  Foehn winds, characterized by abrupt temperature increases and wind speed
changes, significantly impact regions on the leeward side of mountain ranges,
e.g., by spreading wildfires. Understanding how foehn occurrences change under
climate change is crucial. Unfortunately, foehn cannot be measured directly but
has to be inferred from meteorological measurements employing suitable
classification schemes. Hence, this approach is typically limited to specific
periods for which the necessary data are available. We present a novel approach
for reconstructing historical foehn occurrences using a combination of
unsupervised and supervised probabilistic statistical learning methods. We
utilize in-situ measurements (available for recent decades) to train an
unsupervised learner (finite mixture model) for automatic foehn classification.
These labeled data are then linked to reanalysis data (covering longer periods)
using a supervised learner (lasso or boosting). This allows to reconstruct past
foehn probabilities based solely on reanalysis data. Applying this method to
ERA5 reanalysis data for six stations across Switzerland and Austria achieves
accurate hourly reconstructions of north and south foehn occurrence,
respectively, dating back to 1940. This paves the way for investigating how
seasonal foehn patterns have evolved over the past 83 years, providing valuable
insights into climate change impacts on these critical wind events.
