不同闪电参数化方案对海南岛一次强对流闪电过程的数值模拟研究

doi:10.3969/j.issn.1673-503X.2023.03.003

摘要/Abstract

摘要：

利用耦合非感应起电方案和闪电参数化方案的WRF V3.9.1中尺度模式, 模拟了2018年6月26日发生在海南岛的一次强对流过程, 讨论模式中3种闪电参数化方案(LPI方案、PR92w方案和PR92z方案)对热带岛屿地区闪电活动的模拟能力。结果表明: 模式能较好地反演出此次强对流过程的雷达回波特征, 垂直速度和水成物粒子是影响模式预报闪电的重要因素, 云内发展较强的垂直速度和丰富的冰相粒子有利于云内起电过程的发展。LPI方案和PR92w方案模拟出闪电的分布形式和闪电上正下负的结构, 起电区域均位于8~15 km, PR92z方案模拟闪电分散, 偏差较大; LPI方案的ETS评分(Equitable Threat Score)表现最优, 为0.65;进一步分析LPI值与闪电频数的相关关系发现两者之间具有显著的正相关关系, 区域平均LPI与闪电频数相关系数为0.9, 拟合优度为0.83。

关键词: LPI(雷电潜势指数), 闪电参数化, WRF模式, 强对流天气, 闪电预报

Abstract:

The WRF V3.9.1 mesoscale model coupled with the non-inductive electrification scheme and the lightning parameterization scheme was used to simulate a strong convection process occurred in Hainan Island on June 26, 2018.The simulation ability of lightning activity in tropical islands was discussed using three lightning parameterization schemes (LPI scheme, PR92w scheme, PR92z scheme) in the model.The results show that the model can well reproduce the radar echo characteristics of the strong convective process, the vertical velocity and hydrometeor particles are important factors affecting the model prediction of lightning, and the strong vertical velocity and abundant ice particles in the cloud are conducive to the development of the electric process in the cloud.LPI and PR92w schemes reproduce the distribution pattern of lightning and the structure of positive and negative lightning with an electric charge area between 8~15 km, while the lightning simulated by the PR92z scheme shows a scattering pattern, with a large deviation.The Equitable Threat Score (ETS) of the LPI scheme is the best with a value of 0.65.Further analysis of the correlation between LPI and lightning frequency shows that there is a significant positive correlation between them.The correlation coefficient between the regional average LPI and lightning frequency is 0.9, and the goodness of fitting value is 0.83.

Key words: LPI (Lightning Potential Index), Lightning parameterization, WRF model, Severe convection weather, Lightning forecast

中图分类号:

P435

杨薇,李勋,林建兴,陈有龙,杨滨瑜. 不同闪电参数化方案对海南岛一次强对流闪电过程的数值模拟研究[J]. 气象与环境学报, 2023, 39(3): 20-30.

Wei YANG,Xun LI,Jian-xing LIN,You-long CHEN,Bin-yu YANG. Numerical simulation of a strong convective lightning process in Hainan island with different lightning parameterization schemes[J]. Journal of Meteorology and Environment, 2023, 39(3): 20-30.

