Shape Recognition of DMT Airborne Cloud Particle Images and Its Application

Zhang Rong; Li Hongyu; Zhou Xu; Li Hao; Hu Xiangfeng; Xia Qiang

doi:10.11898/1001-7313.20210608

Vol.32, NO.6, 2021

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Article Navigation > Journal of Applied Meteorological Science > 2021 > 32(6): 735-747

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.

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.

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Shape Recognition of DMT Airborne Cloud Particle Images and Its Application

DOI: 10.11898/1001-7313.20210608

Zhang Rong^{1, 2
,
,},
Li Hongyu^{1, 2},
Zhou Xu^{1, 2},
Li Hao³,
Hu Xiangfeng⁴,
Xia Qiang⁵

1.
Key Laboratory for Cloud Physics of China Meteorological Administration, Beijing 100081
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
3.
Weather Modification Center of Henan Province, Zhengzhou 450003
4.
Hebei Provincial Weather Modification Center, Shijiazhuang 050021
5.
China Huayun Meteorological Technology Group Corporation, Beijing 100081

Received Date: 2021-07-15
Rev Recd Date: 2021-08-27
Publish Date: 2021-11-23

Abstract

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.
- cloud particle;
- particle image;
- particle shape;
- hydrometeor content

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References(39)

Figures and Tables

Fig. 1 Schematic diagram for the image produced as particles passing across optical array

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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)

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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)

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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)

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

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Fig. 6 Geometric parameters used for particle shape classification

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Fig. 7 Examples of distinguishing particle shapes

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

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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

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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D_x≥4D_y或D_y≥4D_x)	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
⑤	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
⑦	F＜10且d<32	F＜10且d<32	板状
⑧	F<16或D_x≤7	F<16或D_x≤7	不规则状
⑧	其余粒子	其余粒子	枝状

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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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Table 3 Statistical results

粒子形状	个例20181210		个例20190108		个例20190226
粒子形状	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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References

