Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 2
Articles | Volume 26, issue 2
https://doi.org/10.5194/acp-26-1415-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-1415-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 2
Research article
 | 
28 Jan 2026
Research article |  | 28 Jan 2026

Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability

Yufei Chu, Guo Lin, Min Deng, Lulin Xue, Weiwei Li, Hyeyum Hailey Shin, Jun A. Zhang, Hanqing Guo, and Zhien Wang

Viewed

Total article views: 1,761 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,426 291 44 1,761 44 39 51
  • HTML: 1,426
  • PDF: 291
  • XML: 44
  • Total: 1,761
  • Supplement: 44
  • BibTeX: 39
  • EndNote: 51
Views and downloads (calculated since 04 Aug 2025)
Cumulative views and downloads (calculated since 04 Aug 2025)

Viewed (geographical distribution)

Total article views: 1,761 (including HTML, PDF, and XML) Thereof 1,727 with geography defined and 34 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Feb 2026
Download
Short summary
We developed a new machine learning approach to estimate the height of the mixing layer in the lower atmosphere, which is important for predicting weather and air quality. By using daily temperature and heat patterns, the model learns how the atmosphere changes throughout the day. It gives accurate results across different locations and seasons, helping improve future climate and weather forecasts through better understanding of surface–atmosphere interactions.
Read more
Share
Altmetrics
Final-revised paper
Preprint