVENT DETECTION; OCEANOGRAPHIC DATA; NYHOPS; NEW YORK HARBOUR OBSERVING AND PREDICTION SYSTEM

Document Type: Research article

Publication date: 2009-02-01

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The Journal of Operational Oceanography is the only international peer reviewed journal that links the latest research in marine science and technology to its application as part of a sustained system for observing and forecasting our oceans and seas.
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