基于数值天气模式及其模式输出的闪电预报研究进展

doi:10.11867/j.issn.1001-8166.2017.01.0044

地球科学进展 ›› 2017, Vol. 32 ›› Issue (1): 44 -55. doi: 10.11867/j.issn.1001-8166.2017.01.0044

王昊亮 ¹(

), 刘玉宝 ^2, ^3,,A; ^*(

), 赵天良 ¹, 郭凤霞 ¹, 冯双磊 ², 王勃 ²

1.气象灾害预报预警与评估协同创新中心,南京信息工程大学,江苏南京 210044
2.新能源与储能运行控制国家重点实验室,中国电力科学研究院,北京 100085
3.应用研究实验室,美国国家大气研究中心(NCAR), Boulder,Colorado,USA 80307

收稿日期:2016-08-13 修回日期:2016-12-26 出版日期:2017-01-20
通讯作者: 刘玉宝 E-mail:whl198969@163.com;yliu@ucar.edu
基金资助:
*国家电网公司科学技术项目“基于集合四维资料同化的电力气象多尺度数值预报技术研究及应用”(编号:SGTYHT/14-JS-188);江苏省普通高校学术学位研究生科研创新计划项目“基于中尺度数值天气模式的闪电资料同化技术”(编号:KYLX16_0939)资助.

Progress in Lightning Forecast by Using Numerical Weather Models and Model Outputs

Haoliang Wang ¹( ), Yubao Liu ^2, ^3, ^*( ), Tianliang Zhao ¹, Fengxia Guo ¹, Shuanglei Feng ², Bo Wang ²

1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems, China Electric Power Research Institute, Beijing 100085, China
3.Research Application Laboratory, National Center for Atmospheric Research, Boulder, Colorado 80307, America

Received:2016-08-13 Revised:2016-12-26 Online:2017-01-20 Published:2017-01-10
Contact: Yubao Liu E-mail:whl198969@163.com;yliu@ucar.edu
About author:
First author:Wang Haoliang(1989-),male,Ping’an County,Qinghai Province,Ph.D student. Research areas include forecast of severe convective weather and lightning.E-mail:whl198969@163.com

*Corresponding author:Liu Yubao(1963-),male,Suqian City,Jiangsu Province,Professor. Research areas include cloud scale numerical weather forecast, severe weather process, power meteorology, typhoon, operational weather forecast system.E-mail:yliu@ucar.edu
Supported by:
Project supported by the State Grid Science and Technology Project “Multiscale ensemble FDDA and NWP technologies for electric-power applications”(No.SGTYHT/14-JS-188);the Jiangsu Province University Academic Degrees Postgraduate Research and Innovation Project “Lightning data assimilation based on mesoscale numerical weather model”(No. KYLX16_0939).

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鉴于闪电对人类生命、设备安全的威胁、对强对流天气的指示作用及其在大气化学反应链中的重要角色,国内外大量研究人员在中尺度数值天气模式、全球气候模式中开展了闪电预报的研究工作。在数值天气模式中,闪电预报方法可以分为3种:基于闪电活动发生时的天气背景场统计关系的闪电数值诊断预报;依据云动力、微物理场与起电活动的关系建立闪电率参数化方案;在数值天气模式中耦合详细的起电、放电参数化方案。详细综述了这3种预报方法的研究进展,总结了它们各自的特点,并且对现存的难点问题及未来的发展方向进行了探讨。

关键词: 数值天气模式, 闪电预报, 强对流天气, 闪电参数化方案, 大气化学模式

Lightning can threaten human and equipment safety. An indicator of sever convective weather, it plays an important role in atmospheric chemistry. The intensive studies have advanced the lightning forecast in the mesoscale weather models and its application in global climate models. There are three methods to forecast lightning by using numerical weather models: Numerical diagnosis prediction based on synoptic background filed statistical relations; Flash rate parameterization developed with the relationship between dynamical, microphysical and electrification processes, and The numerical weather model coupled with the explicit electrification and lightning parameterization schemes. In this paper, the research progress in lightning forecast with three above-mentioned methods were reviewed, and the future research issues on lightning forecast were also discussed.

