Preprints
https://doi.org/10.5194/egusphere-2023-1085
https://doi.org/10.5194/egusphere-2023-1085
10 Jul 2023
 | 10 Jul 2023

Supercooled liquid water cloud classification using lidar backscatter peak properties

Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot

Abstract. The use of depolarization lidar to measure atmospheric volume depolarization ratio (VDR) is a common technique to classify cloud phase (liquid or ice). Previous work using a machine learning framework, applied to peak properties derived from co-polarised attenuated backscatter data, has been demonstrated to effectively detect supercooled liquid water containing clouds (SLCC). However, the training data from Davis Station, Antarctica, includes no warm liquid water clouds (WLCC), potentially limiting the model’s accuracy in regions where WLCC are present. In this work, we apply the Davis model to a 9-month Micro Pulse Lidar dataset collected in Christchurch, New Zealand, a location which includes WLCC. We then evaluate the results relative to a reference VDR cloud phase mask. We found that the Davis model performed relatively poorly at detecting SLCC with an accuracy of 0.62, often misclassifying WLCC as SLCC. We then trained a new model, using data from Christchurch, to perform SLCC detection on the same set of co-polarized attenuated backscatter peak properties. Our new model performed well, with accuracy scores as high as 0.89, highlighting the effectiveness of the machine learning technique when appropriate training data relevant to the location is used.

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Journal article(s) based on this preprint

02 Oct 2024
Supercooled liquid water cloud classification using lidar backscatter peak properties
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024,https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1085', Anonymous Referee #1, 07 Dec 2023
    • AC1: 'Reply on RC1', Luke Whitehead, 08 Jun 2024
  • RC2: 'Comment on egusphere-2023-1085', Anonymous Referee #2, 24 Feb 2024
    • AC2: 'Reply on RC2', Luke Whitehead, 08 Jun 2024

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1085', Anonymous Referee #1, 07 Dec 2023
    • AC1: 'Reply on RC1', Luke Whitehead, 08 Jun 2024
  • RC2: 'Comment on egusphere-2023-1085', Anonymous Referee #2, 24 Feb 2024
    • AC2: 'Reply on RC2', Luke Whitehead, 08 Jun 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Luke Whitehead on behalf of the Authors (08 Jun 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (13 Jun 2024) by Bernhard Mayer
RR by Anonymous Referee #2 (01 Jul 2024)
RR by Anonymous Referee #1 (01 Aug 2024)
ED: Publish subject to technical corrections (02 Aug 2024) by Bernhard Mayer
AR by Luke Whitehead on behalf of the Authors (16 Aug 2024)  Manuscript 

Journal article(s) based on this preprint

02 Oct 2024
Supercooled liquid water cloud classification using lidar backscatter peak properties
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024,https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot

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Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.