DOI: 10.1002/2016JD025348
论文题名: Seasonal variability of warm boundary layer cloud and precipitation properties in the Southern Ocean as diagnosed from A-Train data
作者: Mace G.G. ; Avey S.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2017
卷: 122, 期: 2 起始页码: 1015
结束页码: 1032
语种: 英语
英文关键词: clouds
; precipitation processes
; remote sensing
; Southern Ocean
Scopus关键词: algorithm
; boundary layer
; brightness temperature
; cloud condensation nucleus
; cloud cover
; cloud droplet
; CloudSat
; condensation
; MODIS
; precipitation (climatology)
; radar
; remote sensing
; satellite data
; seasonal variation
; Southern Ocean
英文摘要: The extensive cloudiness and resulting high albedo of the Southern Oceans (SO) are predominantly due to the occurrence of widespread marine boundary layer clouds. Recent work finds correlations between biogenically enhanced cloud condensation nuclei concentrations and cloud droplet number concentrations derived from passive satellite data. The active remote sensors in the A-Train have created a unique and long-term record of these clouds that include vertical profiles of radar reflectivity and microwave brightness temperature from CloudSat that can be combined with solar reflectances from Moderate Resolution Imaging Spectroradiometer. We examine this data record using a unique algorithm to infer warm-topped cloud and precipitation properties. We find seasonal variations in cloud properties where summer season clouds demonstrate higher cloud droplet number concentrations on average and require higher liquid water contents to produce similar precipitation rates. ©2016. The Authors. 资助项目: NNX10AM42G
; NNX13A169G
; NNX15AK17G
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/62752
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, United States
Recommended Citation:
Mace G.G.,Avey S.. Seasonal variability of warm boundary layer cloud and precipitation properties in the Southern Ocean as diagnosed from A-Train data[J]. Journal of Geophysical Research: Atmospheres,2017-01-01,122(2)