  Upward lightning (UL) has become an increasingly important threat to wind
turbines as ever more of them are being installed for renewably producing
electricity. The taller the wind turbine the higher the risk that the type of
lightning striking the man-made structure is UL. UL can be much more
destructive than downward lightning due to its long lasting initial continuous
current leading to a large charge transfer within the lightning discharge
process. Current standards for the risk assessment of lightning at wind
turbines mainly take the summer lightning activity into account, which is
inferred from LLS. Ground truth lightning current measurements reveal that less
than 50% of UL might be detected by lightning location systems (LLS). This
leads to a large underestimation of the proportion of LLS-non-detectable UL at
wind turbines, which is the dominant lightning type in the cold season. This
study aims to assess the risk of LLS-detectable and LLS-non-detectable UL at
wind turbines using direct UL measurements at the Gaisberg Tower (Austria) and
S\"antis Tower (Switzerland). Direct UL observations are linked to
meteorological reanalysis data and joined by random forests, a powerful machine
learning technique. The meteorological drivers for the non-/occurrence of
LLS-detectable and LLS-non-detectable UL, respectively, are found from the
random forest models trained at the towers and have large predictive skill on
independent data. In a second step the results from the tower-trained models
are extended to a larger study domain (Central and Northern Germany). The
tower-trained models for LLS-detectable lightning is independently verified at
wind turbine locations in that domain and found to reliably diagnose that type
of UL. Risk maps based on case study events show that high diagnosed
probabilities in the study domain coincide with actual UL events.
