Continuous global monitoring of lightning has been important in recent years to study a possible relationship with global warming. Consequently, several networks to detect lightning have been installed at various spatial scales (regional and global). One of these networks is the World Wide Lightning Location Network (WWLLN), which has been monitoring lightning since 2003. It is also important to monitor the occurrence of thunderstorms and this can be roughly archived by detecting lightning clusters. In this work, we propose a lightning grouping methodology, in order to estimate the global number of thunderstorms. Our methodology consists of grouping the WWLLN data into a density matrix with a spatial resolution of $0.1°$ x $0.1°$ (1 pixel) and a temporal resolution of one hour, then the algorithm identifies the pixels with lightning and groups these pixels with the adjacent pixels to form the thunderstorm. Then, we calculate monthly, seasonal and annual averaged daily curves of the number of thunderstorms. The data set under study includes years 2012 and 2013. Our methodology is validated by calculating the linear correlation coefficient (R) between the annual daily thunderstorm curve and the “universal” Carnegie curve (R = 0.97) and with the Vostok electric field measurements (R = 0.98). Additionally, we found higher correlation in September–October–November months (R = 0.98) compared with June–July–August months (R = 0.75) for Carnegie and for Vostok station (R=0.99 and R=0.88, respectively).

近年来，对闪电的持续全球监测对于研究与全球变暖的可能关系非常重要。因此，已经在不同的空间尺度（区域和全球）安装了几个检测闪电的网络。这些网络之一是全球闪电定位网络（WWLLN），它自 2003 年以来一直在监测闪电。监测雷暴的发生也很重要，这可以通过检测闪电簇来大致存档。在这项工作中，我们提出了一种闪电分组方法，以估计全球雷暴的数量。我们的方法包括将 WWLLN 数据分组为空间分辨率为$0.1°$ X $0.1°$(1 个像素) 和 1 小时的时间分辨率，然后算法识别出有闪电的像素，并将这些像素与相邻像素分组以形成雷暴。然后，我们计算出雷暴次数的月、季和年平均日曲线。研究中的数据集包括 2012 年和 2013 年。我们的方法通过计算年度每日雷暴曲线与“通用”卡内基曲线（R = 0.97）之间的线性相关系数（R）以及 Vostok 电场测量值（ R = 0.98）。此外，我们发现卡内基站和东方站（分别为 R=0.99 和 R=0.88）在 9 月至 10 月至 11 月（R = 0.98）与 6 月至 7 月至 8 月（R = 0.75）的相关性更高。