Calibration for Data Observed by Airborne Hot-wire Liquid Water Content Sensor
-
摘要: 机载含水量仪是目前云中液态水含量唯一的探测仪器,其准确性直接影响人工增雨作业条件判别。基于2015年和2017年四川盆地南部开展的10架次飞机云物理探测试验,考察机载热线含水量仪LWC-100探测数据发现存在异常极大值、负值数量多等问题。通过分析DMT(Droplet Measurement Technologies)公司云粒子探头(cloud droplet probe,CDP)、云粒子图像探头(cloud imaging probe,CIP)、降水粒子图像探头(precipitation imaging probe,PIP)数据,提出对入云前的干功率进行重新计算的3种方法:方法1以CDP探头的不同粒子尺度分档为标准,不低于某一档尺度的粒子数浓度大于0记为入云;方法2以CDP的数浓度大于10 cm-3为入云判定条件;方法3以CDP,CIP,PIP 3种探头探测的粒子数浓度同时大于0记为入云。结果显示:3种方法均有效纠正液态水含量不为0的情况,负值数量也较探测数据明显减少。方法1以不小于5 μm的粒子数浓度大于0记为入云,校验计算得到的液态水含量以负值数量和大小作为评价依据较方法2和方法3更优。Abstract: Based on the cloud microphysical detection data of 10-sortie aircraft over southern Sichuan Basin in 2015 and 2017, the liquid water content measured by DMT (Droplet Measurement Technologies) hot-wire liquid water content sensor is examined, and abnormal values in maximum, minimum and negative values are found.There are 4 possible causes for the abnormal maximum, minimum and negative values of liquid water content. First, the errors are caused by multiple parameters such as temperature, air pressure and vacuum velocity, which may lead to the error superposition of calculated values. Second, the on-board operators didn't calibrate the zero before entering the cloud. Third, the on-board operators only calibrate the zero once before entering the cloud during the whole flight. Fourth, the interval between cloud entry and exit is too short, so that the manual zero calibration is inaccurate.Using cloud particle spectrum data from cloud droplet probe (CDP), cloud imaging probe (CIP) and precipitation imaging probe (PIP), three solutions are proposed for calibrating hot-wire liquid water content sensor. Solution 1 is to set the criteria for entering cloud as the concentration of particle above a certain size from CDP probe greater than 0. Solution 2 is to set the criteria for entering cloud as the number concentration of cloud particles greater than 10 cm-3 from CDP probe. Solution 3 is to set the criteria for entering cloud as the number concentration from CDP, CIP and PIP probe greater than 0. The results show that when the number concentration is 0 from CDP, CIP and PIP probe, the original non-zero liquid water content problems are corrected by these solutions.To avoid the influence of ice phase particles on CDP number concentration, the verification is carried out in the positive temperature zone. All the test results show that the negative proportion of liquid water content is also significantly reduced compared with the original data. Solution 1 reduces the negative proportion of liquid water content, and make the minimum and maximum more reasonable than other scales. The liquid water content measured by Solution 1 are more reasonable than Solution 2 and 3.
-
图 3 2015年12月1日飞机探测要素随时间变化(a)飞行高度和温度,(b)粒子数浓度,(c)液态水含量,(d)飞机与微波辐射计水平距离
Fig. 3 Factors of time series from flight detection on 1 Dec 2015 (a)flight altitude and temperature, (b)particles number concentration, (c)liquid water content, (d)horizontal distance between aircraft and groud-based microwave radiometer
表 1 机载云物理探测系统
Table 1 Airborne cloud microphysical detection system
设备类型 测量范围 探测要素 热线含水量仪 0~3 g·m-3 液态水含量 云粒子探头 2~50 μm 小云粒子谱 云粒子图像探头 25~1550 μm 大云粒子谱、二维图像 降水粒子图像探头 100~6200 μm 降水粒子谱、二维图像 飞机综合气象要素测量系统 温、压、湿、风、GPS轨迹 表 2 液态水含量数据概况
Table 2 Overview of liquid water content data
