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Cloud Parameter Characteristics of Three Strengthening Convective Systems During Downhill Processes in Beijing
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摘要: 利用FY-4A资料, 识别北京地区3次对流下山加强过程(2021年7月1日、2022年6月12日和2022年8月4日)的中尺度对流系统(mesoscale convective system, MCS), 结合京津冀自动气象站降水资料、中国气象局北京快速更新循环数值预报系统(CMA-BJ)产品分析MCS云特征以指示MCS的形成、成熟阶段和演变特征。结果表明:形成阶段MCS面积增长缓慢, 红外亮温梯度大, 亮温值快速下降, 最低值小于-65 ℃;成熟阶段MCS面积迅速增大, 面积最大时地面短时强降水站数最多, 但变温区面积、降温幅度和亮温梯度均小于形成阶段。水汽与红外亮温差(简称亮温差)可表示对流云发展强度, 亮温差随时间变化呈缓慢波动增长-快速增长-稳定维持的特征, 指示MCS不同发展阶段。MCS形成阶段后期, 云顶高度升高, 红外亮温和水汽亮温下降, 亮温差接近于0 ℃;MCS成熟阶段, 云顶高度、红外亮温和水汽亮温均达到极值, 高空出流明显, 云团面积增至最大。以上特征可揭示北京地区对流下山过程中MCS的发展阶段, 为判断MCS强度变化和地面强降雨、大风落区提供参考。
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关键词:
- 中尺度对流系统(MCS);
- FY-4A静止卫星;
- 亮温;
- 对流下山
Abstract: Based on FY-4A geostationary satellite multi-channel data, mesoscale convective systems (MCSs) during three downhill processes of convection strengthening (1 July 2021, 12 June 2022 and 4 August 2022) in Beijing are identified by temperature threshold method. Precipitation data from Beijing-Tianjin-Hebei automatic weather stations, along with data from CMA-BJ mesoscale numerical model, are also utilized to analyze cloud characteristics of MCSs in formation and mature stages. Results indicate that the area of the convective cloud increases slowly, but the brightness temperature gradient is high, the brightness temperature decreases rapidly, the lowest temperature is below -65 ℃ and the maximum temperature variation rate in the area is -40 ℃·(15 min)-1 in the formation stage of MCSs. The maximum bright temperature gradient area corresponds to the inflow and water vapor convergence area, and it is located on the side of the cloud movement direction. In the mature stage, the area of the convective cloud increases rapidly. The number of short-time heavy precipitation stations is the largest, and the rainfall intensity is higher when the area is the largest. The brightness temperature is maintained at a relatively low value, but the area and amplitude of the temperature variation zone, as well as the brightness temperature gradient in the mature stage are smaller than that in the formation stage. The brightness temperature difference between water vapor brightness temperature and infrared brightness temperature can represent convective cloud development intensity, showing characteristics of slow fluctuation increase-fast increase-stable maintenance over time which indicate stages of MCSs. In the view of kinematic characteristics of the cloud cluster, it can be seen that strongly convergent and strong upward motion are located in the inflow region, the cloud top height increases rapidly, the infrared brightness and water vapor brightness temperature decreases rapidly, and the brightness temperature is close to 0 ℃ in the initial stage. In the mature stage, the cloud top height, infrared brightness and water vapor brightness temperature stay a relatively stable state, strong updraft and downdraft coexist, there is obvious outflow within the upper air, and the convective area increases to the maximum. The downdraft in the cloud body creates a strong outflow ahead of the low-level movement, leading to the extreme wind on the ground. The convergence of environmental wind and outflow strengthens the updraft movement in front of the MCS, which is conducive to the formation of new cells and the strengthening of MCS. Above results can reveal the development stage of MCSs and provide reference for determining whether MCSs in Beijing are strengthening or weakening during the downhill processes, as well as the potential area of severe weather on the surface. -
