Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3921-2025
https://doi.org/10.5194/gmd-18-3921-2025
Development and technical paper
 | 
01 Jul 2025
Development and technical paper |  | 01 Jul 2025

Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression

Gunho Loren Oh and Philip H. Austin

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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 egusphere-2024-352', Anonymous Referee #1, 06 May 2024
    • AC2: 'Reply on RC1', Gunho Oh, 25 Jun 2024
    • AC4: 'Reply on RC1', Gunho Oh, 12 Feb 2025
  • CEC1: 'Comment on egusphere-2024-352 - No Compliance with GMD's policy', Juan Antonio Añel, 11 May 2024
    • AC1: 'Reply on CEC1', Gunho Oh, 28 May 2024
  • RC2: 'Comment on egusphere-2024-352', Anonymous Referee #2, 01 Jun 2024
    • AC3: 'Reply on RC2', Gunho Oh, 25 Jun 2024
    • AC5: 'Reply on RC2', Gunho Oh, 12 Feb 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Gunho Oh on behalf of the Authors (12 Feb 2025)  Author's response   Author's tracked changes 
EF by Anna Mirena Feist-Polner (13 Feb 2025)  Manuscript 
ED: Publish as is (05 Apr 2025) by Richard Neale
AR by Gunho Oh on behalf of the Authors (07 Apr 2025)
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Short summary
It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
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