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Dynamical Mechanism in Meteorological Factors Using Detrended Cross-correlation Analysis

机译:趋势互相关分析的气象因素动力机制

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We simulate and analyze the temporal dependence of fine dust particle with sizes less than 10 microns, denoted as PM10, on influencing factors such as temperature, humidity, and wind velocity in eight South Korean cities. We employ the detrended cross-correlation analysis method to extract the overall tendencies of the hourly variations of those dependences. The relationships between PM10 and the meteorological factors are established by using the cross-correlation coefficients. Particularly, we ascertain from a specific interval 3≤ n ≤168 of the hourly time series data that a city in Korea, Andong has the largest cross-correlation coefficient while another city in Korea, Busan, has the smallest value in the correlation between the dust density PM10 and the wind velocity. A city in Korea, Donghae has the largest negative value of the cross-correlation coefficient between PM10and the humidity. We find that the cross-correlation is statistically significant for the hourly time intervals n = 12, 24, and 48 for meteorological time series data.
机译:我们模拟并分析了韩国八个城市中小于10微米(称为PM10)的细尘颗粒对温度,湿度和风速等影响因素的时间依赖性。我们采用去趋势的互相关分析方法来提取这些依赖项每小时变化的总体趋势。通过使用互相关系数来建立PM10与气象因子之间的关系。特别是,我们从每小时时间序列数据的特定间隔3≤n≤168中确定,韩国的一个城市安东的互相关系数最大,而韩国的另一个城市釜山的互相关系数最小。灰尘密度PM10和风速。韩国的东海市,PM10与湿度之间的互相关系数的负值最大。我们发现,对于气象时间序列数据,对于小时时间间隔n = 12、24和48,互相关具有统计学意义。

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