the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Collaborative development of the Lidar Processing Pipeline (LPP)
- 1Centro de Investigaciones en Láseres y Aplicaciones, UNIDEF (CITEDEF-CONICET), Buenos Aires, Argentina
- 2Universidade Federal Fluminense, Volta Redonda, Brazil
- 3Instituto de Pesquisas Energéticas e Nucleares, São Paulo, Brazil
- 4Universidade Federal de Uberlândia, Ituiutaba, Brazil
- 5University of Maryland Baltimore County, Baltimore, United States
- 6Universidade de São Paulo, São Paulo, Brazil
- 1Centro de Investigaciones en Láseres y Aplicaciones, UNIDEF (CITEDEF-CONICET), Buenos Aires, Argentina
- 2Universidade Federal Fluminense, Volta Redonda, Brazil
- 3Instituto de Pesquisas Energéticas e Nucleares, São Paulo, Brazil
- 4Universidade Federal de Uberlândia, Ituiutaba, Brazil
- 5University of Maryland Baltimore County, Baltimore, United States
- 6Universidade de São Paulo, São Paulo, Brazil
Abstract. Lidars can simultaneously measure clouds and aerosols with high temporal and spatial resolution and hence help understand their interactions, which are the source of the largest uncertainties in current climate projections. However, lidars are typically custom-built, so there are significant differences between them. In this sense, lidar networks play a crucial role as they coordinate the efforts of different groups, providing the guidelines for quality-assured routine measurements aiming to homogenize the physical retrievals. With that in mind, this work describes an ongoing effort to develop a lidar processing pipeline (LPP) collaboratively. The LPP is a collection of tools developed in C/C++, python, and Linux script that handle all the steps of a typical lidar analysis. The first publicly released version of LPP produces data files at levels 0 (raw and metadata), 1 (averaging and layer-mask), and 2 (aerosol optical properties). We discussed the application of LPP for two case studies for Sao Paulo and Amazon, which shows the capabilities of the current release but also highlights the need for new features. From this exercise, we developed and presented a roadmap to guide future development, accommodating the needs of our community.
Juan V. Pallotta et al.
Status: open (until 22 Feb 2023)
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RC1: 'Comment on gi-2022-19', Anonymous Referee #1, 31 Jan 2023
reply
The manuscript for Pallotta et al. provides an overview of a processing chain which was developed for the South American lidar Network. The developments are based on open science and are all available on github, which is very honourable and I really appreciate the efforts and work the authors have made.
However, I have strong doubts that the current manuscript fulfils the requirements for a research article and is thus appropriate for this journal. It is currently more a technical report, which, however, is not a valid manuscript type within this journal. The reasons are the following:
- The is nothing scientifically new in this manuscript as the authors use existing approaches to homogenise the different hardware of the lidars in South America in terms of data analysis
- Mostly, there are pure technical descriptions which are not relevant for a research article reader
- On the other hand, the current manuscript is incomplete in terms of methodology description (e.g., how to determine the reference height and value, how does the feature mask really work
- Most of this missing information might be available on github, BUT I have to judge the current manuscript and the information stored on github are not persistent. I.e., just today I went to the repository and see that the most recent changes are only some days back, while the manuscript was submitted weeks ago.
- Many of the action items listed in the outlook have also been already published by other groups for other processing chains but are not yet referenced. Thus, the paper lacks also a kind of more complete literature review.
Therefore, I propose the following: The authors should publish their Lidar Processing Chain Code and Description as it is on github via some platform which allow to issue DOI. For example, zenodo can be used for this.
Then, they either use this published code as publication only with the current draft manuscript as introductory comment and skip the publication of a manuscript in this journal
Or, after also having published the code and description with a DOI, they revise the current manuscript in a way, that it can be a research paper. For this, the scientific methods should be more intensively described, and differences to other processing chains (e.g. SCC, PollyNET, Ad-Net..) need to be highlighted. Furthermore, the used methods need to be referenced or, if new, described. Of particular interest would be, e.g., how dead time correction is done (if done), how the reference value and height for the Fernald method is determined, how exactly the background subtraction is performed …
Furthermore, in the introduction or an own section, the possible differences of different lidar systems should be more detailed described (e.g., analog detection vs. photon counting, pre-trigger vs. far-range background, scanning vs. stare mode etc.). Only by doing so, the reader which is not (yet) a complete Lidar expert can understand the need for this chain and why some variables might be needed and what they mean.
Beside this. some minor language editing would be needed.
I again have to say, that I really appreciate the efforts and work the authors have done, but as a reviewer, I have to judge in this case a manuscript submitted as a research article which in my opinion the current manuscript is not.
Juan V. Pallotta et al.
Juan V. Pallotta et al.
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