图 1 研究区域(灰色框内) 及地形高度(填色)
Fig. 1 Terrian height (the shaded) and study area (the grey box)
图 3 3次过程MCS面积和短时强降水站数变化
(灰线和橙线分别表示面积为6400 km2和形成与成熟阶段分界)
Fig. 3 MCS area and number of stations with short-time heavy rainfall of 3 cases
(the grey line and the orange line denote the area of 6400 km2 and the boundary between formation stage and mature stage of MCS,respectively)
图 4 3次过程MCS形成阶段和成熟阶段850 hPa风场(风羽)、短时强降水站点(黑点) 与强降水前1 h红外亮温(填色，紫线表示-52 ℃亮温)
Fig. 4 850 hPa wind field (the barb), short-time heavy rainfall sites (the black dot) and infrared brightness temperature 1 h in advance (the shaded, the purple line denotes brightness temperature of -52 ℃) in formation stage and mature stage of MCS in 3 cases
图 5 3次过程MCS形成阶段和成熟阶段红外亮温(紫线和红线分别表示-32 ℃和-52 ℃亮温) 与亮温梯度(填色)
Fig. 5 Infrared brightness temperature (the purple line and the red line denote brightness temperature of -32℃ and -52℃) and brightness temperature gradient (the shaded) in formation stage and mature stage of MCS in 3 cases
图 6 3次过程MCS最低红外亮温与最低水汽亮温变化
Fig. 6 Variations of minimum infrared and vapor brightness temperature of MCS in 3 cases
图 7 3次过程MCS形成阶段和成熟阶段红外亮温变温率(填色)
(紫线和红线分别表示-32 ℃和-52 ℃亮温)
Fig. 7 Infrared brightness temperature variation rate (the shaded) in formation stage and mature stage of MCS in 3 cases (the purple line and red line denote brightness temperature of -32 ℃ and -52 ℃, respectively)
图 8 3次过程MCS形成阶段和成熟阶段亮温差(填色)
(紫线和蓝线分别表示-32 ℃和-52 ℃红外亮温)
Fig. 8 Brightness temperature difference (the shaded) in formation stage and mature stage of MCS in 3 cases (the purple line and the blue line denote infrared brightness temperature of -32 ℃ and -52 ℃, respectively)
图 9 3次过程MCS形成阶段和成熟阶段亮温差变温率(填色)
(紫线和红线分别表示-32 ℃和-52 ℃红外亮温)
Fig. 9 Brightness temperature difference variation rate (the shaded) in formation stage and mature stage of MCS in 3 cases (the purple line and the red line denote infrared brightness temperature of -32 ℃ and -52 ℃, respectively)
图 10 “6·12”过程MCS形成阶段和成熟阶段红外亮温(填色) 与850 hPa (黑色风羽)、200 hPa风场(蓝色风羽)(紫线和红线分别表示-32 ℃和-52 ℃亮温)(灰线分别表示116.6°E、117.12°E)
Fig. 10 Infrared brightness temperature (the shaded, the purple isoline and the red isoline denote brightness temperature of -32 ℃ and -52 ℃, respectively) and the wind field of 850 hPa (the black barb) and 200 hPa (the blue barb) in formation stage and mature stage of MCS in case "6·12" (the grey line denotes longitude of 116.6°E and 117.12°E, respectively)
图 11 “6·12”过程MCS形成阶段沿116.6°E和成熟阶段沿117.12°E的垂直剖面(填色为垂直速度；黑色箭头为v和w合成的沿经向的垂直环流风场；灰色等值线为雷达反射率因子，单位：dBZ；紫线为云顶高度，单位：km；橙线为红外亮温，单位：℃；绿线为水汽亮温，单位：℃)
Fig. 11 Vertical section along 116.6°E in formation stage and 117.12°E in mature stage of MCS in case "6·12" (the shaded denotes vertical velocity;the black vector denotes meridional wind field composed of v and w;the grey isoline denotes reflectivity factor(unit:dBZ);the purple line, the orange line and the green line denote cloud top height(unit:km), infrared brightness temperature(unit:℃), and water vapor brightness temperature(unit:℃), respectively)
表 1 北京地区3次典型对流下山过程
Table 1 Mesoscale convective systems(MCSs) during 3 downhill processes in Beijing
过程 时间 路径 强对流天气 形成阶段 成熟阶段 “6·12” 2022-06-12 西—东 短时强降水、大风、冰雹 16:38—19:23 19:23—22:23 “7·1” 2021-07-01 西北—东南 短时强降水、大风、冰雹 11:38—14:34 14:34—18:19 “8·4” 2022-08-04 西北—东南 短时强降水、大风 15:34—18:15 18:15—22:38 
