Multifractal analysis of meteorological time series to assess climate impacts
Multifractal analysis of meteorological time series to assess climate impacts
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DOI：
10.3354/cr01321
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发表时间：
2015-01-01
期刊：
影响因子：
1.1
通讯作者：
Gluza, Andrzej
中科院分区：
文献类型：
--
作者：
Baranowski, Piotr;Krzyszczak, Jaromir;Gluza, Andrzej
农业气象量往往是时间序列的形式，关于其时间尺度特性的知识是将当地测量的波动转换为更大尺度的基础，反之亦然。然而，这些量的标度分析是复杂的，由于存在本地化的趋势和nonstationarities。本研究的目的是通过多重分形去趋势波动分析（MFDFA）来验证所选农业气象量的标度特性（即统计自相似性）。为此，MFDFA进行了11 322日的空气温度，风速，相对空气湿度，全球辐射和降水量的时间序列（31年），从位于芬兰，德国，波兰和西班牙的站。经验奇异谱显示了它们的多重分形结构。研究的多重分形的丰富性进行了评估，其频谱的宽度，表明相当大的差异，在动态和发展。在所有气象参数的累积分布的对数-对数图中，线性函数对于响应的高值占上风，表明这些分布与幂律渐近行为一致。此外，我们研究了类型的多重分形的基础上的q-依赖的广义赫斯特指数通过分析相应的洗牌和替代时间序列。对于大多数研究的气象参数，多重分形是由于不同的长期相关性的小和大的波动。只有降水的多重分形主要由宽概率函数产生。这一特点对于评估气候动态变化的影响可能特别有价值。
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.