FY-4A卫星闪电资料与ADTD闪电资料在四川省冕宁暴雨中的对比分析及数值模拟

赵婥; 徐国强; 黄守友; 程佳佳

doi:10.3878/j.issn.1006-9895.2111.21110

FY-4A卫星闪电资料与ADTD闪电资料在四川省冕宁暴雨中的对比分析及数值模拟

doi: 10.3878/j.issn.1006-9895.2111.21110

1.
中国气象科学研究院, 北京100081
2.
中国气象局地球系统数值预报中心, 北京100081
3.
中国科学院软件研究所, 北京100190

基金项目: 国家重点研发计划项目2018YFC1506603，风云卫星应用先行计划 FY-APP-ZX-2022.01，国家自然科学基金项目42175167

详细信息

作者简介:
赵婥，女，1998年出生，硕士研究生，主要从事数值模式的物理过程研究。E-mail: zhao_chuo@163.com

通讯作者:
徐国强，E-mail: xugq@cma.gov.cn

中图分类号: P456
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- 被引次数: 0
出版历程
- 收稿日期: 2021-06-28
- 录用日期: 2021-11-15
- 网络出版日期: 2022-03-02
- 刊出日期: 2023-05-15

Comparative Analysis and Numerical Simulation of Lightning Detection Data from FY-4A Satellite and ADTD for Rainstorm in Mianning, Sichuan Province

1.
Chinese Academy of Meteorological Sciences, Beijing 100081
2.
Center for Earth System Modeling and Prediction of China Meteorological Administration, Beijing 100081
3.
Institute of Software, Chinese Academy of Sciences, Beijing 100190

Funds: National Key Research and Development Program of China (Grant 2018YFC1506603), Fengyun Application Pioneering Project (Grant FY-APP-ZX-2022.01), National Natural Science Foundation of China (Grant 42175167)

摘要

摘要: FY-4A卫星闪电资料与地基ADTD（Advanced Direction and Time of arrival Detecting system）闪电资料对于研究暴雨等强对流天气具有一定的指示意义，通过对四川省冕宁暴雨个例的研究，对比分析了这两种闪电资料的差别，设计了将两种闪电资料引入数值预报模式的多组数值试验。结果表明：（1）两种闪电资料在不同地区有不同的探测效果，ADTD闪电资料范围更广且分散，FY-4A卫星监测到的闪电数量更密集、分布更集中；两种闪电资料在进入模式之后所转化成的代理雷达回波具有很好的一致性；对于低频次的闪电来说，ADTD闪电定位仪可能比FY-4A闪电成像仪探测效率更高。（2）引入任何一种闪电资料都对降水预报具有正效果，其中ADTD闪电资料的应用对于短时降水预报准确率的提高更为有效。（3）两种闪电资料对于云微物理量的调整作用，在不同的区域有不同效果，说明这两种闪电资料的分布不完全一致，揭示出这两种闪电资料具有一定的互补性。
- 闪电资料 /
- 对比分析 /
- 云分析 /
- 数值试验
Abstract: Lightning data obtained from the FY-4A satellite and ADTD (Advanced Direction and Time of arrival Detecting system) are significant for studying rainstorms and severe convection weather. This paper compares and analyzes the differences between the two lightning data through a case study of a rainstorm in Mianning, Sichuan Province. A series of numerical experiments are designed to introduce the two kinds of lightning data into a numerical prediction model. The main conclusions are: (1) Two kinds of lightning data have different detection effects in different areas. The ADTD lightning data are more extensive and scattered, whereas the number and distribution of lightning detected by the FY-4A satellite are more intensive. However, there is a good consistency between the two kinds of surrogate radar echo transformed by the two lightning data kinds. For low-frequency lightning, the ADTD lightning localizer may be more efficient than FY-4A LMI (Lightning Mapping Imager). (2) The introduction of these two types of lightning data has positive effects on precipitation forecast, and the application of ADTD lightning data is more effective for improving the accuracy of short-time precipitation forecast. (3) The two types of lightning data have different effects on the cloud microphysical quantities adjustment in different regions. This shows that the distribution of the two types of lightning data is not entirely consistent, but they are complementary to each other.
- Lightning data /
- Comparative analysis /
- Cloud analysis /
- Numerical experimentation

