Articles | Volume 2, issue 3
Weather Clim. Dynam., 2, 581–608, 2021
https://doi.org/10.5194/wcd-2-581-2021
Weather Clim. Dynam., 2, 581–608, 2021
https://doi.org/10.5194/wcd-2-581-2021

Research article 12 Jul 2021

Research article | 12 Jul 2021

An unsupervised learning approach to identifying blocking events: the case of European summer

Carl Thomas 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 wcd-2021-1', Anonymous Referee #1, 26 Feb 2021
    • AC1: 'Reply on RC1', Carl Thomas, 06 Apr 2021
  • RC2: 'Comment on wcd-2021-1', Anonymous Referee #2, 04 Mar 2021
    • AC2: 'Reply on RC2', Carl Thomas, 06 Apr 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Carl Thomas on behalf of the Authors (06 Apr 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (12 Apr 2021) by Silvio Davolio
RR by Anonymous Referee #2 (14 Apr 2021)
RR by Anonymous Referee #1 (24 May 2021)
ED: Publish subject to minor revisions (review by editor) (25 May 2021) by Silvio Davolio
AR by Carl Thomas on behalf of the Authors (03 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (04 Jun 2021) by Silvio Davolio
AR by Carl Thomas on behalf of the Authors (04 Jun 2021)  Author's response    Manuscript
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Short summary
Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.