Analyzing monthly extreme sea levels with a time-dependent GEV model

Ver/Abrir
Identificadores
URI: http://hdl.handle.net/10902/1635
ISSN: 1520-0426
ISSN: 0739-0572
Compartir
Estadísticas
Ver Estadísticas
Google Scholar
Registro completo
Mostrar el registro completo DC
Autoría
Méndez Incera, Fernando JavierAutoridad Unican; Menéndez García, MelisaAutoridad Unican; Luceño Vázquez, AlbertoAutoridad Unican; Losada Rodríguez, Íñigo
Fecha
2007-05
Derechos
© Copyright [2007] American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyrights@ametsoc.org.
Publicado en
Journal of Atmospheric and Oceanic Technology, 2007, 24(5), 894–911.
Editorial
American Meteorological Society
Enlace a la publicación
Resumen/Abstract
A statistical model to analyze different time scales of the variability of extreme high sea levels is presented. This model uses a time-dependent generalized extreme value (GEV) distribution to fit monthly maxima series and is applied to a large historical tidal gauge record (San Francisco, California). The model allows the identification and estimation of the effects of several time scales —such as seasonality, interdecadal variability, and secular trends— in the location, scale, and shape parameters of the probability distribution of extreme sea levels. The inclusion of seasonal effects explains a large amount of data variability, thereby allowing a more efficient estimation of the processes involved. Significant correlation with the Southern Oscillation index and the nodal cycle, as well as an increase of about 20% for the secular variability of the scale parameter have been detected for the particular dataset analyzed. Results show that the model is adequate for a complete analysis of seasonal-to-interannual sea level extremes providing time-dependent quantiles and confidence intervals.
Colecciones a las que pertenece