Zhang Rong, Li Hongyu, Zhou Xu, et al. Shape recognition of DMT airborne cloud particle images and its application. J Appl Meteor Sci, 2021, 32(6): 735-747. DOI:  10.11898/1001-7313.20210608.
Citation: Zhang Rong, Li Hongyu, Zhou Xu, et al. Shape recognition of DMT airborne cloud particle images and its application. J Appl Meteor Sci, 2021, 32(6): 735-747. DOI:  10.11898/1001-7313.20210608.

Shape Recognition of DMT Airborne Cloud Particle Images and Its Application

DOI: 10.11898/1001-7313.20210608
  • Received Date: 2021-07-15
  • Rev Recd Date: 2021-08-27
  • Publish Date: 2021-11-23
  • 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.
  • Fig. 1  Schematic diagram for the image produced as particles passing across optical array

    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)

    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)

    Fig. 4  The original particle image with discrete points(a) and the image after removing the discrete points(b)

    Fig. 5  Examples of noisy points(a) and streaking particles(b) with only one pixel in the direction of diode array or flight

    Fig. 6  Geometric parameters used for particle shape classification

    Fig. 7  Examples of distinguishing particle shapes

    Fig. 8  The occurrence frequency, average area of each shape of particles and typical particle images of three cases

    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

    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
    a<25 a<23 微小
    r2≥0.4或(d<64且Dx≥4DyDy≥4Dx) r2≥0.4或(d<64且d≥4w) 线状
    d>160 d>100 聚合状
    S≥0.7 S≥0.7 霰状
    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
    F≤5.5 F≤5.5 球状
    F<10且d≥32 F<10且d≥32 霰状
    F<10且d<32 F<10且d<32 板状
    F<16或Dx≤7 F<16或Dx≤7 不规则状
    其余粒子 其余粒子 枝状
    DownLoad: Download CSV

    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
    DownLoad: Download CSV

    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
    DownLoad: Download CSV
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