架次 日期 整段飞行 NCDP=NCIP=NPIP=0 L1/(g·m-3) L2/(g·m-3) L1/(g·m-3) L1方差 L2/(g·m-3) L2方差 1 2015-11-28 -1.70~1.15 0~2.68 -1.67~0.31 0.050 0~1.65 1.600 2 2015-12-01(白天) -0.30~0.60 -0.28~1.26 -0.29~0.27 0.009 -0.28~1.25 0.040 3 2015-12-01(夜间) -1.86~0.88 0~2.21 -1.86~0.23 0.065 0~1.47 1.380 4 2015-12-10 -2.12~2.99 0~5.31 -0.48~0.16 0.038 0~1.54 1.000 5 2015-12-12 -1.57~31.52 -0.90~2.64 -1.57~31.52 1.150 -0.90~1.33 0.090 6 2015-12-13 -3.78~40.74 -11.54~9.11 -1.41~40.74 2.940 -0.004~6.78 0.150 7 2015-12-18 -1.64~44.19 0~49.61 -0.52~1.40 0.060 0.66~3.27 1.000 8 2017-10-31 -2.26~0.04 -1.54~1.25 -1.42~-0.22 0.080 -0.64~0.15 0.003 9 2017-11-27 -0.81~69.13 -3.78~1.12 -0.81~69.13 159.580 -3.78~1.11 0.180 10 2017-12-01 -1.15~-0.42 -0.67~0.85 -0.99~-0.61 0.410 -0.42~0.85 0.010 表 3 入云判别方法与指标阈值
Table 3 Solutions and thresholds for in-cloud determination
方法 参考因素 入云的判别指标阈值 备注 1 尺度、数浓度 Ni>0 Ni为不低于第i档尺度粒子数浓度 2 数浓度 NCDP>10 cm-3 NCDP为CDP探头测得粒子总数浓度 3 数浓度 NCDP>0
或NCIP>0
或NPIP>0NCIP为CIP探头测得粒子总数浓度,
NPIP为PIP探头测得粒子总数浓度表 4 正温区的液态水含量的探测数据及3种方法校验值对比
Table 4 Comparisons between probe data and those determined by three solutions for liquid water content in the positive temperature levels
架次 探测数据 方法1 方法2 方法3 L2/(g·m-3) 负值占比/% 液态水含量/ (g·m-3) 负值占比/% 液态水含量/ (g·m-3) 负值占比/% 液态水含量/ (g·m-3) 负值占比/% 1 0~2.68 0 -0.18~0.51 2.48 -0.22~0.49 3.64 -0.13~0.53 8.45 2 -0.11~1.26 5.58 -0.23~0.95 1.43 -0.93~0.94 1.50 -0.66~0.95 13.83 3 0~2.21 0 -0.12~0.36 0.50 -0.25~0.33 1.03 -0.29~3.14 19.57 4 0~1.91 0 -0.14~0.29 1.09 -0.16~0.29 0.94 -0.83~5.39 15.32 5 -0.02~2.64 1.91 -0.28~1.33 4.08 -0.28~1.33 4.34 -2.06~1.53 8.27 6 -11.54~2.86 4.93 -0.02~0.10 0.10 0 0 -0.40~0.39 14.48 7 0~1.42 0 -0.18~0.07 1.97 0 0 -0.37~5.21 15.27 8 -1.78~1.25 81.95 -0.03~1.27 0.59 -0.007~1.27 0.32 -0.03~1.27 0.45 9 -0.128~1.12 10.98 -0.003~0.17 0.36 0~0.17 0 -1.15~1.10 2.73 10 -0.67~0.05 37.36 -0.005~0.01 1.35 0~0.003 0 -0.21~0.02 21.90 -
[1] 段英, 吴志会.利用地基遥感方法监测大气中汽态、液态水含量分布特征的分析.应用气象学报, 1999, 10(1):34-40. http://qikan.camscma.cn/article/id/19990133Duan Y, Wu Z H. Monitoring the distribution characteristics of liquid and vapour water content in the atmosphere using ground-based remote sensing. J Appl Meteor Sci, 1999, 10(1): 34-40. http://qikan.camscma.cn/article/id/19990133 [2] 亓鹏, 郭学良, 卢广献, 等. 华北太行山东麓一次稳定性积层混合云飞机观测研究: 对流云/对流泡和融化层结构特征. 大气科学, 2019, 43(6): 1365-1384. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201906012.htmQi P, Guo X L, Lu G X, et al. Aircraft measurements of a stable stratiform cloud with embedded convection in eastern Taihang Mountain of North China: Characteristics of embedded convection and melting layer structure. Chinese J Atmos Sci, 2019, 43(6): 1365-1384. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201906012.htm [3] 杨洁帆, 胡向峰, 雷恒池, 等. 太行山东麓层状云微物理特征的飞机观测研究. 大气科学, 2021, 45(1): 88-106. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202101006.htmYang 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): 88-106. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202101006.htm [4] 陈文选, 王俊, 刘文. 一次冷涡过程降水的微物理机制分析. 应用气象学报, 1999, 10(2): 190-198. http://qikan.camscma.cn/article/id/19990258Chen W X, Wang J, Liu W. Analysis of the microphysical precipitation mechanism for a cold vortex process. J Appl Meteor Sci, 1999, 10(2): 190-198. http://qikan.camscma.cn/article/id/19990258 [5] 孙玉稳, 董晓波, 李宝东, 等. 太行山东麓一次低槽冷锋降水云系云物理结构和作业条件的飞机观测研究. 高原气象, 2019, 38(5): 971-982. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201905006.htmSun Y W, Dong X B, Li B D, et al. The physical properties and seeding potential analysis of a low trough cold front cloud system at Mountain Taihang based on aircraft observations. Plateau Meteorology, 2019, 38(5): 971-982. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201905006.htm [6] 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 [7] 王维佳, 董晓波, 石立新, 等. 一次多层云系云物理垂直结构探测研究. 高原气象, 2011, 30(5): 1368-1375. