A probabilistic analysis of wind gusts using extreme value statistics

Friederichs, Petra; Göber, Martin; Bentzien, Sabrina; Lenz, Anne; Krampitz, Rebekka

doi:10.1127/0941-2948/2009/0413

Cited by 43 publications

(36 citation statements)

References 31 publications

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“…These approaches include multiple linear regression (Wigley et al, 1990;Huth, 2002), kriging (Biau et al, 1999), neural networks (Snell et al, 2000;Cannon and Whitfield, 2002;Hewitson and Crane, 2002), logistic regression or quantile regression (Koenker and Bassett, 1978). Friederichs et al (2009) reviewed the performance of different regression approaches in modeling wind gust observations, while Vrac et al (2007b) and Salameh et al (2009) discussed a nonlinear extension of these approaches using GAM models.…”

Section: Downscaling Methodologiesmentioning

confidence: 99%

Extreme events: dynamics, statistics and prediction

Ghil

Yiou

Hallegatte

et al. 2011

Nonlin. Processes Geophys.

234

167

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Abstract. We review work on extreme events, their causes and consequences, by a group of European and American researchers involved in a three-year project on these topics. The review covers theoretical aspects of time series analysis and of extreme value theory, as well as of the deterministic modeling of extreme events, via continuous and discrete dynamic models. The applications include climatic, seismic and socio-economic events, along with their prediction.Two important results refer to (i) the complementarity of spectral analysis of a time series in terms of the continuous and the discrete part of its power spectrum; and (ii) the need for coupled modeling of natural and socio-economic systems. Both these results have implications for the study and prediction of natural hazards and their human impacts.

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Section: Downscaling Methodologiesmentioning

confidence: 99%

Extreme events: dynamics, statistics and prediction

Ghil

Yiou

Hallegatte

et al. 2011

Nonlin. Processes Geophys.

234

167

View full text Add to dashboard Cite

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“…In the United States, Orwig and Schroeder (2007) calculated a factor of 1.5 triggered by a rear-flank downdraft of a supercell and a value of 3.8 during a derecho. Regarding return values, Friederichs et al (2009) have quantified the probability of wind gusts to exceeding a certain warning level in Germany, but they did not differentiate between convective and turbulent gusts. However, as already shown by Gomes and Vickery (1978), , and Holmes (2002), both gust forms have very different exceedance probability curves and thus have to be separated for proper extreme value analysis.…”

Section: Introductionmentioning

confidence: 99%

Statistical characteristics of convective wind gusts in Germany

Mohr

Kunz

Richter

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Due to the small-scale and non-stationary nature of the convective wind gusts usually associated with thunderstorms, there is a considerable lack of knowledge regarding their characteristics and statistics. In an effort to remedy this situation, we investigated in this study a set of 110 climate stations of the German Weather Service between 1992 and 2014 to analyze the temporal and spatial distribution, intensity, and occurrence probability of convective gusts.Similar to thunderstorm activity, the frequency of convective gusts decreases gradually from southern to northern Germany. No further spatial structures, such as a relation to orography or climate conditions, can be identified regarding their strength or likelihood. Rather, high wind speeds of above 30 m s −1 can be expected everywhere in Germany with almost similar occurrence probabilities. A comparison of the 20-year return values of convective gusts with those of turbulent gusts demonstrates that the latter have higher frequencies, especially in northern Germany. However, for higher return periods, this effect can be reversed at some stations.The values of the convective gust factors are mainly in a range between 1 and 4 but can even reach values up to 10. Besides the dependency from the averaging time period of the mean wind, the values of the gust factors additionally depend on the event duration and the storm type, respectively.

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“…Begueria, 2005;Freiderichs et al, 2009). This approach is also known as a 'Peaks-Over-Threshold' (POT) or partial duration series approach.…”

Section: Datamentioning

confidence: 99%