the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Aug 2026
Linking cloud hydrometeor growth and surface precipitation to synoptic forcing at Dumont d’Urville, East Antarctica
Abstract. We investigate how weather regimes influence surface precipitation and associated cloud microphysical processes at Dumont d’Urville (DDU; 66.7° S, 140.0° E) using data from the Antarctic Precipitation: Remote Sensing from Surface and Space (APRES3) project. Two precipitation-producing regimes, Summer Warm Air Advection (S-WAA) and Low-Pressure (Low-P), account for 94.2 % of total precipitation at DDU. Hydrometeor growth processes were analysed through comprehensive case studies of Low-P and S-WAA events. The polarimetric signatures during the Low-P event indicate riming and secondary ice production (SIP) within the −10 to −14 °C layer (2–3.5 km), which falls within the dendritic growth zone (DGZ; approximately −10 to −20 °C). Riming intensifies between 1 and 2 km (−7 to −10 °C), whereas below 1 km, downslope flow leads to pronounced hydrometeor sublimation and sublimation-induced SIP. Thus, the surface precipitation associated with this event displays an intermittent pattern, with a predominance of graupel. In contrast, the S-WAA regime is associated with a moist boundary layer, favouring persistent surface precipitation. The polarimetric signatures in the upper half of the DGZ (−14 to −20 °C, 2.5–3.5 km) suggest hydrometeor growth through vapour deposition, while signatures below down to 1 km (−8 to −14 °C) align with aggregation processes. Enhanced riming and SIP driven by the Hallett–Mossop process are observed below 1 km. Our findings emphasise that synoptic weather regimes primarily impact precipitation growth, while boundary-layer flow and moisture play a significant role in snowfall efficiency at DDU in East Antarctica.
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Status: open (until 15 Oct 2026)
- RC1: 'Comment on egusphere-2026-4159', Haoran Li, 16 Sep 2026 reply
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RC2: 'Comment on egusphere-2026-4159', Anonymous Referee #2, 08 Oct 2026
reply
The manuscript assesses the influence of synoptic conditions on microphysical processes of precipitation events in Dumont d’Urville in Antarctica. The authors analyze the evolution of ice hydrometeors during two strong precipitation cases using polarimetric radar observations, and link the hydrometeor characteristics to synoptic regimes on a case study basis. They motivate their analyses by uncertainties in the representation of mixed-phase clouds and precipitation in climate models.
The paper reads well, with concise and clear explanations and descriptions. The figures are well chosen. The manuscript is a timely contribution to ACP, and I recommend publication after the authors address the following minor comments.Specific comments:
- line 51 – 55: The authors describe the lack of in situ observations and the uncertainty of modeling mixed-phase clouds in Antarctica Here, I am missing some more references on remote-sensing field deployments targeting some of the discussed microphysical processes over the Antarctic continent, such as, e.g., Tridon et al. (2022).- line 53 – 59: the authors motivate their study by uncertainties in the representation of mixed-phase clouds in climate models. It would be great to include a more detailed statement in the Conclusions Section on how the results (which are nicely summarized in Fig. 9) could contribute to resolving some of the modeling uncertainties and which process representation in particular.
- Line 83: Was the processing applied to the data before or after adding the 6 dBZ?
- Line 87: More details should be added on the installation of the Pluvio gauge as this seems crucial to the analysis. At which height is Pluvio positioned and is there a shield for the Pluvio?
- line 92: It is unclear whether ERA-5 or observational winds were used for filtering the observations.
- Line 124: The authors should include more details on why the SP category is excluded in further analyses.
- Line 165: It looks like W-Trough occurs more often in May than in July. Also, there’s a general high occurrence during all winter months, why not say: from April to October?
- Line 166: The low-pressure system extends even further than just from 120◦ to 160◦E.
- Line 270: Fig. S1 is missing in the current form of manuscript.
- Figure 1: For a quicker overlook of the variables shown, the T and Td legend should be placed in each sub-panel within the column (as opposed to only in panel (n) ). The Figure caption should be updated as it mentions red and green lines for these respective panels.
- Figure 2: Adding a line for the cross-sections shown in Fig. 4 (b) and (c) would be helpful for readability, as well as marking the time of the radiosondes that are shown in Fig. 3a-c in Fig. 2 (b) and (d).
- Figure 3: Adding the height as a secondary y-axis would facilitate comparison to the other Figures.
- Figure 6: For the Low-P case, the phases 1 and 2 are very easily distinguishable in Fig. a-d as the colors are different for both phases. Adjusting the color scale for S-WAA so that phase one and two are more easily distinguishable (e.g., red from 0:00 to 1:00 and gray from 1:00 to 3:00) would help with following the descriptions of hydrometeor evolution.