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图 1 暖季（a）不同闪电频次对应的最大雷达反射率因子，（b）四组反射率的权重系数随高度变化

Figure 1. (a) Maximum radar reflectivity corresponding to different lightning frequencies and (b) weight coefficients of four groups of reflectivity varying with height in the warm season

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图 2 2020年6月12日12时（北京时，下同）所选区域（23.5°～35.5°N, 96°～111°E）实际雷达反射率回波（单位：dBZ）。红色方框区域表示冕宁地区

Figure 2. Actual radar reflectivity (unit: dBZ) in selected areas (23.5°–35.5°N, 96°–111°E) at 1200 BJT (Beijing time) 12 June 2020. The red square area represents Mianning area

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图 3 2020年6月26日12时（a）ADTD闪电和（b）FY-4A卫星LMIE闪电资料散点图。区域① 、 ② 、③分别表示四川西北部、中南部冕宁地区、东南部地区，下同

Figure 3. Scatter plots of (a) ADTD (Advanced Direction and Time of arrival Detecting system) lightning and (b) FY-4A satellite LMIE (Lightning Mapping Imager Event) lightning data at 1200 BJT 12 June 2020. Regions ①, ②, ③ represent northwest, Mianning area in central south, southeast of Sichuan Province, respectively, the same below

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图 4 2020年6月26日12时（a）ADTD闪电资料、（b）FY-4A卫星闪电资料转换为雷达回波的分布（单位：dBZ）

Figure 4. Radar echo (units: dBZ) changed from (a) ADTD lightning data and (b) FY-4A satellite LMIE lightning data at 1200 BJT 12 June 2020

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图 5 2020年6月26日12时雷达资料与（a）ADTD闪电资料、（b）FY-4A卫星闪电资料转换的代理雷达回波分布（单位：dBZ ）以及（c）二者差值（单位：dBZ ）

Figure 5. Proxy radar echo (units: dBZ ) converted from radar and (a) ADTD lightning data, (b) FY-4A satellite LMIE lightning data, (c) the differences (units: dBZ ) between Fig. a and Fig. b at 1200 BJT 12 June 2020

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图 6 2020年6月26日12时代理雷达回波相关性：（a）ADTD闪电资料与FY-4A闪电资料转换的代理雷达回波；（b）加入雷达资料的基础上，ADTD闪电资料与FY-4A闪电资料转化的雷达代理回波

Figure 6. Proxy radar echo correlation: (a) Proxy radar echo converted from ADTD lightning data and FY-4A satellite LMIE lightning data; (b) proxy radar echo converted from ADTD lightning data and FY-4A satellite LMIE lightning data on the basis of adding radar data at 1200 BJT 12 June 2020

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图 7 2020年6月26日12时至13时1 h累计降水量（单位：mm）：（a）实际降水；（b）test 1.1试验；（c）test 1.2试验；（d）test 2.1试验；（e）test 2.2试验；（f）test 2.3试验

Figure 7. One-hour accumulated rainfall (units: mm) from 1200 BJT to 1300 BJT on 26 June 2020: (a) The actual precipitation; (b) experiment test 1.1; (c) experiment test 1.2; (d) experiment test 2.1; (e) experiment test 2.2; (f) experiment test 2.3

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图 8 2020年6月26日12时至15时3 h累计降水量（单位：mm）：（a）实际降水；（b）test 1.1试验；（c）test 1.2试验；（d）test 2.1试验；（e）test 2.2试验；（f）test 2.3试验

Figure 8. Three-hour accumulated rainfall (units: mm) from 1200 BJT to 1500 BJT on 26 June 2020: (a) The actual precipitation; (b) experiment test 1.1; (c) experiment test 1.2; (d) experiment test 2.1; (e) experiment test 2.2; (f) experiment test 2.3

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图 9 2020年6月26日12时至18时6 h累计降水量（单位：mm）：（a）实际降水；（b）test 1.1试验；（c）test 1.2试验；（d）test 2.1试验；（e）test 2.2试验；（f）test 2.3试验

