人工增雨效果物理检验方法的建立及应用

沙修竹; 褚荣浩; 黄毅梅

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

doi: 10.3878/j.issn.1006-9895.2105.20237

沙修竹^{1, 2,},
褚荣浩³,
黄毅梅^{1, 2}

1.
中国气象局河南省农业气象保障与应用技术重点实验室, 郑州 450000
2.
河南省人工影响天气中心, 郑州 450000
3.
安徽省公共气象服务中心, 合肥 230000

基金项目: 中国气象局河南省农业气象保障与应用技术重点实验室应用技术研究项目KM201923、KM202135

详细信息

作者简介:
沙修竹，女，1990年出生，工程师，主要从事云降水与人工影响天气研究。E-mail: xiuzhu1990@163.com

中图分类号: P481
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出版历程
- 收稿日期: 2020-11-30
- 录用日期: 2021-08-30
- 网络出版日期: 2021-10-08
- 刊出日期: 2022-07-19

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

Xiuzhu SHA^{1, 2
,},
Ronghao CHU³,
Yimei HUANG^{1, 2}

1.
Henan Key Laboratory of Agrometeorological Support and Applied Technique, China Meteorological Administration, Zhengzhou 450000
2.
Weather Modification Center of Henan Province, Zhengzhou 450000
3.
Meteorological Service Center of Anhui Province, Hefei 230000

Funds: Henan Key Laboratory of Agrometeorological Support and Applied Technique Research Project of China Meteorological Administration (Grants KM201923, KM202135)

摘要

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。
- 增雨效果物理检验 /
- HYSPLIT模型 /
- APC系数 /
- 无量纲化 /
- PIDI指数
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.
- Physical inspection of precipitation enhancement effect /
- HYSPLIT model /
- Coefficient of APC /
- Dimensionless /
- Index of PIDI

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图 1 人工增雨效果物理检验PIDI指数方法技术路线

Figure 1. Technical route of the PIDI (Physical Inspection Dimensionless Index) method of physical inspection for artificial precipitation enhancement effect

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图 2 2014～2019年24架次增雨作业的（a1–a24）飞机航线叠加雷达回波平面和（b1–b24）垂直剖面图

Figure 2. Overlay of the airline and radar echo plane (a1–a24), vertical radar profile (b1–b24) of 24 aircraft precipitation enhancement operations from 2014 to 2019

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2 （续）

2. (Continued)

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图 3 2014～2019年（a1–a24）24架次飞机增雨作业飞机航线、0～3 h逐小时影响区和对比区的三维地理空间配置。红色实线为飞机航线；空中蓝色、橘黄色区域分别为影响区、对比区；地面蓝色、橘黄色区域分别为空中影响区、对比区在地面上的投影

Figure 3. Three-dimensional geospatial configuration of the airline and 0–3 h hourly influence area and contrast area of (a1–a24) the 24 aircraft precipitation enhancement operations. The solid red lines represent the airline; the blue and orange areas in the air represent the influence area and contrast area, respectively; the blue and orange areas on the ground represent the ground projection of the influence area and contrast area in the air, respectively

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3 （续）

3. (Continued)

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图 4 （a1–a12）日间个例的物理检验各指标PIDI_i及综合指数PIDI。PIDI_ttop、PIDI_ref、PIDI_optn、PIDI_lwp、PIDI_CR、PIDI_30echo、PIDI_VIL、PIDI_rh分别表征影响区人工催化引起的云顶温度、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、小时降水量的变化率，PIDI表征影响区人工催化引起的前7项指标平均变化率

Figure 4. (a1–a12) PIDI_i of each indicator and comprehensive index PIDI of physical inspection of diurnal cases. PIDI_ttop、PIDI_ref、PIDI_optn、PIDI_lwp、PIDI_CR、PIDI_30echo、PIDI_VIL、PIDI_rh represent the change rate of cloud top temperature, effective particle radius, optical thickness, liquid water path, combined reflectivity, ≥30dBZ echo area, vertical cumulative liquid water content, hourly precipitation of influence area due to artificial catalysis, respectively. PIDI represents the average change rate of the first seven indices of influence area due to artificial catalysis

