Abstract
AbstractExtreme wave heights are climate‐related events. Therefore, special attention should be given to the large‐scale weather patterns responsible for wave generation in order to properly understand wave climate variability. We propose a classification of weather patterns to statistically downscale daily significant wave height maxima to a local area of interest. The time‐dependent statistical model obtained here is based on the convolution of the stationary extreme value model associated to each weather type. The interdaily dependence is treated by a climate‐related extremal index. The model's ability to reproduce different time scales (daily, seasonal, and interannual) is presented by means of its application to three locations in the North Atlantic: Mayo (Ireland), La Palma Island, and Coruña (Spain).
Highlights
The need to understand the frequency and intensity of natural hazards and develop resilient, long-term infrastructures has promoted extreme value theory as a relevant discipline for engineering and applied science over the last century
Extreme value theory typically provides a statistical description of the maxima of a stationary process, where stochastic properties are considered constant in time
4. fitting a stationary extreme model (e.g., Generalized Extreme Value (GEV)) on the predictand associated to each weather type, 5. obtaining the extremal index associated to each weather type, 6. performing the convolution of the distribution functions of the weather types to obtain associated return periods, and 7. applying the model to different temporal periods
Summary
The need to understand the frequency and intensity of natural hazards and develop resilient, long-term infrastructures has promoted extreme value theory as a relevant discipline for engineering and applied science over the last century. Longer records result in smaller errors, and the record should be long enough to encompass the range of variability in extremes [Serafin and Ruggiero, 2014] This has prompted the development of probabilistic methods to simulate thousands of estimates of wave climates [Hawkes et al, 2002; Mendez et al, 2006; Callaghan et al, 2008; Menendez et al, 2009; Cannon, 2010; Wahl et al, 2012; Corbella and Stretch, 2013]. The use of a weather-type approach to estimate return values of a variable of interest, in this case significant wave height, allow analysis of extreme wave climate variability at different time scales such as seasons or years and it may open the possibility to explore a multivariate analysis.
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