Journal ArticleDOI
Indication of a Universal Persistence Law Governing Atmospheric Variability
E. Koscielny-Bunde,Armin Bunde,Shlomo Havlin,H. Eduardo Roman,Yair Goldreich,Hans Joachim Schellnhuber +5 more
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TLDR
In this paper, the authors studied the temporal correlations in the atmospheric variability by 14 meteorological stations around the globe, the variations of the daily maximum temperatures from their average values, and found that the persistence, characterized by the correlation C(s) of temperature variations separated by s days, approximately decays.Abstract:
We study the temporal correlations in the atmospheric variability by 14 meteorological stations around the globe, the variations of the daily maximum temperatures from their average values. We apply several methods that can systematically overcome possible nonstationarities in the data. We find that the persistence, characterized by the correlation C(s) of temperature variations separated by s days, approximately decays $C(s)\ensuremath{\sim}{s}^{\ensuremath{-}\ensuremath{\gamma}}$, with roughly the same exponent $\ensuremath{\gamma}\ensuremath{\cong}0.7$ for all stations considered. The range of this universal persistence law seems to exceed one decade, and is possibly even larger than the range of the temperature series considered.read more
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Journal ArticleDOI
Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series
Jan W. Kantelhardt,Jan W. Kantelhardt,Stephan Zschiegner,Eva Koscielny-Bunde,Eva Koscielny-Bunde,Shlomo Havlin,Shlomo Havlin,Armin Bunde,H. Eugene Stanley +8 more
TL;DR: In this article, the authors developed a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA).
Journal ArticleDOI
Detecting long-range correlations with detrended fluctuation analysis
TL;DR: It is shown that deviations from scaling which appear at small time scales become stronger in higher orders of detrended fluctuation analysis, and a modified DFA method is suggested to remove them.
Journal ArticleDOI
Effect of trends on detrended fluctuation analysis.
TL;DR: It is shown how to use DFA appropriately to minimize the effects of trends, how to recognize if a crossover indicates indeed a transition from one type to a different type of underlying correlation, or if the crossover is due to a trend without any transition in the dynamical properties of the noise.
Journal ArticleDOI
Effect of nonstationarities on detrended fluctuation analysis.
TL;DR: In this article, the effects of three types of non-stationarities often encountered in real data were studied. And the authors compared the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types and showed how the characteristics of these crossovers depend on the fraction and size of the parts cut out from the signal, the concentration of spikes and their amplitudes.
Effect of Nonstationarities on Detrended Fluctuation Analysis
TL;DR: It is found that introducing nonstationarities to stationary correlated signals leads to the appearance of crossovers in the scaling behavior and it is shown how to develop strategies for preprocessing "raw" data prior to analysis, which will minimize the effects of non stationarities on the scaling properties of the data.
References
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Book
Fractal Concepts in Surface Growth
TL;DR: The first chapter of this important new text is available on the Cambridge Worldwide Web server: http://www.cup.cam.ac.uk/onlinepubs/Textbooks/textbookstop.html as discussed by the authors.
BookDOI
Climate trend atlas of Europe based on observations, 1891-1990
TL;DR: In this article, the authors present a method of analysis and reliability problems for upper-air data and present a list of station and related homogeneity properties of Spectral Variance.