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Clouds over Hyyti?l?, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements

机译:Hyyti的云彩?L?,芬兰:一种基于太阳辐射和云基高度测量来分类云的算法

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We developed a simple algorithm to classify clouds based on global radiation and cloud base height measured by pyranometer and ceilometer, respectively. We separated clouds into seven different classes (stratus, stratocumulus, cumulus, nimbostratus, altocumulus + altostratus, cirrus + cirrocumulus + cirrostratus and clear sky + cirrus). We also included classes for cumulus and cirrus clouds causing global radiation enhancement, and we classified multilayered clouds, when captured by the ceilometer, based on their height and characteristics (transmittance, patchiness and uniformity). The overall performance of the algorithm was nearly 70 % when compared with classification by an observer using total-sky images. The performance was best for clouds having well-distinguishable effects on solar radiation: nimbostratus clouds were classified correctly in 100 % of the cases. The worst performance corresponds to cirriform clouds (50 %). Although the overall performance of the algorithm was good, it is likely to miss the occurrences of high and multilayered clouds. This is due to the technical limits of the instrumentation: the vertical detection range of the ceilometer and occultation of the laser pulse by the lowest cloud layer. We examined the use of clearness index, which is defined as a ratio between measured global radiation and modeled radiation at the top of the atmosphere, as an indicator of clear-sky conditions. Our results show that cumulus, altocumulus, altostratus and cirriform clouds can be present when the index indicates clear-sky conditions. Those conditions have previously been associated with enhanced aerosol formation under clear skies. This is an important finding especially in the case of low clouds coupled to the surface, which can influence aerosol population via aerosol–cloud interactions. Overall, caution is required when the clearness index is used in the analysis of processes affected by partitioning of radiation by clouds.
机译:我们开发了一种简单的算法,分别基于由绘制计和CeiLometer测量的全局辐射和云底高度来分类云。我们将云分离成七种不同的课程(层状,划分,巨大,Nimbostratus,Altocumulus + Altostratus,CiRrus + Cirrocumulus + Cirrostratus和Clear Sky + Cirrus)。我们还包括云Ulus和卷云云的课程,导致全球辐射增强,我们根据其高度和特征(透射率,斑块和均匀性)被Ceilometer捕获,我们分类多层云。与使用总天空图像的观察者的分类相比,算法的整体性能接近70%。性能最适合对太阳辐射具有良好区分的云:Nimbostratus云在100%的情况下正确分类。最糟糕的性能对应于岩体云(50%)。虽然算法的整体性能良好,但很可能会错过高和多层云的出现。这是由于仪器的技术限制:Ceirometer的垂直检测范围和最低云层激光脉冲的清层。我们检查了使用透明度指数,该透明度指数被定义为测量的全球辐射与大气顶部的模型辐射之间的比率,作为清晰天空条件的指标。我们的结果表明,当索引表示清晰天空条件时,云Ulus,高度,Altostratus和螺旋云可以存在。这些条件以前在晴天下与增强的气溶胶形成有关。这是一个重要的发现,特别是在耦合到表面的低云的情况下,这可以通过气溶胶云相互作用影响气溶胶群。总的来说,当透明度指数用于分析受云分配辐射的过程分析时,需要注意。

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