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[1] Maddox R A.Meoscale convective complexes.Bull Amer Meteor Soc, 1980, 61(11):1374-1387. doi: 10.1175/1520-0477(1980)061<1374:MCC>2.0.CO;2 [2] Houze R A Jr. Mesoscale convective systems. Rev Geophys, 2004, 42(4). DOI: 10.1029/2004RG000150. [3] 张夕迪, 沈文强, 杨舒楠, 等. 2020年7月4—10日长江流域极端强降雨过程MCS活动特征分析. 暴雨灾害, 2020, 39(6): 593-602. doi: 10.3969/j.issn.1004-9045.2020.06.007Zhang X D, Shen W Q, Yang S N, et al. Analysis of MCS activity characteristics during extreme heavy rainfall in the Yangtze River Basin from July 4 to 10, 2020. Torrential Rain Disasters, 2020, 39(6): 593-602. doi: 10.3969/j.issn.1004-9045.2020.06.007 [4] 王晓芳, 崔春光. 暴雨中尺度对流系统研究的若干进展. 暴雨灾害, 2011, 30(2): 97-106. https://www.cnki.com.cn/Article/CJFDTOTAL-HBQX201102002.htmWang X F, Cui C G. A number of advances of the research on heavy rain mesoscale convective systems. Torrential Rain Disasters, 2011, 30(2): 97-106. https://www.cnki.com.cn/Article/CJFDTOTAL-HBQX201102002.htm [5] Zheng L L, Sun J H, Zhang X L, et al. Organizational modes of mesoscale convective systems over central East China. Wea Forecasting, 2013, 28(5): 1081-1098. doi: 10.1175/WAF-D-12-00088.1 [6] 段炼, 郭鹏宇. 利用静止气象卫星识别和追踪MCS方法概述. 民航学报, 2021, 5(6): 76-80. https://www.cnki.com.cn/Article/CJFDTOTAL-MHXE202106016.htmDuan L, Guo P Y. Overview of MCS identification and tracking using geostationary meteorological satellite. J Civ Aviat, 2021, 5(6): 76-80. https://www.cnki.com.cn/Article/CJFDTOTAL-MHXE202106016.htm [7] 覃丹宇, 方宗义. 利用静止气象卫星监测初生对流的研究进展. 气象, 2014, 40(1): 7-17. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201401002.htmQin D Y, Fang Z Y. Research progress of geostationary satellite-based convective initiation. Meteor Mon, 2014, 40(1): 7-17. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201401002.htm [8] 张琪, 任景轩, 肖递祥, 等. 青藏高原东部初生雷暴云图特征及成因. 气象科技, 2018, 46(5): 943-950. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201805013.htmZhang Q, Ren J X, Xiao D X, et al. Satellite image characteristics and formation mechanisms of newborn thunderstorms in eastern Qinghai-Xizang Plateau. Meteor Sci Technol, 2018, 46(5): 943-950. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201805013.htm [9] Mecikalski J R, Bedka K M. Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon Wea Rev, 2006, 134(1): 49. doi: 10.1175/MWR3062.1 [10] Vila D A, Machado L A T, Laurent H, et al. Forecast and tracking the evolution of cloud clusters(ForTraCC) using satellite infrared imagery: Methodology and validation. Wea Forecasting, 2008, 23(2): 233-245. doi: 10.1175/2007WAF2006121.1 [11] Walker J R, MacKenzie W M, Mecikalski J R, et al. An enhanced geostationary satellite-based convective initiation algorithm for 0-2 h nowcasting with object tracking. J Appl Meteor Climatol, 2012, 51(11): 1931-1949. doi: 10.1175/JAMC-D-11-0246.1 [12] 何兴伟, 冯小虎, 韩琦, 等. 世界各国静止气象卫星发展综述. 气象科技进展, 2020, 10(1): 22-29. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ202001007.htmHe X W, Feng X H, Han Q, et al. Advances of the geostationary meteorological satellite in the world: A review. Adv Meteor Sci Tech, 2020, 10(1): 22-29. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ202001007.htm [13] 俞小鼎, 郑永光. 中国当代强对流天气研究与业务进展. 气象学报, 2020, 78(3): 391-418. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202003006.htmYu X D, Zheng Y G. Advances in severe convective weather research and operational service in China. Acta Meteor Sinica, 2020, 78(3): 391-418. