On the estimation of boundary layer heights: a machine learning approach

Krishnamurthy, Raghavendra; Newsom, Rob K.; Berg, Larry K.; Xiao, Heng; Ma, Po-Lun; Turner, David D.

doi:https://doi.org/10.5194/amt-14-4403-2021

Articles | Volume 14, issue 6

https://doi.org/10.5194/amt-14-4403-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-14-4403-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 6

Research article

|

15 Jun 2021

Research article |

| 15 Jun 2021

On the estimation of boundary layer heights: a machine learning approach

Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner

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Final revised paper (published on 15 Jun 2021)
Preprint (discussion started on 17 Dec 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review for AMT-2020-439', Anonymous Referee #1, 18 Feb 2021
- AC1: 'Reply on RC1', Raghavendra Krishnamurthy, 31 Mar 2021
RC2: 'Review of use of ML for deriving PBL', Anonymous Referee #2, 03 Mar 2021
- AC2: 'Reply on RC2', Raghavendra Krishnamurthy, 31 Mar 2021

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Raghavendra Krishnamurthy on behalf of the Authors (31 Mar 2021) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (11 Apr 2021) by Domenico Cimini

RR by Anonymous Referee #2 (12 Apr 2021)

RR by Anonymous Referee #1 (27 Apr 2021)

ED: Publish as is (27 Apr 2021) by Domenico Cimini

Short summary

Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.