Articles | Volume 18, issue 14
https://doi.org/10.5194/amt-18-3453-2025
https://doi.org/10.5194/amt-18-3453-2025
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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-3959', Anonymous Referee #1, 07 Mar 2025
    • AC1: 'Reply on RC1', Damao Zhang, 26 Apr 2025
  • RC2: 'Comment on egusphere-2024-3959', Anonymous Referee #2, 24 Mar 2025
    • AC2: 'Reply on RC2', Damao Zhang, 26 Apr 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Damao Zhang on behalf of the Authors (26 Apr 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (28 Apr 2025) by Jorge Luis Chau
RR by Anonymous Referee #1 (01 May 2025)
RR by Anonymous Referee #2 (12 May 2025)
ED: Publish subject to minor revisions (review by editor) (13 May 2025) by Jorge Luis Chau
AR by Damao Zhang on behalf of the Authors (16 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (19 May 2025) by Jorge Luis Chau
AR by Damao Zhang on behalf of the Authors (19 May 2025)
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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.
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