Extreme waves seasonality analysis: An application in the<scp>M</scp>editerranean<scp>S</scp>ea

Sartini, Ludovica; Cassola, Federico; Besio, Giovanni

doi:10.1002/2015jc011061

Cited by 48 publications

(46 citation statements)

References 65 publications

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“…In the following, the peak over threshold (POT) method in the form described for instance in [29][30][31][32][33] was followed to produce a set of extreme significant wave height values. There is here a choice to be made as to which extreme value distribution should be fitted to the data.…”

Section: Methodsmentioning

confidence: 99%

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Dentale

Furcolo

Carratelli

et al. 2018

Water

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Abstract:Estimating the extreme values of significant wave height (H S ), generally described by the H S return period T R function H S (T R ) and by its confidence intervals, is a necessity in many branches of coastal science and engineering. The availability of indirect wave data generated by global and regional wind and wave model chains have brought radical changes to the estimation procedures of such probability distribution-weather and wave modeling systems are routinely run all over the world, and H S time series for each grid point are produced and published after assimilation (analysis) of the ground truth. However, while the sources of such indirect data are numerous, and generally of good quality, many aspects of their procedures are hidden to the users, who cannot evaluate the reliability and the limits of the H S (T R ) deriving from such data. In order to provide a simple engineering tool to evaluate the probability of extreme sea-states as well as the quality of such estimates, we propose here a procedure based on integrating H S time series generated by model chains with those recorded by wave buoys in the same area.

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Section: Methodsmentioning

confidence: 99%

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Dentale

Furcolo

Carratelli

et al. 2018

Water

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“…All these sources are affected by errors in various ways and to different extents: the limitations of models and of satellite altimeters as a source of historical data are obvious and are discussed elsewhere (Ganguly et al 2015;Passaro et al 2015;Sartini et al 2015a;Kudryavtseva and Soomere 2016). In situ wave-meters, when available, are normally the best choice but -as it will be shown in the following -in certain circumstances they are also affected by a strong bias when used to determine extreme values.…”

Section: Introductionmentioning

confidence: 99%

Sampling Bias in the Estimation of Significant Wave Height Extreme Values

Dentale

Reale

D’Alessandro

et al. 2017

Int. Conf. Coastal. Eng.

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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.

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“…Sartini et al, 2015a) and it is known that throughout the basin winter is richer in cyclones and, in turn, in wave storms. However, regional differences are significant.…”

Section: Introductionmentioning

confidence: 99%

Time clustering of wave storms in the Mediterranean Sea

Besio

Briganti

Romano

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. In this contribution we identify storm time clustering in the Mediterranean Sea through a comprehensive analysis of the Allan factor. This parameter is evaluated from a long time series of wave height provided by oceanographic buoy measurements and hindcast reanalysis of the whole basin, spanning the period 1979-2014 and characterized by a horizontal resolution of about 0.1 • in longitude and latitude and a temporal sampling of 1 h (Mentaschi et al., 2015). The nature of the processes highlighted by the AF and the spatial distribution of the parameter are both investigated. Results reveal that the Allan factor follows different curves at two distinct timescales. The range of timescales between 12 h to 50 days is characterized by a departure from the Poisson distribution. For timescales above 50 days, a cyclic Poisson process is identified. The spatial distribution of the Allan factor reveals that the clustering at smaller timescales is present to the north-west of the Mediterranean, while seasonality is observed across the whole basin. This analysis is believed to be important for assessing the local increased flood and coastal erosion risks due to storm clustering.

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Extreme waves seasonality analysis: An application in theMediterraneanSea

Cited by 48 publications

References 65 publications

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Sampling Bias in the Estimation of Significant Wave Height Extreme Values

Time clustering of wave storms in the Mediterranean Sea

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