the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jul 2025
Microphysical properties of various precipitation systems worldwide classified via objective methods based on dual-frequency precipitation radar observations
Abstract. Microphysical properties play crucial roles in physical processes related to the development of precipitation. In this study, Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) data were processed to demonstrate the microphysical properties of different precipitation systems (PSs) that are objectively classified with the k-means clustering algorithm. Four types of regular/non-extreme PS (high-latitude shallow PS, subtropical shallow PS, moderate PS, deep PS) and four types of extreme PS (extreme deep PS, strong PS, extreme strong PS, and marine extreme PS) were recognized. These eight types of PS exhibit differences in spatial-temporal features and convection characteristics, such as storm height, rain intensity, and vertical structures. For example, with the highest radar echo top and the largest mean mass-weighted mean diameter (Dm), the extreme strong PS mainly locate over tropical continent, while the high-latitude shallow PS have the least precipitation rate and mean normalized intercept parameter (Nw) values. The relationships between convection features and microphysical properties also vary among the eight types of PSs. For extreme PS, maximum precipitation rate near the surface generally exceeds 100 mm h-1 and balanced breakup and coalescence processes play a dominant role compared with non-extreme PS. In contrary, the coalescence processes dominate near the surface in two types of shallow PS. These results highlight the diversity of global precipitation microphysics and emphasize the necessity of global studies to increase the understanding of precipitation processes.
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Status: open (until 08 Oct 2025)
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RC1: 'Comment on egusphere-2025-2199', Anonymous Referee #1, 26 Aug 2025
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The paper investigates the microphysical properties of global precipitation systems (PSs) using GPM Dual-frequency Precipitation Radar (DPR) data from 2018–2022. An objective k-means clustering approach (with PCA for dimensionality reduction) is applied to classify precipitation systems into eight distinct types:
- Non-extreme PSs: high-latitude shallow, subtropical shallow, moderate, deep.
- Extreme PSs: extreme deep, strong, extreme strong, marine extreme.
Key findings:
- Continental PSs generally have larger mean drop diameters (Dm) than oceanic PSs, while oceanic PSs have higher normalized intercept parameters (Nw).
- Extreme PSs show balanced raindrop breakup and coalescence, while shallow PSs are dominated by coalescence.
- Clear land–ocean contrasts and latitudinal variations are found in microphysical structures.
- Diurnal and seasonal cycles differ by PS type, with continental systems peaking in the afternoon/summer, while shallow oceanic PSs peak at night.
The manuscript is generally well written and presents novel results. However, it would benefit from a few clarifications and structural improvements. I therefore recommend a minor revision addressing the following points:
- The methodology and results section would benefit from a clearer and more logical narrative. At present, the text shifts between clustering, physical interpretation, and microphysical discussion in a way that can confuse the reader. A more transparent structure would be to explicitly present the workflow as follows:
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Application of clustering algorithm
State clearly that the clustering was applied to the precipitation feature (PF) database, which contains ~9 million PFs identified from GPM DPR data. PF input variables include: precipitation rate, radar echo top heights, drop size distribution (Dm, Nw), convective/stratiform fractions, and spatial metrics.
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Selection of optimal number of clusters
Explain that the Davis–Bouldin index and Calinski–Harabasz score were evaluated for different values of k, and the minimum DB index at k=8 was taken as evidence that eight classes provided the best compromise between compactness and separation. Clarify if the Elbow Method applied to the Within-Cluster Sum of Squares suggests the same number of classes. This will justify the choice of k=8. -
Characterization of each cluster
Emphasize that each cluster is then characterized by its distinctive features, such as mean Dm or cloud top height... Based on these distinguishing characteristics, the clusters are named descriptively (e.g., “shallow,” “deep,” “extreme strong,” “marine extreme”). This step should be made explicit, because the current version sometimes reads as if the naming were imposed rather than derived. -
Emergent spatial and temporal patterns
Only after the clusters are defined should the manuscript show that these objectively derived groups exhibit coherent spatial distributions (e.g., shallow clusters dominating high latitudes, extreme clusters in the tropics).This is an important and interesting result: the clustering, based purely on precipitation properties, also reflects geophysical organisation in space, suggesting that the classification captures physically meaningful regimes.
- The methodology should also clarify how the stratiform, convective, land, and marine percentages are computed. Are these:
(a) computed for each PF individually and then averaged across all PFs in a cluster, or
(b) computed directly from the total number of pixels across all PFs in a cluster?- Extreme events context: The discussion of extreme precipitation systems should be better grounded in previous literature, particularly Zipser et al. (2006), Ni et al. (2017), and Bang and Cecil (2021), which provide benchmarks for extreme convective systems observed by satellites.
- Logical inconsistency (line 287): The argument is circular. Earlier, weak updrafts were inferred from low 40 dBZ echo heights. Later, the rapid decrease in reflectivity with height is attributed again to weak updrafts. This leads to a logical loop and should be clarified.
- Balanced processes would result in no change in the reflectivity and Dm.- Phase-change influence (line 303): The observed change in Dm likely corresponds to changes in precipitation phase across the melting layer. Please reference Mroz et al. (2024). Additional CFADs of Z, Dm, and Nw as a function of temperature or height relative to the freezing level would strengthen the analysis and could be added as supplementary material.
- Algorithm-induced correlations (section 3.4): The observed correlations between Dm and Nw may be artifacts of retrieval assumptions, since the GPM algorithm enforces a correlation between Dm and precipitation rate (see Chase et al., 2020). This must be acknowledged explicitly.
- Extreme precipitation rates: DPR is not well-suited for quantifying extreme rain rates because Ku/Ka frequencies are strongly affected by attenuation and multiple scattering in heavy precipitation. Values above ~100 mm h⁻¹ should be treated with caution and interpreted in light of retrieval limitations (see Battaglia et al.).
- Language and Clarity Issues: Many sentences exceed 40 words; shortening them would improve readability. Descriptions of Figs. 7–8 are overly detailed in-text.Citation: https://doi.org/10.5194/egusphere-2025-2199-RC1
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