Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 14
Articles | Volume 18, issue 14
https://doi.org/10.5194/amt-18-3453-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-3453-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 14
Research article
 | 
28 Jul 2025
Research article |  | 28 Jul 2025

Best estimate of the planetary boundary layer height from multiple remote sensing measurements

Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao

Viewed

Total article views: 1,025 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
817 181 27 1,025 39 35 44
  • HTML: 817
  • PDF: 181
  • XML: 27
  • Total: 1,025
  • Supplement: 39
  • BibTeX: 35
  • EndNote: 44
Views and downloads (calculated since 14 Feb 2025)
Cumulative views and downloads (calculated since 14 Feb 2025)

Viewed (geographical distribution)

Total article views: 1,025 (including HTML, PDF, and XML) Thereof 1,023 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 30 Aug 2025
Download
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Read more
Share