the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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.
- Preprint
(9593 KB) - Metadata XML
-
Supplement
(2444 KB) - BibTeX
- EndNote
Status: open (until 17 Apr 2026)
- RC1: 'Comment on egusphere-2026-1091', Anonymous Referee #1, 31 Mar 2026 reply
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 178 | 94 | 16 | 288 | 46 | 29 | 32 |
- HTML: 178
- PDF: 94
- XML: 16
- Total: 288
- Supplement: 46
- BibTeX: 29
- EndNote: 32
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
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