冰雹云卫星早期识别与自动预警

徐小红; 余兴; 刘贵华; 岳治国; 朱延年

doi:10.3878/j.issn.1006-9895.2104.21010

冰雹云卫星早期识别与自动预警

doi: 10.3878/j.issn.1006-9895.2104.21010

徐小红^{1, 2,},
余兴^{1, 2, ,},
刘贵华¹,
岳治国³,
朱延年¹

1.
陕西省气象科学研究所，西安 710016
2.
秦岭和黄土高原生态环境重点实验室，西安 710016
3.
陕西省人工影响天气中心，西安 710016

基金项目: 国家重点研发计划项目2018YFC1507903，中国气象局西北区域人影科学试验项目RYSY201905，陕西省重点研发计划项目2020SF-429，中国气象局创新发展专项CXFZ2021J040

详细信息

作者简介:
徐小红，女，1971年出生，硕士，高工，主要从事云降水和人工影响天气研究。E-mail: xu_xiaohong16@163.com

通讯作者:
余兴，E-mail: yu_xing23@163.com

中图分类号: P482
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出版历程
- 收稿日期: 2021-01-21
- 录用日期: 2021-07-08
- 网络出版日期: 2021-08-17
- 刊出日期: 2022-01-18

Early Identification and Automatic Warning of Hail Clouds by Satellite

Xiaohong XU^{1, 2
,},
Xing YU^{1, 2
, ,},
Guihua LIU¹,
Zhiguo YUE³,
Yannian ZHU¹

1.
Meteorological Institute of Shaanxi Province, Xi’an 710016
2.
Key Laboratory of Eco-Environment and Meteorology for the Qinling Moutains and Loess Plateau, Xi’an 710016
3.
Center of Weather Modification of Shaanxi Province, Xi’an 710016

Funds: National Key Research and Development Program of China (Grant 2018YFC1507903), Northwest Weather Modification Scientific Experiment of China Meteorological Administration (Grant RYSY201905), Key Research and Development Program of Shaanxi Province (Grant 2020SF-429), Innovation and Development Project of China Meteorological Administration (Grant CXFZ2021J040)

