Publication: Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases
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2012-02-01
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American Meteorological Society
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Abstract
ThThe development of numerical wave prediction models for hindcast applications allows a detailed description
of wave climate in locations where long-term instrumental records are not available. Wave hindcast
databases (WHDBs) have become a powerful tool for the design of offshore and coastal structures, offering
important advantages for the statistical characterization of wave climate all over the globe (continuous time
series, wide spatial coverage, constant time span, homogeneous forcing, and more than 60-yr-long time series).
However, WHDBs present several deficiencies reported in the literature. One of these deficiencies is
related to typhoons and hurricanes, which are inappropriately reproduced by numerical models. The main
reasons are (i) the difficulty of specifying accurate wind fields during these events and (ii) the insufficient
spatiotemporal resolution used. These difficulties make the data related to these events appear as ‘‘outliers’’
when compared with instrumental records. These bad data distort results from calibration and/or correction
techniques. In this paper, several methods for detecting the presence of typhoons and/or hurricane data are
presented, and their automatic outlier identification capabilities are analyzed and compared. All the methods
are applied to a global wave hindcast database and results are compared with existing hurricane and buoy
databases in the Gulf of Mexico, Caribbean Sea, and North Atlantic Ocean.
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Keywords
Error analysis, Ocean models, Regression analysis, Statistical techniques
Bibliographic citation
Mínguez, R., Reguero, B. G., Luceño, A., & Méndez, F. J. (2012). Regression Models for Outlier Identification (Hurricanes and Typhoons) in Wave Hindcast Databases. Journal of Atmospheric and Oceanic Technology, 29 (2), pp. 267-285.