Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data

The remote sensing of water clouds is useful for studying their spatial and temporal variations and constraining physical processes in climate and weather prediction models. However, radar-only detection provides inadequate information for the cloud droplet size distribution. Here, we propose a nove...

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Autores principales:	Yinchao Zhang, Su Chen, Wangshu Tan, Siying Chen, He Chen, Pan Guo, Zhuoran Sun, Rui Hu, Qingyue Xu, Mengwei Zhang, Wei Hao, Zhichao Bu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	water cloud radar multiwavelength lidar optical properties microphysical properties Science Q
Acceso en línea:	https://doaj.org/article/6608d53079ac4735ac7a7e2e43f42fe7
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spelling	oai:doaj.org-article:6608d53079ac4735ac7a7e2e43f42fe72021-11-11T18:55:27ZRetrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data10.3390/rs132143962072-4292https://doaj.org/article/6608d53079ac4735ac7a7e2e43f42fe72021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4396https://doaj.org/toc/2072-4292The remote sensing of water clouds is useful for studying their spatial and temporal variations and constraining physical processes in climate and weather prediction models. However, radar-only detection provides inadequate information for the cloud droplet size distribution. Here, we propose a novel lookup-table method, which combines lidar (1064, 532 nm) and radar (8.6 mm) to retrieve profiles of cloud optical (backscatter coefficient and extinction coefficient) and microphysical properties (effective diameter and liquid water content). Through the iteration of the extinction-to-backscatter ratio, more continuous cloud optical characteristics can be obtained. Sensitivity analysis shows that a 10% error of the lidar constant will lead to a retrieval error of up to 30%. The algorithm performed precise capture of the ideal cloud signal at a specific height and at full height and the maximum relative error of the backscatter coefficients at 1064 nm and 532 nm were 6% and 4%, respectively. With the application of the algorithm in the two observation cases on single or multiple cloud layers, the results indicate that the microphysical properties mostly agree with the empirical radar measurements but are slightly different when larger particles cause signal changes of different extents. Consequently, the synergetic algorithm is capable of computing the cloud droplet size distribution. It provides continuous profiles of cloud optical properties and captures cloud microphysical properties well for water cloud studies.Yinchao ZhangSu ChenWangshu TanSiying ChenHe ChenPan GuoZhuoran SunRui HuQingyue XuMengwei ZhangWei HaoZhichao BuMDPI AGarticlewater cloudradarmultiwavelength lidaroptical propertiesmicrophysical propertiesScienceQENRemote Sensing, Vol 13, Iss 4396, p 4396 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	water cloud radar multiwavelength lidar optical properties microphysical properties Science Q
spellingShingle	water cloud radar multiwavelength lidar optical properties microphysical properties Science Q Yinchao Zhang Su Chen Wangshu Tan Siying Chen He Chen Pan Guo Zhuoran Sun Rui Hu Qingyue Xu Mengwei Zhang Wei Hao Zhichao Bu Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data
description	The remote sensing of water clouds is useful for studying their spatial and temporal variations and constraining physical processes in climate and weather prediction models. However, radar-only detection provides inadequate information for the cloud droplet size distribution. Here, we propose a novel lookup-table method, which combines lidar (1064, 532 nm) and radar (8.6 mm) to retrieve profiles of cloud optical (backscatter coefficient and extinction coefficient) and microphysical properties (effective diameter and liquid water content). Through the iteration of the extinction-to-backscatter ratio, more continuous cloud optical characteristics can be obtained. Sensitivity analysis shows that a 10% error of the lidar constant will lead to a retrieval error of up to 30%. The algorithm performed precise capture of the ideal cloud signal at a specific height and at full height and the maximum relative error of the backscatter coefficients at 1064 nm and 532 nm were 6% and 4%, respectively. With the application of the algorithm in the two observation cases on single or multiple cloud layers, the results indicate that the microphysical properties mostly agree with the empirical radar measurements but are slightly different when larger particles cause signal changes of different extents. Consequently, the synergetic algorithm is capable of computing the cloud droplet size distribution. It provides continuous profiles of cloud optical properties and captures cloud microphysical properties well for water cloud studies.
format	article
author	Yinchao Zhang Su Chen Wangshu Tan Siying Chen He Chen Pan Guo Zhuoran Sun Rui Hu Qingyue Xu Mengwei Zhang Wei Hao Zhichao Bu
author_facet	Yinchao Zhang Su Chen Wangshu Tan Siying Chen He Chen Pan Guo Zhuoran Sun Rui Hu Qingyue Xu Mengwei Zhang Wei Hao Zhichao Bu
author_sort	Yinchao Zhang
title	Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data
title_short	Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data
title_full	Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data
title_fullStr	Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data
title_full_unstemmed	Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data
title_sort	retrieval of water cloud optical and microphysical properties from combined multiwavelength lidar and radar data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6608d53079ac4735ac7a7e2e43f42fe7
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Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data

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