摘要

摘要: 利用陕西、山东、贵州和新疆等地近十年日间降雹记录和对应的极轨卫星数据，采用卫星云微物理反演技术，定量分析冰雹云微物理特征，比较不同地区间差异，并利用FY-4A静止卫星定量分析一次冰雹过程云微物理特征演变，探讨冰雹云卫星识别预警应用潜力。结果表明：（1）陕西、山东等地冰雹云微物理特征具有一致性，卫星早期识别指标为：晶化温度（T_g）较冷，均值为−33°C；全部冰晶化时T_g对应的云粒子有效半径r_e（表征为r_eg）未饱和（<40 μm），均值36.9 μm，且r_eg 越小冰雹云越强；云顶呈现r_e随高度减小带。（2）各地冰雹云早期识别指标在数值上存在一定差异，实际应用时应针对各地进行相应调整。（3）在静止卫星上，冰雹云微物理特征与极轨卫星相一致，将早期识别指标应用于FY-4A静止卫星，跟踪云团发展演变，实现自动预警。（4）经过4次降雹过程中应用，FY-4A卫星自动预警与实况吻合22次，漏报2次，自动预警平均提前约2小时。FY-4A卫星自动预警对及时有效组织实施人工防雹作业具有重要现实意义。
- 冰雹云 /
- 云微物理特征 /
- FY-4A静止卫星 /
- 卫星早期识别 /
- 自动预警
Abstract: Meteorological satellites have provided useful information for improving weather forecasting, environmental monitoring, and short-term climate prediction. In the field of the weather forecast, satellites provide a powerful means for the forecast of typhoons, rainstorms, hail, sandstorms, and other severe weather conditions. In this study, the microstructure of hail clouds was analyzed by satellite observation data based on nearly a decade of hail event records of Shaanxi, Shandong, Guizhou, and Xinjiang. The comparison between the hail cloud and deep convective precipitation cloud characteristics retrieved by polar orbit satellites showed different cloud properties such as cloud top temperature/effective radius and cloud glaciation temperatures. Based on distinct cloud properties between hail clouds and convective clouds, this work summarized the characteristics and further applied them to the FY-4A geostationary satellite, which captures the hail cycle, which occurred on August 16, 2019, in the Shandong area. Results showed that the satellite has the potential to capture a hail cloud during its developing stage and use it as an application of early warning. The hail cloud shows the following characteristics: (1) There are considerable differences in the cloud’s physical characteristics between hail clouds and deep convective precipitation clouds. Microphysical characteristics of hail clouds observed by satellites are shown in three aspects: Glaciation temperature（T_g）is cooler with an average value of −33°C. The hail cloud reaches the glaciation temperature with a smaller effective radius (<40 μm) with an average of 36.9 μm when the clouds are fully glaciated. It also shows that the smaller the r_eg (effective radius corresponding to glaciation temperature), the stronger the hail cloud. Additionally, hail cloud tops often have a reduction zone of r_e with increasing height. (2) All the studied areas have consistent cloud properties such as a lower T_g, smaller r_eg, and a decreased r_e compared to those of adjacent convective clouds. However, it still showed regional variabilities that indicate the need to establish different indicators for identifying hail clouds for early warning purposes. (3) The case study of the FY-4A geostationary satellite shows that the geostationary satellite can track the evolution of hail clouds. By tracking the hail cloud, the geostationary satellite has a response consistent with that of the polar orbit satellite, providing a method for monitoring and early warning service of hail weather. The geostationary satellite can be used to track the development and evolution of the cloud cluster at any time when the satellite detects a strong hail signal because of the high time resolution. Combining the satellite’s early warning with radar observation, the location of hail occurrence can be determined precisely. (4) Combining the indicators summarized by polar orbit satellites with the FY-4A to track the hail cloud evolution. Four hail storms that occurred in Shaanxi and Shandong were applied for early warnings. Ground observations reported 24 hail events in the two regions, of which the satellite successfully warned 22 times in advance and missed two times. The average early warning time is about two hours before the hail disaster. All of these suggest that the automatic warning of hail by the FY-4A satellite has important practical significance for timely and effective organization and implementation of operational hail mitigation.
- Hail cloud /
- Cloud properties /
- FY-4A /
- Early identification /
- Automatic warning

HTML全文

图 1 （a）冰雹云和深对流降水云的粒子有效半径随温度变化特征及（b）两类云云顶温度（T_top）和晶化温度（T_g）的均值和偏差分布。（a）中蓝色和绿色实线分别为冰雹云和深对流降水云个例在相同温度层r_e中值的平均值，蓝色和绿色阴影分别为冰雹云和深对流降水云对应温度层r_e中值的范围；（b）中圆点和方点分别表示同类型云所有个例T_g和T_top的平均值，实线表示T_g和T_top的变化范围及对应r_eg和r_etop的变化范围

Figure 1. (a) T–r_e (temperature–effective radius) profiles of hail clouds and deep convective clouds and (b) average values and deviations of cloud top temperature (T_top) and glaciation temperature (T_g) of hail clouds and deep convective clouds. Blue and green solid lines and shaded areas in (a) represent the average of the median r_e and its range at the same levels of temperature for hail clouds and deep convective clouds, respectively. The dots and squares in (b) represent the average values of T_g and T_top for the same type of cloud, respectively, and solid lines represent the variation range of T_g and T_top and the variation range of r_eg and r_etop

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图 2 陕西、山东、贵州和新疆冰雹云T_top和T_g均值和偏差分布，图中圆点和菱形点分别表示各地所有个例T_g和T_top的平均值，实线表示T_g和T_top的变化范围及对应r_eg和r_etop的变化范围

Figure 2. Average values and deviations of T_top and T_g of hail clouds at Shaanxi, Shandong, Guizhou, and Xinjiang. The circle and diamond represent the average values of T_g and T_top for all cases in each region, while the lines represent the variation range of T_g and T_top and the variation range of r_eg and r_etop

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图 3 2019年8月16日13:30～17:30冰雹云（a）T–r_e分布及（b）T_top、T_g随时间的变化

Figure 3. Temporal evolution of (a) T–r_e profiles and (b) T_top and T_g of hail clouds from 1330 BJT (Beijing time) to 1730 BJT on August 16, 2019

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图 4 5·23冰雹（a）11:38、（b）12:19、（c）13:38和（d）16:00卫星云微物理合成图（Lensky and Rosenfeld，2008），绿色圆点为降雹地点，红线为11:00～17:00 8 km高度前向气流轨迹，红色星号为卫星预警信号，下同

Figure 4. Day microphysical composition（Lensky and Rosenfeld，2008）at (a) 1138 BJT, (b) 1219 BJT, (c) 1338 BJT, and (d) 1600 BJT on May 23, 2020. Purple lines represent the forward trajectory at the height of 8 km from 1100 BJT to1700 BJT, the red hexagram represents an early warning from the satellite, and the green circle represents the hail events, the same below

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图 5 2019年8月16日冰雹（a）14:45、（b）16:15、（c）17:30和2020年5月21日（d）13:45、（e）14:30、（f）17:34及2020年6月24日（g）12:00、（h）13:23、（i）18:00卫星云微物理合成图

Figure 5. Day microphysical composition at (a) 1445 BJT, (b) 1615 BJT, and (c) 1730 BJT on August 16, 2019; (d) 1345 BJT, (e) 1430 BJT, and (f) 1734 BJT on May 21, 2020; and (g) 1200 BJT, (h) 1323 BJT, and (i) 1800 BJT on June 24, 2020

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表 1 2019年8月16日不同时次冰雹云的$T_{\rm{top}} $、$T_{\rm{g}} $、$r_{\rm{etop}} $和$r_{\rm{eg}} $值

Table 1. $T_{\rm{top}} $, $T_{\rm{g}} $, $r_{\rm{etop}} $, and $r_{\rm{eg}} $ of hail clouds at different times on August 16, 2019

时间	T_top/°C	T_g/°C	r_etop/μm	r_eg/μm
13:30	−25	−23	40	40
14:00	−24	−21	40	40
14:30	−39	−26	39.8	40
15:00	−47	−28	40	40
16:00	−48	−40	31	32.1
16:30	−49	−40	28.1	30.9
17:00	−50	−40	32.5	29.1
17:30	−56	−40	34.3	29.3

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表 2 5·23冰雹地面降雹实况和卫星预警

Table 2. Early warning time for hail clouds from FY-4A in comparison with the actual event time on May 23, 2020

序号	降雹开始时间	降雹地点	卫星预警初现时间	时间提前量 /min	备注
1	12:00	烟台市	11:38	22	吻合
2	12:40	济南钢城区	12:19	21	吻合
3	12:40	淄博博山区	12:19	21	吻合
4	13:00	淄博沂源县	12:19	41	吻合
5	13:00	临沂平邑县	13:38	−38	漏报
6	14:00	烟台龙口市	11:38	142	吻合
7	14:40	烟台蓬莱市	11:38	182	吻合
8	15:00	临沂沂水县	13:38	82	吻合
9	17:00	烟台栖霞市	11:38	322	吻合

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表 3 山东、陕西冰雹个例地面降雹实况和卫星预警

Table 3. Early warning time for the hail cloud from the FY-4A in comparison with the actual event time for different dates at Shandong and Shaanxi

日期	降雹开始时间	降雹地点	卫星预警初现时间	时间提前量/min	备注
2019年8月16日	14:00	潍坊安丘市	14:45	−45	漏报
	15:25	潍坊诸城市	14:45	40	吻合
	15:30	临沂沂水县	14:45	45	吻合
	15:30	日照五莲县	14:45	45	吻合
	16:57	临沂莒南县	14:45	132	吻合
	17:00	日照莒县	14:45	135	吻合
2020年5月21日	14:01	延安延长县	13:45	16	吻合
	17:11	渭南白水县	14:30	161	吻合
	17:20	铜川耀州区	14:30	170	吻合
	17:21	铜川印台区	14:30	171	吻合
2020年6月24日	15:16	延安吴起县	13:23	113	吻合
	15:25	延安宝塔区	12:00	205	吻合
	16:36	延安延川县	12:00	276	吻合
	16:40	延安富县	13:23	197	吻合
	16:51	延安黄陵县	13:23	208	吻合

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