Articles | Volume 19, issue 3
https://doi.org/10.5194/amt-19-1059-2026
© 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/amt-19-1059-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Feb 2026
Research article |
| 16 Feb 2026
| 16 Feb 2026
Improved estimation of diurnal variations in near-global PBLH through a hybrid WCT and transfer learning approach
Yarong Li
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
State Key Laboratory of Severe Weaher Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing, China
Zeyang Liu
College of Electronic and Information Engineering, West Anhui University, Lu'an, China
Jianjun He
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weaher Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing, China
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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.
An attention-augmented ResNet and a transfer training are implemented to derive diurnal...
