Articles | Volume 19, issue 18
https://doi.org/10.5194/gmd-19-8855-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Evaluation of plume rise parameterizations in GEM-MACHv2 with analysis of image data using a deep convolutional neural network
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- Final revised paper (published on 21 Sep 2026)
- Supplement to the final revised paper
- Preprint (discussion started on 15 Oct 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on egusphere-2025-4582', Anonymous Referee #1, 21 Nov 2025
- AC2: 'Reply on RC1', Mark Gordon, 20 Apr 2026
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CEC1: 'Comment on egusphere-2025-4582 - No compliance with the policy of the journal', Juan Antonio Añel, 07 Dec 2025
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AC1: 'Reply on CEC1', Mark Gordon, 09 Jan 2026
- CEC2: 'Reply on AC1', Juan Antonio Añel, 11 Jan 2026
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AC1: 'Reply on CEC1', Mark Gordon, 09 Jan 2026
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RC2: 'Comment on egusphere-2025-4582', Anonymous Referee #2, 09 Feb 2026
- AC2: 'Reply on RC1', Mark Gordon, 20 Apr 2026
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RC3: 'Comment on egusphere-2025-4582', Riccardo Simionato, 13 Mar 2026
- AC2: 'Reply on RC1', Mark Gordon, 20 Apr 2026
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Mark Gordon on behalf of the Authors (01 May 2026)
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ED: Referee Nomination & Report Request started (04 May 2026) by Luke Western
RR by Riccardo Simionato (22 May 2026)
RR by Anonymous Referee #1 (04 Jun 2026)
ED: Reconsider after major revisions (09 Jun 2026) by Luke Western
AR by Mark Gordon on behalf of the Authors (21 Jul 2026)
Author's response
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ED: Referee Nomination & Report Request started (22 Jul 2026) by Luke Western
RR by Riccardo Simionato (06 Aug 2026)
RR by Anonymous Referee #1 (25 Aug 2026)
ED: Publish subject to minor revisions (review by editor) (25 Aug 2026) by Luke Western
AR by Mark Gordon on behalf of the Authors (05 Sep 2026)
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ED: Publish as is (08 Sep 2026) by Luke Western
AR by Mark Gordon on behalf of the Authors (09 Sep 2026)
Manuscript
The paper provides a comparison of the Briggs plume rise parameterisations against a deep convolutional neural network for determining plume rise from imagery (DPRNet).
I felt that there was a lack of understanding of the Briggs formulae. The underlying basis of the Briggs plume rise equations are the governing conservation equations of mass, momentum and heat. Solving these governing equations under specific conditions (buoyancy or momentum dominated, or particular meteorological conditions e.g., zero crosswind, constant buoyancy frequency) leads to the Briggs formulations. The ‘dimensionless constants’ are entrainment parameters. The Briggs formulae are commonly used for predicting plume rise but there are reasons why they may not work well, for example if the assumptions made in deriving the Briggs formulae from the underlying conservation equations do not hold true (e.g., the true atmospheric meteorological profiles may differ from that assumed). Indeed, the authors do make use of a modification for the interaction with the boundary layer top. Other authors have made direct use of the underlying conservation equations, considering the true atmospheric and release conditions (Webster and Thomson, 2002) and more elaborate models do also account for latent heat release from moisture in the rising plume / entrained atmospheric air (Fathi et al., 2025).
I found the text a bit vague in places, a little repetitive and verbose in other places and with many references to supplementary information and over-/under-predictions. It is worth the authors considering what key points / results they would like to present in the main paper and being both concise and precise. In addition, there are quite a lot of typographical errors which, with more care and attention, could have been avoided. All in all, this makes the paper quite hard to follow. Some further details are given in the detailed points below. The conclusion section was, however, well written and provided a good summary.
How are the Briggs formulae for momentum and buoyancy applied to calculate plume rise for plumes with both momentum and buoyancy? Are they just added together and, if so, is this appropriate? The calculation of the proportion of the plume rise due to momentum in section 3.2.3 assumes this simple addition but I’m not sure this is true in reality. Understanding the underlying conservation equations and the derivation of the Briggs equations (and the assumptions made) may shed some light on this.
Presumably DPRNet has been trained on an earlier dataset? What dataset was this and will the trained model be applicable to the images processed here? What about uncertainties / errors in the observations? Line of sight and plume direction are mentioned. Indeed, a sensitivity analysis of the wind direction on the determined observed plume height is conducted. Two methods for obtaining the plume rise from the observations are used, but conclusions are drawn on the performance of the Briggs formulae to these observations without consideration of uncertainties in the observations. Indeed, the authors state under- or overpredictions which are small compared to the uncertainties, say, in the observed plume rise height due to the wind direction presented in section 3.5.1.
Is Figure 3 in the horizontal plane? Is this a valid assumption? I can imagine that the plume height may not be at the same height as the camera. The caption mentions a similar transformation in the vertical plane to determine the plume rise height. A reference is given but it would be helpful to give more detail here on the calculation, what is measured (presumably SP’), what is calculated (presumably SP) and how.
Minor points:
Some minor points on the text are listed here: