Spatio-seasonal risk assessment of upward lightning at tall objects using meteorological reanalysis data

Abstract

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.

Figures (11)

• Figure 1: Topographic overview of study area and location of the instrumented Gaisberg Tower (Salzburg, Austria). Colors indicates the elevation above mean sea level according to data taken from the Shuttle Radar Topography Mission with a 90 m spatial resolution farr2000.
• Figure 2: Panel a: accumulated number of objects with effective heights $\geq$100 m in ERA5 grid cells ($0.25^\circ \times 0.25^\circ$). Panel b: all objects with effective heights $\geq$100 m coded by color.
• Figure 3: Panel a: number of objects per effective height range. Panel b: number of flash-hours scaled by the number of objects per effective height range.
• Figure 4: Panel a: total number of flash-hours in ERA5 grid cell (including DL to the ground and lightning at tall objects) between 2021 and 2023. Panel b: accumulated number of flash-hours at objects with effective heights $\geq$100 m. Panel c: proportion of hours exclusively having lightning at tall objects to the total flash-hours 10 km around each object. Excluded are those flash-hours, where also DL to the ground occurred around the object. Panel d: proportion of wind turbines to the total number of objects in cell. One flash-hour is defined by at least one lightning flash within a grid cell and within one hour.
• Figure 5: Seasonal (panels a--d) and annual (panel e) UL risk at tall objects modeled by the Gaisberg Tower-trained random forest models. Risk is quantified by counting the number of hours exceeding a conditional probability of 0.5. Red dots are LLS-detected flash-hours at tall objects accumulated to the 1 km$^2$ grid cell size. The size category numbers are the upper limit, e.g., size category 5 includes flash-hours from 1 to 5. Light beige shaded cells are cells without tall objects.