Covariate extreme value analysis using wave spectral partitioning

Jesús Portilla-Yandún, Edwin Jácome

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

3 Citas (Scopus)


An important requirement in extreme value analysis (EVA) is for the working variable to be identically distributed. However, this is typically not the case in wind waves, because energy components with different origins belong to separate data populations, with different statistical properties. Although this information is available in the wave spectrum, the working variable in EVA is typically the total significant wave height Hs, a parameter that does not contain information of the spectral energy distribution, and therefore does not fulfill this requirement. To gain insight in this aspect, we develop here a covariate EVA application based on spectral partitioning. We observe that in general the total Hs is inappropriate for EVA, leading to potential over-or underestimation of the projected extremes. This is illustrated with three representative cases under significantly different wave climate conditions. It is shown that the covariate analysis provides a meaningful understanding of the individual behavior of the wave components, in regard to the consequences for pro-jecting extreme values.

Idioma originalInglés
Páginas (desde-hasta)873-888
Número de páginas16
PublicaciónJournal of Atmospheric and Oceanic Technology
EstadoPublicada - may. 2020
Publicado de forma externa


Profundice en los temas de investigación de 'Covariate extreme value analysis using wave spectral partitioning'. En conjunto forman una huella única.

Citar esto