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图 5 （a1–a12）夜间个例的物理检验各指标PIDI_i及综合指数PIDI。PIDI表征影响区人工催化引起的PIDI_ttop、PIDI_CR、PIDI_30echo、PIDI_VIL四项指标平均变化率）

Figure 5. (a1–a12) PIDI_i of each indicator and comprehensive index PIDI of the physical inspections of the night cases. PIDI represents the average change rate of PIDI_ttop, PIDI_CR, PIDI_30echo, and PIDI_VIL of the influence area due to artificial catalysis

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图 6 2017年10月1日（a）增雨作业PIDI综合指数与各指标K值的对比以及（b）PIDI_rh指数与小时降水量K值的对比。PIDI表示影响区人工催化引起的7项检验指标平均变化率；PIDI_rh表示影响区人工催化引起的小时降水量变化率；K_ttop、K_ref、K_optn、K_lwp、K_CR、K_30echo、K_VIL、K_rh分别表示影响区与对比区的云顶温度、云粒子有效半径、光学厚度、液水路径、组合反射率、≥30 dBZ组回波面积、垂直累积液态含水量、小时降水量的观测值比值；横坐标0表示催化结束时刻，0～3表示催化结束后的3小时时段

Figure 6. (a) Comparison of the PIDI and K value of each index; (b) comparison of the PIDI_rh and K value of the hourly precipitation of the precipitation enhancement operation on October 1, 2017. PIDI represents the average change rate of the seven indices of the influence area due to artificial catalysis; PIDI_rh represents the change rate of hourly precipitation of the influence area due to artificial catalysis; K_ttop、K_ref、K_optn、K_lwp、K_CR、K_30echo、K_VIL、K_rh represent the ratio of cloud top temperature, effective particle radius, optical thickness, liquid water path, combined reflectivity, ≥30 dBZ echo area, vertical cumulative liquid water content, hourly precipitation observed in the influence area to the contrast area, respectively; 0 on the x-coordinate represents the moment when seeding agent ends, 0–3 on the x-coordinate represents the 3 hours after seeding

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表 1 对比区选取指标（序号1～7）、PIDI指数构成指标（序号1～8）的参数说明

Table 1. Parameter description of the contrast area selection index (numbers 1–7) and the constitution index of PIDI

序号	探测方式	探测或反演参数	定义	指导作用	单位
1	FY-2E/2G或FY-4A静止气象卫星	云顶温度（ttop）	云顶所在高度的温度	人工增雨云系播云温度窗的选择	°C
2		云粒子有效半径（ref）	指假设云层在垂直方向均匀条件下云粒子的有效半径	判断云中粒子大小，反映云可降水量	μm
3		云光学厚度（optn）	云系在整个路径上云消光总和	了解云系垂直方向云体厚实程度	无量纲
4		液水路径（lwp）	云体单位面积上的垂直方向液水总量（或称柱液水量）	了解垂直方向的云水丰沛程度	mm
5	多普勒天气雷达	组合反射率(CR)	气象雷达接收周围一定范围内不同高度云层反射雷达波的比率	反映雷达体扫对所有回波在对应格点上的最大反射率因子值	dBZ
6		≥30 dBZ回波面积(30echo)	组合反射率数值≥30 dBZ面积大小	表示≥30 dBZ反射率的实际范围	km²
7		垂直累积液态含水量（VIL）	降水云体中某一确定面积的垂直柱体内液态水总量的分布	反映将反射率因子数据转换成等价液态水值	kg m⁻²
8	自动气象站	小时降水量（rh）	1小时内的总降水量	反映云降水结果的宏观及直观特征	mm

