Profiling the atmospheric turbulence kinetic energy (TKE) is of great significance for improving our understanding of energy conversion, dissipation and material exchange in the atmospheric boundary layer, which is in turn able to affect the evolution of convection process.  Due to limitations in previous detection methods, it has been challenging to visually observe the vertical structure of various budget terms of TKE, particularly the buoyancy generation term. This study provides a meaningful advancement by intuitively presenting the vertical characteristics of various TKE budget terms using high-resolution wind lidar data.

The manuscript offers a comprehensive and detailed analysis of the TKE budget in the Shenzhen area based on long-term observations from coherent wind lidar, which provides valuable insights into atmospheric boundary layer dynamics. One of the most notable and intriguing findings of this study is the discovery that the TKE tendency term transitions around 14:00 in all seasons, revealing critical insights into the diurnal variation of turbulence. The study presents important implications for atmospheric turbulence modeling and parameterization in climate models. Additionally, the analysis of seasonal variations in buoyancy and shear generation provides a more refined understanding of turbulence energy transfer processes at different altitudes.

The manuscript is well-structured, clearly written, and presents significant findings that contribute to improving our understanding of TKE budget dynamics. However, there are a few areas where minor revisions can further enhance the clarity and completeness of the study. Below are my suggested modifications:

Specific Comments:

 1）The wind profile radar can also measure the dissipation rate in the TKE budget term. Add some literature on this topic in the second paragraph of the introduction. For example, Solanki, R., et al., Elucidating the atmospheric boundary layer turbulence by combining UHF radar wind profiler and radiosonde measurements over urban area of Beijing. Urban Climate, 2022. 43,

2）Line 144, The assumption that pressure transport (Tp) is negligible is common in turbulence studies, but it is important to provide a clear justification for this decision. While Tp is often small in comparison to other TKE budget terms, its significance can vary depending on meteorological conditions and observational techniques. Adding supporting references on why Tp can be safely ignored in this study would increase the scientific rigor of the methodology. This will also help readers unfamiliar with turbulence budget analysis better understand the reasoning behind this assumption.

3）Line 323, Figure 18 provides a quantitative breakdown of the relative contributions of different TKE budget terms across various heights in the boundary layer. However, the manuscript does not clearly explain the computational method used to derive these contributions. Clarifying whether the values are obtained from normalized budget term magnitudes, fractional contributions, or other statistical methods would improve transparency.

4）The reduced number of available observational days in June and August raises questions about potential data collection biases (Table 2). Since the reliability of turbulence analysis is dependent on a continuous and representative dataset, it is crucial to clarify the cause of missing data. If the missing days are due to weather conditions (such as persistent cloud cover, heavy precipitation, or typhoon events), this should be explicitly stated.

5）Lines 216 and 242 respectively mention the buoyancy and shear generation terms as the reasons why TKE has the greatest impact height in autumn, but why are only buoyancy generation terms mentioned in the abstract and conclusion?

6）What are the time resolution and spatial resolution of TKE budget terms? The manuscript should explicitly state the time and spatial resolution at which the TKE budget terms were derived. Resolution details are critical for interpreting turbulence measurements, as they influence how small-scale versus large-scale processes are captured. Given that the wind lidar operates at a temporal resolution of 5 seconds and a spatial resolution of 15 meters, it would be helpful to confirm whether the same resolution applies to all derived TKE terms or if additional temporal/spatial averaging was performed. Including this information in the methodology section would strengthen the manuscript’s transparency and help readers better assess the scale of the analysis.

