Peter Kuma
Science and Software
Article

Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals Open access

Adrien Guyot1, Alain Protat1, Simon P. Alexander2, Andrew Klekociuk2, Peter Kuma3, Adrian McDonald4

1Australian Bureau of Meteorology, Melbourne, Victoria, Australia
2Australian Antarctic Division, Kingston, Tasmania, Australia
3Department of Meteorology, Stockholm University, Stockholm, Sweden
4School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa/New Zealand

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Abstract

Cloud and aerosol lidars measuring backscatter and depolarization ratio are the most suitable lidars to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water containing clouds (SLCC) based on ceilometers measuring only co-polarization backscatter. We utilize observations collected at Davis, Antarctica, where low-level, mixed-phase clouds, including supercooled liquid water (SLW) droplets and ice crystals, remain poorly understood due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018 to February 2019, with a variety of instruments including a depolarization lidar and a W-band cloud radar which were used to build a two-dimensional cloud phase mask distinguishing SLW and mixed-phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarization ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar–lidar approach with an accuracy as high as 0.91. This study provides a new SLCC retrieval approach based on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations. Finally, the two algorithms were applied to a full year of ceilometer observations to retrieve cloud phase and frequency of occurrences of SLCC: SLCC was present 29 ± 6 % of the time for T19 and 24 ± 5 % of the time for G22-Davis over that annual cycle.
Journal:
Atmospheric Measurement Techniques
Volume:
15
Number:
12
Pages:
3663–3681
DOI:
10.5194/amt-15-3663-2022
Submitted:
10 January 2022
Accepted:
18 May 2022
Published:
20 June 2022
License:
Open access / Creative Commons Attribution 4.0 International (CC BY 4.0)
BibTeX: @article{guyot2022,
  journal={Atmospheric Measurement Techniques},
  year={2022},
  volume={15},
  number={12},
  pages={3663-3681},
  doi={10.5194/amt-15-3663-2022},
  url={https://doi.org/10.5194/amt-15-3663-2022},
  author={Guyot, Adrien and Protat, Alain and Alexander, Simon P. and Klekociuk, Andrew and Kuma, Peter and McDonald, Adrian},
  title={Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals}
}

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