Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 509–533, 2022
https://doi.org/10.5194/gmd-15-509-2022
Geosci. Model Dev., 15, 509–533, 2022
https://doi.org/10.5194/gmd-15-509-2022

Methods for assessment of models 25 Jan 2022

Methods for assessment of models | 25 Jan 2022

An aerosol classification scheme for global simulations using the K-means machine learning method

Jingmin Li 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 gmd-2021-191', Anonymous Referee #1, 30 Aug 2021
    • AC1: 'Reply on RC1', Jingmin Li, 15 Sep 2021
  • RC2: 'Comment on gmd-2021-191', Anonymous Referee #2, 27 Sep 2021
  • AC2: 'Reply to reviewers' comments', Jingmin Li, 02 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jingmin Li on behalf of the Authors (02 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (17 Nov 2021) by Havala Pye
RR by Anonymous Referee #2 (14 Dec 2021)
ED: Publish as is (15 Dec 2021) by Havala Pye
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Short summary
The growing complexity of global aerosol models results in a large number of parameters that describe the aerosol number, size, and composition. This makes the analysis, evaluation, and interpretation of the model results a challenge. To overcome this difficulty, we apply a machine learning classification method to identify clusters of specific aerosol types in global aerosol simulations. Our results demonstrate the spatial distributions and characteristics of these identified aerosol clusters.