Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 535–551, 2022
https://doi.org/10.5194/gmd-15-535-2022
Geosci. Model Dev., 15, 535–551, 2022
https://doi.org/10.5194/gmd-15-535-2022

Methods for assessment of models 25 Jan 2022

Methods for assessment of models | 25 Jan 2022

A method for assessment of the general circulation model quality using the K-means clustering algorithm: a case study with GETM v2.5

Urmas Raudsepp and Ilja Maljutenko

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-68', Astrid Kerkweg, 08 Jun 2021
    • AC2: 'Reply on CEC1', Urmas Raudsepp, 26 Aug 2021
  • RC1: 'Comment on gmd-2021-68', Anonymous Referee #1, 11 Aug 2021
  • RC2: 'Comment on gmd-2021-68', Anonymous Referee #2, 01 Oct 2021
    • AC3: 'Reply on RC2', Urmas Raudsepp, 09 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Urmas Raudsepp on behalf of the Authors (12 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (14 Dec 2021) by Olivier Marti
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Short summary
A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.