Preprints
https://doi.org/10.5194/gi-2022-19
https://doi.org/10.5194/gi-2022-19
 
19 Dec 2022
19 Dec 2022
Status: this preprint is currently under review for the journal GI.

Collaborative development of the Lidar Processing Pipeline (LPP)

Juan V. Pallotta1, Silvania Carvalho2, Fabio J. S. Lopes3, Alexandre Cacheffo4, Eduardo Landulfo3, and Henrique M. J. Barbosa5,6 Juan V. Pallotta et al.
  • 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

Juan V. Pallotta et al.

Juan V. Pallotta et al.

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Short summary
Lidar networks coordinate efforts of different groups, providing guidelines to homogenize retrievals from different instruments. We describe an ongoing effort to develop a lidar processing pipeline (LPP) collaboratively. It is a collection of tools developed in C/C++, python, and Linux script that handle all the steps of a typical lidar analysis. We show case studies for Sao Paulo and Amazon, from which we envision a roadmap to guide future development, accommodating the needs of our community.