This study investigates lightning at tall objects and evaluates the risk of
upward lightning (UL) over the eastern Alps and its surrounding areas. While
uncommon, UL poses a threat, especially to wind turbines, as the long-duration
current of UL can cause significant damage. Current risk assessment methods
overlook the impact of meteorological conditions, potentially underestimating
UL risks. Therefore, this study employs random forests, a machine learning
technique, to analyze the relationship between UL measured at Gaisberg Tower
(Austria) and $35$ larger-scale meteorological variables. Of these, the
larger-scale upward velocity, wind speed and direction at 10 meters and cloud
physics variables contribute most information. The random forests predict the
risk of UL across the study area at a 1 km$^2$ resolution. Strong near-surface
winds combined with upward deflection by elevated terrain increase UL risk. The
diurnal cycle of the UL risk as well as high-risk areas shift seasonally. They
are concentrated north/northeast of the Alps in winter due to prevailing
northerly winds, and expanding southward, impacting northern Italy in the
transitional and summer months. The model performs best in winter, with the
highest predicted UL risk coinciding with observed peaks in measured lightning
at tall objects. The highest concentration is north of the Alps, where most
wind turbines are located, leading to an increase in overall lightning
activity. Comprehensive meteorological information is essential for UL risk
assessment, as lightning densities are a poor indicator of lightning at tall
objects.

通过应用随机森林机器学习算法分析上升闪电的测量数据与 35 个大尺度气象变量之间的关系，这项研究评估了东阿尔卑斯山及其周边地区上升闪电的风险，发现大尺度上行速度、风速和方向以及云物理变量对上升闪电风险评估贡献最大，并预测了研究区域每平方公里的上升闪电风险。