设为首页
加入收藏
Shape Recognition of DMT Airborne Cloud Particle Images and Its Application
-
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.
-
Key words:
- cloud particle;
- particle image;
- particle shape;
- hydrometeor content
-
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 
下载: 导出CSV
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
下载: 导出CSV
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
下载: 导出CSV
-
[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] 李军霞, 李培仁, 陶玥, 等. 山西春季层状云系数值模拟及与飞机探测对比. 应用气象学报, 2014, 25(1): 22-32. http://qikan.camscma.cn/article/id/20140103Li 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] 姚展予. 中国气象科学研究院人工影响天气研究进展回顾. 应用气象学报, 2006, 17(6): 786-795. http://qikan.camscma.cn/article/id/200606127Yao 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] 郭学良, 方春刚, 卢广献, 等. 2008-2018年我国人工影响天气技术及应用进展. 应用气象学报, 2019, 30(6): 641-650. doi: 10.11898/1001-7313.20190601Guo 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] 蔡兆鑫, 蔡淼, 李培仁, 等. 大陆性积云不同发展阶段宏观和微观物理特性的飞机观测研究. 大气科学, 2019, 43(6): 1191-1203. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201906001.htmCai 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] 李德泉, 李抗抗, 李宏宇, 等. 飞机作业监测移动应用系统的设计与实现. 应用气象学报, 2019, 30(6): 745-758. doi: 10.11898/1001-7313.20190610Li 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] 党娟, 刘卫国, 陶玥. 一次降水性层积云系的微物理特征分析. 高原气象, 2016, 35(6): 1639-1649. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201606021.htmDang 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] 杨洁帆, 胡向峰, 雷恒池, 等. 太行山东麓层积混合云微物理特征的飞机观测研究. 大气科学, 2021, 45(1): 1-19.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] 陈琪, 张华, 荆现文, 等. 冰晶粒子不同形状假定对辐射收支和气候的影响. 气象学报, 2017, 75(4): 607-617. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201704007.htmChen 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] 徐舒扬, 吴翀, 刘黎平. 双偏振雷达水凝物相态识别算法的参数改进. 应用气象学报, 2020, 31(3): 350-360. doi: 10.11898/1001-7313.20200309Xu 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] 吴翀, 刘黎平, 仰美霖, 等. X波段双偏振雷达相态识别与拼图的关键技术. 应用气象学报, 2021, 32(2): 200-216. doi: 10.11898/1001-7313.20210206Wu 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] 任建奇, 严卫, 叶晶, 等. 云相态的卫星遥感研究进展. 地球科学进展, 2010, 25(10): 1051-1060. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201010009.htmRen 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] 楼小凤, 傅瑜, 孙晶. 一次浙江对流云催化数值模拟试验. 应用气象学报, 2019, 30(6): 665-676. doi: 10.11898/1001-7313.20190603Lou 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] 郭欣, 郭学良, 陈宝君, 等. 一次大冰雹形成机制的数值模拟. 应用气象学报, 2019, 30(6): 651-664. doi: 10.11898/1001-7313.20190602Guo 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] 董全, 张峰, 宗志平. 基于ECMWF集合预报产品的降水相态客观预报方法. 应用气象学报, 2020, 31(5): 527-542. doi: 10.11898/1001-7313.20200502Dong 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] 黄敏松, 雷恒池, 陈家田, 等. 机载光阵探头探测期间云粒子的破碎. 大气科学, 2016, 40(3): 647-656. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201603016.htmHuang 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] 黄敏松, 雷恒池, 金玲. 机载云降水粒子成像仪所测数据中伪粒子的识别. 大气科学, 2017, 41(5): 1113-1124. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201705016.htmHuang 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] 王磊, 李成才, 赵增亮, 等. 二维粒子形状分类技术在云微物理特征分析中的应用. 大气科学, 2014, 38(2): 201-212. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201402001.htmWang 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] 黄敏松, 雷恒池. 改进的Holroyd云粒子形状识别方法及其应用. 气象学报, 2020, 78(2): 289-300. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202002011.htmHuang 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] 李宏宇, 周旭, 张荣, 等. 不同机载设备观测的气象要素与飞行参数对比分析. 气象, 2020, 46(9): 1154-1163. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202009002.htmLi 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 -
图(9) / 表(3)
计量
- 摘要浏览量: 1725
- HTML全文浏览量: 561
- PDF下载量: 206
- 被引次数: 0
