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Shape Recognition of DMT Airborne Cloud Particle Images and Its Application
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摘要: 利用机载云粒子探测设备入云进行观测是目前获取云粒子微物理特征最直接有效的手段。国内已有多家单位引进美国DMT（Droplet Measurement Technologies）公司的云粒子图像探头CIP（cloud imaging probe）。由于其配套软件不能输出逐个粒子的详细信息,在很大程度上限制了对云粒子图像探测数据的深入挖掘和分析。基于解析粒子图像原始数据,对粒子图像数据进行质量控制,并根据粒子形状几何特征将粒子形状分为8类（微小、线状、聚合状、霰状、球状、板状、枝状和不规则状）。利用2018年12月—2019年3月河南省3次冬季航测获取的灰度CIP探测数据,分析云粒子形状及各形状粒子面积的统计特征,并对比基于不同形状粒子的质量-尺度关系与将所有粒子视作球形液滴计算所得的粒子水凝物含量,发现后者超过前者约1个量级。Abstract: Currently, the most direct and effective way to obtain cloud precipitation microphysical characteristics is from in-situ measurements acquired by airborne imaging probes. There are many studies based on CIP (cloud imaging probe) and PIP (precipitation imaging probe) detection data, which are mostly based on the limited output from the software PADS (Particle Analysis and Display System) provided by DMT (Droplet Measurement Technologies). Since PADS only outputs the second-by-second statistical results rather than the detailed particle-by-particle information, it greatly limits the deep mining and analyzing of cloud particle image data. Besides, particle shapes in previous studies are mainly classified through naked-eye observations, which is time consuming, subjective, and unreliable to conduct statistical analysis on thousands of cloud particle images. Therefore, it is impossible to calculate the hydrometeor content based on mass-dimension relationships for particles of different shapes in ice or mixed cloud observations.The operation principle of airborne two-dimensional optical array probes is introduced. Then techniques of recognition and elimination of shattering particles and fake particles are illustrated in detail. Particle shapes are divided into 8 types (tiny, linear, aggregated, graupel, spherical, plate, dendritic and irregular) based on geometric characteristics of particle shapes. Statistical characteristics of different cloud particle shapes and their areas are analyzed by using gray CIP data detected in three wintertime stratiform clouds in Henan Province. The recognition of particle shapes are basically consistent with results through naked-eye observations, and also consistent with dominant particle shapes in each temperature range obtained by previous studies. The hydrometeor content obtained using the mass-dimension relationship for particles of different shapes is compared with that from treating all particles as spherical liquid particles (i.e., the algorithm used by PADS). It is found that when all particles are treated as spherical liquid particles, the hydrometeor content is roughly one magnitude higher than that from considering different particle shapes, indicating the technique of particle shape classification can improve the accuracy of hydrometeor content in ice or mixed clouds. In addition, some matters needing attention in the use of two-dimensional particle image data are pointed out to ensure proper use of two-dimensional particle image data.
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Key words:
- cloud particle;
- particle image;
- particle shape;
- hydrometeor content
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图 1 粒子通过光电二极管阵列时形成粒子图像的原理图
Fig. 1 Schematic diagram for the image produced as particles passing across optical array
图 2 CIP相邻粒子间隔时间和间隔距离(a)及相邻粒子间隔时间分布(b)
Fig. 2 The interval time and distance between adjacent particles detected by CIP(a) and the histogram of the interval time between adjacent particles(b)
图 3 利用相邻粒子时间间隔阈值法剔除破碎粒子示例
(红色矩形所标记粒子为破碎粒子,灰色竖线用以分割不同粒子)
Fig. 3 Typical shattering particles eliminated by the inter-arrival time method
(particles marked in red rectangles are shattering particles, gray vertical lines are used to separate different particles)
图 4 粒子本身存在离散点的原始图像(a)及剔除离散点后的图像(b)
(红圈所示)
Fig. 4 The original particle image with discrete points(a) and the image after removing the discrete points(b)
图 5 在二极管阵列方向或飞行方向上只有1个像素的噪点(a)及条状粒子(b)
Fig. 5 Examples of noisy points(a) and streaking particles(b) with only one pixel in the direction of diode array or flight
图 8 各形状粒子出现频率、平均面积及不同个例典型粒子图像
Fig. 8 The occurrence frequency, average area of each shape of particles and typical particle images of three cases
图 9 根据不同形状粒子的质量公式所得水凝物含量及将所有粒子视作球形液滴所得水凝物含量
Fig. 9 The hydrometeor content obtained according to the mass formulas for particles of different shapes and when all particles treated as spherical liquid droplets
表 1 粒子形状判别流程
Table 1 The decision procedure of particle shape classification
步骤 原判别条件[26] 本文判别条件 粒子形状 ① a<25 a<23 微小 ② r2≥0.4或(d<64且Dx≥4Dy或Dy≥4Dx) r2≥0.4或(d<64且d≥4w) 线状 ③ d>160 d>100 聚合状 ④ S≥0.7 S≥0.7 霰状 ⑤ d≥64且F≤13 d≥51且F≤9 霰状 d≥64且F>13 d≥51且F>9 聚合状 ⑥ F≤5.5 F≤5.5 球状 ⑦ F＜10且d≥32 F＜10且d≥32 霰状 F＜10且d<32 F＜10且d<32 板状 ⑧ F<16或Dx≤7 F<16或Dx≤7 不规则状 其余粒子 其余粒子 枝状 
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表 2 分析时段的个例情况
Table 2 Information of cases during the analysis period
个例 时段 探测高度/m 温度/℃ 20181210 17:14—17:37 2100 -7 20190108 22:58—23:46 3600 -10~-8 20190226 15:36—15:48 4200 -17
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表 3 统计结果
Table 3 Statistical results
粒子形状 个例20181210 个例20190108 个例20190226 频率/% 平均面积/(105 μm2) 频率/% 平均面积/(105 μm2) 频率/% 平均面积/(105 μm2) 微小 21.17 0.0846 8.52 0.0819 6.62 0.0684 线状 1.52 1.1014 14.33 0.9684 3.94 1.4017 聚合状 0.01 6.2185 0.45 5.5009 2.73 6.3862 霰状 0.46 3.3971 2.84 4.0957 15.12 3.8128 球状 65.76 0.2833 22.72 0.5188 13.04 0.7995 板状 9.84 0.6050 46.84 0.6802 43.28 1.3404 不规则状 1.24 0.7487 3.81 1.2645 14.22 2.8251 枝状 0.01 1.6271 0.5 1.9824 1.04 1.8786
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