It has been shown before, and it is intuitively evident, that in a Significant Wave Height (SWH) time series, the longer the sampling interval, the lower is the number of events which are above a given threshold value. As a consequence, the use of data with a low time resolution (such as a 3 h sampling, for instance) causes a considerable undervaluation of the extreme SWH values for a given return time RT. In this paper an example of such a bias is provided, and a method is suggested to estimate it on a regional basis. Results may help to improve the use of historical wave meters data which were often collected with a low time resolution, and may also provide a tool to improve the application of Numerical Meteo-Wave models to the evaluation of extremes.
Sampling bias in the estimation of significant wave height extreme values / F. Dentale, F. Reale, F. D'Alessandro, L. Damiani, A. Di Leo, E.P. Carratelli, G.R. Tomasiccchio - In: Proceedings of the Coastal Engineering Conference / [a cura di] P. Lynett. - [s.l] : American Society of Civil Engineers (ASCE), 2016. - ISBN 9780989661133. (( Intervento presentato al 35. convegno International Conference on Coastal Engineering tenutosi a Antalya nel 2016.
Sampling bias in the estimation of significant wave height extreme values
F. D'Alessandro;
2016
Abstract
It has been shown before, and it is intuitively evident, that in a Significant Wave Height (SWH) time series, the longer the sampling interval, the lower is the number of events which are above a given threshold value. As a consequence, the use of data with a low time resolution (such as a 3 h sampling, for instance) causes a considerable undervaluation of the extreme SWH values for a given return time RT. In this paper an example of such a bias is provided, and a method is suggested to estimate it on a regional basis. Results may help to improve the use of historical wave meters data which were often collected with a low time resolution, and may also provide a tool to improve the application of Numerical Meteo-Wave models to the evaluation of extremes.| File | Dimensione | Formato | |
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