Multifractal analysis of air temperature in Brazil

Silva, Hérica Santos da; Stošić, Tatijana

doi:10.1016/j.physa.2020.124333

Cited by 32 publications

(24 citation statements)

References 65 publications

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“…Fine particulate matter pollutants have been of interest for several research endeavors [16][17][18], while [19], for example, used a multifractal analysis on a time series from the European carbon futures markets. Multifractal analysis is often used for temperature analysis like [20] did for air temperature, or [21] for global methane concentrations and remotely-sensed temperature anomalies.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Kojić

Mitić

Dimovski³

et al. 2021

Mathematics

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One of the most challenging endeavors of contemporary research is to describe and analyze the dynamic behavior of time series arising from real-world systems. To address the need for analyzing long-range correlations and multifractal properties of multivariate time series, we generalize the multifractal detrended moving average algorithm (MFDMA) to the multivariate case and propose a multivariate MFDMA algorithm (MV-MFDMA). The validity and performance of the proposed algorithm are tested by conducting numerical simulations on synthetic multivariate monofractal and multifractal time series. The MV-MFDMA algorithm is then utilized to analyze raw, seasonally adjusted, and remainder components of five air pollutant time series. Results from all three cases reveal multifractal properties with persistent long-range correlations.

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Section: Introductionmentioning

confidence: 99%

Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Kojić

Mitić

Dimovski³

et al. 2021

Mathematics

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“…Multifractal detrended fluctuation analysis (MFDFA) was introduced by Kantelhardt et al (2002) and has been widely used to analyze data in physiology, geophysics, ecology, climatology and finances (da Silva et al, 2020;De Benicio et al, 2013;Movahed et al, 2006;Telesca & Toth, 2016;Zorick & Mandelkern, 2013;Zunino et al, 2008).…”

Section: Multifractal Detrended Fluctuation Analysis (Mf-dfa)mentioning

confidence: 99%

Multifractal analysis of rainfall in coastal area in Pernambuco, Brazil

Barreto

Stošić

2021

RSD

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Global warming and climate change are the mayor concerns of scientists, engineers and policy makers because they affect every aspect of nature and human life. Rainfall and air temperature are the most important variables used to detect climate change, through the statistical analysis of set of indices that describe temperature and rainfall extremes. Over the last decades concepts and methods from complex system science were applied in analysis of hydrological data to describe variability of hydrological processes on multiple temporal and spatial scales. In this work we analyzed daily rainfall temporal series in Recife, Brazil (during the period from 1962 to 2019) using Multifractal Detrended Fluctuation analysis (MFDFA) in order to study long term correlations in subsets of small and large rainfall fluctuations. We calculated multifractal parameters (that quantify position of maximum, width and asymmetry of multifractal spectrum) which are related to different properties of rainfall fluctuations. By comparing the values of these parameters for two subperiods of 29 years, we found that after 1990, rainfall dynamics changed towards stronger persistency, weaker multifractality and decreased dominance of small fluctuations.

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“…In this study, a deseasonalized time series z(t) is obtained from a procedure of the original series x(t). e method is performed by adjusting the data with the seasonal mean and standard deviation as in [18,34,35]:…”

Section: Seasonal Detrendingmentioning

confidence: 99%

“…Many case studies indicated that the regional and local climate is a huge and complex and dynamic system that needs strong and appropriate study tools [16][17][18]. Shen et al [19] have demonstrated that climate change has selfmemory and self-similarity characteristics, which are important features of fractal and multifractal time series [19].…”

Section: Introductionmentioning

confidence: 99%

Assessing Nonlinear Dynamics and Trends in Precipitation by Ensemble Empirical Mode Decomposition (EEMD) and Fractal Approach in Benin Republic (West Africa)

et al. 2021

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Climate dynamics and trends have significant environmental and socioeconomic impacts; however, in the Benin Republic, they are generally studied with diverse statistical methods ignoring the nonstationarity, nonlinearity, and self-similarity characteristics contained in precipitation time series. This can lead to erroneous conclusions and an unclear understanding of climatic dynamics. Based on daily precipitation data observed in the six synoptic stations of Benin Republic, in the period from 1951 to 2010, we have proposed (i) determining the local trends of precipitations, (ii) investigating precipitation nonlinear dynamics, and (iii) assessing climatic shift in the study period by Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Fluctuation Analysis (MFDFA) method. To overcome the detrending issue in the standard MFDFA method, the EEMD algorithm is embedded into the MFDFA. The study period is subdivided into three subperiods: 1951–1970, 1971–1990, and 1991–2010. Intrinsic Mode Functions (IMFs) are obtained according to the climatic region in which the stations are located. Results show that precipitation variation is significantly governed by the five first IMFs, in which oscillation periods vary from 1 to 25 days. The trend curves decrease at all the synoptic stations, and their slope values vary accordingly to the subperiods. Referring to the values of the multifractal spectrum parameters, α 0 , and the width of the spectrum w , consistent changes are observed regardless of the subperiods and the concerned stations. The spatial and temporal variability of precipitation indicates that the multifractal properties are good indicators for assessing changes in precipitation dynamics, and the changes in their features could be explained by the global change than by the local climate variation (climatic zones). Despite the observed differences in multifractal spectra properties from the three subperiods, it is not possible to verify the subdivision of 1951–2010 in three subperiods as it is done by previous studies in West Africa. Our findings can be used in the validation of global and regional climate models since a valid model should explain empirically detected scaling properties in observed data.

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Multifractal analysis of air temperature in Brazil

Cited by 32 publications

References 65 publications

Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Multifractal analysis of rainfall in coastal area in Pernambuco, Brazil

Assessing Nonlinear Dynamics and Trends in Precipitation by Ensemble Empirical Mode Decomposition (EEMD) and Fractal Approach in Benin Republic (West Africa)

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