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.

Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot

Status: final response (author comments only)

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
  • RC2: 'Comment on egusphere-2023-1085', Anonymous Referee #2, 24 Feb 2024
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.