TY - JOUR
T1 - Simultaneous Retrieval Algorithm of Water Cloud Optical and Microphysical Properties by High-Spectral-Resolution Lidar
AU - Zhang, Kai
AU - Wu, Lingyun
AU - Rosenfeld, Daniel
AU - Muller, Detlef
AU - Li, Chengcai
AU - Zhao, Chuanfeng
AU - Landulfo, Eduardo
AU - Jimenez, Cristofer
AU - Wang, Shuaibo
AU - Hu, Xianzhe
AU - Li, Weize
AU - Li, Xiaotao
AU - Sun, Yao
AU - Liu, Sihan
AU - Wu, Lan
AU - Wan, Xueping
AU - Chen, Wentai
AU - Liu, Chong
AU - Bai, Jian
AU - Li, Jing
AU - Sun, Wenbo
AU - Venkataraman, Sivakumar
AU - Zhou, Yudi
AU - Deng, Zhiji
AU - Liu, Ming
AU - Cheng, Miao
AU - Fu, Zhewei
AU - Pan, Weilin
AU - Liu, Dong
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - The uncertainty of water cloud feedback on radiative forcing is one of the largest obstacles to producing confident projections of the global climate. Sufficient measurements of water clouds are crucial to addressing this issue. However, existing techniques based on remote sensing or in situ instruments face limitations in data capacity attributed to the short lifetime, high temporal variability, and complex vertical structure of water clouds. In this study, taking advantage of a dual-field-of-view (dual-FOV) high-spectral-resolution lidar (HSRL), we developed a novel algorithm to obtain diurnal simultaneous profiles of water cloud optical and microphysical properties with high temporal-spatial resolution. This technique does not rely on the widely used subadiabatic assumption about the vertical structure of water clouds. The retrieval algorithm, validated by simulations and cloud radar measurements, was applied to field experiment data collected at the Beijing and Hangzhou sites in China. The relationship functions between water cloud properties are presented to enhance our understanding of the underlying processes. Furthermore, the vertical distributions of retrieved properties are compared to the subadiabatic assumption. The dual-FOV HSRL technique enables comprehensive observations, enhancing our understanding of water clouds and providing significant insights into the interactions among clouds, aerosols, precipitation, and radiation.
AB - The uncertainty of water cloud feedback on radiative forcing is one of the largest obstacles to producing confident projections of the global climate. Sufficient measurements of water clouds are crucial to addressing this issue. However, existing techniques based on remote sensing or in situ instruments face limitations in data capacity attributed to the short lifetime, high temporal variability, and complex vertical structure of water clouds. In this study, taking advantage of a dual-field-of-view (dual-FOV) high-spectral-resolution lidar (HSRL), we developed a novel algorithm to obtain diurnal simultaneous profiles of water cloud optical and microphysical properties with high temporal-spatial resolution. This technique does not rely on the widely used subadiabatic assumption about the vertical structure of water clouds. The retrieval algorithm, validated by simulations and cloud radar measurements, was applied to field experiment data collected at the Beijing and Hangzhou sites in China. The relationship functions between water cloud properties are presented to enhance our understanding of the underlying processes. Furthermore, the vertical distributions of retrieved properties are compared to the subadiabatic assumption. The dual-FOV HSRL technique enables comprehensive observations, enhancing our understanding of water clouds and providing significant insights into the interactions among clouds, aerosols, precipitation, and radiation.
KW - Dual-field-of-view (dual-FOV) high-spectral-resolution lidar (HSRL)
KW - multiple scattering
KW - properties
KW - vertical structure
KW - water clouds
UR - http://www.scopus.com/inward/record.url?scp=85196545377&partnerID=8YFLogxK
U2 - 10.1109/tgrs.2024.3416493
DO - 10.1109/tgrs.2024.3416493
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AN - SCOPUS:85196545377
SN - 0196-2892
VL - 62
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
M1 - 4106011
ER -