Figure 9. Six-hour accumulated rainfall (units: mm) from 1200 BJT to 1800 BJT on 26 June 2020: (a) The actual precipitation; (b) experiment test 1.1; (c) experiment test 1.2; (d) experiment test 2.1; (e) experiment test 2.2; (f) experiment test 2.3

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图 10 2020年6月26日12时起报的（a、d）1 h、（b、e）3 h、（c、f）6 h累计降水（a–c）TS评分和（d–f）BS评分

Figure 10. TS and BS scores of (a, d) 1-h, (b, e) 3-h, and (c, f) 6-h accumulated precipitation from 1200 BJT 12 June 2020

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图 11 2020年6月26日12:05试验一的（a0、a1、a2）云水含量（q_c）、（b0、b1、b2）云雨含量（q_r）以及（c0、c1、c2）云冰含量（q_i）（单位：g kg⁻¹）沿斜线[（28.6°N, 101.5°E）～（27.6°N, 106.4°E）]的剖面：（a0、b0、c0）ctrl1.0试验；（a1、b1、c1）test1.1试验；（a2、b2、c2）：test1.2试验

Figure 11. Vertical cross sections of content (units: g kg⁻¹) for (a0, a1, a2) cloud water (q_c), (b0, b1, b2) cloud rain (q_r), and (c0, c1, c2) cloud ice (q_i) along line [(28.6°N, 101.5°E)–(27.6°N, 106.4°E)] in experiment 1 at 1205 BJT on 26 June 2020: (a0, b0, c0) Experiment ctrl 1.0; (a1, b1, c1) experiment test 1.1; (a2, b2, c2) experiment test 1.2

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图 12 2020年6月26日12:05试验二的（a0、a1、a2）云水含量（q_c）、（b0、b1、b2）云雨含量（q_r）以及（c0、c1、c2）云冰含量（q_i）（单位：g kg⁻¹）沿线段[（28.6°N, 101.5°E）～（27.6°N, 106.4°E）]的剖面：（a0、b0、c0）ctrl2.0试验；（a1、b1、c1）test2.1试验；（a2、b2、c2）test2.2试验

Figure 12. Vertical cross sections of content (units: g kg⁻¹) for (a0, a1, a2) cloud water (q_c), (b0, b1, b2) cloud rain (q_r), and (c0, c1, c2) cloud ice (q_i) along line [(28.6°N, 101.5°E)–(27.6°N, 106.4°E)] in experiment 2 at 1205 BJT on 26 June 2020: (a0, b0, c0) Experiment ctrl 2.0; (a1, b1, c1) experiment test 2.1; (a2, b2, c2) experiment test 2.2

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图 13 2020年6月26日12:05试验一700 hPa云水含量（上）、850 hPa云雨含量（中）、250 hPa云冰含量（下）水平分布（单位：g kg⁻¹）：（a、d、g）ctrl 1.0试验；（b、e、h）test 1.1试验；（c、f、i）：test 1.2试验

Figure 13. Contents (units: g kg⁻¹) of 700-hPa cloud water (top), 850-hPa cloud rain (middle), and 250-hPa cloud ice (bottom) in experiment 1 at 1205 BJT on 26 June 2020: (a, d, g) Experiment ctrl 1.0; (b, e, h) experiment test 1.1; (c, f, i) experiment test 1.2

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图 14 2020年6月26日12:05试验二700 hPa云水含量（上）、850 hPa云雨含量（中）、250 hPa云冰含量（下）水平分布（单位：g kg⁻¹）：（a、d、g）ctrl 2.0试验；（b、e、h）test 2.1试验；（c、f、i）test 2.2试验

Figure 14. Contents (units: g kg⁻¹) of 700-hPa cloud water (top), 850-hPa cloud rain (middle), and 250-hPa cloud ice (bottom) in experiment 2 at 1205 BJT on 26 June 2020: (a, d, g) Experiment ctrl 2.0; (b, e, h) experiment test 2.1; (c, f, i) experiment test 2.2

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表 1 加入GFS背景场及卫星资料的基础上两种闪电资料对比试验（试验一：无雷达资料）

