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Shape dependence of snow crystal fall speed

机译:雪晶秋季速度的塑造依赖

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Improved snowfall predictions require accurate knowledge of the properties of ice crystals and snow particles, such as their size, cross-sectional area, shape, and fall speed. The fall speed of ice particles is a critical parameter for the representation of ice clouds and snow in atmospheric numerical models, as it determines the rate of removal of ice from the modelled clouds. Fall speed is also required for snowfall predictions alongside other properties such as ice particle size, cross-sectional area, and shape. For example, shape is important as it strongly influences the scattering properties of these ice particles and thus their response to remote sensing techniques. This work analyzes fall speed as a function of particle size (maximum dimension), cross-sectional area, and shape using ground-based in situ measurements. The measurements for this study were done in Kiruna, Sweden, during the snowfall seasons of 2014 to 2019, using the ground-based in situ instrument Dual Ice Crystal Imager (D-ICI). The resulting data consist of high-resolution images of falling hydrometeors from two viewing geometries that are used to determine particle size (maximum dimension), cross-sectional area, area ratio, orientation, and the fall speed of individual particles. The selected dataset covers sizes from about 0.06 to 3.2?mm and fall speeds from 0.06 to 1.6?m?s ?1 . Relationships between particle size, cross-sectional area, and fall speed are studied for different shapes. The data show in general low correlations to fitted fall speed relationships due to large spread observed in fall speed. After binning the data according to size or cross-sectional area, correlations improve, and we can report reliable parameterizations of fall speed vs. particle size or cross-sectional area for part of the shapes. For most of these shapes, the fall speed is better correlated with cross-sectional area than with particle size. The effects of orientation and area ratio on the fall speed are also studied, and measurements show that vertically oriented particles fall faster on average. However, most particles for which orientation can be defined fall horizontally.
机译:改善的降雪预测需要准确地了解冰晶和雪粒的性质,例如它们的尺寸,横截面积,形状和下降速度。冰颗粒的秋季速度是大气数值模型中冰云和雪的表示的关键参数,因为它决定了从模型云中去除冰的速率。降雪的速度也需要降雪预测,以及冰粒径,横截面积和形状等其他性质。例如,形状很重要,因为它强烈影响这些冰颗粒的散射性质,从而影响它们对遥感技术的响应。该工作分析了逐次速度作为粒度(最大尺寸),横截面积和形状的函数,使用地面基于原位测量。这项研究的测量在2014年至2019年的降雪季节,瑞典在2014年至2019年的基础上的原位仪器双层冰晶成像器(D-ICI)中,在瑞典Kiruna进行了完成。由此产生的数据包括来自用于确定粒度(最大尺寸),横截面积,面积比,取向和单个颗粒的横截面积,方向和落速的两个观察几何图中的高分辨率图像。所选择的数据集盖尺寸为约0.06至3.2?mm,下降速度从0.06到1.6?m?s?1。针对不同形状研究了粒度,横截面积和下降速度之间的关系。由于在秋季速度下观察到的大扩散,数据显示了一般的低相关性以拟合跌倒速度关系。在根据尺寸或横截面积分布数据后,相关性改进,我们可以报告秋季速度与粒径或横截面积的可靠参数,以实现部分形状。对于大多数这些形状,下降速度与横截面积更好地与粒径相比。研究了取向和面积比对秋季速度的影响,测量结果表明,垂直定向的粒子平均下降得更快。然而,可以定义方向的大多数粒子水平落下。

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