Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7079-2021
https://doi.org/10.5194/amt-14-7079-2021
Research article
 | 
11 Nov 2021
Research article |  | 11 Nov 2021

The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning

Rachel Atlas, Johannes Mohrmann, Joseph Finlon, Jeremy Lu, Ian Hsiao, Robert Wood, and Minghui Diao

Viewed

Total article views: 2,382 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,631 686 65 2,382 62 54
  • HTML: 1,631
  • PDF: 686
  • XML: 65
  • Total: 2,382
  • BibTeX: 62
  • EndNote: 54
Views and downloads (calculated since 21 May 2021)
Cumulative views and downloads (calculated since 21 May 2021)

Viewed (geographical distribution)

Total article views: 2,382 (including HTML, PDF, and XML) Thereof 2,244 with geography defined and 138 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
Many clouds with temperatures between 0 °C and −40 °C contain both liquid and ice particles, and the ratio of liquid to ice particles influences how the clouds interact with radiation and moderate Earth's climate. We use a machine learning method called random forest to classify images of individual cloud particles as either liquid or ice. We apply our algorithm to images captured by aircraft within clouds overlying the Southern Ocean, and we find that it outperforms two existing algorithms.