globalchange  > 气候减缓与适应
DOI: 10.1029/2017JD028102
Scopus记录号: 2-s2.0-85048952733
论文题名:
Retrieval of Cloud Condensation Nuclei Number Concentration Profiles From Lidar Extinction and Backscatter Data
作者: Lv M.; Wang Z.; Li Z.; Luo T.; Ferrare R.; Liu D.; Wu D.; Mao J.; Wan B.; Zhang F.; Wang Y.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期:11
起始页码: 6082
结束页码: 6098
语种: 英语
英文关键词: CCN vertical profile ; lidar measurement ; look-up table ; observational cases ; simulation
Scopus关键词: aerosol ; algorithm ; backscatter ; cloud condensation nucleus ; concentration (composition) ; lidar ; observational method ; particle size ; satellite data ; simulation ; size distribution ; supersaturation ; vertical distribution
英文摘要: The vertical distribution of aerosols and their capability of serving as cloud condensation nuclei (CCN) are important for improving our understanding of aerosol indirect effects. Although ground-based and airborne CCN measurements have been made, they are generally scarce, especially at cloud base where it is needed most. We have developed an algorithm for profiling CCN number concentrations using backscatter coefficients at 355, 532, and 1,064 nm and extinction coefficients at 355 and 532 nm from multiwavelength lidar systems. The algorithm considers three distinct types of aerosols (urban industrial, biomass burning, and dust) with bimodal size distributions. The algorithm uses look-up tables, which were developed based on the ranges of aerosol size distributions obtained from the Aerosol Robotic Network, to efficiently find optimal solutions. CCN number concentrations at five supersaturations (0.07–0.80%) are determined from the retrieved particle size distributions. Retrieval simulations were performed with different combinations of systematic and random errors in lidar-derived extinction and backscatter coefficients: systematic errors range from −20% to 20% and random errors are up to 15%, which fall within the typical error ranges for most current lidar systems. The potential of this algorithm to retrieve CCN concentrations is further evaluated through comparisons with surface-based CCN measurements with near-surface lidar retrievals. This retrieval algorithm would be valuable for aerosol-cloud interaction studies for which virtually none has employed CCN at cloud base because of the lack of such measurements. ©2018. American Geophysical Union. All Rights Reserved.
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/113693
Appears in Collections:气候减缓与适应

Files in This Item:

There are no files associated with this item.


作者单位: State Key Laboratory of Earth Surface Processes and Resource Ecology and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Department of Atmospheric Science, University of Wyoming, Laramie, WY, United States; Earth System Science Interdisciplinary Center and Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States; Key Laboratory of Atmospheric Composition and Optical Radiation, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, China; NASA Langley Research Center, Hampton, VA, United States; School of Physics, Peking University, Beijing, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Recommended Citation:
Lv M.,Wang Z.,Li Z.,et al. Retrieval of Cloud Condensation Nuclei Number Concentration Profiles From Lidar Extinction and Backscatter Data[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(11)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Lv M.]'s Articles
[Wang Z.]'s Articles
[Li Z.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Lv M.]'s Articles
[Wang Z.]'s Articles
[Li Z.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Lv M.]‘s Articles
[Wang Z.]‘s Articles
[Li Z.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.