Multifractal Detrended Fluctuation Analysis of Temperature Reanalysis Data over Greece

Abstract

The Multifractal Detrended Fluctuation Analysis (MF-DFA) is used to examine the scaling behavior and the multifractal characteristics of the mean daily temperature time series of the ERA-Interim reanalysis data for a domain centered over Greece. The results showed that the time series from all grid points exhibit the same behavior: they have a positive long-term correlation and their multifractal structure is insensitive to local fluctuations with a large magnitude. Special emphasis was given to the spatial distribution of the main characteristics of the multifractal spectrum: the value of the Hölder exponent, the spectral width, the asymmetry, and the truncation type of the spectra. The most interesting finding is that the spatial distribution of almost all spectral parameters is decisively determined by the land-sea distribution. The results could be useful in climate research for examining the reproducibility of the nonlinear dynamics of reanalysis datasets and model outputs.

A New Approach to Predict Potential Sleep Disorder based on Fractal Analysis from Non-overlapping Single Lead ECG Using Support Vector Machine

“…The feature extraction from electroencephalogram (EEG) signals time series [23]. The time series analysis of daily temperature [24]. The leaf image feature analysis using multifractal analysis [25].…”

Investigating the Implications of COVID-19 on PM2.5 in Pakistan

“…Short-term time series of air quality can identify such characteristics (or signals) in medium and long-term time series for air pollutants, since time series of air pollutants intrinsically characterize as having long-range dependence, fractality (self-similarity), persistence and scale-invariance (Taqqu et al, 1995;Lee, 2002;Lee et al, 2003b;Shi et al, 2009). Since the concept popularized three decades ago (Mandelbrot, 1982), studies have successfully applied fractal analysis (mono-fractality and multi-fractality) to time series of financial markets (Mandelbrot et al, 1997;Kumar and Deo, 2009;Wang et al, 2010), natural sciences (Udovichenko and Strizhak, 2002;Makowiec et al, 2009;Philippopoulos et al, 2019), behavioral sciences (Drożdż et al, 2010;Ihlen and Vereijken, 2013) and air pollutants (Lee and Lin, 2008;Meraz et al, 2015;Stan et al, 2020).…”

Multi-fractal Behaviors of long term daily relative humidity and temperature observed over Benin synoptic stations (West Africa)

“…Researchers have explained these findings by the fact that the atmosphere and the natural processes taking place in it are a complex natural system (Kantz and Schreiber, 2004;Peters and Neelin, 2006;Vassoler and Zebende, 2012;Shi, 2014). Therefore, meteorological parameters are characterized by a high degree of nonlinearity, non-stationarity and complexity (He, 2015;Agbazo et al, 2019a;Philippopoulos et al, 2019;Kalamaras et al, 2017Kalamaras et al, , 2019Jiang et al, 2016;Burgueño et al, 2014 andDong et al, 2016). In this context, multifractal methods are suitable to analyze processes that obey to nonlinearity characteristics.…”

“…For example, recently, MFDFA has been applied to time series of: precipitation by Efstathiou and Varotsos (2012); Agbazo et al (2019a); temperature by Kalamaras et al (2017); Kalamaras et al (2019); Jiang et al (2016); Burgueño et al (2014); Dong et al (2016); wind speed by Kavasseri and Nagarajan (2005);Feng et al (2009), and many others. The authors found that MFDFA methods can help reveal some properties, which could not be detected by linear methods (Kantelhardt et al, 2002;Kalamaras et al, 2017Kalamaras et al, , 2019Philippopoulos et al, 2019;Jiang et al, 2016).…”