This study presents the first high-resolution dataset of low-level atmospheric turbulence parameters in China by combining radar wind profiler and radiosonde observations. It reveals vertical attenuation patterns (e.g., linear decrease of turbulent dissipation rate, ε, with altitude) and seasonal variations (stronger turbulence in spring/summer), providing critical data support for model parameterization. The research design is robust, data sources are reliable, and the methodology demonstrates innovation. The findings hold significant value for understanding boundary layer turbulence dynamics and aviation safety applications. The manuscript is suitable for publication after minor revisions.

Specific Suggestions and Clarifications Required:

1. Ensure all abbreviations (e.g., Turbulence dissipation rate (ε)) are defined only onceat their first mention. Avoid redundant definitions in the Abstract, Introduction, or other sections. Check consistency for other terms.
2. Line 123，revise "see Chen et al., 2022b" to "Chen et al., 2022b" (remove "see").
3. Equation 1, define the variable h (height/altitude).Ensure consistency in height representation: Equation 4 uses z for altitude, while other equations (e.g., Equation 1) should use the same notation.
4. Equation 2, define T (temperature) and P (pressure) explicitly.
5. Symbol Consistency in Equations 4 and 6: Equation 4 uses φ (phi), while Equation6 uses ψ (psi). Clarify their definitions and ensure consistency in notation.
6. Equation 6, define ϕ (phi) in the context of the equation.
7. Equation 8, specify the value of kinematic viscosity (v) and cite relevant references for its calculation.
8. Clarify how N² is calculated at times other than 00 and 12 UTC, given its reliance on twice-daily radiosonde data. Explicitly state the temporal resolution of N² in the methodology.

Low-level turbulence in the atmospheric boundary layer remains a critical component for understanding near-surface exchange processes, mesoscale dynamics, and safe aviation operations, yet remains under-characterized due to data scarcity. This manuscript presents a new turbulence dataset derived from a synergistic combination of radar wind profiler (RWP) and radiosonde observations at different sites across China. The authors employ the Doppler spectral width method to estimate a suite of turbulence-related parameters including 𝜀, 𝑁², κ, 𝑙₀, and 𝐿𝐵, and offer a comprehensive analysis of their spatial, vertical, and seasonal patterns. The paper is technically sound and analyses low-level turbulence climatology over China. As the dataset can be very important and helpful for boundary layer modeling, I believe this study is suitable for publication following a few minor revisions. 

Minor Comments:

1. The manuscript would benefit from improved clarity and precision in defining symbols and variables, especially within different equations. For example, in Equation (1), the height variable (ℎ) is introduced without definition, while other equations such as (4) use 𝑧 for altitude. A consistent notation may be easy for readers to understand. 

2. The variables (e.g., 𝑇, 𝑃, 𝜙, 𝜓, 𝜈) are mainly given their names. I suggest the authors add the physical meaning of these variables in section 2 for clarity, thus being more accessible for readers unfamiliar with the PBL scheme.

3. The term "high-resolution" is mentioned several times, which may cause confusion. It would be clearer to specify that the dataset offers high vertical resolution, as the temporal resolution is limited to twice daily observations. 

4. Line 123: The use of "see" before the citation appears unnecessary and is inconsistent with the citation style used elsewhere in the manuscript. 

5. Abbreviations such as 𝜀, 𝑁², and 𝐾 are repeatedly redefined. For example,  𝜀 defined third times in Sections 1-2.