Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 19
Articles | Volume 17, issue 19
https://doi.org/10.5194/amt-17-5765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-5765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 19
Research article
 | 
02 Oct 2024
Research article |  | 02 Oct 2024

Supercooled liquid water cloud classification using lidar backscatter peak properties

Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot

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Alexander, S. P. and Protat, A.: Cloud Properties Observed From the Surface and by Satellite at the Northern Edge of the Southern Ocean, J. Geophys. Res.-Atmos., 123, 443–456, https://doi.org/10.1002/2017JD026552, 2018. a
Bhatti, Y. A., Revell, L. E., Schuddeboom, A. J., McDonald, A. J., Archibald, A. T., Williams, J., Venugopal, A. U., Hardacre, C., and Behrens, E.: The sensitivity of Southern Ocean atmospheric dimethyl sulfide (DMS) to modeled oceanic DMS concentrations and emissions, Atmos. Chem. Phys., 23, 15181–15196, https://doi.org/10.5194/acp-23-15181-2023, 2023. a
Blanchard, Y., Pelon, J., Eloranta, E. W., Moran, K. P., Delanoë, J., and Sèze, G.: A Synergistic Analysis of Cloud Cover and Vertical Distribution from A-Train and Ground-Based Sensors over the High Arctic Station Eureka from 2006 to 2010, J. Appl. Meteorol. Climatol., 53, 2553–2570, https://doi.org/10.1175/JAMC-D-14-0021.1, 2014. a
Bodas-Salcedo, A., Hill, P. G., Furtado, K., Williams, K. D., Field, P. R., Manners, J. C., Hyder, P., and Kato, S.: Large Contribution of Supercooled Liquid Clouds to the Solar Radiation Budget of the Southern Ocean, J. Climate, 29, 4213–4228, https://doi.org/10.1175/JCLI-D-15-0564.1, 2016. a
Brodersen, K. H., Ong, C. S., Stephan, K. E., and Buhmann, J. M.: The Balanced Accuracy and Its Posterior Distribution, in: 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23–26 August 2010, 3121–3124, https://doi.org/10.1109/ICPR.2010.764, 2010. a
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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.
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