Articles | Volume 14, issue 11
Atmos. Meas. Tech., 14, 7079–7101, 2021
https://doi.org/10.5194/amt-14-7079-2021
Atmos. Meas. Tech., 14, 7079–7101, 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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-123', Darrel Baumgardner, 16 Jun 2021
    • AC1: 'Reply on RC1', Rachel Atlas, 14 Aug 2021
  • RC2: 'Comment on amt-2021-123', Annika Lauber, 02 Jul 2021
    • AC2: 'Reply on RC2', Rachel Atlas, 14 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Rachel Atlas on behalf of the Authors (14 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (28 Aug 2021) by Hartmut Herrmann

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Rachel Atlas on behalf of the Authors (03 Nov 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (06 Nov 2021) by Hartmut Herrmann
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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.