Upward Lightning at Wind Turbines: Risk Assessment From Larger‐Scale Meteorology.

Upward lightning (UL) has become a major threat to the growing number of wind turbines producing renewable electricity. It can be much more destructive than downward lightning due to the large charge transfer involved in the discharge process. Ground‐truth lightning current measurements indicate that less than 50% of UL could be detected by lightning location systems (LLS). UL is expected to be the dominant lightning type during the cold season. However, current standards for assessing the risk of lightning at wind turbines mainly consider summer lightning, which is derived from LLS. This study assesses the risk of LLS‐detectable and LLS‐undetectable UL at wind turbines using direct UL measurements at instrumented towers. These are linked to meteorological data using random forests. The meteorological drivers for the absence/occurrence of UL are found from these models. In a second step, the results of the tower‐trained models are extended to a larger study area (central and northern Germany). The tower‐trained models for LLS‐detectable lightning are independently verified at wind turbine sites in this area and found to reliably diagnose this type of UL. Risk maps based on cold season case study events show that high probabilities in the study area coincide with actual UL flashes. This lends credibility to the application of the model to all UL types, increasing both risk and affected areas. Plain Language Summary: The need to produce renewable energy has recently led to an increase not only in the number of wind turbines, but also in their size. The taller the man‐made structure, the greater the likelihood of upward lightning (UL) to initiate from the wind turbine. Each UL flash has an initial continuous current, potentially making it much longer and much more destructive than a downward lightning flash. As UL has become a major weather‐related hazard to wind turbines, proper risk assessment has become essential. The problem: Ground‐truth current measurements at an instrumented tower in Austria show that less than 50% of UL is actually detected by lightning location systems (LLS). This study shows that a new approach based on vertically resolved larger‐scale meteorology and direct UL measurements from specially instrumented towers, combined with flexible machine learning techniques, succeeds in diagnosing the risk of both LLS‐detectable and LLS‐undetectable UL at wind turbines in the colder season over a larger study area. Key Points: Tower‐trained random forests can discriminate between high and low risk of upward lightning (UL) at wind turbines from larger‐scale meteorologyLarge values of convective precipitation, vertical velocity and ice crystals, together with convective available potential energy are most important for ULRegions with elevated terrain and coastlines perpendicular to the impinging wind field have an increased risk of UL [ABSTRACT FROM AUTHOR]