Evaluating the impact of water vapor for aerosol and carbon dioxide detection lidar CO<sub>2</sub> measurement onboard the atmospheric environment monitoring satellite

Global warming, driven by greenhouse gas emissions from human activities, poses significant environmental challenges. Accurate greenhouse gas measurement data are crucial for effective emission reduction policies and international cooperation. The spaceborne integrated path differential absorption lidar offers high precision for monitoring global atmospheric carbon dioxide (CO<sub>2</sub>) concentrations on both days and nights. However, its accuracy can be compromised by water vapor interference. We evaluated the impact of water vapor on CO<sub>2</sub> detection, focusing on measurements from an aerosol and carbon dioxide detection lidar onboard the atmospheric environment monitoring satellite. The bias due to water vapor absorption was negligible. However, water vapor broadened the absorption spectrum, causing molecular interference, which could introduce considerable CO<sub>2</sub> column concentrations (XCO<sub>2</sub>) bias. In areas with high water vapor, the bias could exceed 1 ppm. Globally, the annual average bias of XCO<sub>2</sub> due to water vapor broadening effects was 0.42 ppm. The analysis highlights the importance to account for water vapor spectrum broadening effects, not only in spaceborne lidar measurements such as ACDL but also in other atmospheric measurement techniques to improve CO<sub>2</sub> measurement accuracy and enhance our understanding of global climate change and the carbon cycles.

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Publication Details

Journal
Optics Express
Published
2024-12-10
DOI
https://doi.org/10.1364/oe.546903
Citations
3
Primary Topic
Atmospheric and Environmental Gas Dynamics
Type
article
Field-Weighted Citation Impact
0.85
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article

Evaluating the impact of water vapor for aerosol and carbon dioxide detection lidar CO2 measurement onboard the atmospheric environment monitoring satellite

Cheng Chen, Xiaopeng Zhu, Zitong Wu, Sihan Liu et al.
3 citations
Optics Express
Atmospheric and Environmental Gas Dynamics
0.85
article

Evaluating the impact of water vapor for aerosol and carbon dioxide detection lidar CO2 measurement onboard the atmospheric environment monitoring satellite

Cheng Chen, Xiaopeng Zhu, Zitong Wu, Sihan Liu, Jiqiao Liu, Chuncan Fan, Zhongting Wang, Jianbo Hu, Weibiao Chen, Yuan Xie, Jin Rong Yang, Lu Zhang
article en
3 citations

Abstract

Global warming, driven by greenhouse gas emissions from human activities, poses significant environmental challenges. Accurate greenhouse gas measurement data are crucial for effective emission reduction policies and international cooperation. The spaceborne integrated path differential absorption lidar offers high precision for monitoring global atmospheric carbon dioxide (CO<sub>2</sub>) concentrations on both days and nights. However, its accuracy can be compromised by water vapor interference. We evaluated the impact of water vapor on CO<sub>2</sub> detection, focusing on measurements from an aerosol and carbon dioxide detection lidar onboard the atmospheric environment monitoring satellite. The bias due to water vapor absorption was negligible. However, water vapor broadened the absorption spectrum, causing molecular interference, which could introduce considerable CO<sub>2</sub> column concentrations (XCO<sub>2</sub>) bias. In areas with high water vapor, the bias could exceed 1 ppm. Globally, the annual average bias of XCO<sub>2</sub> due to water vapor broadening effects was 0.42 ppm. The analysis highlights the importance to account for water vapor spectrum broadening effects, not only in spaceborne lidar measurements such as ACDL but also in other atmospheric measurement techniques to improve CO<sub>2</sub> measurement accuracy and enhance our understanding of global climate change and the carbon cycles.

Optics ExpressVol. 32(27)
China Meteorological Administration (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 26%
Atmospheric and Environmental Gas Dynamics
0.85
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