A multifractal description of wind speed records

Kavasseri, Rajesh G.; Nagarajan, Radhakrishnan

doi:10.1016/s0960-0779(04)00533-8

Cited by 98 publications

(46 citation statements)

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“…Several previous reports have indicated the multifractal nature of many atmospheric and terrestrial physical and meteorological records, such as cloud distribution (Schertzer & Lovejoy 1988), wind speed (Kavasseri & Nagarajan 2005, Feng et al 2009), air temperature (Koscielny-Bunde et al 1998, Király & Jánosi 2005, Bartos & Jánosi 2006, Lin & Fu 2008, Yuan et al 2013, ocean temperature (Fraedrich & Blender 2003), ground surface and soil temperature (Jiang et al 2013), precipitation (Deidda 2000, García-Marín et al 2008, de Lima & de Lima 2009, Gemmer et al 2011, Lovejoy et al 2012, and ozone concentration (Jimenez-Hornero et al 2010). Although long-range correlations in air temperature time series have been discussed by a number of authors, there is no consistency in the final conclusions (Maraun et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Multifractal analysis of meteorological time series to assess climate impacts

Baranowski¹,

Krzyszczak²,

Sławiński³

et al. 2015

Clim. Res.

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Agro-meteorological quantities are often in the form of time series, and knowledge about their temporal scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and nonstationarities. The objective of this study was to characterise scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). For this purpose, MFDFA was performed with 11 322 measured time series (31 yr) of daily air temperature, wind velocity, relative air humidity, global radiation and precipitation from stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. In log-log plots of the cumulative distributions of all meteorological parameters the linear functions prevailed for high values of the response, indicating that these distributions were consistent with power-law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent by analysing the corresponding shuffled and surrogate time series. For most of the studied meteorological parameters, the multifractality is due to different long-range correlations for small and large fluctuations. Only for precipitation does the multifractality result mainly from broad probability function. This feature may be especially valuable for assessing the effect of change in climate dynamics.

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Section: Introductionmentioning

confidence: 99%

Multifractal analysis of meteorological time series to assess climate impacts

Baranowski¹,

Krzyszczak²,

Sławiński³

et al. 2015

Clim. Res.

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show abstract

“…To further characterize the air temperature record, we extend the study to include all moments of the full distribution of the fluctuations and adopt different scaling exponents, using a fairly robust and powerful technique called multi-fractal detrended fluctuation analysis (MF-DFA) [8]. This method provides a systematic means to identify and more importantly quantify the multiple scaling exponents in the data [8], and has been successfully utilized in different fields to study multi-fractals [6,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A universal model to characterize different multi-fractal behaviors of daily temperature records over China

Lin

2008

Physica A: Statistical Mechanics and its Applications

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“…(7) is only effective for stationary time series theoretically, because the concepts of the Renyi exponent τ(q) and the singularity spectrum f(α) based on the partition function are merely suitable for the multifractal description of a stationary process. As a result, the extended applications of multifractal spectrum to the nonstationary time series such as the tourists' time sequences and wind speed records [29,33] may incur considerable computational errors, and those numerical results of f(α) are questionable.…”

Section: Numerical Procedures Of Multifractal Detrended Fluctuation Anmentioning

confidence: 99%

“…Thus, MF-DFA as a preferable method of multifractal analysis has been widely applied to multidisciplinary areas, e.g., air temperature shifts, river runoff records, traffic flow series, crude oil price and financial time series, as well as wind speed records, seismic data etc. [33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%