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表 2 人工增雨作业过程合理性分析条件

Table 2. Reasonableness analysis condition of the artificial precipitation enhancement process

指标	要求
作业条件	（1）云系类型为层状云、层积混合云（2）天气条件（降水云系）和水汽输送等适合增雨作业
作业时机	（1）作业位于水汽含量、液态水含量充沛区域，作业区云层存在较强雷达回波（2）作业云体存在过冷层，且具有一定厚度（3）云顶温度＜−10°C
作业部位	（1）作业催化处于播云窗，利于催化剂最大核化（2）航线设计合理，作业区具有一定面积且实现充分播撒
催化剂量	催化剂量合理且充足，实现充分播撒

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表 3 2014～2019年24架次飞机增雨作业信息

Table 3. Information of the 24 aircraft precipitation enhancement operations in Henan from 2014 to 2019

编号	作业日期	作业机型	催化时段（北京时）	催化时长 /h	催化平均高度/km	催化剂量				催化区域
编号	作业日期	作业机型	催化时段（北京时）	催化时长 /h	催化平均高度/km	干冰 /kg	AgI烟条 /根	AgI焰弹 /发	AgI烟管 /根	催化区域
19	20140823	运8	09:39～12:01	2.4	5.7	48	16	200	/	平顶山—许昌—漯河—南阳
20	20140823	运8	14:58～17:36	2.6	6.5	/	19	187	/	平顶山—许昌—漯河—周口—驻马店
21	20140826	运8	15:21～17:36	2.3	6.5	120	1	120	/	平顶山—许昌—漯河—周口—驻马店
56	20160507	运8	16:17～17:20	1.1	5.6	/	15	193	/	洛阳—焦作—郑州
58	20160520	运8	14:07～15:56	1.8	6.2	/	17	195	/	许昌—平顶山—漯河—周口—驻马店
65	20170404	运12	09:45～11:26	1.7	3.8	/	13	/	/	平顶山—许昌
67	20170408	运12	10:39～11:42	1.1	4.5	/	13	/	/	洛阳—焦作
69	20170410	运12	09:45～11:21	1.6	4.5	/	13	/	/	洛阳—平顶山—南阳
72	20170924	MA60	14:02～17:10	3.1	5.3	/	20	180	/	南阳
73	20170925	MA60	11:57～14:59	3.0	6.2	/	18	194	/	南阳
75	20171001	MA60	11:10～12:00	0.8	6.2	/	26	191	/	南阳
76	20171001	MA60	17:19～19:43	2.4	6.2	/	37	175	/	南阳
78	20180124	MA60	09:50～11:50	2.0	4.2	/	16	182	/	平顶山—南阳
79	20180124	MA60	15:20～16:40	1.3	4.2	/	16	189	/	平顶山—漯河—南阳—驻马店
80	20180126	MA60	21:29～22:40	1.2	4.2	/	15	192	/	南阳
90	20181105	MA60	09:10～10:59	1.8	4.0	/	/	/	33	洛阳—南阳
91	20181105	MA60	14:55～17:25	2.5	4.3	/	/	/	37	洛阳—三门峡—平顶山
93	20181106	MA60	16:42～17:30	0.8	4.3	/	/	/	36	许昌—漯河—平顶山
94	20181107	MA60	15:11～17:35	2.4	4	/	/	/	35	平顶山—南阳
97	20181203	MA60	16:02～18:12	2.2	4	/	/	/	35	周口
99	20181210	MA60	16:18～18:15	2.0	2.7	/	24	183	/	漯河—周口—驻马店
100	20181219	MA60	14:11～16:00	1.8	4.2	/	16	187	/	南阳—驻马店
101	20181219	MA60	19:15～20:50	1.6	3.6	/	16	177	/	驻马店—南阳—信阳
105	20190226	MA60	15:15～16:50	1.6	4.2	/	18	/	/	南阳
注：烟条AgI含量每根125 g及每根35 g两种规格；焰弹AgI含量每发3.6 g；烟管AgI含量每根12.5 g。

