Realistic Noise Generation to Enhance Realism of Virtual Lidar Scans

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript presents a practical and data-driven framework for generating realistic noise in virtual liDAR scanning, especially for the simulation of atmospheric wind liDAR measurements. The author combines theoretical insights with extensive field data analysis to provide a site-specific signal-to-noise ratio (SNR) characterization and a robust noise generation model. This work fills a significant gap in the literature, enabling filtering and data assimilation algorithms to be more accurately benchmarked, which is of great value to both the remote sensing and wind energy fields. Manuscripts are generally well-written, with reasonable methods and good references. Before being accepted, the following minor issues should be considered:

The authors emphasize that the framework can be adapted to other lidars or sites with minimal modification. However, most of the detailed characterization (e.g., noise standard deviation vs SNR) is based on the Halo Photonics Streamline XR system. Could the authors elaborate on the specific requirements or steps for adapting the model to a different lidar architecture, especially with differing wavelength, detection method (e.g., coherent vs direct-detection), or internal signal processing?

How sensitive is the noise model to changes in atmospheric conditions or to instrument aging/maintenance? Is periodic recalibration recommended?

The current framework is well validated under typical ABL conditions, but it is not clear how it performs during events like heavy precipitation, dust storms, or abrupt boundary layer transitions. Consider adding a brief discussion of the model’s limitations in extreme or rapidly changing atmospheric scenarios, or clarify if such events were filtered out in your QC process.

While the value and rationale of the current approach are well explained, a brief quantitative comparison with existing noise simulation or virtual lidar models (if available) would help readers further appreciate the improvement.

 

Author Response

Please see attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors propose a simplified and generalizable model for generating realistic noise in lidar simulated data, focusing in particular on noise modulation according to the signal-to-noise ratio (SNR) of the backscattered signal. The model takes into account laser energy attenuation and aerosol stratification by height and distance, which allows simulations close to real measurements. A general model for the standard deviation of wind speed measured as a function of the SNR is presented, which can be adapted for different scanning configurations of a lidar system.
The main contribution of the work is the development of a practical and transferable model for generating realistic noise in lidar virtual data.

Revisions and weakness: 
1.    Above figure 3, I don't understand what [b!] means;
2.    Although the proposed model is generalizable, practical testing and validation on other types of lidar systems (from different manufacturers or with distinct architectures) and in other geographical locations, with varied climatologies is not presented
3.    Lidar noise is not just a function of SNR. There may be additive or correlated effects from sources such as electronic noise, thermal drift, multipath reflections, mechanical disturbances, and more rarely: precipitation, condensation on optics, etc. It would be useful if the model also allowed the weight of these noise sources to be adjusted or the sensitivity to be tested.
Although it is clear that the general purpose and theoretical framework are argued, the article does not lead the reader step by step, with concrete examples and impact comparisons, to the conclusion that the proposed model is beneficial and can be used on most lidar systems.
The authors transparently present some limitations, highlighting that the proposed model represents a significant step towards the realism of lidar simulations. However, the article left the impression of the lack of a complete “red thread” and of an applied/coherent justification throughout the work.
However, the article would significantly benefit from a restructuring that would more clearly highlight the 'red thread' of the argumentation and the practical impact of the proposed model:
4.    I suggest to introduce a dedicated section that presents direct and quantifiable comparisons between the simulations made with and without the proposed noise model, concretely illustrating the differences in results and interpretations.
5.    It would be useful to include a complete case study that follows step by step how the proposed model influences practical decisions in the planning of a lidar campaign or in the evaluation of data 
6.    I recommend restructuring the conclusions to explicitly highlight the quantitative (not just qualitative) benefits 
7.    I suggest adding a synthesis figure that graphically illustrates the entire logical chain, from noise theory to practical applications (e.g. how a decision would change if you apply the model or not), thus facilitating the understanding of the general contribution of the work [I consider that there is room for improvement with regard to figure 3].

Author Response

Please see attchment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The work has been improved by the authors and their effort and response are appreciated.
Minor aspects:
1.    The numbers on the coordinate axes in Figure 2 are slightly overlapping. I therefore ask the authors to address this issue.

Author Response

Thanks for catching the graphical issue. We uploaded a revised figure.