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首页> 外文期刊>Journal of Geophysical Research, D. Atmospheres: JGR >Assessing lidar-based classification schemes for polar stratospheric clouds based on 16 years of measurements at Esrange, Sweden
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Assessing lidar-based classification schemes for polar stratospheric clouds based on 16 years of measurements at Esrange, Sweden

机译:基于瑞典埃斯兰奇16年的测量结果,评估基于激光雷达的极地平流层云分类方案

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摘要

Lidar measurements of polar stratospheric clouds (PSCs) are commonly analyzed in classification schemes that apply the backscatter ratio and the particle depolarization ratio. This similarity of input data suggests comparable results of different classification schemes-despite measurements being performed with a variety of mostly custom-made instruments. Based on a time series of 16 years of lidar measurements at Esrange (68°N, 21°E), Sweden, we show that PSC classification differs substantially depending on the applied scheme. The discrepancies result from varying threshold values of lidar-derived parameters used to define certain PSC types. The resulting inconsistencies could impact the understanding of long-term PSC observations documented in the literature. We identify two out of seven considered classification schemes that are most likely to give reliable results and should be used in future lidar-based studies. Using polarized backscatter ratios gives the advantage of increased contrast for observations of weakly backscattering and weakly depolarizing particles. Improved confidence in PSC classification can be achieved by a more comprehensive consideration of the effect of measurement uncertainties. The particle depolarization ratio is the key to a reliable identification of different PSC types. Hence, detailed information on the calibration of the polarization-sensitive measurement channels should be provided to assess the findings of a study. Presently, most PSC measurements with lidar are performed at 532 nm only. The information from additional polarization-sensitive measurements in the near infrared could lead to an improved PSC classification. Coincident lidar-based temperature measurements at PSC level might provide useful information for an assessment of PSC classification.
机译:极性平流层云(PSC)的激光雷达测量通常在分类方案中进行分析,这些分类方案采用反向散射比和粒子去极化比。输入数据的这种相似性表明,尽管使用各种大多数定制的仪器进行测量,但不同分类方案的结果可比较。根据瑞典Esrange(68°N,21°E)的16年激光雷达测量的时间序列,我们显示PSC分类根据所应用的方案而有很大不同。差异是由用于定义某些PSC类型的激光雷达衍生参数的阈值变化引起的。由此产生的不一致可能会影响对文献中记录的PSC长期观测的理解。我们在七个考虑到的分类方案中确定了两个,这些分类方案最有可能给出可靠的结果,应该在未来基于激光雷达的研究中使用。使用偏振后向散射比可为观察弱后向散射和弱去极化粒子提供增强的对比度。通过更全面地考虑测量不确定性的影响,可以提高对PSC分类的信心。颗粒去极化率是可靠识别不同PSC类型的关键。因此,应提供有关偏振敏感测量通道校准的详细信息,以评估研究结果。目前,大多数使用激光雷达的PSC测量仅在532 nm处进行。来自近红外中其他偏振敏感测量的信息可能会导致改进的PSC分类。在PSC级别基于激光雷达的同时温度测量可能为评估PSC分类提供有用的信息。

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