the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 May 2025
Opinion: Inferring Process from Snapshots of Cloud Systems
Abstract. The cloudy atmospheric boundary layer is a complex, open, dynamical system that is difficult to fully characterize through observations. Aircraft measurements provide cloud dynamical, thermodynamical, and microphysical properties along a flightpath, at high spatial/temporal resolution (order 10 m/0.1 s). These data are essentially contiguous "snapshots" in time of the state of the cloud and its environment. Polar-orbiting satellite-based remote sensing yields snapshots of retrieved cloud and aerosol properties once or twice a day at spatial scales on the order of 250 m, but these are usually averaged to scales of ≈ 20–100 km to reduce uncertainties. Neither approach tracks a parcel of air in time, a view that would yield more direct insights into the evolving system. Nevertheless, our long experience with aircraft and satellite-based remote sensing has taught us much about atmospheric processes, suggesting that one can gain insights into processes from these snapshots. Using mostly previously published work we present examples of collections of observation snapshots that reveal various degrees of process- level understanding. We couch the discussion in terms of the concepts of space-time exchange, ergodicity, and process vs. observation timescales. It is our hope that this paper will encourage the atmospheric sciences community to explore the value of these concepts more deeply.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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RC1: 'Comment on egusphere-2025-1869', Anonymous Referee #1, 23 Jun 2025
This manuscript delivers an elegant and much-needed synthesis of how and when snapshot observations of clouds can justifiably be interpreted as proxies for time-resolved processes. Its intellectual clarity and breadth of examples promise to influence both observationalists and modelers.
Inferring process from snapshots of cloud systems is a thought-provoking synthesis: it distils scattered intuitions about when spatial statistics can stand in for temporal evolution, and it offers a clear vocabulary (ergodicity, D-number, Type 1 vs. Type 2) that would help future cloud research.
In my opinion, apart from a few minor modifications, the paper should be published. It is an “opinion” paper, and as such, it highlights an important question that is highly relevant to cloud/rain/aerosol/climate research in a rather qualitative manner.
Minor comments:
1) In general, I miss a consideration of the variance of the explored processes. In all of the examples, the mapping is not one-to-one. The relevant variables are represented by a distribution (r_e or LWP). When sampling the state space, one must be sure that the variability is covered.
The variance is not necessarily a reflection of the many states. It could reflect variations around a given state. The text in Line 141, for example. The step of translating the satellite snapshot into a few hours of observation is critical. It distills the essence of the paper and should be better explained. We know that the r_e slices (per T, Z, or P) can be highly variable. I miss a discussion of the need for fully covering the statistical variance.
2) On the same note, what is a sufficiently large snapshot? How to scale the spatial length of it to the time it covers? What is the right mapping constant? Is it advection?
3) Line 237: “Because the data derive from many different conditions, the observation timescale t_obs is on the order of many days …” Please explain why, when mixing many observations of different thermodynamical states, we can scale the observation time to days? I guess that by doing so, we average over many thermodynamic scenarios? Again, in this case, the variance of the timescale is important. What is the meaning of ergodicity in the case of averaging many states? I think that discussing it in the introduction would be beneficial to the general message of the paper. Can any system be averaged such that taking enough samples to cover the state distribution will yield an ergodic system?
Citation: https://doi.org/10.5194/egusphere-2025-1869-RC1 -
AC2: 'Reply on RC1', Graham Feingold, 05 Aug 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1869/egusphere-2025-1869-AC2-supplement.pdf
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AC2: 'Reply on RC1', Graham Feingold, 05 Aug 2025
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CC1: 'Comment on egusphere-2025-1869', Jesse Loveridge, 01 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1869/egusphere-2025-1869-CC1-supplement.pdf
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AC3: 'Reply on CC1', Graham Feingold, 05 Aug 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1869/egusphere-2025-1869-AC3-supplement.pdf
-
AC3: 'Reply on CC1', Graham Feingold, 05 Aug 2025
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RC2: 'Comment on egusphere-2025-1869', Anonymous Referee #2, 03 Jul 2025
General comment:
This paper provides a nice overview of how to obtain process information from snapshot measurements of cloud systems through reviewing the literature of previous observational and modeling studies. The previous relevant works are reviewed in the well-organized manner that classifies the past approaches according to relative magnitudes of time scales of phenomena and observations. I think that this review is enlightening and encouraging to further explore key fundamental processes of cloud systems through deliberate use of measurement data to obtain observation-based process understanding that is highly required these days to essentially advance numerical modeling of clouds. I only have a couple of minor comments listed below that I hope can be addressed easily by the authors. After the authors address those points, I recommend this manuscript to be published.