Table 1. Comparative test of two types of lightning data added GFS (Global Forecast System) background field and satellite data (Experiment 1: No radar data)

	GFS背景场以及卫星（TBB、CTA）资料	ADTD 闪电资料	LMIE 闪电资料
ctrl 1.0试验	√	×	×
test 1.1试验	√	√	×
test 1.2试验	√	×	√
注：√、×分别表示加入该资料和不加入该资料，下同。

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表 2 加入GFS背景场及卫星资料的基础上两种闪电资料对比试验（试验二：有雷达资料）

Table 2. Comparative test of two types of lightning data added GFS background field and satellite data (Experiment 2: Radar data available)

	GFS背景场以及卫星（TBB、CTA）资料	雷达反射率	ADTD 闪电资料	LMIE 闪电资料
ctrl 2.0试验	√	√	×	×
test 2.1试验	√	√	√	×
test 2.2试验	√	√	×	√
test 2.3试验	√	√	√	√

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参考文献(39)

[1]	Albers S C, McGinley J A, Birkenheuer D L, et al. 1996. The Local Analysis and Prediction System (LAPS): Analyses of clouds, precipitation, and temperature [J]. Wea. Forecasting, 11(3): 273−287. doi: 10.1175/1520-0434(1996)011<0273:TLAAPS>2.0.CO;2
[2]	蔡河章, 洪加肯, 彭涛. 2008. ADTD雷电监测定位系统简介 [J]. 科技资讯(31): 122. doi: 10.3969/j.issn.1672-3791.2008.31.094 Cai Hezhang, Hong Jiaken, Peng Tao. 2008. Brief introduction of ADTD lightning monitoring and positioning system [J]. Science & Technology Information (in Chinese)(31): 122. doi: 10.3969/j.issn.1672-3791.2008.31.094
[3]	曹冬杰. 2016. 风云四号静止卫星闪电成像仪监测原理和产品算法研究进展 [J]. 气象科技进展, 6(1): 94−98. doi: 10.3969/j.issn.2095-1973.2016.01.014 Cao Dongjie. 2016. The development of product algorithm of the Fengyun-4 geostationary Lightning Mapping Imager [J]. Advances in Meteorological Science and Technology (in Chinese), 6(1): 94−98. doi: 10.3969/j.issn.2095-1973.2016.01.014
[4]	曹冬杰, 陆风, 张晓虎, 等. 2018. 风云四号卫星闪电探测产品在强对流天气监测中的应用 [J]. 卫星应用(11): 18−23. doi: 10.3969/j.issn.1674-9030.2018.11.007 Cao Dongjie, Lu Feng, Zhang Xiaohu, et al. 2018. Application of FY-4 lightning detection products in severe convective weather monitoring [J]. Satellite Application (in Chinese)(11): 18−23. doi: 10.3969/j.issn.1674-9030.2018.11.007
[5]	Chang D E, Weinman J A, Morales C A, et al. 2001. The effect of spaceborne microwave and ground-based continuous lightning measurements on forecasts of the 1998 Groundhog Day storm [J]. Mon. Wea. Rev., 129(8): 1809−1833. doi: 10.1175/1520-0493(2001)129<1809:TEOSMA>2.0.CO;2
[6]	丁一汇. 1994. 暴雨和中尺度气象学问题 [J]. 气象学报, 52(3): 274−284. doi: 10.11676/qxxb1994.036 Ding Yihui. 1994. Some aspects of rainstorm and meso-scale meteorology [J]. Acta Meteorologica Sinica (in Chinese), 52(3): 274−284. doi: 10.11676/qxxb1994.036
[7]	Fierro A O, Mansell E R, Ziegler C L, et al. 2012. Application of a lightning data assimilation technique in the WRF-ARW model at cloud-resolving scales for the tornado outbreak of 24 May 2011 [J]. Mon. Wea. Rev., 140(8): 2609−2627. doi: 10.1175/MWR-D-11-00299.1
[8]	何静, 刘舜, 陈敏, 等. 2017. 闪电观测资料与雷达反射率的比较研究及其同化应用[C]//第34届中国气象学会年会S8观测推动城市气象发展——第六届城市气象论坛论文集. 中国气象学会, 112. He Jing, Liu Shun, Chen Min, et al. 2017. Comparative Study of lightning observation data and radar reflectivity and its assimilation application [C]//S8 Observations Promote the Development of Urban Meteorology at the 34th Annual Meeting of the Chinese Meteorological Society—Proceedings of the 6th Urban Meteorological Forum (in Chinese). Chinese Meteorological Society, 112.
[9]	Hong S Y, Lim J O J. 2006. The WRF single-moment 6-class microphysics scheme (WSM6) [J]. Journal of the Korean Meteorological Society, 42(2): 129−151.
[10]	Hu M, Weygandt S S, Benjamin S G, et al. 2009. Assimilation of lightning data using cloud analysis within the rapid refresh [C]//89th American Meteorological Society Annual Meeting.
[11]	黄富祥. 2007. FY-4卫星闪电成像仪的使命和挑战 [J]. 气象科技, 35(Z1): 35−42. doi: 10.3969/j.issn.1671-6345.2007.z1.008 Huang Fuxiang. 2007. Lightning imaging sensor on FY-4 meteorological satellite: Mission and challenge [J]. Meteorological Science and Technology (in Chinese), 35(Z1): 35−42. doi: 10.3969/j.issn.1671-6345.2007.z1.008
[12]	黄守友. 2020. FY4A资料在模式热启动中的初步研究与应用 [D]. 中国气象科学研究院硕士学位论文. Huang Shouyou. 2020. Preliminary study and application of FY4A data in model hot start [D]. M. S. thesis (in Chinese), Chinese Academy of Meteorological Sciences.
[13]	黄守友, 徐国强. 2020. FY4A的LMIE闪电数据对云信息初始化的影响及数值试验 [J]. 高原气象, 39(2): 378−392. doi: 10.7522/j.issn.1000-0534.2019.00110 Huang Shouyou, Xu Guoqiang. 2020. Influence of LMIE lightning data of FY4A on cloud information initialization and numerical experiment [J]. Plateau Meteorology (in Chinese), 39(2): 378−392. doi: 10.7522/j.issn.1000-0534.2019.00110
[14]	Jian G J, Shieh S L, McGinley J A. 2003. Precipitation simulation associated with typhoon Sinlaku (2002) in Taiwan area using the LAPS diabatic initialization for MM5 [J]. Terrestrial Atmospheric and Oceanic Sciences, 14(3): 261−288. doi: 10.3319/TAO.2003.14.3.261(A
[15]	Krishnamurti T N, Ingles K, Cocke S, et al. 1984. Details of low latitude medium range numerical weather prediction using a global spectral model. Part II. Effects of orography and physical initialization [J]. J. Meteor. Soc. Japan, 62(4): 613−649. doi: 10.2151/jmsj1965.62.4_613
[16]	Kristjánsson J E. 1992. Initialization of cloud water in a numerical weather prediction model [J]. Meteor. Atmos. Phys., 50(1): 21−30. doi: 10.1007/BF01025502
[17]	Liu R X, Liu J, Pessi A, et al. 2019. Preliminary study on the influence of FY-4 lightning data assimilation on precipitation predictions [J]. Journal of Tropical Meteorology, 25(4): 528−541. doi: 10.16555/j.1006-8775.2019.04.009
[18]	Petersen W A, Rutledge S A. 1998. On the relationship between cloud-to-ground lightning and convective rainfall [J]. J. Geophys. Res.: Atmos., 103(D12): 14025−14040. doi: 10.1029/97JD02064
[19]	郄秀书, 刘冬霞, 孙竹玲. 2014. 闪电气象学研究进展 [J]. 气象学报, 72(5): 1054−1068. doi: 10.11676/qxxb2014.048 Qie Xiushu, Liu Dongxia, Sun Zhuling. 2014. Recent advances in research of lightning meteorology [J]. Acta Meteorologica Sinica (in Chinese), 72(5): 1054−1068. doi: 10.11676/qxxb2014.048
[20]	郄秀书, 袁善锋, 陈志雄, 等. 2021. 北京地区雷电灾害天气系统的动力—微物理—电过程观测研究 [J]. 中国科学: 地球科学, 51(1): 46–62. Qie Xiushu, Yuan Shanfeng, Chen Zhixiong, et al. 2021. Understanding the dynamical–microphysical–electrical processes associated with severe thunderstorms over the Beijing metropolitan region [J]. Science China Earth Sciences, 64(1): 10–26. doi:10.1007/s11430-020-9656-8
[21]	任绪伟, 陈晓燕, 蔡迪花, 等. 2021. GRAPES_Meso模式及其云分析系统在中国西北地区降水预报中的应用评估 [J]. 干旱气象, 39(2): 333−344. doi: 10.11755/j.issn.1006-7639(2021)-02-0333 Ren Xuwei, Chen Xiaoyan, Cai Dihua, et al. 2021. Evaluation of precipitation forecast based on GRAPES_Meso model and its cloud analysis system in Northwest China [J]. Journal of Arid Meteorology (in Chinese), 39(2): 333−344. doi: 10.11755/j.issn.1006-7639(2021)-02-0333
[22]	Rudlosky S D, Fuelberg H E. 2013. Documenting storm severity in the Mid-Atlantic region using lightning and radar information [J]. Mon. Wea. Rev., 141(9): 3186−3202. doi: 10.1175/MWR-D-12-00287.1
[23]	孙凌, 陈志雄, 徐燕, 等. 2019. 北京一次强飑线过程的闪电辐射源演变特征及其与对流区域和地面热力条件的关系 [J]. 大气科学, 43(4): 759−772. doi: 10.3878/j.issn.1006-9895.1805.18128 Sun Ling, Chen Zhixiong, Xu Yan, et al. 2019. Evolution of lightning radiation sources of a strong squall line over Beijing metropolitan region and its relation to convection region and surface thermodynamic condition [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 43(4): 759−772. doi: 10.3878/j.issn.1006-9895.1805.18128
[24]	孙婵, 徐国强. 2019. 闪电定位和雷达观测资料在云分析中的应用及数值试验 [J]. 大气科学, 43(1): 131−141. doi: 10.3878/j.issn.1006-9895.1804.17233 Sun Chan, Xu Guoqiang. 2019. Application of lightning location and radar data in cloud analysis system and numerical experiments [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 43(1): 131−141. doi: 10.3878/j.issn.1006-9895.1804.17233
[25]	陶诗言, 丁一汇, 周晓平. 1979. 暴雨和强对流天气的研究 [J]. 大气科学, 3(3): 227−238. doi: 10.3878/j.issn.1006-9895.1979.03.05 Tao Shiyan, Ding Yihui, Zhou Xiaoping. 1979. Study on heavy rain and severe convective weather [J]. Chinese Journal of Atmospheric Sciences (Scientia Atmospherica Sinica) (in Chinese), 3(3): 227−238. doi: 10.3878/j.issn.1006-9895.1979.03.05
[26]	王红艳, 刘黎平, 王改利, 等. 2009. 多普勒天气雷达三维数字组网系统开发及应用 [J]. 应用气象学报, 20(2): 214−224. doi: 10.11898/1001-7313.20090211 Wang Hongyan, Liu Liping, Wang Gaili, et al. 2009. Development and application of the Doppler weather radar 3-D digital mosaic system [J]. Journal of Applied Meteorological Science (in Chinese), 20(2): 214−224. doi: 10.11898/1001-7313.20090211
[27]	Wang Y, Yang Y, Wang C H. 2014. Improving forecasting of strong convection by assimilating cloud-to-ground lightning data using the physical initialization method [J]. Atmospheric Research, 150: 31−41. doi: 10.1016/j.atmosres.2014.06.017
[28]	王莹, 杨毅, 邱晓滨. 2015. 集合均方根滤波同化地闪资料的试验研究 [J]. 干旱气象, 33(5): 761−768. doi: 10.11755/j.issn.1006-7639(2015)-05-0761 Wang Ying, Yang Yi, Qiu Xiaobin. 2015. Assimilating cloud-to-ground lightning data using ensemble square root filter [J]. Journal of Arid Meteorology (in Chinese), 33(5): 761−768. doi: 10.11755/j.issn.1006-7639(2015)-05-0761
