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

Xu, Xinpeng; Shu, Shoujuan; Wang, Guochen; Li, Weijun

doi:10.5194/acp-26-6507-2026

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.

https://doi.org/10.5194/acp-26-6507-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 9

Research article

|

15 May 2026

Research article |

| 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, Shoujuan Shu, Guochen Wang, and Weijun Li

Supplement

https://doi.org/10.5194/acp-26-6507-2026-supplement

Data sets

Hourly/Sub-Hourly Observational Data NOAA https://www.ncei.noaa.gov/maps/hourly/

ECMWF Reanalysis v5 | ECMWF ECMWF https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5

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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.