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201105024.htmWang W J, Dong X B, Shi L X, et al. Study on vertical microphysical structure of cloud for a multi-layer cloud system. Plateau Meteorology, 2011, 30(5): 1368-1375. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201105024.htm [8] 樊志超, 周盛, 汪玲, 等. 湖南秋季积层混合云系飞机人工增雨作业方法. 应用气象学报, 2018, 29(2): 200-216. doi: 10.11898/1001-7313.20180207Fan Z C, Zhou S, Wang L, et al. Methods of aircraft-based precipitation enhancement operation for convective-stratiform mixed clouds in autumn in Hunan Province. J Appl Meteor Sci, 2018, 29(2): 200-216. doi: 10.11898/1001-7313.20180207 [9] 王维佳, 刘建西, 石立新, 等. 四川盆地降水云系飞机云物理观测个例分析. 气象, 2011, 37(11): 1389-1394. doi: 10.7519/j.issn.1000-0526.2011.11.009Wang W J, Liu J X, Shi L X, et al. Case analysis of micophysical characterstics of precipitation cloud system in Sichuan Basin. Meteor Mon, 2011, 37(11): 1389-1394. doi: 10.7519/j.issn.1000-0526.2011.11.009 [10] 张佃国, 郭学良, 龚佃利, 等. 山东省1989-2008年23架次飞机云微物理结构观测试验结果. 气象学报, 2011, 69(1): 195-207. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201101017.htmZhang D G, Guo X L, Gong D L, et al. The observational results of the clouds microphysical structure based the data obtained by 23 sorties between 1989 and 2008 in Shandong Province. Acta Meteor Sinica, 2011, 69(1): 195-207. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201101017.htm [11] 刘佩, 银燕, 陈倩, 等. 吸湿性播撒对暖性对流云减雨影响的数值模拟. 应用气象学报, 2019, 30(2): 211-222. doi: 10.11898/1001-7313.20190208Liu P, Yin Y, Chen Q, et al. Numerical simulation of hygroscopic seeding effects on warm convective clouds and rainfall reduction. J Appl Meteor Sci, 2019, 30(2): 211-222. doi: 10.11898/1001-7313.20190208 [12] 王黎俊, 银燕, 姚展予, 等. 三江源地区秋季一次层积云飞机人工增雨催化试验的微物理响应. 气象学报, 2013, 71(5): 925-939. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201305012.htmWang L J, Yin Y, Yao Z Y, et al. Microphysical responses as seen in a stratocumulus aircraft seeding experiment in autumn over the Sanjiangyun National Nature Reserve. Acta Meteor Sinica, 2013, 71(5): 925-939. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201305012.htm [13] 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 [14] 齐晨, 金晨曦, 郭文利, 等. 基于模糊逻辑的飞机积冰预测指数. 应用气象学报, 2019, 30(5): 619-628. doi: 10.11898/1001-7313.20190510Qi C, Jin C X, Guo W L, et al. Icing potential index of aircraft icing based on fuzzy logic. J Appl Meteor Sci, 2019, 30(5): 619-628. doi: 10.11898/1001-7313.20190510 [15] Gultepe I, Isaac G A, Leaitch W R, et al. Parameterization of marine stratus microphysics based on in situ observations: Implications for GCMs. J Climate, 1996, 9: 345-357. http://www.researchgate.net/profile/Ismail_Gultepe/publication/255252684_parameterizations_of_Marine_Stratus_Microphysics_Based_on_In_Situ_Observations_Implications_for_GCMS/links/565478d808aefe619b19e8f1.pdf [16] Gultepe I, Isaac G A. Aircraft observations of cloud droplet number concentration: Implications for climate studies. Quart J Roy Meteor Soc, 2004, 130(602): 2377-2390. http://www.researchgate.net/profile/Ismail_Gultepe/publication/229737863_Aircraft_observations_of_cloud_droplet_number_concentration_Implications_for_climate_studies/links/543b6d990cf204cab1dafddd [17] Zhang Q, Quan J N, Tie X X, et al. Impact of aerosol particles on cloud formation: Aircraft measurements in China. Atmospheric Environment, 2011, 45(3): 665-672. http://www.researchgate.net/profile/Jiannong_Quan/publication/241112880_Impact_of_aerosol_particles_on_cloud_formation_Aircraft_measurements_in_China/links/5555a34a08ae6943a871cf61.pdf [18] 王柏忠, 刘卫国, 王广河, 等. KLWC-5含水量仪原理及在人工增雨中的应用. 气象科技, 2004, 32(4): 294-296. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200404022.htmWang B Z, Liu W G, Wang G H, et al. Principles of KLWC-5 liquid water content guage and its application in cloud seeding. Meteorological Science and Technology, 2004, 32(4): 294-296. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200404022.htm [19] 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 [20] 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. Progresses 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 [21] 郭学良, 于子平, 杨泽后, 等. 高性能机载云粒子成像仪研制及应用. 气象学报, 2020, 78(6): 1050-1064. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202006013.htmGuo X L, Yu Z P, Yang Z H, et al. Development and application of the high-performance airborne cloud particle imager. Acta Meteor Sinica, 2020, 78(6): 1050-1064. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202006013.htm [22] 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): 1143-1152. 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): 1143-1152. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202009002.htm [23] 李义宇, 孙鸿娉, 杨俊梅, 等. 华北中部夏季气溶胶和云分布特征. 应用气象学报, 2021, 32(6): 665-676. doi: 10.11898/1001-7313.20210603Li Y Y, Sun H P, Yang J M, et al. Characteristics of aerosol and cloud in summer in the central plain of North China. J Appl Meteor Sci, 2021, 32(6): 665-676. doi: 10.11898/1001-7313.20210603 [24] 蔡兆鑫, 蔡淼, 李培仁, 等. 华北地区一次气溶胶与浅积云微物理特性的飞机观测研究. 大气科学, 2021, 45(2): 393-406. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202102011.htmCai Z X, Cai M, Li P R, et al. An in-situ case study on micro physical properties of aerosol and shallow cumulus clouds in North China. Chinese J Atmos Sci, 2021, 45(2): 393-406. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202102011.htm [25] 张正国, 卢广献, 汤达章, 等. 广西秋季层状云微物理特征分析. 气象科技, 2018, 46(3): 545-555. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201803019.htmZhang Z G, Lu G X, Tang D Z, et al. Microphysical characteristics of stratiform clouds in autumn in Guangxi. Meteorological Science and Technology, 2018, 46(3): 545-555. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201803019.htm [26] 孙鸿娉, 李培仁, 闫世明, 等. 山西省2008-2010年64架次飞机云物理观测结果分析. 气象科技, 2014, 42(4): 682-689. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201404028.htmSun H P, Li P R, Yan S M, et al. Characteristics of cloud microphysical structure based on aircraft data in 2008-2010 in Shanxi Province. Meteorological Science and Technology, 2014, 42(4): 682-689. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201404028.htm [27] 段婧, 楼小凤, 陈勇, 等. 基于航测的珠三角气溶胶垂直分布及活化特性. 应用气象学报, 2019, 30(6): 677-689. doi: 10.11898/1001-7313.20190604Duan J, Lou X F, Chen Y, et al. Aircraft measurements of aerosol vertical distributions and its activation efficiency over the Pearl River Delta. J Appl Meteor Sci, 2019, 30(6): 677-689. doi: 10.11898/1001-7313.20190604 [28] 李义宇, 杨俊梅, 李培仁, 等. 山西省层状云微物理结构探测分析. 气候与环境研究, 2012, 17(6): 693-703. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH201206007.htmLi Y Y, Yang J M, Li P R, et al. Detection analysis of microphysical structure of stratiform cloud in Shanxi Province. Climatic and Environmental Research, 2012, 17(6): 693-703. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH201206007.htm [29] 刘晓璐, 刘东升, 郭丽君, 等. 国产MWP967KV型地基微波辐射计探测精度. 应用气象学报, 2019, 30(6): 731-744. doi: 10.11898/1001-7313.20190609Liu X L, Liu D S, Guo L J, et al. The observational precision of domestic MWP967KV ground-based microwave radiometer. J Appl Meteor Sci, 2019, 30(6): 731-744. doi: 10.11898/1001-7313.20190609 [30] Rangno A L, Hobbs P V. Microstructures and precipitation development in cumulus and small cumulonimbus clouds over the warm pool of the tropical Pacific Ocean. Quart J Roy Meteor Soc, 2005, 131: 639-673. http://www.onacademic.com/detail/journal_1000034849616510_8817.html