the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Sep 2025
Identification of Snowfall Riming and Aggregation Processes Using Ground-Based Triple-Frequency Radar
Abstract. Riming and aggregation are critical ice-phase microphysical processes in winter clouds, but their overlapping signatures and dynamic transitions pose challenges for conventional single-frequency radar detection. We introduce a novel gradient-based identification method using ground-based triple-frequency dual-polarization radar observations. By analyzing vertical gradients of triple-frequency radar variables, rather than their absolute values, we discern these microphysical processes through physically based thresholds that reflect particle growth regimes. This approach captures subtle spatiotemporal variations in riming and aggregation that conventional threshold methods would miss, particularly in resolving layered riming-aggregation transitions. The dynamic gradient-based method demonstrates the enhanced physical consistency and adaptability near process boundaries, which obviously improve the tracking of ice-particle evolution. These advances provide a pathway to refine microphysical parameterizations and enhance high-resolution snowfall forecasting.
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Status: open (until 16 Dec 2025)
- RC1: 'Comment on egusphere-2025-4233', Anonymous Referee #1, 02 Oct 2025 reply
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CC1: 'Comment on egusphere-2025-4233', Leonie von Terzi, 28 Oct 2025
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Thank you for using the data collected by our team. However, I have noted several inconsistencies. Please correct them and in the future make sure which dataset you are using, which instruments are employed, which processing steps were done and who were the people conducting the campaign.
You say you are using the TRIPEx-pol dataset. This is incorrect, the campaign conducted in winter of 2015 and 2016, and published in Dias-Neto et al. 2019 was from the TRIPEx campaign. The TRIPEx-pol campaign took place in winter 2018-2019, and was published in von Terzi et al. 2022. The radars employed in TRIPEx differ from the TRIPEx-pol campaign significantly: we changed the X-Band radar and had an additional W-Band radar on the roof platform in Jülich.
In Line 94-95 you write that both X and Ka-Band radars were from METEK. This is not correct, see Dias-Neto et al. 2019, Section 2.1. The X-Band was a mobile Meteor 50DX radar, manufactured by Selex ES (Gematronik).
In Line 105 you write that the TRIPEx-pol campaign was conducted by DWD, this is incorrect. The DWD has nothing to do with any of the TRIPEx campaigns. They were conducted by the Emmy-Noether group OPTIMICE under Stefan Kneifel, at the University of Cologne.
You cite Myagkov et al. 2020 and Karrer et al. 2022 as examples of studies that have used your dataset. Both studies used the TRIPEx-pol dataset, however, your study used the TRIPEx dataset. Please correct that!
I am a bit confused about your attenuation correction. Did you do a correction yourself? If so, then why are you only correcting for gas? Liquid and ice are essential to be corrected for (see explanations in Dias-Neto et al. 2019), especially since I see a clear melting layer in your Figure 2, so liquid attenuation is important for the W-Band. Or are you using the corrected data from Dias-Neto? Then make clear that they did this correction and that dataset is not only correcting for gas but also for liquid and ice, that was the whole point of that paper.
I have a few more general comments and major concerns that you may want to consider:
Mainly I think the study is misinterpreting the signatures observed in the night hours of the case study, namely the reduction of Ze alongside an increase of DWR. In my opinion this is caused by sublimation. Since sublimation causes the smallest particles to fully sublimate, the particle size distribution changes, shifting D0 towards larger sizes. This is consistent with the reduction in Ze. I do not understand how Ze should decrease if the particles sedimenting into this region have been rimed above and are now aggregating. I think the authors are missing important previous work of e.g. Kumjian et al. 2022 and especially Mason et al. 2019 were the importance of the size distribution on DWRKaW and DWRXKa are explained
I am also missing a thourough evaluation of the methods. Testing against the triple-frequency space is not enough as both DWRKaW and DWRXKa were used to determine the regions of riming and aggregation. Without a validation with other methods or in-situ observations it is not possible to tell if the methods are actually identifying correct regions or not. The manuscript is short enough to allow for this analysis to also be undertaken.
