Multifractal characterization of global temperature anomalies

Mali, Provash

doi:10.1007/s00704-014-1268-y

Cited by 47 publications

(39 citation statements)

References 34 publications

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“…If the multifractality source is the long-range correlation, the shuffled series exhibits non-multifractal scaling, since the shuffling of time series destroys the longrange correlation. In contrast, if the multifractality in the time series is due to a broad probability density function, the spectra obtained for the AAFT surrogate data indicate no multifractality (Min et al 2013, Mali 2014. To better compare the impact of a broad probability density function on multifractality, we applied the procedure (Clauset et al 2009) based on the maximum likelihood method and the KolmogorovSmir nov statistic.…”

Section: Data Analysesmentioning

confidence: 99%

Multifractal analysis of meteorological time series to assess climate impacts

Baranowski¹,

Krzyszczak²,

Sławiński³

et al. 2015

Clim. Res.

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Agro-meteorological quantities are often in the form of time series, and knowledge about their temporal scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and nonstationarities. The objective of this study was to characterise scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). For this purpose, MFDFA was performed with 11 322 measured time series (31 yr) of daily air temperature, wind velocity, relative air humidity, global radiation and precipitation from stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. In log-log plots of the cumulative distributions of all meteorological parameters the linear functions prevailed for high values of the response, indicating that these distributions were consistent with power-law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent by analysing the corresponding shuffled and surrogate time series. For most of the studied meteorological parameters, the multifractality is due to different long-range correlations for small and large fluctuations. Only for precipitation does the multifractality result mainly from broad probability function. This feature may be especially valuable for assessing the effect of change in climate dynamics.

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Section: Data Analysesmentioning

confidence: 99%

Multifractal analysis of meteorological time series to assess climate impacts

Baranowski¹,

Krzyszczak²,

Sławiński³

et al. 2015

Clim. Res.

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“…MF-DFA has successfully been applied to fields as diverse as finance and stock markets [43][44][45], seismicity [46][47][48], mineral grade detection [49], climate change [50][51][52][53][54], traffic flow [55,56], speech signal characteristics [57], plant species identification [58], air pollution [59,60], and heart rate dynamics [61]. It is very valuable to introduce this method into the analysis of shoulder-lines.…”

Section: Introductionmentioning

confidence: 99%

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Cao

et al. 2017

IJGI

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Abstract:The Loess Plateau in China is internationally known for its unique geographical features and has therefore been studied by many researchers. This research exploits the regional differences in the spatial morphological characteristics of Loess shoulder lines in different landform types as an important basis for geomorphological regionalization. In this study, we used ensemble empirical mode decomposition (EEMD), multi-fractal detrended fluctuation analysis (MF-DFA), and detrended cross-correlation analysis (DCCA) to analyze topographic data series extracted from shoulder lines. Loess shoulder-line land variations series data from the Suide, Ganquan, and Chunhua areas on the Loess Plateau were selected and a combination of the two above-mentioned methods was used to study land variations at these three sample sites. The results revealed differences in the topographic variations of the multi-fractal characteristics and the topographic spatial variation in the Loess shoulder line of the three sample sites. Furthermore, the extent to which the results were affected by noise and the analysis scale differed among the three areas.

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“…Kantelhardt et al [21] have extended the DFA method for nonstationary and multifractal series, and the generalized DFA also known as the multifractal DFA (MF-DFA) method, is a robust and powerful technique for the verification of multifractal behavior of time series data. The MF-DFA technique has so far been applied to various time series data, such as stock markets [22,23], foreign exchange markets [24,25], geophysical time series [26,27], and medicine [28,29]. Obviously, the spectrum of references on the application of MF-DFA is not a complete one.…”

Section: Introductionmentioning

confidence: 99%