Due to the lack of full boundary layer turbulence detection methods in the past, it was not possible to provide the continuous vertical distribution of turbulence within the boundary layer, resulting in a certain degree of insufficient understanding of turbulence generation, transport, and dissipation processes. This study, based on high temporal and spatial resolution data from wind lidar throughout the year, inverts the vertical profiles of TKE (Turbulent Kinetic Energy) budget terms (such as dissipation rate, shear/buoyancy generation terms, etc.), systematically revealing the diurnal and seasonal variation patterns of TKE and its budget terms, and deepening the understanding of the evolution of turbulence across the entire boundary layer. It clarifies unique phenomena in the Shenzhen area, such as the dominance of shear generation in summer and the significant buoyancy effect at high altitudes in autumn, which enhances the understanding of the boundary layer in coastal cities and provides observational evidence for climate model parameterization, offering practical application significance. The paper has a novel perspective, solid datasets, a reasonable structure, and detailed content, making it almost ready for publication. However, I believe there are still a few small issues that need attention:

Minor Points：

1. The lidar-based dissipation rate inversion method mentioned in the reference (Xian et al., 2025) needs to be explained in detail (e.g., is it based on inertial subrange spectrum fitting?).
2. The temporal resolution of the wind lidar is 0.2 Hz/5s, but this does not necessarily mean that the turbulence kinetic energy and its budget terms have a time resolution of 5 seconds. Typically, a certain time period is required to compute the turbulence spectrum, which raises the question: is the turbulence assumed to be steady during this period? How is this ensured?
3. The authors neglected the pressure transport term in the method. Is there any basis for neglecting this term? Please add relevant references.
4. The specific meanings of the three wind speed components in Equation 1 (u, v, w) should be provided (e.g., longitudinal direction, latitudinal direction, vertical direction).
5. In the Introduction section, line 76-78, I agree that the author state the important of atmospheric turbulence and its impact on weather and climate change, please add relevant references. However, it is also crucial to air quality, please revise the sentence and add relevant reference (Retrieval of Boundary Layer Height and Its Influence on PM_2.5 Concentration Based on Lidar Observation over Guangzhou. https://doi.org/10.46267/j.1006-8775.2021.027).

The quantitative analysis of turbulent kinetic energy (TKE) budget is a crucial support for understanding the formation mechanism of turbulence. This paper, using coherent Doppler lidar observation data, visually presents the vertical structure and seasonal variation characteristics of TKE budget in the boundary layer of Shenzhen area. The research method of this paper has obvious innovation, and the conclusions obtained also have scientific significance. The study reveals the vertical distribution characteristics of TKE in different seasons in the coastal area of South China (for example, the TKE in the lower layer is the strongest in summer), and clarifies the relative contributions of buoyancy generation and shear generation. The features of the time evolution analysis (for example, the transformation of the trend term of TKE around 14:00) and the explanation of the mechanism (buoyancy generation dominates) are logically clear. This research provides new evidence for the formation mechanism of the boundary layer turbulence in subtropical coastal cities and theoretical support and data support for the parameterization scheme of the boundary layer turbulence process. Although this research is structurally rigorous and scientifically strong, some technical details and methodological explanations still need to be provided to enhance the completeness and universality of the paper.

1. ​(Line 140): The symbol θ is referenced in the TKE budget equation but lacks explicit definition. Given the critical role of thermal stratification in buoyancy-driven turbulence, the potential temperature (θ)should be explicitly defined to avoid ambiguity.
2. Vertical Coordinate Clarification (Line 138): The vertical coordinate z is ambiguously described as "height." Please specify whether z represents altitude above mean sea level (AMSL) or height above ground level (AGL) in the methodology section.
3. Pressure Transport Term (Line 144): The manuscript omits the pressure transport term (Tp) in the TKE budget analysis without theoretical or empirical justification. Cite peer-reviewed studies (e.g., Zhou et al., 1985; Nilsson et al., 2016a) that validate the negligible contribution of Tp in similar boundary layer regimes to strengthen methodological credibility.
4. Data Generalizability Limitations: the exclusion of complex weather conditions (e.g., precipitation, cloud cover) limits the applicability of findings to idealized scenarios. Explicitly acknowledge this limitation in the Conclusions section, emphasizing the need for future studies under diverse meteorological conditions.
5. Temporal Reference in Figures: Figures 3, 5, 7, 9, and 10 display diurnal cycles without specifying the time zone. Label all temporal axes as "Local Time (UTC+8)" to align with Shenzhen’s geographic context to avoid misunderstanding.