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202003006.htm [14] 王婧羽, 崔春光, 陈杨瑞雪, 等. 西南山区5—8月产生突发性暴雨事件的中尺度对流系统的时空分布特征. 气象学报, 2022, 80(1): 21-38. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202201002.htmWang J Y, Cui C G, Chen Y R X, et al. Temporal and spatial characteristics of mesoscale convective systems associated with abrupt heavy rainfall events over Southwest China during May—August. Acta Meteor Sinica, 2022, 80(1): 21-38. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202201002.htm [15] 万夫敬, 刁秀广. 两次MCC红外云图特征与天气实况异同性分析. 气象, 2018, 44(6): 759-770. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201806004.htmWan F J, Diao X G. Comparative analysis on cloud evolution features and weather of two MCCs. Meteor Mon, 2018, 44(6): 759-770. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201806004.htm [16] 冯晋勤, 刘铭, 蔡菁. 闽西山区"7·22" 极端降水过程中尺度对流特征. 应用气象学报, 2018, 29(6): 748-758. doi: 10.11898/1001-7313.20180610Feng J Q, Liu M, Cai J. Meso-scale convective characteristics of "7·22" extreme rain in the west mountainous area of Fujian. J Appl Meteor Sci, 2018, 29(6): 748-758. doi: 10.11898/1001-7313.20180610 [17] 黄小彦, 孙继松, 刘文婷. 地形作用下低空急流的演变与强降水对流风暴系统的相互作用. 气象学报, 2020, 78(4): 551-567. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202004001.htmHuang X Y, Sun J S, Liu W T. The interaction between low-level jet evolution and severe convective rainstorms under topographic effect. Acta Meteor Sinica, 2020, 78(4): 551-567. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202004001.htm [18] 马瑞阳, 郑栋, 姚雯, 等. 雷暴云特征数据集及我国雷暴活动特征. 应用气象学报, 2021, 32(3): 358-369. doi: 10.11898/1001-7313.20210308Ma R Y, Zheng D, Yao W, et al. Thunderstorm feature dataset and characteristics of thunderstorm activities in China. J Appl Meteor Sci, 2021, 32(3): 358-369. doi: 10.11898/1001-7313.20210308 [19] 蓝渝, 郑永光, 毛冬艳, 等. 华北区域冰雹天气分型及云系特征. 应用气象学报, 2014, 25(5): 538-549. http://qikan.camscma.cn/article/id/20140503Lan Y, Zheng Y G, Mao D Y, et al. Classification and satellite nephogram features of hail weather in North China. J Appl Meteor Sci, 2014, 25(5): 538-549. http://qikan.camscma.cn/article/id/20140503 [20] 孙继松, 雷蕾, 于波, 等. 近10年北京地区极端暴雨事件的基本特征. 气象学报, 2015, 73(4): 609-623. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201504001.htmSun J S, Lei L, Yu B, et al. The fundamental features of the extreme severe rain events in the recent 10 years in the Beijing Area. Acta Meteor Sinica, 2015, 73(4): 609-623. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201504001.htm [21] 周鑫, 周顺武, 覃丹宇, 等. 利用FY-2F快速扫描资料分析对流初生阶段的云顶物理量特征. 气象, 2019, 45(2): 216-227. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201902007.htmZhou X, Zhou S W, Qin D Y, et al. Analysis of cloud top features during convective initiation using FY-2F satellite scan data. Meteor Mon, 2019, 45(2): 216-227. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201902007.htm [22] 陆风, 张晓虎, 陈博洋, 等. 风云四号气象卫星成像特性及其应用前景. 海洋气象学报, 2017, 37(2): 1-12. https://www.cnki.com.cn/Article/CJFDTOTAL-SDQX201702001.htmLu F, Zhang X H, Chen B Y, et al. FY-4 geostationary meteorological satellite imaging characteristics and its application prospects. J Mar Meteor, 2017, 37(2): 1-12. https://www.cnki.com.cn/Article/CJFDTOTAL-SDQX201702001.htm [23] 齐道日娜, 何立富, 王秀明, 等. "7·20" 河南极端暴雨精细观测及热动力成因. 应用气象学报, 2022, 33(1): 1-15. doi: 10.11898/1001-7313.20220101Chyi D, He L F, Wang X M, et al. Fine observation characteristics and thermodynamic mechanisms of extreme heavy rainfall in Henan on 20 July 2021. J Appl Meteor Sci, 2022, 33(1): 1-15. doi: 10.11898/1001-7313.20220101 [24] 高安春, 申高航. 