Journal Article

Spatio‐Seasonal Risk Assessment of Upward Lightning at Tall Objects Using Meteorological Reanalysis Data

Mayr, Georg J., Zeileis, Achim, Stucke, Isabell, Schulz, Wolfgang, Simon, Thorsten, Morgenstern, Deborah, Pichler, Hannes, Diendorfer, Gerhard

2025

Overview

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 m and cloud physics variables contribute most information. The random forests predict the risk of UL across the study area at a 1 km2${\\text{km}}^{2}$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. Plain Language Summary This study investigates the risk of upward lightning (UL) in the eastern Alps and surrounding regions, which is critical for tall objects such as wind turbines. Current risk assessments often overlook meteorological conditions, potentially underestimating the hazard. Using random forests, a machine learning method, the study analyzes UL at the Gaisberg Tower in Austria, taking into account 35 meteorological factors. Key contributors include wind speed, wind direction, and cloud physics. The model predicts UL risk at a resolution of 1 km2${\\text{km}}^{2}$ , highlighting higher‐risk areas influenced by near‐surface winds and terrain. Risk varies daily and seasonally, peaking in winter north of the Alps and shifting southward in warmer months. Winter predictions are consistent with observed lightning at tall objects, particularly concentrated north of the Alps where wind turbines are prevalent. This study highlights the importance of detailed meteorological data for accurate UL risk assessment and demonstrates that general lightning densities are inadequate indicators of the safety of tall objects. Key Points Strong winds near the surface and upward deflection by obstructing terrain increase the risk of upward lightning at tall objects Lightning at tall wind turbines can account for up to 20% of total lightning activity north of the Alps High‐risk areas are north and east of the Alps in winter and shift southward in the transition seasons and summer

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU)
• Subject: Climate change / Cloud physics / Efficiency / EUCLID / Gaisberg Tower / Height / Lightning / Machine learning / meteorological reanalysis data / random forest / Risk assessment / Turbines / upward lightning at wind turbines / Variables / Wind power / Wind speed / Winter