Long-term foehn reconstruction combining unsupervised and supervised learning

TL;DR

This paper introduces a novel combined unsupervised and supervised learning approach to reconstruct historical foehn wind occurrences from reanalysis data, enabling climate change impact analysis over the past 83 years.

Contribution

It presents a new probabilistic method that links in-situ foehn measurements with reanalysis data for long-term foehn reconstruction.

Findings

Accurate hourly foehn occurrence reconstructions from 1940 to present.

Method effectively captures seasonal foehn pattern changes over decades.

Enables climate change impact studies on foehn wind events.

Abstract

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.