中国东南区域闪电活动特征及其与大气环境参数的关系

Characteristics of lightning activity and its relationship with the atmospheric environment parameters in Southeast China

利用近18年(1997~2013)的星载LIS/OTD(lightning imaging sensor and optical transient detector)闪电观测资料、NCEP/NCAR(National Center for Environmental Protection/National Center for Atmospheric Research)和ERA-Interim再分析资料,对北纬30°以南、东经100°以东的中国陆地地区(17.5~30°N,100~122.5°E)的闪电活动特征及其与大气环境因素的关系进行了分析.结果表明:中国东南闪电高发区闪电活动季节性特征明显,春夏两季为闪电高发期,厄尔尼诺事件对其闪电活动的年际变化有较强的影响,在此期间冬春季闪电活动有明显的增强.东南闪电高发区闪电活动与地面温度、700~400hPa平均相对湿度、850hPa位温等热力学参数有明显正相关,与多种稳定度参数有强相关,显示出大气层结不稳定性越大,闪电活动越强.进一步通过多元逐步线性回归方法,建立了东南闪电高发区区域平均闪电密度与地面温度、CAPE(convective available potential energy)、BR(Bowen ratio)、LI(lifted index)、SI(Showalter index)的回归方程,有助于利用多种参数开展预报区域闪电活动.

Based on space-borne lightning observation data from Lightning Imaging Sensor （LIS） and Optical Transient Detector （OTD）, and reanalysis data of National Center for Environmental Protection/ National Center for Atmospheric Research （NCEP/NCAR） and ERA-Interim in an 18-year period from 1997 to 2013, the lightning activity over southeastern China （17.5 ~ 30°N, 100 ~ 122.5°E） and itsassociations with atmospheric environmental factors were investigated. The results indicate that the lightning activities show strong seasonal variabilities with high frequency of lightning events in the spring and summer. On the inter-annual time scale, the E1 Nifio-Southern Oscillation （ENSO） has a significant impact on lightning variability. The E1 Nifio events can significantly intensify the lightning activity in winter and spring. The high frequency of lightning events in southeastern China is associated with the increase of thermodynamic parameters, such as surface air temperature, 700~400 hPa averaged relative humidity and 850 hPa potential temperature. The atmospheric stability parameters are also closely related to lightning activity, and a larger unstable atmospheric layer is associated with a stronger lightning activity. Furthermore, multivariate step linear regression is applied to establish the relationship between predictor variables （e. g., surface air temperature, convective available potential energy, Bowen ratio, lifted index and Showalter index） and prediction （averaged lightning activity concentration）. The multiple stepwise linear regression equation is beneficial for regional lightning activity forecast.