Key words: Numerical weather model, Lightning forecast, Lightning parameterization scheme, Sever convective weather, Atmospheric chemistry model.

中图分类号:

P457

表1 基于统计关系的闪电数值诊断预报的研究概况

Table 1 Summary of numerical diagnosis lightning prediction based on the statistical relationship

统计方式	数值模式平台	统计时间范围	相关分析方法	预报方程中包括的参量	研究人员
MOS	NGM	1987—1990年	多元线性回归	K指数、950 mb相对涡度、相对湿度、500 mb水平风速、1 000 mb抬升指数、1 000 mb温度等	Reap^{[ 50 ]}
	GSM	1997—2001年	多元线性回归	平均高相对湿度、500 hPa及700 hPa的垂直速度、地表CAPE、925 hPa及850 hPa的水汽通量散度	Hughes^{[ 51 , 52 ]}
	GEM	2000—2001年	分类回归树	肖沃特稳定指数、平均海平面气压、对流层可降水量、灾害性天气征兆指数等	Burrows等^{[ 53 ]}
	WRF	2003—2009年	二进制逻辑回归	700~500 hPa的相当位温差、抬升指数、500 hPa的最大位势涡度、700~500 hPa的相对湿度差等	Sousa等^{[ 54 ]}
PP	RUC ETA	1995—2002年	二进制逻辑回归	400~700 mb的相当位温差、抬升凝结高度至平衡层的距离、云底温度、抬升凝结高度至-20 ℃层的CAPE等	Bothwell^{[ 56 ]}
	RUC13 NAM12 WRF	2002—2005年	二进制逻辑回归	对流层可降水量、不稳定度指数、边界层强迫力、低层风速	Shafer等^{[ 58 ]}
	RUC13 NAM12	2005—2008年	二进制逻辑回归	对流可降水量、抬升指数、K指数、全总指数、肖沃特稳定指数	Saunders^{[ 59 ]}

表1 基于统计关系的闪电数值诊断预报的研究概况

Table 1 Summary of numerical diagnosis lightning prediction based on the statistical relationship

统计方式	数值模式平台	统计时间范围	相关分析方法	预报方程中包括的参量	研究人员
MOS	NGM	1987—1990年	多元线性回归	K指数、950 mb相对涡度、相对湿度、500 mb水平风速、1 000 mb抬升指数、1 000 mb温度等	Reap^{[ 50 ]}
	GSM	1997—2001年	多元线性回归	平均高相对湿度、500 hPa及700 hPa的垂直速度、地表CAPE、925 hPa及850 hPa的水汽通量散度	Hughes^{[ 51 , 52 ]}
	GEM	2000—2001年	分类回归树	肖沃特稳定指数、平均海平面气压、对流层可降水量、灾害性天气征兆指数等	Burrows等^{[ 53 ]}
	WRF	2003—2009年	二进制逻辑回归	700~500 hPa的相当位温差、抬升指数、500 hPa的最大位势涡度、700~500 hPa的相对湿度差等	Sousa等^{[ 54 ]}
PP	RUC ETA	1995—2002年	二进制逻辑回归	400~700 mb的相当位温差、抬升凝结高度至平衡层的距离、云底温度、抬升凝结高度至-20 ℃层的CAPE等	Bothwell^{[ 56 ]}
	RUC13 NAM12 WRF	2002—2005年	二进制逻辑回归	对流层可降水量、不稳定度指数、边界层强迫力、低层风速	Shafer等^{[ 58 ]}
	RUC13 NAM12	2005—2008年	二进制逻辑回归	对流可降水量、抬升指数、K指数、全总指数、肖沃特稳定指数	Saunders^{[ 59 ]}

