Articles | Volume 19, issue 3
https://doi.org/10.5194/amt-19-1059-2026
https://doi.org/10.5194/amt-19-1059-2026
Research article
 | 
16 Feb 2026
Research article |  | 16 Feb 2026

Improved estimation of diurnal variations in near-global PBLH through a hybrid WCT and transfer learning approach

Yarong Li, Zeyang Liu, and Jianjun He

Related authors

Upper-air secondary pollutants downward invade to planetary boundary layer by strong turbulence at the eastern steep slope of Tibetan Plateau: results from BLMP-SCB
Suping Zhao, Shaofeng Qi, Jianjun He, Ye Yu, Longxiang Dong, Tong Zhang, Guo Zhao, and Yiting Lv
EGUsphere, https://doi.org/10.5194/egusphere-2025-3130,https://doi.org/10.5194/egusphere-2025-3130, 2025
Preprint archived
Short summary
Development and application of a street-level meteorology and pollutant tracking system (S-TRACK)
Huan Zhang, Sunling Gong, Lei Zhang, Jingwei Ni, Jianjun He, Yaqiang Wang, Xu Wang, Lixin Shi, Jingyue Mo, Huabing Ke, and Shuhua Lu
Atmos. Chem. Phys., 22, 2221–2236, https://doi.org/10.5194/acp-22-2221-2022,https://doi.org/10.5194/acp-22-2221-2022, 2022
Short summary
Assessment of meteorology vs. control measures in the China fine particular matter trend from 2013 to 2019 by an environmental meteorology index
Sunling Gong, Hongli Liu, Bihui Zhang, Jianjun He, Hengde Zhang, Yaqiang Wang, Shuxiao Wang, Lei Zhang, and Jie Wang
Atmos. Chem. Phys., 21, 2999–3013, https://doi.org/10.5194/acp-21-2999-2021,https://doi.org/10.5194/acp-21-2999-2021, 2021
Short summary

Cited articles

Altmann, A., Toloşi, L., Sander, O., and Lengauer, T.: Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340–1347, 2010. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Che, H., Gui, K., Xia, X., Wang, Y., Holben, B. N., Goloub, P., Cuevas-Agulló, E., Wang, H., Zheng, Y., Zhao, H., and Zhang, X.: Large contribution of meteorological factors to inter-decadal changes in regional aerosol optical depth, Atmos. Chem. Phys., 19, 10497–10523, https://doi.org/10.5194/acp-19-10497-2019, 2019. 
Chen, S., Tong, B., Russell, L. M., Wei, J., Guo, J., Mao, F., Li, Z., and Deng, Z.: Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate, Remote Sens. Environ., 281, 113243, https://doi.org/10.1016/j.rse.2022.113243, 2022. 
Davy, R. and Esau, I.: Differences in the efficacy of climate forcings explained by variations in atmospheric boundary layer depth, Nat. Commun., 7, 11690, https://doi.org/10.1038/ncomms11690, 2016. 
Download
Short summary
An attention-augmented ResNet and a transfer training are implemented to derive diurnal variations in near-global planetary boundary layer height. The transfer-trained model shows superior performances compared to conventional algorithms and non-transfer trained mode. The model predicted more reliable diurnal behaviors, with daily amplitude and peak timing approaching radiosonde results.
Share