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沙修竹, 褚荣浩, 黄毅梅. 2022. 人工增雨效果物理检验方法的建立及应用[J]. 大气科学, 46(4): 819−834. doi: 10.3878/j.issn.1006-9895.2105.20237
引用本文: 沙修竹, 褚荣浩, 黄毅梅. 2022. 人工增雨效果物理检验方法的建立及应用[J]. 大气科学, 46(4): 819−834. doi: 10.3878/j.issn.1006-9895.2105.20237
SHA Xiuzhu, CHU Ronghao, HUANG Yimei. 2022. Establishment and Application of a Physical Inspection Method for the Artificial Precipitation Enhancement Effect [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 819−834. doi: 10.3878/j.issn.1006-9895.2105.20237
Citation: SHA Xiuzhu, CHU Ronghao, HUANG Yimei. 2022. Establishment and Application of a Physical Inspection Method for the Artificial Precipitation Enhancement Effect [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 819−834. doi: 10.3878/j.issn.1006-9895.2105.20237

人工增雨效果物理检验方法的建立及应用

Establishment and Application of a Physical Inspection Method for the Artificial Precipitation Enhancement Effect

  • 摘要: 本文针对基于多源探测数据的人工增雨效果物理检验,建立对比区选取的相似性度量系数(APC,Analogy Deviation-Pearson Correlation Coefficient),建立人工增雨效果物理检验的无量纲化指数PIDI(Physical Inspection Dimensionless Index)方法。结果表明:(1)人工增雨效果物理检验PIDI指数方法,能够实现以相似性度量系数APC最大程度削减增雨作业催化云体及降水的自然变率影响,以无量纲化处理方法综合多种具有量纲差异的云物理探测参数,最终以一个百分数变化率的数值形式综合度量多种云物理参数的整体变化趋势及程度。(2)应用PIDI方法对2014~2019年24架次飞机增雨作业进行增雨效果物理检验。人工增雨催化引起作业后3 h的云顶温度、云粒子有效半径、光学厚度、液水路径、组合反射率、≥30 dBZ回波面积、垂直累积液态含水量7项指标平均变化率3.4%~19.6%。18次作业的小时降水量变化率呈0~58.3%的增雨效果,6次作业的小时降水量变化率呈−37.5%~0的减雨效果。多数增雨作业引起的云物理参数变化明显小于降水变化。(3)具有增雨正效果的18次增雨作业,人工催化引起多数作业的云顶温度、组合反射率、垂直累积液态含水量呈增加趋势,多数作业的云粒子有效半径、光学厚度、液水路径呈减小趋势。(4)利用飞机增雨个例对比PIDI指数方法与K值方法异同。对于降水量变化趋势的检验二者具有一致性。二者差别在于PIDI指数方法能够反映人工催化引起的所有检验指标平均变化率。

     

    Abstract: For a physical inspection of the artificial precipitation enhancement effect based on multi-source detection data, this study established the similarity measurement coefficient, APC (Analogy Deviation-Pearson Correlation Coefficient), of a contrast area selection and the dimensionless PIDI (Physical Inspection Dimensionless Index) index method for a physical inspection of the artificial precipitation enhancement effect. The results revealed the following: (1) The PIDI index method of a physical inspection for the artificial precipitation enhancement effect can minimize the variable influence of a seeding cloud body and precipitation with the similarity coefficient APC. In addition, a variety of dimensionless cloud physical detection parameters can be synthesized using a dimensionless method. Finally, the percentage change rate was used to measure the overall variations and the degree of various cloud physical parameters. (2) The PIDI index method was applied to inspect the precipitation enhancement effect of 24 aircraft from 2014 to 2019. The average change rate of seven indices (cloud top temperature, effective particle radius, optical thickness, liquid water path, combined reflectivity, ≥30 dBZ echo area, and vertical cumulative liquid water content) caused by artificial precipitation enhancement was 3.4%–19.6%. The change rate of hourly precipitation of 18 operations was 0–58.3%; the change rate of 6 operations was −37.5% to 0. The changes in the cloud physical parameters caused by most precipitation-increasing operations are smaller than the changes in precipitation. (3) For the 18 operations with a positive effect of precipitation enhancement, the cloud top temperature, combined reflectivity, and the vertical cumulative liquid water content for most operations were increased due to the artificial catalysis, effective particle radius, and optical thickness. Moreover, the liquid water path for most operations was decreased by artificial catalysis. (4) The PIDI index and K-value methods were compared using an aircraft precipitation enhancement operation. For the test of precipitation variation trend, the two methods were consistent. The main difference was that the PIDI index method could reflect the average change rate of all inspection indices caused by artificial catalysis.

     

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