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

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  • Guignard, Fabian
  • Lovallo, Michele
  • Laib, Mohamed
  • Golay, Jean
  • Kanevski, Mikhail
  • Helbig, Nora
  • Telesca, Luciano

Abstract

In this paper, the time dynamics of the daily means of wind speed measured in complex mountainous regions are investigated. For 293 measuring stations distributed over all Switzerland, the Fisher information measure and the Shannon entropy power are calculated. The results reveal a clear relationship between the computed measures and both the elevation of the wind stations and the slope of the measuring sites. In particular, the Shannon entropy power and the Fisher information measure have their highest (respectively lowest) values in the Alps, where the time dynamics of wind speed follows a more disordered pattern. The spatial mapping of the calculated quantities allows the identification of two regions, which is in agreement with the topography of the Swiss territory. The present study could contribute to a better characterization of the temporal dynamics of wind speed in complex mountainous terrain.

Suggested Citation

  • Guignard, Fabian & Lovallo, Michele & Laib, Mohamed & Golay, Jean & Kanevski, Mikhail & Helbig, Nora & Telesca, Luciano, 2019. "Investigating the time dynamics of wind speed in complex terrains by using the Fisher–Shannon method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 611-621.
  • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:611-621
    DOI: 10.1016/j.physa.2019.02.048
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    References listed on IDEAS

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    1. Telesca, Luciano & Lovallo, Michele, 2017. "On the performance of Fisher Information Measure and Shannon entropy estimators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 569-576.
    2. Laib, Mohamed & Golay, Jean & Telesca, Luciano & Kanevski, Mikhail, 2018. "Multifractal analysis of the time series of daily means of wind speed in complex regions," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 118-127.
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    4. Telesca, Luciano & Lovallo, Michele & Hsu, Han-Lun & Chen, Chien-Chih, 2011. "Analysis of dynamics in magnetotelluric data by using the Fisher–Shannon method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1350-1355.
    5. Martin, M.T. & Perez, J. & Plastino, A., 2001. "Fisher information and nonlinear dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 291(1), pages 523-532.
    6. Telesca, Luciano & Lovallo, Michele & Ramirez-Rojas, Alejandro & Angulo-Brown, Fernando, 2009. "A nonlinear strategy to reveal seismic precursory signatures in earthquake-related self-potential signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(10), pages 2036-2040.
    7. Telesca, Luciano & Lovallo, Michele & Kanevski, Mikhail, 2016. "Power spectrum and multifractal detrended fluctuation analysis of high-frequency wind measurements in mountainous regions," Applied Energy, Elsevier, vol. 162(C), pages 1052-1061.
    8. Telesca, Luciano & Lovallo, Michele & Chamoli, Ashutosh & Dimri, V.P. & Srivastava, K., 2013. "Fisher–Shannon analysis of seismograms of tsunamigenic and non-tsunamigenic earthquakes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3424-3429.
    9. Nematollahi, Omid & Hoghooghi, Hadi & Rasti, Mehdi & Sedaghat, Ahmad, 2016. "Energy demands and renewable energy resources in the Middle East," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1172-1181.
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    Cited by:

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