Specific points:
Line 74-75: It is a bit unclear to me what the “observation timescale” means. At first, I interpreted it to be a “temporal resolution” of observation, but later I realized that this means what is more like a “duration of observation”. Is this interpretation correct? I would appreciate the authors to clarify this point to avoid possible confusion for interpreting the meaning of the Deborah number and the classification into Type 1 and 2.
Section 2: Given a remarkable progress in satellite observations with active sensors in this couple of decades, I’m just curious how various types of statistical analysis with vertical profiling data from radar/lidar are classified into the two types the authors defined. In particular, I’m wondering how three statistical methods of compositing the vertical cloud profiles, namely, Contoured Frequency by Altitude Diagram (CFAD), Contoured Frequency by Temperature Diagram (CFED), and Contoured Frequency by Optical Depth Diagram (CFODD), are classified into the two types or any other type. A-Train satellite data is touched on in Section 4, but more detailed discussion of active sensor-based analysis would be appreciated.
Section 2.3: As a quick note on Stephens and Haynes (2007), I like to point out that the left-hand side and right-hand side of equation (2) are not obtained from independent measurement information. The left-hand side quantity (P times h) is derived from re, COD and Z, according to the expression on the right-hand side. By carefully looking at the right-hand side, the timescale of auto-conversion, represented by the slope in Figure 3, is solely determined by Z. This correspondence of Z to the timescale comes from the assumption of Long’s collection kernel proportional to sixth power of particle radius that happens to coincide with the dependence of Z on particle radius (which is also sixth power). Constrained by this assumption, the variability range of the timescale (or slope in Figure 3) simply reflects the variability range of Z bracketed between -15dBZ and 0dBZ. This understanding of Stephens and Haynes (2007) should be more clearly described in the authors’ argument of Line 224-230 to interpret “why the process rates are relatively poorly constrained”. Again, the diversity of the timescale is just a simple translation from the diversity of Z, according to equation (2).
Citation: https://doi.org/10.5194/egusphere-2025-1869-RC2 -
AC1: 'Reply on RC2', Graham Feingold, 05 Aug 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1869/egusphere-2025-1869-AC1-supplement.pdf
-
AC1: 'Reply on RC2', Graham Feingold, 05 Aug 2025
Status: closed
-
RC1: 'Comment on egusphere-2025-1869', Anonymous Referee #1, 23 Jun 2025
This manuscript delivers an elegant and much-needed synthesis of how and when snapshot observations of clouds can justifiably be interpreted as proxies for time-resolved processes. Its intellectual clarity and breadth of examples promise to influence both observationalists and modelers.
Inferring process from snapshots of cloud systems is a thought-provoking synthesis: it distils scattered intuitions about when spatial statistics can stand in for temporal evolution, and it offers a clear vocabulary (ergodicity, D-number, Type 1 vs. Type 2) that would help future cloud research.
In my opinion, apart from a few minor modifications, the paper should be published. It is an “opinion” paper, and as such, it highlights an important question that is highly relevant to cloud/rain/aerosol/climate research in a rather qualitative manner.
Minor comments:
1) In general, I miss a consideration of the variance of the explored processes. In all of the examples, the mapping is not one-to-one. The relevant variables are represented by a distribution (r_e or LWP). When sampling the state space, one must be sure that the variability is covered.
The variance is not necessarily a reflection of the many states. It could reflect variations around a given state. The text in Line 141, for example. The step of translating the satellite snapshot into a few hours of observation is critical. It distills the essence of the paper and should be better explained. We know that the r_e slices (per T, Z, or P) can be highly variable. I miss a discussion of the need for fully covering the statistical variance.