[1]	Baumgardner D, Brenguier J L, Bucholtz A, et al. Airborne instruments to measure atmospheric aerosol particles, clouds and radiation:A cook's tour of mature and emerging technology. Atmos Res, 2011, 102:10-29. doi: 10.1016/j.atmosres.2011.06.021
[2]	Knollenberg R G. The optical array: An alternative to scattering or extinction for airborne particle size determination. J Appl Meteor, 1970, 9(1): 86-103. doi: 10.1175/1520-0450(1970)009<0086:TOAAAT>2.0.CO;2
[3]	Lawson R P, Jensen E, Mitchell D L, et al. Microphysical and radiative properties of tropical clouds investigated in TC4 and NAMMA. J Geophys Res, 2010, 115, D10(D00J08). DOI: 10.1029/2009JD013017.
[4]	Sukovich E M, Kingsmill D E. Variability of graupel and snow observed in tropical oceanic convection by aircraft during TRMM KWAJEX. J Appl Meteor Climatol, 2009, 48: 185-198. doi: 10.1175/2008JAMC1940.1
[5]	Li J X, Li P R, Tao Y, et al. Numerical simulation and flight observation of stratiform precipitation clouds in spring of Shanxi Province. J Appl Meteor Sci, 2014, 25(1): 22-32. http://qikan.camscma.cn/article/id/20140103
[6]	Leroy D, Fontaine E, Schwarzenboeck A, et al. Ice crystal sizes in high ice water content clouds. Part Ⅱ: Statistics of mass diameter percentiles in tropical convection observed during the HAIC/HIWC project. J Atmos Oceanic Technol, 2017, 34(1): 117-136. doi: 10.1175/JTECH-D-15-0246.1
[7]	Yao Z Y. Review of weather modification research in Chinese Academy of Meteorological Sciences. J Appl Meteor Sci, 2006, 17(6): 786-795. http://qikan.camscma.cn/article/id/200606127
[8]	Guo X L, Fang C G, Lu G X, et al. Progress of weather modification technologies and applications in China from 2008 to 2018. J Appl Meteor Sci, 2019, 30(6): 641-650. doi: 10.11898/1001-7313.20190601
[9]	Cai Z X, Cai M, Li P R, et al. Aircraft observation research on macro and microphysics characteristics of continental cumulus cloud at different development stages. Chinese J Atmos Sci, 2019, 43(6): 1191-1203. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201906001.htm
[10]	Li D Q, Li K K, Li H Y, et al. Design and implementation of mobile application fro real-time monitoring of weather-modification aircraft operations. J Appl Meteor Sci, 2019, 30(6): 745-758. doi: 10.11898/1001-7313.20190610
[11]	Hou T, Lei H, Hu Z. A comparative study of the microstructure and precipitation mechanisms for two stratiform clouds in China. Atmos Res, 2010, 96: 447-460. doi: 10.1016/j.atmosres.2010.02.004
[12]	Dang J, Liu W G, Tao Y. Analysis of cloud microphysical characteristics on a precipitation stratocumulus. Plateau Meteorology, 2016, 35(6): 1639-1649. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201606021.htm
[13]	Yang J F, Hu X F, Lei H C, et al. Airborne observations of microphysical characteristics of stratiform cloud over eastern side of Taihang Mountains. Chinese J Atmos Sci, 2021, 45(1): 1-19.
[14]	Chen Q, Zhang H, Jing X W, et al. Effects of different ice crystal shape assumptions on radiation budget and climate. Acta Meteor Sinca, 2017, 75(4): 607-617. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201704007.htm
[15]	Mitchell D L. Use of mass-and area-dimensional power laws for determining precipitation particle terminal velocities. J Atmos Sci, 1996, 53(12): 1710-1723. doi: 10.1175/1520-0469(1996)053<1710:UOMAAD>2.0.CO;2
[16]	Mason B J. The shapes of snow crystals-Fitness for purpose?. Quart J Roy Meteor Soc, 1994, 120(518): 849-860.
[17]	Xu S Y, Wu C, Liu L P. Parameter improvements of hydrometeor classification algorithm for the dual-polarimetric radar. J Appl Meteor Sci, 2020, 31(3): 350-360. doi: 10.11898/1001-7313.20200309
[18]	Wu C, Liu L P, Yang M L, et al. Key technologies of hydrometeor classification and mosaic algorithm for X-band polarimetric radar. J Appl Meteor Sci, 2021, 32(2): 200-216. doi: 10.11898/1001-7313.20210206
[19]	Ren J Q, Yan W, Ye J, et al. Advances in the study of cloud phase discrimination using satellite remote sensing data. Advance in Earth Science, 2010, 25(10): 1051-1060. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201010009.htm
[20]	Hu Y, Winker D, Vaughan M, et al. CALIPSO/CALIOP cloud phase discrimination algorithm. J Atmos Oceanic Technol, 2009, 26(11): 2293-2309. doi: 10.1175/2009JTECHA1280.1
[21]	Lou X F, Fu Y, Sun J. A numerical seeding simulation of convective precipitation in Zhejiang, China. J Appl Meteor Sci, 2019, 30(6): 665-676. doi: 10.11898/1001-7313.20190603
[22]	Guo X, Guo X L, Chen B J, et al. Numerical simulation on the formation of large-size hailstones. J Appl Meteor Sci, 2019, 30(6): 651-664. doi: 10.11898/1001-7313.20190602
[23]	Dong Q, Zhang F, Zong Z P. Objective precipitation type forcast based on ECMWF ensemble prediction product. J Appl Meteor Sci, 2020, 31(5): 527-542. doi: 10.11898/1001-7313.20200502
[24]	Huang M S, Lei H C, Chen J T, et al. Cloud particle shattering during sampling by airborne optical array probes. Chinese J Atmos Sci, 2016, 40(3): 647-656. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201603016.htm
[25]	Huang M S, Lei H C, Jin L. Pseudo particle identification in the image data from the airborne cloud and precipitation particle image probe. Chinese J Atmos Sci, 2017, 41(5): 1113-1124. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201705016.htm
[26]	Holroyd E W. Some techniques and uses of 2D-C habit classification software for snow particles. J Atmos Oceanic Technol, 1987, 4(3): 498-511. doi: 10.1175/1520-0426(1987)004<0498:STAUOC>2.0.CO;2
[27]	Wang L, Li C C, Zhao Z L, et al. Application of 2D habit classification in cloud microphysics analysis. Chinese J Atmos Sci, 2014, 38(2): 201-212. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201402001.htm
[28]	Huang M S, Lei H C. An improved Holroyd cloud particle habit identification method and its application. Acta Meteor Sinca, 2020, 78(2): 289-300. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202002011.htm
[29]	Droplet Measurement Technologies. Data Analysis User's Guide Chapter Ⅱ: Single Particle Imaging. 2009: 1-34.
[30]	Korolev A, Emery E, Creelman K. Modification and tests of particle probe tips to mitigate effects of ice shattering. J Atmos Oceanic Technol, 2013, 30(4): 690-708. doi: 10.1175/JTECH-D-12-00142.1
[31]	Jackson R C, McFarquhar G M, Stith J, et al. An assessment of the impact of antishattering tips and artifact removal techniques on cloud ice size distributions measured by the 2D cloud probe. J Atmos Oceanic Technol, 2014, 31(12): 2567-2590. doi: 10.1175/JTECH-D-13-00239.1
[32]	Field P R, Wood R, Brown P R A, et al. Ice particle interarrival times measured with a fast FSSP. J Atmos Oceanic Technol, 2003, 20(2): 249-261. doi: 10.1175/1520-0426(2003)020<0249:IPITMW>2.0.CO;2
[33]	Field P R, Heymsfield A J, Bansemer A. Shattering and particle interarrival times measured by optical array probes in ice clouds. J Atmos Oceanic Technol, 2006, 23(10): 1357-1371. doi: 10.1175/JTECH1922.1
[34]	Li H Y, Zhou X, Zhang R, et al. Comparison and analysis of several meteorological elements and flight parameters observed from different airborne detection instruments. Meteor Mon, 2020, 46(9): 1154-1163. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202009002.htm
[35]	Bailey M P, Hallett J. A comprehensive habit diagram for atmospheric ice crystals: Confirmation from the laboratory, AIRS Ⅱ, and other field studies. J Atmos Sci, 2009, 66(9): 2888-2899. doi: 10.1175/2009JAS2883.1
[36]	O'Shea S J, Crosier J, Dorsey J, et al. Revisiting particle sizing using greyscale optical array probes: Evaluation using laboratory experiments and synthetic data. Atmos Meas Tech, 2019, 12(6): 3067-3079. doi: 10.5194/amt-12-3067-2019
[37]	O'Shea S, Crosier J, Dorsey J, et al. Characterising optical array particle imaging probes: Implications for small ice crystal observations. Atmos Meas Tech, 2021, 14(3): 1917-1939. doi: 10.5194/amt-14-1917-2021
[38]	Heymsfield A J, Parrish J L. A computational technique for increasing the effective sampling volume of the PMS two-dimensional particle size spectrometer. J Appl Meteor, 1978, 17(10): 1566-1572. doi: 10.1175/1520-0450(1978)017<1566:ACTFIT>2.0.CO;2
[39]	Baumgardner D, Abel S, Axisa D, et al. Cloud ice properties: In situ measurement challenges. Meteor Monogr, 2017, 58: 9.1-9.23. doi: 10.1175/AMSMONOGRAPHS-D-16-0011.1