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A case study of stratus cloud base height multifractal fluctuations


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  • Ivanova, K.
  • Shirer, H.N.
  • Clothiaux, E.E.
  • Kitova, N.
  • Mikhalev, M.A.
  • Ackerman, T.P.
  • Ausloos, M.


The complex structure of a typical stratus cloud base height (or profile) time series is analyzed with respect to the variability of its fluctuations and their correlations at all experimentally observed temporal scales. Due to the underlying processes that create these time series, they are expected to have multiscaling properties. For obtaining reliable measures of these scaling properties, different methods of statistical analysis are used herein: power spectral density, detrended fluctuation analysis, and multifractal analysis. This broad set of diagnostic techniques is applied to a typical stratus cloud base height (CBH) data set; data were obtained from the Southern Great Plains site of the Atmospheric Radiation Measurement Program of the Department of Energy from a Belfort Laser Ceilometer. First, we demonstrate that this CBH time series is a nonstationary signal with stationary increments. Further, two scaling regimes are found, although the characteristic laws are quite similar ones. Next, the multi-affine scaling properties are confirmed. The scaling properties of the cloud base height profile of such a continental stratus are found to be similar to those of the marine cloud base height profiles studied by us previously. Some physical interpretation in terms of anomalous diffusion (or fractional random walk) is given for the continental case.

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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 308 (2002)
Issue (Month): 1 ()
Pages: 518-532

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Handle: RePEc:eee:phsmap:v:308:y:2002:i:1:p:518-532

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Keywords: Stratus cloud; Cloud base height; Fluctuations; Correlations; Power spectrum; Detrended fluctuation analysis; Multifractals;


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Cited by:
  1. Suárez-García, Pablo & Gómez-Ullate, David, 2014. "Multifractality and long memory of a financial index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 226-234.
  2. Eder Lucio Fonseca & Fernando F. Ferreira & Paulsamy Muruganandam & Hilda A. Cerdeira, 2012. "Identifying financial crises in real time," Papers 1204.3136,, revised Nov 2012.
  3. Cook, Steven, 2007. "A threshold cointegration test with increased power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(6), pages 386-392.
  4. da Fonseca, Eder Lucio & Ferreira, Fernando F. & Muruganandam, Paulsamy & Cerdeira, Hilda A., 2013. "Identifying financial crises in real time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1386-1392.


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