[29]	Wang Y, Yang Y, Liu D X, et al. 2017. A case study of assimilating lightning-proxy relative humidity with WRF-3DVAR [J]. Atmosphere, 8(3): 55. doi: 10.3390/atmos8030055
[30]	Weygandt S S, Benjamin S G, Devenyi D, et al. 2006. Cloud and hydrometeor analysis using metar, radar and satellite data within the RUC/Rapid Refresh model [C]//12th Conference on Aviation Range and Aerospace Meteorology. Atlanta, GA.
[31]	Wolcott S W, Warner T T. 1981. A moisture analysis procedure utilizing surface and satellite data [J]. Mon. Wea. Rev., 109(9): 1989−1998. doi: 10.1175/1520-0493(1981)109<1989:AMAPUS>2.0.CO;2
[32]	Xiao X, Sun J Z, Qie X S, et al. 2018. A new lightning assimilation technique based on 4DVAR [C]//AGU Fall Meeting. San Francisco, USA: AGU.
[33]	徐国强, 黄守友, 赵晨阳. 2020. FY-4A闪电资料在对流天气数值预报中的影响研究 [J]. 气象, 46(9): 1165−1177. doi: 10.7519/j.issn.1000-0526.2020.09.004 Xu Guoqiang, Huang Shouyou, Zhao Chenyang. 2020. Influence of FY-4A lightning data on numerical forecast of convective weather [J]. Meteorological Monthly (in Chinese), 46(9): 1165−1177. doi: 10.7519/j.issn.1000-0526.2020.09.004
[34]	徐燕, 孙竹玲, 周筠珺, 等. 2018. 一次具有对流合并现象的强飑线系统的闪电活动特征及其与动力场的关系 [J]. 大气科学, 42(6): 1393−1406. doi: 10.3878/j.issn.1006-9895.1801.17220 Xu Yan, Sun Zhuling, Zhou Yunjun, et al. 2018. Lightning activity of a severe squall line with cell merging process and its relationships with dynamic fields [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 42(6): 1393−1406. doi: 10.3878/j.issn.1006-9895.1801.17220
[35]	Xue M, Droegemeier K K, Wong V. 2000. The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification [J]. Meteor. Atmos. Phys., 75(3): 161−193. doi: 10.1007/s007030070003
[36]	Zhang R, Zhang Y J, Xu L T, et al. 2017. Assimilation of total lightning data using the three-dimensional variational method at convection-allowing resolution [J]. J. Meteor. Res., 31(4): 731−746. doi: 10.1007/s13351-017-6133-3
[37]	张人禾, 沈学顺. 2008. 中国国家级新一代业务数值预报系统GRAPES的发展 [J]. 科学通报, 53(20): 2393−2395. doi: 10.3321/j.issn:0023-074X.2008.20.001 Zhang Renhe, Shen Xueshun. 2008. Development of China national new generation service numerical forecast system GRAPES [J]. Chinese Science Bulletin (in Chinese), 53(20): 2393−2395. doi: 10.3321/j.issn:0023-074X.2008.20.001
[38]	朱立娟. 2012. GRAPES短临预报的云初始场形成与雷达VAD质控的关键技术研究 [D]. 中国气象科学研究院博士学位论文. Zhu LIjuan. 2012. The key technology research on cloud initial field and radar VAD quality control for GRAPES nowcasting [D]. Ph. D. thesis(in Chinese) , Chinese Academy of Meteorological Sciences.
[39]	朱立娟, 龚建东, 黄丽萍, 等. 2017. GRAPES三维云初始场形成及在短临预报中的应用 [J]. 应用气象学报, 28(1): 38−51. doi: 10.11898/1001-7313.20170104 Zhu Lijuan, Gong Jiandong, Huang Liping, et al. 2017. Three-dimensional cloud initial field created and applied to GRAPES numerical weather prediction nowcasting [J]. Journal of Applied Meteorological Science (in Chinese), 28(1): 38−51. doi: 10.11898/1001-7313.20170104