Articles | Volume 14, issue 8
Geosci. Model Dev., 14, 5205–5215, 2021
https://doi.org/10.5194/gmd-14-5205-2021
Geosci. Model Dev., 14, 5205–5215, 2021
https://doi.org/10.5194/gmd-14-5205-2021

Development and technical paper 18 Aug 2021

Development and technical paper | 18 Aug 2021

Copula-based synthetic data augmentation for machine-learning emulators

David Meyer 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-2020-427', Anonymous Referee #1, 07 Feb 2021
  • RC2: 'Comment on gmd-2020-427', Anonymous Referee #2, 12 Mar 2021
  • RC3: 'Comment on gmd-2020-427', Anonymous Referee #3, 13 Mar 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by David Meyer on behalf of the Authors (31 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (15 Jun 2021) by Sylwester Arabas
RR by Anonymous Referee #2 (01 Jul 2021)
ED: Publish as is (05 Jul 2021) by Sylwester Arabas

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by David Meyer on behalf of the Authors (11 Aug 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (16 Aug 2021) by Sylwester Arabas
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Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.