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参考文献 40

1	马明, 吕伟涛, 张义军, 等. 1997-2006年我国雷电灾情特征[J]. 应用气象学报, 2008, 19 (4): 393- 400. doi: 10.3969/j.issn.1001-7313.2008.04.002
2	王凯, 周丽雅, 鞠晓雨, 等. 2011-2019年中国长江三角洲区域闪电时空分布特征[J]. 气象与环境学报, 2021, 37 (4): 100- 106.
3	Gatlin P N , Goodman S J . A total lightning trending algorithm to identify severe thunderstorms[J]. Journal of Atmospheric and Oceanic Technology, 2010, 27 (1): 3- 22. doi: 10.1175/2009JTECHA1286.1
4	Kain J S , Coniglio M C , Correia J , et al. A feasibility study for probabilistic convection initiation forecasts based on explicit numerical guidance[J]. Bulletin of the American Meteorological Society, 2013, 94 (8): 1213- 1225. doi: 10.1175/BAMS-D-11-00264.1
5	郑永光, 周康辉, 盛杰, 等. 强对流天气监测预报预警技术进展[J]. 应用气象学报, 2015, 26 (6): 641- 657.
6	周康辉, 郑永光, 蓝渝. 基于闪电数据的雷暴识别、追踪与外推方法[J]. 应用气象学报, 2016, 27 (2): 173- 181.
7	王昊亮, 刘玉宝, 赵天良, 等. 基于数值天气模式及其模式输出的闪电预报研究进展[J]. 地球科学进展, 2017, 32 (1): 44- 55.
8	Reap R M . Analysis and prediction of lightning strike distributions associated with synoptic map types over Florida[J]. Monthly Weather Review, 1994, 122 (8): 1698- 1715. doi: 10.1175/1520-0493(1994)122<1698:AAPOLS>2.0.CO;2
9	郑栋, 张义军, 吕伟涛, 等. 大气不稳定度参数与闪电活动的预报[J]. 高原气象, 2005, 24 (2): 196- 203. doi: 10.3321/j.issn:1000-0534.2005.02.010
10	Shafer P E , Fuelberg H E . A perfect prognosis scheme for forecasting warm-season lightning over Florida[J]. Monthly Weather Review, 2008, 136 (6): 1817- 1846. doi: 10.1175/2007MWR2222.1
11	周明薇, 王道平, 罗龙友, 等. 基于闪电定位和雷达资料的邵阳地区雷电预报预警研究[J]. 气象与环境学报, 2021, 37 (3): 117- 124.
12	Sousa J F , Fragoso M , Mendes S , et al. Statistical-dynamical modeling of the cloud-to-ground lightning activity in Portugal[J]. Atmospheric Research, 2013, 132-133, 46- 64. doi: 10.1016/j.atmosres.2013.04.010
13	周丽雅, 王凯, 刘倪. 基于Logistic回归蚌埠地区致灾雷电预报研究[J]. 气象与环境学报, 2016, 32 (5): 147- 153.
14	袁湘玲, 马虹旭, 陈石定, 等. 黑龙江省闪电活动特征及其与地面相对湿度的响应关系[J]. 气象与环境学报, 2020, 36 (5): 91- 96.
15	田琨, 郭凤霞, 曾庆峰, 等. 南京地区雷暴活动强度潜势预报[J]. 气象科技, 2013, 41 (1): 177- 183.
16	周威, 张武龙, 康岚, 等. 基于EC模式闪电格点概率预报模型及应用[J]. 气象科技, 2020, 48 (6): 862- 870.
17	Barthe C , Pinty J P . Simulation of a supercellular storm using a three-dimensional mesoscale model with an explicit lightning flash scheme[J]. Journal of Geophysical Research: Atmospheres, 2007, 112 (D6): D06210.
18	Lynn B , Yair Y . Prediction of lightning flash density with the WRF model[J]. Advances in Geosciences, 2010, 23, 11- 16.
19	Lynn B H , Yair Y , Price C , et al. Predicting clou-to-ground and intracloud lightning in weather forecast models[J]. Weather and Forecasting, 2012, 27 (6): 1470- 1488.
20	Federico S , Avolio E , Petracca M , et al. Simulating lightning into the RAMS model: implementation and preliminary results[J]. Natural Hazards and Earth System Sciences, 2014, 14 (11): 2933- 2950.
21	徐良韬, 张义军, 王飞, 等. 雷暴起电和放电物理过程在WRF模式中的耦合及初步检验[J]. 大气科学, 2012, 36 (5): 1041- 1052.
22	Fierro A O , Mansell E R , MacGorman D R , et al. The implementation of an explicit charging and discharge lightning scheme within the WRF-ARW model: benchmark simulations of a continental squall line, a tropical cyclone, and a winter storm[J]. Monthly Weather Review, 2013, 141 (7): 2390- 2415.