表2 几种具有代表性的闪电率参数化方案

Table 2 Some representative flash rate parameterization schemes

方案	预报因子	预报公式
Price等^{[ 70 ]}	最大上升气流速度/(m/s)	f=(5.0×10^-6)×
Price等^{[ 70 ]}	云顶高度/km	f=(3.44×10^-5)×H^4.9
McCaul等^{[ 76 ]}	混合相态区冰相物粒子通量/(m/s)	f=0.042(wq_g)_m
McCaul等^{[ 76 ]}	冰相粒子垂直积分总量/(kg/m²)	f=0.2∫ρ(q_g+q_s+q_i)dz
Deierling等^{[ 80 ]}	可沉降冰相物粒子质量/kg	f=(3.4×10^-8)×PIM-18.1
Li等^{[ 82 ]}	可沉降冰相物粒子质量/kg	f=4.5×10^-⁵[(ρ×Δx×Δy×Δz×q_p)dz]-27.3
Li等^{[ 82 ]}	非沉降冰相粒子质量/kg	f=2.7×10^-⁴[(ρ×Δx×Δy×Δz×q_np)dz]-41.3
Yair等^{[ 85 ]}	最大上升气流速度/(m/s)、冰水、液水混合比/(g/g)	LPI=1/V∫∫∫εw²dxdydz ε=2(Q_iQ_l)⁰^.⁵/(Q_i + Q_l)

表2 几种具有代表性的闪电率参数化方案

Table 2 Some representative flash rate parameterization schemes

方案	预报因子	预报公式
Price等^{[ 70 ]}	最大上升气流速度/(m/s)	f=(5.0×10^-6)×
Price等^{[ 70 ]}	云顶高度/km	f=(3.44×10^-5)×H^4.9
McCaul等^{[ 76 ]}	混合相态区冰相物粒子通量/(m/s)	f=0.042(wq_g)_m
McCaul等^{[ 76 ]}	冰相粒子垂直积分总量/(kg/m²)	f=0.2∫ρ(q_g+q_s+q_i)dz
Deierling等^{[ 80 ]}	可沉降冰相物粒子质量/kg	f=(3.4×10^-8)×PIM-18.1
Li等^{[ 82 ]}	可沉降冰相物粒子质量/kg	f=4.5×10^-⁵[(ρ×Δx×Δy×Δz×q_p)dz]-27.3
Li等^{[ 82 ]}	非沉降冰相粒子质量/kg	f=2.7×10^-⁴[(ρ×Δx×Δy×Δz×q_np)dz]-41.3
Yair等^{[ 85 ]}	最大上升气流速度/(m/s)、冰水、液水混合比/(g/g)	LPI=1/V∫∫∫εw²dxdydz ε=2(Q_iQ_l)⁰^.⁵/(Q_i + Q_l)

表3 几种详细的起电、放电参数化方案

Table 3 Some explicit electrification and lightning parameterization schemes

方案	模式平台	非感应起电方案	是否区分云闪、地闪
Barthe等^{[ 100 ]}	MesoNH	TAK78^{[ 108 ]}, GAR85^{[ 109 ]}, SAU91^{[ 110 ]}, SP98^{[ 111 ]}	是
Fierro等^{[ 103 ]}	WRF	MAN05^{[ 112 ]}	否
徐良韬等^{[ 104 ]}	WRF	MAN05^{[ 112 ]}	否
李万莉等^{[ 106 ]}, Liu等^{[ 107 ]}	RAMS	TAK78^{[ 108 ]}, SAU91^{[ 110 ]}	否

表3 几种详细的起电、放电参数化方案

Table 3 Some explicit electrification and lightning parameterization schemes

方案	模式平台	非感应起电方案	是否区分云闪、地闪
Barthe等^{[ 100 ]}	MesoNH	TAK78^{[ 108 ]}, GAR85^{[ 109 ]}, SAU91^{[ 110 ]}, SP98^{[ 111 ]}	是
Fierro等^{[ 103 ]}	WRF	MAN05^{[ 112 ]}	否
徐良韬等^{[ 104 ]}	WRF	MAN05^{[ 112 ]}	否
李万莉等^{[ 106 ]}, Liu等^{[ 107 ]}	RAMS	TAK78^{[ 108 ]}, SAU91^{[ 110 ]}	否

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