2) On the same note, what is a sufficiently large snapshot? How to scale the spatial length of it to the time it covers? What is the right mapping constant? Is it advection?
3) Line 237: “Because the data derive from many different conditions, the observation timescale t_obs is on the order of many days …” Please explain why, when mixing many observations of different thermodynamical states, we can scale the observation time to days? I guess that by doing so, we average over many thermodynamic scenarios? Again, in this case, the variance of the timescale is important. What is the meaning of ergodicity in the case of averaging many states? I think that discussing it in the introduction would be beneficial to the general message of the paper. Can any system be averaged such that taking enough samples to cover the state distribution will yield an ergodic system?
Citation: https://doi.org/10.5194/egusphere-2025-1869-RC1 -
AC2: 'Reply on RC1', Graham Feingold, 05 Aug 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1869/egusphere-2025-1869-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Graham Feingold, 05 Aug 2025
-
CC1: 'Comment on egusphere-2025-1869', Jesse Loveridge, 01 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1869/egusphere-2025-1869-CC1-supplement.pdf
-
AC3: 'Reply on CC1', Graham Feingold, 05 Aug 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1869/egusphere-2025-1869-AC3-supplement.pdf
-
AC3: 'Reply on CC1', Graham Feingold, 05 Aug 2025
-
RC2: 'Comment on egusphere-2025-1869', Anonymous Referee #2, 03 Jul 2025
General comment:
This paper provides a nice overview of how to obtain process information from snapshot measurements of cloud systems through reviewing the literature of previous observational and modeling studies. The previous relevant works are reviewed in the well-organized manner that classifies the past approaches according to relative magnitudes of time scales of phenomena and observations. I think that this review is enlightening and encouraging to further explore key fundamental processes of cloud systems through deliberate use of measurement data to obtain observation-based process understanding that is highly required these days to essentially advance numerical modeling of clouds. I only have a couple of minor comments listed below that I hope can be addressed easily by the authors. After the authors address those points, I recommend this manuscript to be published.
Specific points:
Line 74-75: It is a bit unclear to me what the “observation timescale” means. At first, I interpreted it to be a “temporal resolution” of observation, but later I realized that this means what is more like a “duration of observation”. Is this interpretation correct? I would appreciate the authors to clarify this point to avoid possible confusion for interpreting the meaning of the Deborah number and the classification into Type 1 and 2.
Section 2: Given a remarkable progress in satellite observations with active sensors in this couple of decades, I’m just curious how various types of statistical analysis with vertical profiling data from radar/lidar are classified into the two types the authors defined. In particular, I’m wondering how three statistical methods of compositing the vertical cloud profiles, namely, Contoured Frequency by Altitude Diagram (CFAD), Contoured Frequency by Temperature Diagram (CFED), and Contoured Frequency by Optical Depth Diagram (CFODD), are classified into the two types or any other type. A-Train satellite data is touched on in Section 4, but more detailed discussion of active sensor-based analysis would be appreciated.
Section 2.3: As a quick note on Stephens and Haynes (2007), I like to point out that the left-hand side and right-hand side of equation (2) are not obtained from independent measurement information. The left-hand side quantity (P times h) is derived from re, COD and Z, according to the expression on the right-hand side. By carefully looking at the right-hand side, the timescale of auto-conversion, represented by the slope in Figure 3, is solely determined by Z. This correspondence of Z to the timescale comes from the assumption of Long’s collection kernel proportional to sixth power of particle radius that happens to coincide with the dependence of Z on particle radius (which is also sixth power). Constrained by this assumption, the variability range of the timescale (or slope in Figure 3) simply reflects the variability range of Z bracketed between -15dBZ and 0dBZ. This understanding of Stephens and Haynes (2007) should be more clearly described in the authors’ argument of Line 224-230 to interpret “why the process rates are relatively poorly constrained”. Again, the diversity of the timescale is just a simple translation from the diversity of Z, according to equation (2).
Citation: https://doi.org/10.5194/egusphere-2025-1869-RC2 -
AC1: 'Reply on RC2', Graham Feingold, 05 Aug 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1869/egusphere-2025-1869-AC1-supplement.pdf
-
AC1: 'Reply on RC2', Graham Feingold, 05 Aug 2025
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