Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China

Water vapor vertical profiles are important in numerical weather prediction, moisture transport, and vertical flux calculation. This study presents the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) retrieval algorithm for water vapor vertical profiles and the retrieved results a...

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Published in:Remote Sensing
Main Authors: Hua Lin, Cheng Liu, Chengzhi Xing, Qihou Hu, Qianqian Hong, Haoran Liu, Qihua Li, Wei Tan, Xiangguang Ji, Zhuang Wang, Jianguo Liu
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2020
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Online Access:https://doi.org/10.3390/rs12193193
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spelling ftmdpi:oai:mdpi.com:/2072-4292/12/19/3193/ 2023-08-20T03:59:11+02:00 Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China Hua Lin Cheng Liu Chengzhi Xing Qihou Hu Qianqian Hong Haoran Liu Qihua Li Wei Tan Xiangguang Ji Zhuang Wang Jianguo Liu 2020-09-29 application/pdf https://doi.org/10.3390/rs12193193 EN eng Multidisciplinary Digital Publishing Institute Urban Remote Sensing https://dx.doi.org/10.3390/rs12193193 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 19; Pages: 3193 MAX-DOAS water vapor vertical profiles HEIPRO Text 2020 ftmdpi https://doi.org/10.3390/rs12193193 2023-08-01T00:12:11Z Water vapor vertical profiles are important in numerical weather prediction, moisture transport, and vertical flux calculation. This study presents the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) retrieval algorithm for water vapor vertical profiles and the retrieved results are validated with corresponding independent datasets under clear sky. The retrieved Vertical Column Densities (VCDs) and surface concentrations are validated with the Aerosol Robotic Network (AERONET) and National Climatic Data Centre (NCDC) datasets, achieving good correlation coefficients (R) of 0.922 and 0.876, respectively. The retrieved vertical profiles agree well with weekly balloon-borne radiosonde measurements. Furthermore, the retrieved water vapor concentrations at different altitudes (100–2000 m) are validated with the corresponding European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim datasets, achieving a correlation coefficient (R) varying from 0.695 to 0.857. The total error budgets for the surface concentrations and VCDs are 31% and 38%, respectively. Finally, the retrieval performance of the MAX-DOAS algorithm under different aerosol loads is evaluated. High aerosol loads obstruct the retrieval of surface concentrations and VCDs, with surface concentrations more liable to severe interference from such aerosol loads. To summarize, the feasibility of detecting water vapor profiles using MAX-DOAS under clear sky is confirmed in this work. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 12 19 3193
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic MAX-DOAS
water vapor
vertical profiles
HEIPRO
spellingShingle MAX-DOAS
water vapor
vertical profiles
HEIPRO
Hua Lin
Cheng Liu
Chengzhi Xing
Qihou Hu
Qianqian Hong
Haoran Liu
Qihua Li
Wei Tan
Xiangguang Ji
Zhuang Wang
Jianguo Liu
Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China
topic_facet MAX-DOAS
water vapor
vertical profiles
HEIPRO
description Water vapor vertical profiles are important in numerical weather prediction, moisture transport, and vertical flux calculation. This study presents the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) retrieval algorithm for water vapor vertical profiles and the retrieved results are validated with corresponding independent datasets under clear sky. The retrieved Vertical Column Densities (VCDs) and surface concentrations are validated with the Aerosol Robotic Network (AERONET) and National Climatic Data Centre (NCDC) datasets, achieving good correlation coefficients (R) of 0.922 and 0.876, respectively. The retrieved vertical profiles agree well with weekly balloon-borne radiosonde measurements. Furthermore, the retrieved water vapor concentrations at different altitudes (100–2000 m) are validated with the corresponding European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim datasets, achieving a correlation coefficient (R) varying from 0.695 to 0.857. The total error budgets for the surface concentrations and VCDs are 31% and 38%, respectively. Finally, the retrieval performance of the MAX-DOAS algorithm under different aerosol loads is evaluated. High aerosol loads obstruct the retrieval of surface concentrations and VCDs, with surface concentrations more liable to severe interference from such aerosol loads. To summarize, the feasibility of detecting water vapor profiles using MAX-DOAS under clear sky is confirmed in this work.
format Text
author Hua Lin
Cheng Liu
Chengzhi Xing
Qihou Hu
Qianqian Hong
Haoran Liu
Qihua Li
Wei Tan
Xiangguang Ji
Zhuang Wang
Jianguo Liu
author_facet Hua Lin
Cheng Liu
Chengzhi Xing
Qihou Hu
Qianqian Hong
Haoran Liu
Qihua Li
Wei Tan
Xiangguang Ji
Zhuang Wang
Jianguo Liu
author_sort Hua Lin
title Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China
title_short Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China
title_full Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China
title_fullStr Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China
title_full_unstemmed Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China
title_sort validation of water vapor vertical distributions retrieved from max-doas over beijing, china
publisher Multidisciplinary Digital Publishing Institute
publishDate 2020
url https://doi.org/10.3390/rs12193193
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing; Volume 12; Issue 19; Pages: 3193
op_relation Urban Remote Sensing
https://dx.doi.org/10.3390/rs12193193
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs12193193
container_title Remote Sensing
container_volume 12
container_issue 19
container_start_page 3193
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