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

Lin, Guang; Fu, Zuntao

doi:10.1016/j.physa.2007.10.011

Cited by 36 publications

(16 citation statements)

References 19 publications

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“…Several previous reports have indicated the multifractal nature of many atmospheric and terrestrial physical and meteorological records, such as cloud distribution (Schertzer & Lovejoy 1988), wind speed (Kavasseri & Nagarajan 2005, Feng et al 2009), air temperature (Koscielny-Bunde et al 1998, Király & Jánosi 2005, Bartos & Jánosi 2006, Lin & Fu 2008, Yuan et al 2013, ocean temperature (Fraedrich & Blender 2003), ground surface and soil temperature (Jiang et al 2013), precipitation (Deidda 2000, García-Marín et al 2008, de Lima & de Lima 2009, Gemmer et al 2011, Lovejoy et al 2012, and ozone concentration (Jimenez-Hornero et al 2010). Although long-range correlations in air temperature time series have been discussed by a number of authors, there is no consistency in the final conclusions (Maraun et al 2004).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Multifractal analysis of meteorological time series to assess climate impacts

Baranowski¹,

Krzyszczak²,

Sławiński³

et al. 2015

Clim. Res.

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“…This is in agreement with results obtained by Baranowski et al (2015), who analyzed multifractal properties of meteorological time series coming from different climatic zones and noticed large differences in the multifractal spectra and sources of multifractality for series in different climatic zones. Earlier studies (Bartos and Jánosi, 2006;Lin and Fu, 2008;Trnka et al, 2014) also indicated that the analysis of temporal scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice-versa. which should be included in forecasting models.…”

Section: Resultsmentioning

confidence: 99%

Statistical modelling of agrometeorological time series by exponential smoothing

Murat¹,

Malinowska²,

Hoffmann³

et al. 2016

International Agrophysics

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A b s t r a c t. Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.

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“…In general, strongly correlated fractal properties imply a small α RM range and a small f (α RM ) spectrum width. As the difference of fluctuation in time series widens, in both the α RM value and the interval scale in which α RM is observed, the α RM range increases and f (α RM ) attains a finite width (Lévy-Léhel and Vojak, 1998; Lin, 2008; Lin and Sharif, 2010; Lin and Sharif, 2007; Riedi and Scheuring, 1997). Hence, the width of f (α RM ) of two time series measures a different property from the width of f (α) of a single time series; namely, a smaller (larger) f (α RM ) width implies a stronger (weaker) fractal correlation between the time series.…”

Section: Methodsmentioning

confidence: 99%

Integrated Central-Autonomic Multifractal Complexity in the Heart Rate Variability of Healthy Humans

Lin¹,

Sharif²

2012

Front. Physio.

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Purpose of Study: The aim of this study was to characterize the central-autonomic interaction underlying the multifractality in heart rate variability (HRV) of healthy humans. Materials and Methods: Eleven young healthy subjects participated in two separate ~40 min experimental sessions, one in supine (SUP) and one in, head-up-tilt (HUT), upright (UPR) body positions. Surface scalp electroencephalography (EEG) and electrocardiogram (ECG) were collected and fractal correlation of brain and heart rate data was analyzed based on the idea of relative multifractality. The fractal correlation was further examined with the EEG, HRV spectral measures using linear regression of two variables and principal component analysis (PCA) to find clues for the physiological processing underlying the central influence in fractal HRV. Results: We report evidence of a central-autonomic fractal correlation (CAFC) where the HRV multifractal complexity varies significantly with the fractal correlation between the heart rate and brain data (P = 0.003). The linear regression shows significant correlation between CAFC measure and EEG Beta band spectral component (P = 0.01 for SUP and P = 0.002 for UPR positions). There is significant correlation between CAFC measure and HRV LF component in the SUP position (P = 0.04), whereas the correlation with the HRV HF component approaches significance (P = 0.07). The correlation between CAFC measure and HRV spectral measures in the UPR position is weak. The PCA results confirm these findings and further imply multiple physiological processes underlying CAFC, highlighting the importance of the EEG Alpha, Beta band, and the HRV LF, HF spectral measures in the supine position. Discussion and Conclusion: The findings of this work can be summarized into three points: (i) Similar fractal characteristics exist in the brain and heart rate fluctuation and the change toward stronger fractal correlation implies the change toward more complex HRV multifractality. (ii) CAFC is likely contributed by multiple physiological mechanisms, with its central elements mainly derived from the EEG Alpha, Beta band dynamics. (iii) The CAFC in SUP and UPR positions is qualitatively different, with a more predominant central influence in the fractal HRV of the UPR position.

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

Cited by 36 publications

References 19 publications

Multifractal analysis of meteorological time series to assess climate impacts

Multifractal analysis of meteorological time series to assess climate impacts

Statistical modelling of agrometeorological time series by exponential smoothing

Integrated Central-Autonomic Multifractal Complexity in the Heart Rate Variability of Healthy Humans

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