Multifractal analysis of the time series of daily means of wind speed in complex regions

Abstract

In this paper, we applied the multifractal detrended fluctuation analysis to the daily means of wind speed measured by 119 weather stations distributed over the territory of Switzerland. The analysis was focused on the inner time fluctuations of wind speed, which could be more linked with the local conditions of the highly varying topography of Switzerland. Our findings point out to a persistent behaviour of all the measured wind speed series (indicated by a Hurst exponent significantly larger than 0.5), and to a high multifractality degree indicating a relative dominance of the large fluctuations in the dynamics of wind speed, especially in the Swiss plateau, which is comprised between the Jura and Alp mountain ranges. The study represents a contribution to the understanding of the dynamical mechanisms of wind speed variability in mountainous regions.

Emergence of turbulent epochs in oil prices

“…This is similar to the type of scaling one sees, for instance, in turbulence data [7,8]. Power law type scaling behavior has been observed in many other physical systems also, see for instance [9,10]. See also [11] for a comprehensive overview of such modeling and discussions of various applications and estimation approaches.…”

Investigating the time dynamics of wind speed in complex terrains by using the Fisher–Shannon method

“…Laib et al [18] analyzed the multifractality of the remainder components of wind speed measured by SwissMetNet network and found that the width and the asymmetry of the multifractal spectrum (generally used to quantify the multifractality) showed a certain variability among the measuring sites, likely dependent on the morphology and complex topography of the Swiss territory.…”

“…Although elevation represents an important factor that influences the time dynamics of the wind speed, leading to a larger disorder for wind speed measured at higher stations, topographic features specific to the measuring sites should also be taken into account. In this context, a slope-related parameter used in Laib et al [18] calculated for the stations of the SwissMetNet network the Hurst exponent and mapped it on the Swiss territory ( Fig. 12).…”

“…and µ is the location parameter, λ is the scale parameter and k is the shape [21]. multifractality of wind speed during the same investigation period [18]. Due to the presence of the 24-hour and 12-hour cycles, the daily means of wind speed are analyzed [15].…”

Multifractal processes: Definition, properties and new examples

“…The processes satisfying (1) or some variant of it have received considerable attention in variety of applications like turbulence, finance, climatology, medical imaging, texture classification (see e.g. Mandelbrot et al (1997), Bacry et al (2008), Robert & Vargas (2008), Duchon et al (2012), Lovejoy & Schertzer (2013), Abry et al (2015), Pavlov et al (2016), Kalamaras et al (2017), Laib et al (2018) and the references therein).…”