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 21 Nov 2025)
- RC1: 'Comment on egusphere-2025-4233', Anonymous Referee #1, 02 Oct 2025 reply
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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: