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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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 |
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MAX-DOAS water vapor vertical profiles HEIPRO |
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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 |
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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 |
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Remote Sensing |
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12 |
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19 |
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3193 |
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