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What One Can Learn From the Cloud Condensation Nuclei (CCN) Size Distributions as Monitored by the BEO Moussala?

机译:从云凝结核(CCN)尺寸分布中可以学习哪个是由Beo Moussala监测的分布?

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In this proceeding we report initial studies into the big data set acquired by the Cloud Condensation Nuclei (CCN) counter of the Basic Environmental Observatory (BEO) Moussala over the whole 2016 year at a frequency of 1 Hz. First, we attempt to reveal correlations between the results for CCN number concentrations on the timescale of a whole year (2016) as averaged over 12 month periods with the meteorological parameters for the same period and with the same time step. Then, we "zoom" into these data and repeat the study on the timescale of a month for two months from 2016 - January and July, with a day time step. For the same two months we show the CCN size distributions averaged over day periods. Finally, we arrive at our main result - typical, in terms of maximal and minimal number concentrations, CCN size distributions for chosen hours, one hour for each month of the year, hence 24 distributions in total. These data show a steady pattern of peaks and valleys independent of the concrete number concentration which moves up and down the number concentrations (y-axis) without significant shifts along the sizes (x-axis).
机译:在此过程中,我们将初步研究报告为基本环境天文台(BEO)Moussala的云冷凝核(CCN)计数器以1 Hz的频率在整个2016年中获得的云冷凝核(BEO)摩西大学柜台获得的大数据集。首先,我们试图揭示CCN数量浓度与全年时间尺度(2016年)的时间浓度之间的相关性,平均同期的气象参数和同一时间步骤。然后,我们将“缩放”进入这些数据,并在2016年1月和7月与每月两个月的时间内测量的研究,每次步骤。对于同一个两个月,我们显示了在日期平均平均的CCN尺寸分布。最后,我们到达我们的主要结果 - 典型的,就最大和最小数量的浓度,CCN尺寸分布为选定的小时,每年一个小时,因此总共24个分布。这些数据显示峰和谷的稳定模式,而无关,其独立于混凝土数浓度,该混凝土数浓度在不显着的沿尺寸(x轴)上移动而没有显着的换档。

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