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Classification of particle effective shape ratios in cirrus clouds based on the lidar depolarization ratio

机译:基于激光雷达去极化率的卷云中颗粒有效形状比的分类

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摘要

A shape classification technique for cirrus clouds that could be applied to future spaceborne lidars is presented. A ray-tracing code has been developed to simulate backscattered and depolarized lidar signals from cirrus clouds made of hexagonal-based crystals with various compositions and optical depth, taking into account multiple scattering. This code was used first to study the sensitivity of the linear depolarization rate to cloud optical and microphysical properties, then to classify particle shapes in cirrus clouds based on depolarization ratio measurements. As an example this technique has been applied to lidar measurements from 15 mid-latitude cirrus cloud cases taken in Palaiseau, France. Results show a majority of near-unity shape ratios as well as a strong correlation between shape ratios and temperature: The lowest temperatures lead to high shape ratios. The application of this technique to spaceborne measurements would allow a large-scale classification of shape ratios in cirrus clouds, leading to better knowledge of the vertical variability of shapes, their dependence on temperature, and the formation processes of clouds.
机译:提出了一种可用于未来星载激光雷达的卷云形状分类技术。考虑到多重散射,已经开发了一种光线跟踪代码来模拟来自由具有各种成分和光学深度的六方基晶体制成的卷云产生的反向散射和去极化的激光雷达信号。该代码首先用于研究线性去极化率对云光学和微物理性质的敏感性,然后基于去极化率测量对卷云中的颗粒形状进行分类。例如,该技术已应用于法国Palaiseau拍摄的15个中纬卷云案例的激光雷达测量。结果显示出大多数接近统一的形状比率以及形状比率与温度之间的强相关性:最低温度导致较高的形状比率。这项技术在空间测量中的应用将允许对卷云的形状比率进行大规模分类,从而更好地了解形状的垂直变化,它们对温度的依赖性以及云的形成过程。

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