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Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode

Analysis of the atmospheric Aerosol Particle Size Distribution (APSD) retrieved from Light Detection and Ranging (Lidar) data is one of the popular fields in atmospheric remote sensing. An APSD retrieval method based on the lognormal particle size distribution, the Mie theory, and the Second Simulat...

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Autores principales: Shi, Yuchen, Liu, Wenqing, Dong, Yunsheng, Zhao, Xuesong, Xiang, Yan, Zhang, Tianshu, Lv, Lihui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9350875/
https://www.ncbi.nlm.nih.gov/pubmed/35936234
http://dx.doi.org/10.1016/j.heliyon.2022.e09975
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author Shi, Yuchen
Liu, Wenqing
Dong, Yunsheng
Zhao, Xuesong
Xiang, Yan
Zhang, Tianshu
Lv, Lihui
author_facet Shi, Yuchen
Liu, Wenqing
Dong, Yunsheng
Zhao, Xuesong
Xiang, Yan
Zhang, Tianshu
Lv, Lihui
author_sort Shi, Yuchen
collection PubMed
description Analysis of the atmospheric Aerosol Particle Size Distribution (APSD) retrieved from Light Detection and Ranging (Lidar) data is one of the popular fields in atmospheric remote sensing. An APSD retrieval method based on the lognormal particle size distribution, the Mie theory, and the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) were studied. According to the 6S, this method divides the main body of the aerosols into four basic components and calculates the APSD from Lidar data and the optical and microphysical characteristics of these components. Numerical simulation and experimental observations reveal that this method can obtain the APSD for particle sizes of >0.15 μm in the different vertical layers with good reliability.
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spelling pubmed-93508752022-08-05 Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode Shi, Yuchen Liu, Wenqing Dong, Yunsheng Zhao, Xuesong Xiang, Yan Zhang, Tianshu Lv, Lihui Heliyon Research Article Analysis of the atmospheric Aerosol Particle Size Distribution (APSD) retrieved from Light Detection and Ranging (Lidar) data is one of the popular fields in atmospheric remote sensing. An APSD retrieval method based on the lognormal particle size distribution, the Mie theory, and the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) were studied. According to the 6S, this method divides the main body of the aerosols into four basic components and calculates the APSD from Lidar data and the optical and microphysical characteristics of these components. Numerical simulation and experimental observations reveal that this method can obtain the APSD for particle sizes of >0.15 μm in the different vertical layers with good reliability. Elsevier 2022-07-19 /pmc/articles/PMC9350875/ /pubmed/35936234 http://dx.doi.org/10.1016/j.heliyon.2022.e09975 Text en © 2022 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Shi, Yuchen
Liu, Wenqing
Dong, Yunsheng
Zhao, Xuesong
Xiang, Yan
Zhang, Tianshu
Lv, Lihui
Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode
title Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode
title_full Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode
title_fullStr Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode
title_full_unstemmed Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode
title_short Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode
title_sort atmospheric aerosol particle size distribution from lidar data based on the lognormal distribution mode
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9350875/
https://www.ncbi.nlm.nih.gov/pubmed/35936234
http://dx.doi.org/10.1016/j.heliyon.2022.e09975
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