Related Experiment Video
Updated: May 12, 2026
08:54
Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
Published on: February 13, 2018
Multifractal analysis of validated wind speed time series.
A P García-Marín1, J Estévez, F J Jiménez-Hornero
1Department of Rural Engineering, University of Cordoba, P.O. Box 3048, 14080 Córdoba, Spain. amanda.garcia@uco.es
Chaos (Woodbury, N.Y.)
|April 6, 2013
Summary
This study analyzed multifractal properties of wind speed data from Cadiz, Spain. Results reveal consistent scaling behavior but highlight variations linked to extreme weather events and zero values.
Area of Science:
- Geophysics
- Atmospheric Science
- Complex Systems
Background:
- Wind energy potential assessment requires understanding wind speed variability across diverse time scales.
- Multifractal analysis offers a robust framework for characterizing complex, non-stationary time series like wind speed data.
Purpose of the Study:
- To investigate the multifractal characteristics of 30-minute wind speed data from six locations in Cadiz, Southern Spain.
- To determine how different time scales and data quality influence multifractal parameters.
- To identify the factors contributing to observed variations in multifractal properties.
Main Methods:
- Data validation using quality control tests to ensure reliability.
- Analysis of wind speed moments to obtain empirical scaling exponent functions, K(q).
- Comparison of multifractal parameters (qcrit, γmax, K(0)) across different locations.
Main Results:
- A consistent critical moment (qcrit) was found across all analyzed locations.
- Variations in other multifractal parameters (γmax, K(0)) were observed between sites.
- Differences were attributed to the presence of extreme wind events and zero wind speed data points.
Conclusions:
- The multifractal analysis provides detailed insights into wind speed dynamics at various time scales.
- Data quality and the occurrence of extreme events significantly impact the multifractal signature of wind speed series.
- Understanding these multifractal properties is crucial for accurate wind resource assessment and modeling.
Related Concept Videos
01:15Discrete Fourier Transform
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
01:17Wald-Wolfowitz Runs Test II
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
01:17Wald-Wolfowitz Runs Test I
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
The test works...
01:10Fast Fourier Transform
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
The computational efficiency of the FFT becomes...
01:11Continuous -time Fourier Transform
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
00:56Noncompartmental Analysis: Statistical Moment Theory
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...