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

Viewed

Total article views: 1,520 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,058 411 51 1,520 33 34
  • HTML: 1,058
  • PDF: 411
  • XML: 51
  • Total: 1,520
  • BibTeX: 33
  • EndNote: 34
Views and downloads (calculated since 13 Feb 2024)
Cumulative views and downloads (calculated since 13 Feb 2024)

Viewed (geographical distribution)

Total article views: 1,520 (including HTML, PDF, and XML) Thereof 1,546 with geography defined and -26 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.
Read more
Altmetrics
Final-revised paper
Preprint