Machine-learning-based analysis of influencing factors and synoptic patterns of foehn on the eastern foothills of the Taihang Mountains, China

Abstract. Foehn generates over alpine terrain and is exerting increasingly pronounced impacts on air pollution, heatwaves, wildfires, and human health under global warming. Due to the complexity of influencing factors of foehn, it is difficult for traditional techniques to identify them, thereby limiting foehn’s comprehensive assessments and accurate forecasting. Based on 64 years (1959–2022) of surface-station observations and reanalysis data, this study employs interpretable machine-learning techniques and synoptic analysis, to systematically reveal the key controlling factors, dynamic thresholds, and synoptic patterns of foehn on the eastern foothills of the Taihang Mountains, China. Our results show that foehn formation is predominantly controlled by surface conditions and the influence of foehn factors varies seasonally: the leeward wind speed > 3 m/s from 203°–324° annually, the windward temperature below -17 °C in winter or 9 °C in summer, and the windward specific humidity > 0.07 g/kg in winter or 0.75 g/kg in summer. Synoptic analyses further validate the results obtained from the machine learning model, revealing that foehns preferentially occur in a stably stratified atmospheric environment with a surface pressure pattern characterized by a high- and low-pressure system on the windward and leeward, respectively, accompanied by an upper-level cold trough at 500 hPa and pronounced subsidence at 850 hPa on the leeward. The results provide a scientific basis for improving foehn forecasting capabilities, offering valuable guidance for forecasting compound disaster events associated with foehns.