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

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  • Lin, Guangxing
  • Fu, Zuntao

Abstract

The multi-fractal properties of surface air temperature over China are studied through Multi-fractal detrended fluctuation analysis (MF-DFA). It is shown that there indeed exists the multi-fractal phenomenon in daily surface air temperature time series, reflecting a great deal of fluctuations at various time scales. The multi-fractal properties can be characterized very well by a universal generalized binomial multiplicative cascade model with only two parameters a and b. For different stations, the width of singularity spectrum f(α) is different, indicating different strengths of temperature multi-fractal behavior from station to station.

Suggested Citation

  • Lin, Guangxing & Fu, Zuntao, 2008. "A universal model to characterize different multi-fractal behaviors of daily temperature records over China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 573-579.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:2:p:573-579
    DOI: 10.1016/j.physa.2007.10.011
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    Cited by:

    1. da Silva, Hérica Santos & Silva, José Rodrigo Santos & Stosic, Tatijana, 2020. "Multifractal analysis of air temperature in Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    2. Wang, Jian & Huang, Menghao & Wu, Xinpei & Kim, Junseok, 2023. "A local fitting based multifractal detrend fluctuation analysis method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    3. Jiang, Lei & Zhang, Jiping & Liu, Xinwei & Li, Fei, 2016. "Multi-fractal scaling comparison of the Air Temperature and the Surface Temperature over China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 783-792.
    4. Zunino, Luciano & Figliola, Alejandra & Tabak, Benjamin M. & Pérez, Darío G. & Garavaglia, Mario & Rosso, Osvaldo A., 2009. "Multifractal structure in Latin-American market indices," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2331-2340.
    5. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
    6. Jiang, Lei, 2018. "Mean wind speed persistence over China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 211-217.
    7. Gong, Huanhuan & Fu, Zuntao, 2022. "Beyond linear correlation: Strong nonlinear structures in diurnal temperature range variability over southern China," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    8. Lu, Feiyu & Yuan, Naiming & Fu, Zuntao & Mao, Jiangyu, 2012. "Universal scaling behaviors of meteorological variables’ volatility and relations with original records," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4953-4962.

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