Associating daily meteorological variables of a local climate using DCCA, sample entropy, Lévy-index and Hurst–Kolmogorov exponents: a case study

IF 1.9 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Meteorology and Atmospheric Physics Pub Date : 2024-02-12 DOI:10.1007/s00703-024-01006-2
Humberto Millán, Riccardo Biondi, Ramiro Cumbrera, Everaldo Freitas-Guedes
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Abstract

The nonlinear scaling of meteorological processes is an issue of much interest. The objectives of the present work were (a) to investigate cross-correlations between pairs of meteorological time series using different resolutions and (b) to explore the long-range cross-correlations through different scaling exponents. We used 13 years of daily records of rainfall, relative humidity, cloudiness and vapor pressure ranging from January 1st 1996 to December 31st 2009. Data sets were compiled from Veguita agro-meteorological station at Granma province, Cuba. Detrended cross-correlation analysis, multiscale sample entropy, Lévy-stable laws and Hurst–Kolmogorov dynamics were the main methodological and theoretical tools. The detrended cross-correlation coefficient showed significant cross-correlation between rainfall, relative humidity, cloudiness and actual vapor pressure at all investigated time scales. The individual Hurst exponents were in the range 0.62 ≤ H ≤ 0.72 which is consistent with long-range correlated patterns. Bivariate Hurst exponents (Hxy) were larger than the average exponents of the separate processes (Hx and Hy, respectively). The Hurst–Kolmogorov exponents estimated from the climacograms were in the range 0.6 ≤ H ≤ 0.7 (0.603 ≤ β ≤ 0.798) consistent with the values estimated from detrended fluctuation analysis. Each pair of meteorological variables fitted reasonably well bistable distributions with approximately the same Lévy index (α ≅ 0.736). Hurst–Kolmogorov and infinite variance processes are important drivers of the atmospheric dynamics which can explain the persistence of extreme events (droughts) usually observed in the studied region. The multivariate multiscale sample entropy method and multivariate stable distributions could be valuable candidates for describing daily atmospheric processes.

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利用 DCCA、样本熵、Lévy 指数和 Hurst-Kolmogorov 指数关联当地气候的每日气象变量:案例研究
气象过程的非线性缩放是一个备受关注的问题。本研究的目标是:(a) 利用不同的分辨率研究气象时间序列之间的交叉相关性;(b) 通过不同的缩放指数探索长程交叉相关性。我们使用了 1996 年 1 月 1 日至 2009 年 12 月 31 日 13 年的降雨量、相对湿度、云量和水汽压的每日记录。数据集来自古巴格拉玛省的维吉塔农业气象站。主要的方法和理论工具有脱趋势交叉相关分析、多尺度样本熵、Lévy-稳定定律和 Hurst-Kolmogorov 动力学。去趋势交叉相关系数显示,在所有研究的时间尺度上,降雨量、相对湿度、云量和实际水汽压之间都存在显著的交叉相关性。单个赫斯特指数的范围为 0.62 ≤ H ≤ 0.72,这与长程相关模式一致。双变量赫斯特指数(Hxy)大于单独过程的平均指数(分别为 Hx 和 Hy)。根据气候图估算的赫斯特-科尔莫格罗夫指数在 0.6 ≤ H ≤ 0.7(0.603 ≤ β ≤ 0.798)的范围内,与去趋势波动分析估算的值一致。每对气象变量都合理地拟合了双稳态分布,具有大致相同的莱维指数(α ≅ 0.736)。赫斯特-科尔莫戈罗夫过程和无限方差过程是大气动力学的重要驱动力,可以解释研究区域通常观测到的极端事件(干旱)的持续性。多变量多尺度样本熵方法和多变量稳定分布可能是描述日常大气过程的重要候选方法。
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来源期刊
Meteorology and Atmospheric Physics
Meteorology and Atmospheric Physics 地学-气象与大气科学
CiteScore
4.00
自引率
5.00%
发文量
87
审稿时长
6-12 weeks
期刊介绍: Meteorology and Atmospheric Physics accepts original research papers for publication following the recommendations of a review panel. The emphasis lies with the following topic areas: - atmospheric dynamics and general circulation; - synoptic meteorology; - weather systems in specific regions, such as the tropics, the polar caps, the oceans; - atmospheric energetics; - numerical modeling and forecasting; - physical and chemical processes in the atmosphere, including radiation, optical effects, electricity, and atmospheric turbulence and transport processes; - mathematical and statistical techniques applied to meteorological data sets Meteorology and Atmospheric Physics discusses physical and chemical processes - in both clear and cloudy atmospheres - including radiation, optical and electrical effects, precipitation and cloud microphysics.
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