23	Giannaros T M , Kotroni V , Lagouvardos K . Predicting lightning activity in Greece with the Weather Research and Forecasting(WRF)model[J]. Atmospheric Research, 2015, 156, 1- 13.
24	徐良韬, 陈双, 姚雯, 等. 利用起放电模式开展闪电活动的直接预报试验[J]. 应用气象学报, 2018, 29 (5): 534- 545.
25	李江林, 余晔, 李万莉, 等. 不同非感应起电及感应起电参数化方案对青海东部一次雷暴云电荷结构影响的数值模拟研究[J]. 地球物理学报, 2019, 62 (7): 2366- 2381.
26	赵鹏国, 银燕, 周筠珺, 等. 基于WRF模式对华北一次飑线过程中雷电活动的间接和直接模拟研究[J]. 气象科学, 2017, 37 (3): 293- 303.
27	Mecikalski J R , Williams J K , Jewett C P , et al. Probabilistic 0-1-h convective initiation nowcasts that combine geostationary satellite observations and numerical weather prediction model data[J]. Journal of Applied Meteorology and Climatology, 2015, 54 (5): 1039- 1059.
28	Apke J M , Nietfeld D , Anderson M R . Environmental analysis of GOES-R proving ground convection-initiation forecasting algorithms[J]. Journal of Applied Meteorology and Climatology, 2015, 54 (7): 1637- 1662.
29	Ahijevych D , Pinto J O , Williams J K , et al. Probabilistic forecasts of mesoscale convective system initiation using the random forest data mining technique[J]. Weather and Forecasting, 2016, 31 (2): 581- 599.
30	Lin T Y , Li Q Y , Geng Y A , et al. Attention-based dual-source spatiotemporal neural network for lightning forecast[J]. IEEE Access, 2019, 7, 158296- 158307.
31	Zhou K H , Zheng Y G , Dong W S , et al. A deep learning network for cloud-to-ground lightning nowcasting with multisource data[J]. Journal of Atmospheric and Oceanic Technology, 2020, 37 (5): 927- 942.
32	周康辉, 郑永光, 王婷波. 利用深度学习融合NWP和多源观测数据的闪电落区短时预报方法[J]. 气象学报, 2021, 79 (1): 1- 14.
33	辛吉武, 许向春, 蔡杏尧. 海南儋州雷暴天气气候特征分析[J]. 气象, 2008, 34 (1): 100- 106.
34	郭冬艳, 陈红, 陈有龙, 等. 海南岛地闪活动的时空特征与海陆风关系[J]. 气象科技进展, 2020, 10 (4): 102- 107.
35	Saunders C P R , Keith W D , Mitzeva R P . The effect of liquid water on thunderstorm charging[J]. Journal of Geophysical Research: Atmospheres, 1991, 96 (D6): 11007- 11017.
36	Mansell E R , MacGorman D R , Ziegler C L , et al. Charge structure and lightning sensitivity in a simulated multicell thunderstorm[J]. Journal of Geophysical Research: Atmospheres, 2005, 110 (D12): D12101.
37	Saunders C P R , Peck S L . Laboratory studies of the influence of the rime accretion rate on charge transfer during crystal/graupel collisions[J]. Journal of Geophysical Research: Atmospheres, 1998, 103 (D12): 13949- 13956.
38	Yair Y , Lynn B , Price C , et al. Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting(WRF)model dynamic and microphysical fields[J]. Journal of Geophysical Research: Atmospheres, 2010, 115 (D4): D04205.
39	Price C , Rind D . A simple lightning parameterization for calculating global lightning distributions[J]. Journal of Geophysical Research: Atmospheres, 1992, 97 (D9): 9919- 9933.
40	王雨, 闫之辉. 降水检验方案变化对降水检验评估效果的影响分析[J]. 气象, 2007, 33 (12): 53- 61.

区域	D1	D2	D3
水平格点	170×178	286×286	165×165
分辨率	18 km	6 km	2 km
积云方案	Kain-Fritsch	Kain-Fritsch	不使用
微物理方案	Morrison	Morrison	Morrison
边界层方案	YSU	YSU	YSU
辐射方案	RRTM长波/Dudhia短波	RRTM长波/Dudhia短波	RRTM长波/Dudhia短波
陆面方案	RUC	RUC	RUC
非感应起电方案	SP98	SP98	SP98
闪电方案	LPI/PR92w/PR92z	LPI/PR92w/PR92z	LPI/PR92w/PR92z

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