Foehn wind detection using unsupervised machine learning

Foehn winds are accelerated, warm and dry winds that can have large socioeconomic and environmental impacts as they descend into the lee of a mountain range. They can be found in many alpine areas around the world, including the New Zealand Alps and the McMurdo Dry Valleys (MDV) in Antarctica. In the latter, foehn winds can cause ice and glacial melt and by extension destabilise ice shelves. Hence, detecting past and future foehn events from in-situ sensors or climate models is of significant interest. However, to the best of our knowledge, no unsupervised machine-learning approaches have been applied to this domain to provide a data-driven analysis of foehn wind patterns. In this study, we show that unsupervised methods such as k-means clustering and autoencoder-based anomaly detection (AD) can produce comparable results to current rule-based and supervised approaches with f1-scores of 0.86 and 0.89 respectively.