Although rare, upward lightning poses a threat to wind turbines in particular, as the long-duration current of upward lightning can cause significant damage. Current risk assessment methods overlook the impact of meteorological conditions, potentially underestimating upward lightning risks. Therefore, the underlying study uses random forests, a machine learning technique, to analyze the relationship between upward lightning measured at Gaisberg Tower (Austria) and 35 larger-scale meteorological variables. Of these, larger-scale upward velocity, wind speed and direction at 10 meters, and cloud physics variables contribute the most information. Strong near-surface winds combined with upward deflection by elevated terrain increase the risk of upward lightning. The diurnal cycle of upward lightning 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 expand southward to affect northern Italy in the transition and summer months. The model performs best in winter, with the highest predicted upward lightning 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 assessing upward lightning risk, as lightning densities are a poor indicator of lightning at tall objects. To read the underlying study: https://doi.org/10.22541/essoar.171322767.78278045/v1

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