Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.2172/993103 |
报告编号 | ER64069-1026543-0011666 |
来源ID | OSTI ID: 993103 |
Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties | |
Wang, Zhien | |
2010-06-29 | |
出版年 | 2010 |
语种 | 英语 |
国家 | 美国 |
领域 | 地球科学 |
英文摘要 | The project is mainly focused on the characterization of cloud macrophysical and microphysical properties, especially for mixed-phased clouds and middle level ice clouds by combining radar, lidar, and radiometer measurements available from the ACRF sites. First, an advanced mixed-phase cloud retrieval algorithm will be developed to cover all mixed-phase clouds observed at the ACRF NSA site. The algorithm will be applied to the ACRF NSA observations to generate a long-term arctic mixed-phase cloud product for model validations and arctic mixed-phase cloud processes studies. To improve the representation of arctic mixed-phase clouds in GCMs, an advanced understanding of mixed-phase cloud processes is needed. By combining retrieved mixed-phase cloud microphysical properties with in situ data and large-scale meteorological data, the project aim to better understand the generations of ice crystals in supercooled water clouds, the maintenance mechanisms of the arctic mixed-phase clouds, and their connections with large-scale dynamics. The project will try to develop a new retrieval algorithm to study more complex mixed-phase clouds observed at the ACRF SGP site. Compared with optically thin ice clouds, optically thick middle level ice clouds are less studied because of limited available tools. The project will develop a new two wavelength radar technique for optically thick ice cloud study at SGP site by combining the MMCR with the W-band radar measurements. With this new algorithm, the SGP site will have a better capability to study all ice clouds. Another area of the proposal is to generate long-term cloud type classification product for the multiple ACRF sites. The cloud type classification product will not only facilitates the generation of the integrated cloud product by applying different retrieval algorithms to different types of clouds operationally, but will also support other research to better understand cloud properties and to validate model simulations. The ultimate goal is to improve our cloud classification algorithm into a VAP. |
英文关键词 | Clouds multi-sensor remote sensing Climate |
URL | 查看原文 |
来源平台 | US Department of Energy (DOE) |
引用统计 | |
文献类型 | 科技报告 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/5111 |
专题 | 地球科学 |
推荐引用方式 GB/T 7714 | Wang, Zhien. Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties,2010. |
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