{"title":"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","authors":"Humberto Millán, Riccardo Biondi, Ramiro Cumbrera, Everaldo Freitas-Guedes","doi":"10.1007/s00703-024-01006-2","DOIUrl":null,"url":null,"abstract":"<p>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 ≤ <i>H</i> ≤ 0.72 which is consistent with long-range correlated patterns. Bivariate Hurst exponents (<i>H</i><sub><i>xy</i></sub>) were larger than the average exponents of the separate processes (<i>H</i><sub><i>x</i></sub> and <i>H</i><sub><i>y</i></sub>, respectively). The Hurst–Kolmogorov exponents estimated from the climacograms were in the range 0.6 ≤ <i>H</i> ≤ 0.7 (0.603 ≤ <i>β</i> ≤ 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 (<i>α</i> ≅ 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.</p>","PeriodicalId":51132,"journal":{"name":"Meteorology and Atmospheric Physics","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Associating daily meteorological variables of a local climate using DCCA, sample entropy, Lévy-index and Hurst–Kolmogorov exponents: a case study\",\"authors\":\"Humberto Millán, Riccardo Biondi, Ramiro Cumbrera, Everaldo Freitas-Guedes\",\"doi\":\"10.1007/s00703-024-01006-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 ≤ <i>H</i> ≤ 0.72 which is consistent with long-range correlated patterns. Bivariate Hurst exponents (<i>H</i><sub><i>xy</i></sub>) were larger than the average exponents of the separate processes (<i>H</i><sub><i>x</i></sub> and <i>H</i><sub><i>y</i></sub>, respectively). The Hurst–Kolmogorov exponents estimated from the climacograms were in the range 0.6 ≤ <i>H</i> ≤ 0.7 (0.603 ≤ <i>β</i> ≤ 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 (<i>α</i> ≅ 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.</p>\",\"PeriodicalId\":51132,\"journal\":{\"name\":\"Meteorology and Atmospheric Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Meteorology and Atmospheric Physics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s00703-024-01006-2\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meteorology and Atmospheric Physics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s00703-024-01006-2","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Associating daily meteorological variables of a local climate using DCCA, sample entropy, Lévy-index and Hurst–Kolmogorov exponents: a case study
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.
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.