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首页> 外文期刊>Atmospheric research >Development of a cloud-screening algorithm for direct and diffuse AODs from the Skyradiometer Network
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Development of a cloud-screening algorithm for direct and diffuse AODs from the Skyradiometer Network

机译：从滑雪仪网络直接和漫射AODS的云筛选算法的开发

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We developed a cloud-screening algorithm for direct and diffuse aerosol optical depths (AODs) from the Skyradiometer Network (CSDD). Variabilities of direct AODs were checked not only to screen cloud-contaminated (cloudy) data effectively but also to make direct AODs available for aerosol study, as in the Aerosol Robotic Network (AERONET). Skyradiometer data were used from the Seoul National University (SNU) site for three years from 2012 to 2014. CSDD tested the spectral and temporal variabilities according to the Angstrom exponent at the first stage, and the temporal smoothness in the form of the coefficient of variation at the second stage. The algorithm for CSDD was constructed to minimize the differences with optical properties of cloudy data (and clear-sky data as well) based on the cloud cover from the synoptic station. A number of cloudy data that was not screened by the previous algorithms was removed, and the absolute value of the bias total could be substantially lowered. The performances of algorithms for cloud detection were also examined using lidar observations at the study site in terms of accuracy, probability of detection (POD), and false detection rate. The statistics of cloud detection for CSDD were generally comparable to those of AERONET for direct data, and the POD for diffuse data was improved to the level of direct data.
机译：我们开发了一种用于直接和漫射气雾光学深度（AODS）的云筛选算法（CSDD）。检查直接AODS的可变性不仅要有效地筛选云污染的（多云）数据，而且还可以使可用于气溶胶研究的直接AOD，如气溶胶机器人网络（AERONET）。从2012年到2014年首尔国立大学（SNU）网站使用了SkyRadiometer数据.CSDD根据第一阶段的埃克斯特罗姆指数测试了频谱和时间变量，以及变异系数形式的时间平滑度在第二阶段。构建了CSDD的算法，以最小化基于来自Synoptic站的云覆盖的多云数据（和清晰天空数据）的光学特性的差异。未被先前算法未筛选的许多阴天数据，并且可以基本上降低偏置总量的绝对值。还在准确性，检测概率（POD）和假检测率方面，在研究现场使用LIDAR观测检查云检测算法的性能。 CSDD的云检测的统计数据通常与用于直接数据的机动机构的统计数据，并且用于漫反射数据的POD得到改善到直接数据的水平。

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