2011
DOI: 10.1088/1742-5468/2011/07/p07001
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Analysis of the time dynamics in wind records by means of multifractal detrended fluctuation analysis and the Fisher–Shannon information plane

Abstract: The time structure of more than 10 years of hourly wind data measured in one site in northern Italy from April 1996 to December 2007 is analysed. The data are recorded by the Sodar Rass system, which measures the speed and the direction of the wind at several heights above the ground level. To investigate the wind speed time series at seven heights above the ground level we used two different approaches: i) the Multifractal Detrended Fluctuation Analysis (MF-DFA), which permits the detection of multifractality… Show more

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Cited by 91 publications
(50 citation statements)
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“…[53] We reanalyzed data of Telesca and Lovallo [2011] for wind time series measured at different heights above the ground surface and deduced the variations of the eddy spectrum parameter a with height, as shown in Figure A1. We note that eddy exposure time distributions (required for estimating a) were obtained from the time difference between two consecutive peak and valley in the wind velocity time series, as described in Seo and Lee [1988].…”
Section: Discussionmentioning
confidence: 99%
“…[53] We reanalyzed data of Telesca and Lovallo [2011] for wind time series measured at different heights above the ground surface and deduced the variations of the eddy spectrum parameter a with height, as shown in Figure A1. We note that eddy exposure time distributions (required for estimating a) were obtained from the time difference between two consecutive peak and valley in the wind velocity time series, as described in Seo and Lee [1988].…”
Section: Discussionmentioning
confidence: 99%
“…Hurst parameters and detrended fluctuation analysis (DFA) [33][34] [35]. We shall now illustrate that the MMSE method allows us to characterize different wind regimes in terms of the underlying dynamical complexity.…”
Section: Complexity Analysis Of Different Wind Regimesmentioning
confidence: 99%
“…It is commonly used to model or analyze scaling properties and local irregularities in signals and in image textures. It has been involved in a large variety of real-world applications of very different natures, ranging from biomedical (heart rate variability [1], neurosciences [2,3]), to physics (turbulence [4]), geophysics (rainfalls [5], wind [6], earthquakes [7]), finance [8,9,10], music [11], or Internet traffic [12], to name but a few.…”
Section: Introductionmentioning
confidence: 99%