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Proceedings Paper

Inter-comparison of various approaches of ground-based active remote sensing of cloud water content
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Paper Abstract

This paper provides an inter-comparison study of various ground-based cloud retrieval algorithms that have been developed to obtain cloud water content. The retrieval algorithms are classified into three types, statistical parameterization algorithm, physical retrieval algorithm, and optimal iteration method. Analyses indicate that physical retrieval algorithms are theoretically accurate, however, assumptions used in these methods make it challenging for them to obtain highly reliable results. Empirical parameterization methods are simple and can be easily applied. However, these methods are generally based on very limited cloud samples for certain types of clouds and locations, they have much larger uncertainties. In contrast, the optimal iteration method seems to have relatively higher accuracies since the retrieval results make the forward model simulations match observations. However, the accuracy of optimal iteration method is highly dependent on the reliability of the forward models and the a priori information.

Paper Details

Date Published: 8 November 2014
PDF: 8 pages
Proc. SPIE 9259, Remote Sensing of the Atmosphere, Clouds, and Precipitation V, 92591G (8 November 2014); doi: 10.1117/12.2068653
Show Author Affiliations
Qianqian Wang, Beijing Normal Univ. (China)
Chuanfeng Zhao, Beijing Normal Univ. (China)
Min Lv, Beijing Normal Univ. (China)
Zhanqing Li, Beijing Normal Univ. (China)
Univ. of Maryland, College Park (United States)


Published in SPIE Proceedings Vol. 9259:
Remote Sensing of the Atmosphere, Clouds, and Precipitation V
Eastwood Im; Song Yang; Peng Zhang, Editor(s)

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