多源资料分析鲁西局地大暴雨成因及降水的微物理特征. 气象, 2022, 48(11): 1475-1486. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202211010.htmGao A C, Shen G H. Causes and precipitation microphysical characteristics of localized heavy rainstorm in western Shandong based on multi-source data. Meteor Mon, 2022, 48(11): 1475-1486. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202211010.htm [25] 王莹, 董畅, 易笑园, 等. 天津城区一次γ中尺度短时暴雨的热动力环境分析. 气象, 2021, 47(4): 398-411. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202104002.htmWang Y, Dong C, Yi X Y, et al. The thermal and dynamic environmental field analysis of a meso-γ scale short-time rainstorm in Tianjin. Meteor Mon, 2021, 47(4): 398-411. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202104002.htm [26] 张桂莲, 李一平, 江靖, 等. 多个超级单体风暴诱发的EF3级强龙卷特征分析. 气象, 2023, 49(11): 1315-1327. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202311009.htmZhang G L, Li Y P, Jiang J, et al. Characteristic analysis of EF3 strong tornado induced by multiple supercell storms. Meteor Mon, 2023, 49(11): 1315-1327. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202311009.htm [27] 肖海霞, 张峰, 王亚强, 等. 基于生成对抗网络和卫星数据的云图临近预报. 应用气象学报, 2023, 34(2): 220-233. doi: 10.11898/1001-7313.20230208 Xiao H X, Zhang F, Wang Y Q, et al. Nowcasting of cloud images based on generative adversarial network and satellite data. J Appl Meteor Sci, 2023, 34(2): 220-233. doi: 10.11898/1001-7313.20230208 [28] 张琪, 任景轩, 肖红茹, 等. 基于FY-4A卫星资料的四川盆地MCC初生和成熟阶段特征. 大气科学, 2021, 45(4): 863-873. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202104011.htmZhang Q, Ren J X, Xiao H R, et al. Characteristics of MCC from convective initiation to mature stage over the Sichuan Basin based on FY-4A satellite data. Chinese J Atmos Sci, 2021, 45(4): 863-873. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202104011.htm [29] 高洋, 蔡淼, 曹治强, 等. "21·7" 河南暴雨环境场及云的宏微观特征. 应用气象学报, 2022, 33(6): 682-695. doi: 10.11898/1001-7313.20220604Gao Y, Cai M, Cao Z Q, et al. Environmental conditions and cloud macro and micro features of "21·7" extreme heavy rainfall in Henan Province. J Appl Meteor Sci, 2022, 33(6): 682-695. doi: 10.11898/1001-7313.20220604 [30] 王海平. 高空冷涡对台风烟花(2106)路径的影响. 应用气象学报, 2023, 34(5): 586-597. doi: 10.11898/1001-7313.20230507Wang H P. Impacts of upper tropospheric cold low on the track of Typhoon In-fa in 2021. J Appl Meteor Sci, 2023, 34(5): 586-597. doi: 10.11898/1001-7313.20230507 [31] 闫琳城, 张文娟, 张义军, 等. 南海雷暴大风时空分布及闪电和对流活动特征. 应用气象学报, 2023, 34(4): 503-512. doi: 10.11898/1001-7313.20230410Yan L C, Zhang W J, Zhang Y J, et al. Temporal and spatial distribution of thunderstorms and strong winds with characteristics of lightning and convective activities in the South China Sea. J Appl Meteor Sci, 2023, 34(4): 503-512. doi: 10.11898/1001-7313.20230410 [32] 张萍萍, 刘文婷, 章翠红, 等. 一次强降水过程中FY-4A闪电成像仪观测特征分析. 气象, 2021, 47(11): 1391-1401. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202111008.htmZhang P P, Liu W T, Zhang C H, et al. Observation characteristics of FY-4A lightning mapping imager during a severe rainfall. Meteor Mon, 2021, 47(11): 1391-1401. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202111008.htm [33] 邹耀仁, 王赟, 王淑一, 等. 基于卫星资料的大连地区强对流天气闪电活动特征. 气象与环境学报, 2021, 37(4): 128-133. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX202104018.htmZou Y R, Wang Y, Wang S Y, et al. Characteristics of lightning activity during severe convective weather in Dalian Area based on satellite data. J Meteor Environ, 2021, 37(4): 128-133. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX202104018.htm [34] 支树林, 包慧濛, 李婕. FY-4A卫星闪电资料在台风飑线天气监测中的应用能力分析. 云南大学学报(自然科学版), 2019, 41(6): 1178-1190. https://www.cnki.com.cn/Article/CJFDTOTAL-YNDZ201906015.htmZhi S L, Bao H M, Li J. Application ability analyses of FY-4A satellite flashes data in typhoon squall line weather surveillance. J Yunnan Univ Nat Sci Ed, 2019, 41(6): 1178-1190. https://www.cnki.com.cn/Article/CJFDTOTAL-YNDZ201906015.htm [35] 张晓芸, 魏鸣, 潘佳文. FY-4闪电资料在厦门强降水监测预警中的应用. 遥感技术与应用, 2019, 34(5): 1082-1090. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS201905019.htmZhang X Y, Wei M, Pan J W. Application of FY-4 lightning data in monitoring and warning a heavy precipitation in Xiamen on May 7, 2018. Remote Sens Technol Appl, 2019, 34(5): 1082-1090. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS201905019.htm [36] 任素玲, 赵玮, 曹冬杰, 等. FY-4A白天对流风暴和闪电产品在华北强雷暴天气分析中的应用. 海洋气象学报, 2020, 40(1): 33-46. https://www.cnki.com.cn/Article/CJFDTOTAL-SDQX202001004.htmRen S L, Zhao W, Cao D J, et al. Application of FY-4A daytime convective storm and lightning products in analyzing severe thunderstorm weather in North China. J Mar Meteor, 2020, 40(1): 33-46. https://www.cnki.com.cn/Article/CJFDTOTAL-SDQX202001004.htm [37] 俞小鼎, 姚秀萍, 熊廷南, 等. 多普勒天气雷达原理与业务应用. 北京: 气象出版社, 2006.Yu X D, Yao X P, Xiong T N, et al. Principle and Application of Doppler Weather Radar. Beijing: China Meteorological Press, 2006. [38] 费增坪, 郑永光, 张焱, 等. 基于静止卫星红外云图的MCS普查研究进展及标准修订. 应用气象学报, 2008, 19(1): 82-90. http://qikan.camscma.cn/article/id/20080113Fei Z P, Zheng Y G, Zhang Y, et al. MCS census and modification of MCS definition based on geostationary satellite infrared imagery. J Appl Meteor Sci, 2008, 19(1): 82-90. http://qikan.camscma.cn/article/id/20080113 [39] 费增坪, 王洪庆, 张焱, 等. 基于静止卫星红外云图的MCS自动识别与追踪. 应用气象学报, 2011, 22(1): 115-122. http://qikan.camscma.cn/article/id/20110112Fei Z P, Wang H Q, Zhang Y, et al. MCS identification and tracking based on geo-satellite IR images. J Appl Meteor Sci, 2011, 22(1): 115-122. http://qikan.camscma.cn/article/id/20110112 [40] 段炼, 郭庚常. 风云卫星云图自动识别追踪MCS的方法. 电子科技, 2016, 29(4): 116-119. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKK201604030.htmDuan L, Guo G C. Research on method for MCS automatic identification and tracking based on FY satellite cloud image. Electron Sci Technol, 2016, 29(4): 116-119. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKK201604030.htm [41] 段炼, 刘帆, 吴俊杰. 卫星云图上自动识别追踪MCS方法. 中国民航大学学报, 2014, 32(2): 44-48. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGMH201402011.htmDuan L, Liu F, Wu J J. Antomatical identification and tracking of MCS based on satellite cloud image. J Civ Aviat Univ China, 2014, 32(2): 44-48. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGMH201402011.htm [42] 康岚, 龙柯吉, 黄楚惠, 等. 四川中尺度对流复合体特征分析. 西南大学学报(自然科学版), 2016, 38(11): 149-154. https://www.cnki.com.cn/Article/CJFDTOTAL-XNND201611023.htmKang L, Long K J, Huang C H, et al. The characteristic analysis of mesoscale convective complex over Sichuan Region. J Southwest Univ Nat Sci Ed, 2016, 38(11): 149-154. https://www.cnki.com.cn/Article/CJFDTOTAL-XNND201611023.htm [43] Schmetz J, Tjemkes S A, Gube M, et al. Monitoring deep convection and convective overshooting with METEOSAT. Adv Space Res, 1997, 19(3): 433-441. -
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