Articles | Volume 23, issue 22
https://doi.org/10.5194/acp-23-14293-2023
© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.
Quantifying the dependence of drop spectrum width on cloud drop number concentration for cloud remote sensing
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- Final revised paper (published on 20 Nov 2023)
- Supplement to the final revised paper
- Preprint (discussion started on 14 Jun 2023)
- Supplement to the preprint
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-2023-1176', Anonymous Referee #1, 29 Jun 2023
- RC2: 'Comment on egusphere-2023-1176', Anonymous Referee #2, 13 Jul 2023
- AC1: 'Comment on egusphere-2023-1176', Matthew Lebsock, 26 Aug 2023
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Matthew Lebsock on behalf of the Authors (26 Aug 2023)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (01 Sep 2023) by Odran Sourdeval
RR by Anonymous Referee #1 (13 Sep 2023)
RR by Anonymous Referee #2 (25 Sep 2023)
ED: Publish as is (11 Oct 2023) by Odran Sourdeval
AR by Matthew Lebsock on behalf of the Authors (13 Oct 2023)
Manuscript
This study uses in situ observations of droplet size distributions (DSD) and number concentrations (Nd) to investigate the relationship between DSD width (parameterized by the k parameter) and Nd. Generally, a positive k-Nd relationship is found. This relationship is parameterized and applied to Nd retrievals derived from MODIS level-2 cloud products.
The paper is well written and the subject is definitely relevant and interesting. However, at the end of the introduction the authors state that they claim to address the issue that “the spatial scale of the data is a critical determinant of the derived k-N relationship”, which, in my view, they do not really do. Hence, I would only recommend publication if this issue is indeed addressed. I elaborate on this major comment below, after which some minor comments and suggestions are listed.
Major comment:
As said, the spatial scale on which the DSD is evaluated is critical for the k-Nd relationship. It seems that it is claimed that this issue is addressed by using high resolution (1Hz or ~100m) data. However, there is no demonstration on the scale dependency. More importantly, the resulting parameterization is then applied to correct satellite data that is on the order of 1km^2 resolution. The effective radius that is retrieved for a MODIS footprint would be the one that represents the 3rd over the 2nd moment of all drops near cloud top within that pixel. I would think that the k to derive Nd should the one that represents that MODIS resolution. If the mode (or effective) radii of DSDs at small scales over that footprint vary, this translates into an effectively wider total DSD for the whole footprint. So shouldn’t the in situ observations (and thus derived k-Nd relations) be evaluated over a similar scale as the MODIS observations? It could be that a similar result is then obtained, but this needs to be shown. Or maybe the authors can convince me and the readers that the k-Nd relationship derived at fine scale is correctly applied to the coarser MODIS scales, but then I would argue for a discussion in the paper making this point.
Minor comments and suggestions: