the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Oct 2023
Detection of small drizzle droplets in a large cloud chamber using ultra high-resolution radar
Pavlos Kollias
Raymond Shaw
Alex Kostinski
Steve Krueger
Katia Lamer
Nithin Allwayin
Mariko Oue
Abstract. A large convection cloud chamber has the potential to produce drizzle-sized droplets, thus offering a new opportunity to investigate aerosol-cloud-drizzle interactions at a fundamental level under controlled environmental conditions. One key measurement requirement is the development of methods to detect the low concentration drizzle drops in such a large cloud chamber. In particular, remote sensing methods may overcome some limitations of in situ methods.
Here, the potential of an ultra-high-resolution radar to detect the radar return signal of a small drizzle droplet against the cloud droplet background signal is investigated. It is found that using a small sampling volume is critical to drizzle detection in a cloud chamber to allow a drizzle drop in the radar sampling volume to dominate over the background cloud droplets signal. For instance, a radar volume of 1 cubic centimeter (cm3) would enable the detection of drizzle embryos with diameter larger than 40 μm. However, the probability of drizzle sampling also decreases as the sample volume reduces, leading to a longer observation time. Thus, the selection of radar volume should consider both of the signal power and the drizzle occurrence probability. Finally, observations from the Pi Convection-Cloud Chamber are used to demonstrate the single drizzle particle detection concept using small radar volume. The results presented in this study also suggest new applications of ultra-high-resolution cloud radar for atmospheric sensing.
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Zeen Zhu et al.
Status: open (until 01 Dec 2023)
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RC1: 'Comment on amt-2023-218', Anonymous Referee #1, 14 Nov 2023
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This is an interesting paper that explores the use of very high resolution radar to characterize drizzle drops in a large cloud chamber. The authors find that identification of single drop backscatter against the background cloud droplets is very challenging and at best requires multiple hours to expect observation of a single droplet. While the authors remain optimistic that such a radar can be used for this purpose, its seems that this optimism is a bit premature since the shortest time for detection (order of hours) relies on a droplet concentration theory that, while plausible and published, is perhaps a theory that cannot yet be relied upon to be mature.
I can find no technical issues with the paper and I think it could be published as is. However, I do think the authors should consider a more realistic assessment of the challenges of this methodology being successful. For instance I question whether a 3-hour observational interval between detecting drizzle drops is reasonable? Can the cloud chamber remain in steady state for this long? What exactly can be learned by sensing the presence of a single drizzle drop every several hours? Is drizzle actually produced for the liquid water contents that seem most suitable for generating the SNR needed for detection (much less than 1 g/m3)?
Minor issues:
The manuscript should be proofed for grammar.
Line 56: Is there documentation of this inability to explain drizzle growth by "traditinally-defined condensation growth processes"? It seems that a single 1996 paper is insufficient to establish this statement which is the motivation for using the cloud chamber to study the process
Line 65: What effects?
Figue 2: Color scheme is not freindly to color-blind readers.
Line 235: The likliehood of getting 40 micron drizzle drops in an 0.2 g/m3 LWC cloud seems very unlikely.
Citation: https://doi.org/10.5194/amt-2023-218-RC1 -
RC2: 'Comment on amt-2023-218', Anonymous Referee #2, 15 Nov 2023
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The paper is a fascinating study exploring the potential of radar with ultra-fine sampling volume to increase the possibility of detecting tiny drizzle drops in small concentrations in a cloud chamber. Practical experiments also support the analytical analysis. The study has relevant implications for gathering knowledge on aerosol-cloud interaction and microphysical processes leading to warm rain formation, and for this, I recommend its publication. My only primary concern is that most graphics are color-blind people unfriendly. I suggest that the authors re-elaborate them after exploiting tools to check their plots, which can also be found at https://hiweller.rbind.io/post/using-the-dichromat-package-to-check-if-your-plot-is-colorblind-friendly/
Moreover, I collected some minor comments, corrections of typos, and mere curiosity questions, which I list here:
- Under which hypothesis do you assume the same distribution used for cloud droplets is also valid for drizzle? I understood that you used eq 6 to describe the N(D) of the drizzle drops in 9, and I would like to understand more about this choice.
- L 237: Is it possible to also include, as a reference, dots representing typical in real situ observed relations of LWC/N in Figure 2, as you did for the ones observed in the cloud chamber? It would show how representative the cloud chamber of what happens in reality is—for example, one or two cases of warm maritime and continental clouds from literature studies.
- Fig 3: I think that adding a grey grid on the background of the plots would help the reader to follow your arguments.
- L 264: Where is the 70-micron case?
- L 240: … probability of “detecting, " not detection.
- L 481: Do you think the high inhomogeneous variability in the cloud droplet distributions is happening only in the cloud chamber, or is it a property that can also hold for real clouds? Here, and in general, in the whole paper, it would be great to have a more evident connection to the cloud observations in the environment, maybe highlighting how these studies in the cloud chamber can support them and also discussing possible limitations and differences between what occurs in the cloud chamber and what happens when taking observations outside.
Citation: https://doi.org/10.5194/amt-2023-218-RC2
Zeen Zhu et al.
Zeen Zhu et al.
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