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The multifractal nature of dew point

Author

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  • Tzanis, Chris G.
  • Kalamaras, Nikolaos
  • Philippopoulos, Kostas
  • Deligiorgi, Despina

Abstract

Dew point temperature is a critical air moisture parameter, used widely in the study of climate. In this work, the main multifractal characteristics of dew point temperature are studied using the MF-DFA (Multifractal Detrended Fluctuation Analysis) method. The time series used are extracted from observational records (22 stations located in Greece) and from ECMWF reanalysis dataset. The multifractal spectrum is the basic tool of the study, since significant information about the multifractality can be derived. The results revealed the multifractal nature of all time series and that they exhibit long-range positive correlations. The multifractality of the time series stems mainly from long-range correlations of different fluctuation magnitudes. The spatial distributions of the main multifractal parameters showed their dependency on local topography, weather conditions and the land/sea distribution. Finally, the results from observational and reanalysis data are compared. The basic conclusions about the multifractal characteristics are virtually the same, regardless of whether the time series comes from observations or reanalysis data.

Suggested Citation

  • Tzanis, Chris G. & Kalamaras, Nikolaos & Philippopoulos, Kostas & Deligiorgi, Despina, 2022. "The multifractal nature of dew point," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122005878
    DOI: 10.1016/j.physa.2022.127922
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    References listed on IDEAS

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    1. Kavasseri, Rajesh G. & Nagarajan, Radhakrishnan, 2005. "A multifractal description of wind speed records," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 165-173.
    2. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    3. Kalamaras, N. & Philippopoulos, K. & Deligiorgi, D. & Tzanis, C.G. & Karvounis, G., 2017. "Multifractal scaling properties of daily air temperature time series," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 38-43.
    4. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
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