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

Related authors

Eulerian modelling of spotting using a coupled Fire-Atmosphere approach
Alberto Alonso-Pinar, Jean-Baptiste Filippi, Adrien Guyot, Nicholas McCarthy, Pierre Tulet, and Alexander Filkov
EGUsphere, https://doi.org/10.5194/egusphere-2025-4855,https://doi.org/10.5194/egusphere-2025-4855, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
The sensitivity of Southern Ocean atmospheric dimethyl sulfide (DMS) to modeled oceanic DMS concentrations and emissions
Yusuf A. Bhatti, Laura E. Revell, Alex J. Schuddeboom, Adrian J. McDonald, Alex T. Archibald, Jonny Williams, Abhijith U. Venugopal, Catherine Hardacre, and Erik Behrens
Atmos. Chem. Phys., 23, 15181–15196, https://doi.org/10.5194/acp-23-15181-2023,https://doi.org/10.5194/acp-23-15181-2023, 2023
Short summary
Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model
Zhangcheng Pei, Sonya L. Fiddes, W. John R. French, Simon P. Alexander, Marc D. Mallet, Peter Kuma, and Adrian McDonald
Atmos. Chem. Phys., 23, 14691–14714, https://doi.org/10.5194/acp-23-14691-2023,https://doi.org/10.5194/acp-23-14691-2023, 2023
Short summary
Segmentation of polarimetric radar imagery using statistical texture
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023,https://doi.org/10.5194/amt-16-4571-2023, 2023
Short summary
Earth-system-model evaluation of cloud and precipitation occurrence for supercooled and warm clouds over the Southern Ocean's Macquarie Island
McKenna W. Stanford, Ann M. Fridlind, Israel Silber, Andrew S. Ackerman, Greg Cesana, Johannes Mülmenstädt, Alain Protat, Simon Alexander, and Adrian McDonald
Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023,https://doi.org/10.5194/acp-23-9037-2023, 2023
Short summary
More articles (6)

Cited articles

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
More articles (48)
Download
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.
Read more
Share