Analysis of temporal properties of wind extremes

TL;DR

This study investigates the temporal properties and clustering behavior of wind extremes in Switzerland using run theory, revealing that wind extremes are globally clustered but tend to behave as a Poisson process at higher durations, with clustering more pronounced at higher altitudes.

Contribution

It introduces a novel analysis of wind extreme durations as a temporal point process and examines their clustering behavior across different altitudes and durations.

Findings

Wind extreme durations follow a threshold-independent probability density function.

Wind extremes are globally time-clustered but tend to behave as a Poisson process at higher durations.

Clustering of wind extremes is more pronounced at stations above 2000 m a.s.l.

Abstract

The 10-minute average wind speed series recorded at 132 stations distributed rather homogeneously in the territory of Switzerland are investigated. Wind extremes are defined on the base of run theory: fixing a percentile-based threshold of the wind speed distribution, a wind extreme is defined as a sequence of consecutive wind values (or duration of the extreme) above the threshold. This definition allows to analyse the sequence of extremes as a temporal point process marked by the duration of the extremes. The average probability density function of the duration of the extremes of the wind speed measured in Switzerland does not depend on the percentile-based threshold and decrease with the increase of the extreme duration. The time-clustering behaviour of the sequences of the wind extremes was analysed by using the global and local coefficient of variation and the Allan Factor. The wind extremes are globally time-clustered, although they tend to behave as a Poisson process with the increase of the minimum extreme duration. Locally, the wind extremes tend to be clustered for any percentile-based threshold for stations located above about 2,000 m a.s.l. By using the Allan Factor, it was revealed that wind extremes tend to be clustered even at lower timescales especially for the higher stations.