In the following I have specific comments and note the regions where I do not agree with the interpretations of the authors.
Section 2.2.1: perhaps you might want to consider explaining the method of estimating D0 in more detail. This is not a standard method in my opinion, and estimations of D0 often have significant uncertainty. Scanning over Gaussiat et al. they say they neglect attenuation by ice. However, ice can add several dB of attenuation at W-Band. It is impossible to tell if the attenuation comes from liquid or ice. Also, if you are later using DWR, where W-Band is one of the frequency partners, you need to make sure that attenuation is corrected for both ice and liquid.
Table 1: it is difficult to tell which threshold was derived from which publication. Also, you have 6 thresholds and only 5 “groups” of citations (with groups I mean grouped together by brackets). I am also not sure how you derived a threshold of D0 from my study (von Terzi et al. 2025), we do not retrieve a D0 in this study.
Line 142-145: I am not sure I agree with this criterion. In your Figure 5 you can see that for small DWRKaW, both DWRKaW and DWRXKa increase. Only after the saturation in DWRKaW is reached, then your criterion would be correct. Perhaps you can discuss that a bit more into detail. In addition, can you please cite a study that shows this dependency? Also, previous studies (Mason et al. 2019) have further shown that the shape of the particle size distribution plays a large role in the triple-frequency space, making it not so easy to discriminate between aggregation and riming using DWRKaW and DWRXKa. Can you comment on that?
Line 149: do you have a reference for that statement? (That a negative MDV gradient is observed in aggregation)? In my opinion it is always the question what aggregates. If small ice crystals form a larger aggregate, then initially I would expect the fall velocity to increase.
Line 152: the way you write that here you are expecting only ice crystals to rime. However, it is really likely that all particles in the volume, so also aggregates are riming. This would then not really cause an increase in LDR. Have any other studies investigated this? Or have you done some scattering simulation to show the dependency of LDR on riming degree or aggregation? Otherwise I am not sure you can say that LDR increases with aggregation, but decreases with riming. Especially since if you have needle crystals, that aggregate. Aggregates will always have a smaller LDR than needle crystals. In my opinion you need to investigate this dependency in more detail in order to make those claims of delta LDR here.
In Section 2.2.2 you are not talking about the gradient in spectral width, so perhaps you should include an explanation of why you are using the gradient of SW here.
In your Section 2.2.2 I am missing a citation of Kumjian et al. 2022, and references therein, they have done significant work in identifying fingerprints of ice microphysical processes by studying the gradients of radar variables.
Line 170: is this basically at the lowest range gate? I find it very hard to see the melting layer between approx. 22:30UTC and 03UTC
Line 176: what do you mean by clearly layered structure? Do you mean multiple layers of clouds? In the following sentences you are saying “the low-level cloud”,. “The mid level cloud”, however, usually when a continuous Ze field is observed, only one cloud is assumed to be present. Why do you want to separate into multiple clouds? How do you reason that this is valid?
Line 180-182: why do you say riming? I don't see a MDV increase here at all. Do you have any other indications? Or is this solely based on enhanced Ze? I don’t agree with the statement that the lower Ze regions are aggregation, you can have similar Ze values with riming and aggregation, that’s why most previous studies distinguish riming with the MDV. In my opinion, the region of low Ze could be connected to sublimation processes, or something else. Aggregation usually increases Ze. I would suggest you adapt the MDV colorscale to show the expected values in snow better, perhaps until -4m/s, not until -7.5m/s. Also adapt your LDR colorscale, it is very hard to see anything, since most values are below -20
Line 188: how do you expect rimed particles to slow down due to aggregation again? I would much rather say that sublimation plays a role here. Especially because Ze decreases. If you had aggregation of the previously rimed particles I would Ze to continuously increase (due to the size increase), or at least stay constant.
Figure 2: why is your colorbar limit of DWR KaW so high? Also, on your colorbar you are stating Relfectivity. What is it then? Reflectivity or DWR? If it is DWR we do not expect DWRs to be larger than 15dB in most cases, and even that is already an extreme case. So I would suggest you change our colorbar to reflect the limits of DWR better.
