Early Identification and Automatic Warning of Hail Clouds by Satellite
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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(Tg)较冷,均值为−33°C;全部冰晶化时Tg对应的云粒子有效半径re(表征为reg)未饱和(<40 μm),均值36.9 μm,且reg 越小冰雹云越强;云顶呈现re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。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(Tg)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 reg (effective radius corresponding to glaciation temperature), the stronger the hail cloud. Additionally, hail cloud tops often have a reduction zone of re with increasing height. (2) All the studied areas have consistent cloud properties such as a lower Tg, smaller reg, and a decreased re 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.
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Key words:
- Hail cloud /
- Cloud properties /
- FY-4A /
- Early identification /
- Automatic warning
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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(Ttop)和晶化温度(Tg)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re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re中值的范围;(b)中圆点和方点分别表示同类型云所有个例Tg和Ttop的平均值,实线表示Tg和Ttop的变化范围及对应reg和retop的变化范围
Figure 1. (a) T–re (temperature–effective radius) profiles of hail clouds and deep convective clouds and (b) average values and deviations of cloud top temperature (Ttop) and glaciation temperature (Tg) of hail clouds and deep convective clouds. Blue and green solid lines and shaded areas in (a) represent the average of the median re 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 Tg and Ttop for the same type of cloud, respectively, and solid lines represent the variation range of Tg and Ttop and the variation range of reg and retop
图 2 陕西、山东、贵州和新疆冰雹云Ttop和Tg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Tg和Ttop的平均值,实线表示Tg和Ttop的变化范围及对应reg和retop的变化范围
Figure 2. Average values and deviations of Ttop and Tg of hail clouds at Shaanxi, Shandong, Guizhou, and Xinjiang. The circle and diamond represent the average values of Tg and Ttop for all cases in each region, while the lines represent the variation range of Tg and Ttop and the variation range of reg and retop
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
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
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 Ttop/°C Tg/°C retop/μm reg/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 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 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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