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表 4 24次飞机增雨作业初选对比区APC系数、最佳对比区

Table 4. Coefficient APC of the primary contrast area, best contrast area of the 24 aircraft precipitation enhancement operations

作业号	APC系数值				最佳对比区（APC最小值）	作业号	APC系数值				最佳对比区（APC最小值）
作业号	对比区1	对比区2	对比区3	对比区4	最佳对比区（APC最小值）	作业号	对比区1	对比区2	对比区3	对比区4	最佳对比区（APC最小值）
19	13.13	10.25	10.28	8.57	对比区4	78	2.57	4.63	6.49	1.25	对比区4
20	18.73	33.65	11.74	/	对比区3	79	2.91	1.35	0.47	2.80	对比区3
21	4.26	2.44	1.84	/	对比区3	80	26.18	1.12	11.02	8.12	对比区2
56	160.70	45.47	8.39	/	对比区3	90	4.24	6.65	12.03	5.50	对比区1
58	16.01	26.02	/	/	对比区1	91	4.41	6.04	3.98	/	对比区3
65	16.10	7.40	14.00	22.71	对比区2	93	4.84	5.18	5.00	7.95	对比区1
67	21.96	10.82	5.70	4.08	对比区4	94	10.99	40.59	2.51	/	对比区3
69	6.94	7.95	14.90	27.97	对比区1	97	1.57	13.39	1.56	6.38	对比区3
72	2.41	1.36	1.22	4.59	对比区3	99	47.67	11.45	2.28	2.02	对比区4
73	8.55	34.95	71.27	6.63	对比区4	100	1.86	8.88	2.15	/	对比区3
75	20.78	35.53	13.77	58.23	对比区3	101	11.93	3.39	30.10	11.09	对比区2
76	7.49	7.68	14.96	4.87	对比区1	105	6.62	4.60	176.70	23.16	对比区2

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表 5 具有正效果的18次增雨作业的各检验指标PIDI_i正负值统计

Table 5. Statistics of the positive and negative values of PIDI_i of the 18 precipitation enhancement operations with positive effects

作业号	PIDI_ ttop	PIDI_ ref	PIDI_ optn	PIDI_ lwp	PIDI_ CR	PIDI_ 30echo	PIDI_ VIL	PIDI_ rh
19	-	-	+	+	-	+	-	+
20	-	/	/	/	+	+	+	+
56	-	/	/	/	+	+	+	+
58	+	0	-	-	0	-	0	+
65	+	-	-	-	+	+	+	+
67	-	-	-	-	-	-	+	+
69	+	-	-	-	+	+	+	+
72	+	/	/	/	+	+	+	+
73	+	-	-	-	-	+	-	+
75	-	-	+	-	-	-	-	+
76	+	/	/	/	-	-	-	+
78	+	-	-	-	+	+	+	+
79	+	+	-	-	+	-	+	+
80	+	/	/	/	+	+	-	+
90	+	-	-	+	+	+	+	+
91	0	/	/	/	-	-	-	+
94	+	/	/	/	+	+	+	+
101	0	/	/	/	-	-	-	+
正值作业数	11	1	2	2	10	11	10	/
负值作业数	5	8	8	8	7	7	7	/
0值作业数	2	1	0	0	1	0	1	/
注：“+”表示数值为正值，“-”表示数值为负值，“/”表示不作统计数据。

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表 6 PIDI方法与K值方法对比

Table 6. Comparison of PIDI method and K value method

物理检验方法	计算公式	单位	表征含义
PIDI指数方法	公式（9）和（10）	无	PIDI_i表征影响区人工催化引起的某指标变化率； PIDI表征影响区人工催化引起的所有检验指标平均变化率
K值方法	$ K=\displaystyle\frac{A}{B} $ A为影响区某指标观测值； B为对比区某指标观测值	无	影响区与对比区的某指标观测值比值

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参考文献(26)

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