Articles | Volume 26, issue 9
https://doi.org/10.5194/acp-26-6507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-6507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 May 2026
Research article |
| 15 May 2026
| 15 May 2026
Machine-learning-based identification of influencing factors and synoptic patterns of foehn on the eastern foothills of the Taihang Mountains, China
Xinpeng Xu
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, 310058, China
Shoujuan Shu
CORRESPONDING AUTHOR
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, 310058, China
Guochen Wang
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, 310058, China
Weijun Li
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, 310058, China
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Short summary
We studied warm, dry winds called foehn on the east foothills of China’s Taihang Mountains, where over 30 million people live. Using synopic analysis and machine learning methods, we found these winds are driven mainly by surface conditions and the influence of them varies seasonally. These winds worsen pollution, heatwaves, and fire risk. Our findings help predict these events earlier, protecting health, crops, and air quality in a warming climate.
We studied warm, dry winds called foehn on the east foothills of China’s Taihang Mountains,...
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