Line 198: I would not say you have Graupel here. For Graupel to be formed, large MDV need to be observed (higher than 5m/s). Also, I doubt that Graupel can form in such stratiform conditions.
Line 203: this behaviour could also be consistent with sublimation, as the smallest particles are expected to be sublimated faster than the larger ones, therefore shifting D0 towards larger sizes and increasing DWRKaW and DWRXKa. I would say it is more likely that feature because Ze decreases.
Line 221 and following: How do you come to those conclusions? The MDV looks to be really similar to the early time period you described. Yet here you say aggregation is dominant. Why? Later describing the same time period you say that riming is dominant. I am confused!
Figure 3 and in general the calculation and discussion of the gradients: have you done any averaging? Either in time or range? The data looks really noisy, I find it very hard to see any significant regions here, especially in DWR, as this is already a noisy variable
Figure 5: I am missing the influence of size distribution on your triple-frequency space. As was shown in Mason et al. 2019, this influence is significant and can not be separated in the triple-frequency space. I also find it difficult to use the triple-frequency space as a “validation” of your methods, as both DWRKaW and DWRXKa are used in the methods.
Line 297: where do you see a bimodal distribution? The majority of cases is just in between your theoretical lines, which could indicate e.g. low riming, different internal structure caused by e.g. aggregation of needles vs. aggregation of plates, or a PSD with different shape as the one assumed in your theoretical lines
Figure 5: why are you not discussing Figure 5c?
In general, your manuscript would benefit from more subsections, I would suggest to structure Section 3 like: 3.1 case study description, 3.2: analysis based on gradients
References:
Kumjian, M.R.; Prat, O.P.; Reimel, K.J.; van Lier-Walqui, M.; Morrison, H.C. Dual-Polarization Radar Fingerprints of Precipitation Physics: A Review. Remote Sens. 2022, 14, 3706. https://doi.org/10.3390/rs14153706
Mason, Shannon L., et al. "The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow." Atmospheric Measurement Techniques 12.9 (2019): 4993-5018.
Citation: https://doi.org/10.5194/egusphere-2025-4233-CC1
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The manuscript presents a gradient-based identification method to distinguish riming and aggregation processes using triple-frequency ground-based radar observations. The authors combine both traditional threshold-based diagnostics and a newly developed gradient-based multi-parameter method, applying these to a well-documented snowfall case during the TRIPEx-pol field campaign. The work is carefully written, well referenced, and demonstrates that the authors have carried out a thorough literature review of the state of the art.
The scientific motivation is clear: distinguishing riming and aggregation is a long-standing challenge, and improvements in radar-based diagnostics can directly benefit the representation of microphysics in numerical models. The authors build directly on the foundation of Planat et al. (2021), who introduced a gradient-based approach for single-frequency polarimetric radar data. Here, this idea is extended to triple-frequency radar, which increases the sensitivity to particle density, shape, and size evolution.
A weakness of the current study lies in the lack of independent validation. Without in-situ ground-based hydrometeor observations (e.g., particle imaging or disdrometer measurements), the conclusions cannot be fully verified. Because the melting level was above the surface during this event, the results must be considered a proof of concept rather than a definitive validation of the method. Especially that Mason at al. have shown that the triple frequency (DWR-DWR) signatures can be also modulated by the shape of the PSD. Future work should attempt collocation with in-situ particle imagery or hydrometeor classification to substantiate the gradient-based classifications.
Another important point relates to interpretation. The gradient method identifies the altitude regions where riming and aggregation are most active, but it should be expected that observational signatures of large aggregates (e.g., enhanced DWRX–Ka relative to DWRKa–W) will appear below the regions diagnosed as aggregation-active by the gradient approach. Clarifying this causal relationship would strengthen the physical interpretation.
Overall, this is an innovative application of an existing idea that extends it to triple-frequency radar and demonstrates the advantages of gradient-based methods for identifying transitions. The work is rigorous, clearly presented, and worth publishing after minor revisions.
In the revised version please address these aspects: