Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 11
Articles | Volume 24, issue 11
https://doi.org/10.5194/acp-24-6477-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-6477-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 11
Research article
 | 
04 Jun 2024
Research article |  | 04 Jun 2024

Deep-learning-derived planetary boundary layer height from conventional meteorological measurements

Tianning Su and Yunyan Zhang

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Short summary
The planetary boundary layer is critical to our climate system. This study uses a deep learning approach to estimate the planetary boundary layer height (PBLH) from conventional meteorological measurements. By training data from comprehensive field observations, our model examines the influence of various meteorological factors on PBLH and demonstrates effectiveness across different scenarios, offering a reliable tool for understanding boundary layer dynamics.
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