Articles | Volume 26, issue 2
https://doi.org/10.5194/acp-26-1415-2026
https://doi.org/10.5194/acp-26-1415-2026
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

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Cited articles

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ARM (Atmospheric Radiation Measurement) user facility: Doppler Lidar (DLFPT) data from Southern Great Plains (C1, E32, E37, E39), 2016–2020, compiled by Newsom, R., Shi, Y., and Krishnamurthy, R., ARM Data Center [data set], https://doi.org/10.5439/1025185, 2024. 
Ayazpour, Z., Tao, S., Li, D., Scarino, A. J., Kuehn, R. E., and Sun, K.: Estimates of the spatially complete, observational-data-driven planetary boundary layer height over the contiguous United States, Atmos. Meas. Tech., 16, 563–580, https://doi.org/10.5194/amt-16-563-2023, 2023. 
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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.
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