- Figure 8d is missing in the current text analysis and should be mentioned.Technical Corrections
- Line 111: an and bn should be an and bn
- Line 183: should be ‘Pluvio2’
- Line 219: should be 29 December
- Line 232: parenthesis not closed
- Line 234: should be ‘rimed’
- Line 270: should be ‘likely’
- Line 277: should be ‘The MSLP […]’
Citation: https://doi.org/10.5194/egusphere-2026-4159-RC2
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- 1
Haoran Li (lihr@cma.gov.cn)
Chinese Academy of Meteorological Sciences
This manuscript presents radar analysis of cloud microphysics in summer warm air advection (S-WAA) and Low-Pressure (Low-P) precipitation events over DDU. They report snow riming and SIP in Low-P, while significant aggregation in S-WAA. The observations are unique, and the topic is relevant for ACP readership. I have several questions for the authors prior to publication. I have listed several of my publications, along with papers from Stefan Kneifel’s group, that in my view are closely relevant to this work. For this reason, I have waived my anonymity.
Major Comments
1: Classification of synoptics. It seems to me that the classification was based on thermodynamical profiles over DDU, instead of classical synoptical analysis. I would suggest that you make a straightforward statement that classifications were performed on the thermodynamic profiles.
2: Inference of SIP. A comparison between Nice and Ninp is needed for determining SIP. It is bold for me to discuss SIP simply based on radar polarimetric profiles. From the conceptual diagrams, it appears that the authors are very confident on the SIP story. I am afraid that the current analysis may not support SIP. In particular, if the multi-modal signatures in spectra are absent, I do not believe active SIP is supported in stratiform snowfall. To strength the SIP discussions, I would encourage the authors for more in-depth analysis on spectra data. Newly-formed ice signatures, if they present, are very easy to identify (Figure 3 in Li and Moisseev, 2020; Figure 3 in Li et al., 2021).
Li, H., & Moisseev, D. (2020). Two layers of melting ice particles within a single radar bright band: Interpretation and implications. Geophysical Research Letters, 47(13), e2020GL087499.
Li, H., Möhler, O., Petäjä, T., & Moisseev, D. (2021). Two-year statistics of columnar-ice production in stratiform clouds over Hyytiälä, Finland: environmental conditions and the relevance to secondary ice production. Atmospheric Chemistry and Physics, 21(19), 14671-14686.
Li, H., A. Korolev, and D. Moisseev, 2021, Supercooled liquid water and secondary ice production in Kelvin–Helmholtz instability, Atmospheric Chemistry and Physics, 21(17), 13593–13608.
3: Direct in-situ particle images. The MASC instrument provides high-quality, well-resolved particle images. Rather than only presenting classification results, I encourage the authors to display these raw particle images. Then, your discussion on riming and aggregation will be more convincing.
Minor Comments
L43. I would not say so. Zdr response to riming is rather complicated. Before the fill-in stage, Zdr can increase with riming.
Li, H., Moisseev, D., & von Lerber, A. (2018). How does riming affect dual‐polarization radar observations and snowflake shape?. Journal of Geophysical Research: Atmospheres, 123(11), 6070-6081.
L48. Not sure if we can detect DGZ with radar polarimetry. I would say that signatures of DGZ in radar polarimetry is manifested as ...
L51. This paragraph appears abruptly and disrupts the flow.
L61. It would be valuable to include a mention of China’s contribution. We have been recording precipitation profiling observations at the coastal margin of King George Island since 2023.
Sun, J., Lv, J., Li, H., Zhang, W., Tian, B., & Ding, M. (2026). Brief communication: Two-year MRR observations at Great Wall Station, Antarctic Peninsula region. The Cryosphere, 20(7), 4049-4059.
L91 blowing snow? And, you excluded all blowing snow by implementing the threshold of 15m/s wind? Would this value be too high to exclude blowing snow?
L95. How did you make the resampling? By averaging?
L100. Would be nice to add a figure showing your instruments and scanning modes.
L117. MASC can measure snow size. Our recent work (Li et al., 2026) using triple frequency radars suggests that snow size is rather limited over McMurdo. Not sure if the same to DDU.
Li, Q., Li, H., Sun, X., Zhang, Y., Lyu, W., Ruan, Z., & Zhang, C. (2026). A survey of snow growth signatures from tropics to Antarctica using triple-frequency radar observations. Atmospheric Chemistry and Physics, 26(2), 1249-1264.
L 128. One launch per day?
L229. Riming is more related to in-cloud dynamics. I do not think the large-scale convergence calculated from ERA5 can really help diagnosis riming.
L226. I am confused by this paragraph. The discussion is overly extensive, and the main message is obscured. I am not sure I have caught the core story.
L237 Check this paper for ice-ice collision story.
von Terzi, L., Seifert, A., Siewert, C., Jakub, F., & Kneifel, S. (2026). Investigating ice microphysical processes in the dendritic growth zone by combining Monte-Carlo Lagrangian particle modelling with multi-frequency polarimetric radar observations. EGUsphere, 2026, 1-32.
L244. I would say that multi-modal is more convincing than KDP. It is still not clear how large KDP values an serve as an indicator of SIP.
L305. To support riming, doppler velocity should be given in Figure 5.
Figure 8. Use MDV for Doppler velocity. W is usually used for indicating vertical air motions.