DOI: 10.3354/cr01321
Scopus记录号: 2-s2.0-84945539250
论文题名: Multifractal analysis of meteorological time series to assess climate impacts
作者: Baranowski P. ; Krzyszczak J. ; Slawinski C. ; Hoffmann H. ; Kozyra J. ; Nieróbca A. ; Siwek K. ; Gluza A.
刊名: Climate Research
ISSN: 0936577X
出版年: 2015
卷: 65 起始页码: 39
结束页码: 52
语种: 英语
英文关键词: Agro-meteorological parameters
; Multifractal analysis
; Time series
Scopus关键词: agrometeorology
; air temperature
; climate change
; global perspective
; precipitation (climatology)
; relative humidity
; time series
; Finland
; Germany
; Poland
; Spain
英文摘要: 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 performedwith 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. © Inter-Research 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116471
Appears in Collections: 气候减缓与适应
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Recommended Citation:
Baranowski P.,Krzyszczak J.,Slawinski C.,et al. Multifractal analysis of meteorological time series to assess climate impacts[J]. Climate Research,2015-01-01,65