the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Mar 2026
Cloud vertical structure across China from a national Ka-band cloud radar network: Thermodynamic, dynamical, and land-surface controls
Abstract. Cloud vertical structure plays a central role in regulating Earth’s radiation balance and hydrological cycle, yet it remains poorly represented in weather and climate models due to limited high-resolution observations. Using a newly established national network of 80 Ka-band cloud radars, we provide the first high-spatiotemporal-resolution characterization of cloud vertical structure across China for 2024 and quantify its thermodynamic, dynamical, and land-surface controls. An improved retrieval algorithm accounting for height-dependent radar sensitivity and clutter suppression is applied to derive cloud boundaries. The national annual mean cloud occurrence frequency is 56.7 %, dominated by single-layer clouds (34.7 %), followed by two-layer (14.7 %) and multi-layer clouds (7.1 %). Single-layer clouds prevail over arid northwestern China, whereas multi-layer clouds are more frequent in humid southeastern regions. Cloud base height exhibits strong seasonality, with higher values in summer and lower values in winter, and distinctly lower bases over the Tibetan Plateau. Diurnally, summer clouds show a pronounced afternoon peak between 3 and 9 km, while winter clouds are mainly confined below 3 km with a near-sunrise maximum. Thermodynamic conditions exert primary control on cloud vertical development. Higher low-level humidity favors deeper clouds and higher tops, whereas stronger lower-tropospheric stability suppresses vertical growth. Wind shear generally limits cloud depth, though moderate shear may enhance organization under unstable conditions. Land-surface characteristics further modulate cloud base height, with higher bases over barren land and lower bases over forests. These results provide national-scale observational benchmarks for improving cloud parameterizations in numerical models.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1091', Anonymous Referee #1, 31 Mar 2026
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RC2: 'Comment on egusphere-2026-1091', Anonymous Referee #2, 26 Apr 2026
This paper uses one year of observations from a newly established national Ka-band cloud radar network to characterize cloud vertical structure across China, including cloud occurrence, layer number, CBH, CTH, and CT, and further relates these properties to humidity, lower-tropospheric stability, wind shear, and land cover. The dataset is clearly valuable, especially because the 1-min temporal resolution and 30-m vertical resolution allow the authors to examine seasonal and diurnal variations that are difficult to resolve from satellites or radiosondes. The paper’s main contribution is therefore not only the national-scale description of cloud vertical structure, but also the attempt to connect these observed structures with thermodynamic, dynamical, and land-surface controls. Overall, the study has strong observational potential and could provide useful benchmarks for model evaluation, but several issues need to be addressed before the physical conclusions can be considered fully convincing.
Specific comments:
1. One limitation is the short time period. All major statistics are based on 2024, but the manuscript often presents the results as a national-scale benchmark. Cloud vertical structure over China can be strongly affected by monsoon variability, large-scale circulation anomalies, regional drought or wet conditions, and unusual synoptic patterns in a given year. The authors may discuss how representative 2024 is, for example by comparing cloud fraction, precipitation, humidity, stability, or circulation conditions with multi-year satellite or reanalysis records.
2. The retrieval algorithm may need more validation. The height-dependent sensitivity threshold and clutter-removal method are reasonable, but the final cloud occurrence, layer number, CBH, CTH, and CT depend on several empirical thresholds, including the variance criteria, the −20 dBZ filter, and the thin-layer merging/removal rules. These choices may affect thin clouds, weak high clouds, multilayer clouds, and precipitation-contaminated cases. Is it possible to include systematic validation against independent observations, such as ceilometer, radiosonde-derived cloud layers, lidar, or active satellite products?
3. The spatial representativeness and scale matching should be discussed more carefully. Although the network is impressive, it is still a set of point measurements rather than an area-covering observing system. In addition, the MMCR observations are matched with ERA5 fields at 0.25° resolution, which may not represent local cloud environments well, especially over complex terrain or regions with strong surface heterogeneity. The authors may consider the sensitivity of key results to station distribution, sparse regions, complex terrain, and the point-to-grid matching method.
4. The physical attribution is still mainly based on correlation. The reported relationships between q850, LTS, WS700, and cloud properties are plausible, but the current analysis does not fully separate the effects of season, region, cloud type, and large-scale weather regime. For example, the positive relationship between q850 and CBH may partly reflect the co-occurrence of high humidity, deeper boundary layers, and convective regimes in summer, rather than a direct moisture effect on cloud base.
5. CT should be treated carefully, since it is defined as the distance between the highest cloud top and lowest cloud base and may include clear gaps in multilayer cases. Clarifying this definition would make the interpretation of CT correlations more reliable.
6. The use of fixed pressure-level environmental variables needs caution, especially over the Plateau area. The manuscript uses q850 and an LTS definition based on 700 hPa and 1000 hPa, but these pressure levels may be below ground or not physically representative at high-elevation sites.
Citation: https://doi.org/10.5194/egusphere-2026-1091-RC2
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General Comments
This manuscript presents a comprehensive analysis of cloud vertical structure (CVS) across China using a national network of 80 MMCRs for the year 2024. The authors develop an improved CVS retrieval algorithm that incorporates height-dependent radar sensitivity and variance-based clutter suppression. They systematically characterize the spatial, seasonal, and diurnal variations of CVS metrics (cloud base height, cloud top height, cloud thickness, and cloud layer number) and quantify the regulatory effects of thermodynamic (lower-tropospheric stability, specific humidity at 850 hPa), dynamical (wind shear at 700 hPa), and land-surface factors. The findings provide valuable observational benchmarks for evaluating and improving cloud parameterization schemes in weather and climate models. Overall, the manuscript is well written and the concepts are clearly presented. However, several major and minor issues require further improvement. Therefore, I recommend that the authors revise their manuscript to address the following comments.
Major Comments
Minor Comments