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Open AccessArticle

Wavelet Scale Variance Analysis of Wind Extremes in Mountainous Terrains

1
Institute of Methodologies for Environmental Analysis, National Research Council, 85050 Tito (PZ), Italy
2
IDYST, Faculty of Geosciences and Environment, University of Lausanne, CH-1015 Lausanne, Switzerland
3
WSL Institute for Snow and Avalanche Research SLF, 7260 Davos, Switzerland
*
Author to whom correspondence should be addressed.
Energies 2019, 12(16), 3048; https://doi.org/10.3390/en12163048
Received: 12 June 2019 / Revised: 16 July 2019 / Accepted: 2 August 2019 / Published: 7 August 2019
(This article belongs to the Section Wind, Wave and Tidal Energy)
The 10-min average wind speed series recorded at 130 stations distributed rather homogeneously in the territory of Switzerland are investigated. Fixing a percentile-based threshold of the wind speed distribution, a wind extreme is defined as the duration of the sequence of consecutive wind values above the threshold. This definition allows to analyze the sequence of extremes as a temporal point process marked by their duration. Representing the sequence of wind extremes by the inter-extreme interval series, the wavelet variance, a useful tool to investigate the variance of a time series across scales, was applied in order to find a link between the wavelet scales and several topographic parameters. Our findings suggest that the mean duration of wind extremes and mean inter-extreme time are positively correlated and that such relationship depends on the threshold of the wind speed. Furthermore, the threshold of the wind speed distribution correlates best with a terrain parameter related to the Laplacian of terrain elevations; and, in particular, for wavelet scales less than 3, the terrain exposure may explain the formation of extreme wind speeds. View Full-Text
Keywords: wind; extremes; wavelet wind; extremes; wavelet
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MDPI and ACS Style

Telesca, L.; Guignard, F.; Helbig, N.; Kanevski, M. Wavelet Scale Variance Analysis of Wind Extremes in Mountainous Terrains. Energies 2019, 12, 3048.

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