A satellite perspective on cloud water to rain water conversion rates and relationships with environmental conditions. Issue 12 (26th June 2013)

• Record Type: Journal Article
• Authors: Sorooshian, Armin; Wang, Zhen; Feingold, Graham; L'Ecuyer, Tristan S.
• Is Part Of: Journal of geophysical research. Volume 118:Issue 12(2013:Dec.)
• Page Start: 6643
• Page End: 6650
• Publication Date: 2013-06-26
• Subjects: A‐Train -- coalescence -- aerosol -- precipitation; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/jgrd.50523
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract : [1] A two‐year satellite remote sensing data set from the NASA A‐Train is used to examine conversion rates of cloud water to rain water for warm maritime clouds with different ranges of mean cloud‐layer radar reflectivity and rain rate. Recent work has demonstrated the utility of a novel procedure that relies on the differing sensitivities of passive MODIS measurements and active CloudSat radar measurements to estimate warm cloud conversion rates and associated time scales. That work is extended here to examine regional differences in conversion rates, including sensitivity to environmental parameters such as atmospheric stability and the presence of different aerosol types defined based on values of aerosol optical depth, fine mode fraction, and Ångstrom Exponent. Among eight subregions examined, the tropical Pacific Ocean is characterized by the highest average conversion rate while subtropical stratocumulus cloud regions (far northeastern Pacific Ocean, far southeastern Pacific Ocean, Western Africa coastal region) exhibit the lowest rates. Conversion rates are generally higher at reduced values of lower tropospheric static stability (LTSS). When examining data in two selected ranges for LTSS, higher conversion rates are coincident with higher LWP and factors covarying or rooted in the presence of aerosol types exhibiting lower aerosol index values. Key Points: Cloud‐to‐rain water conversion parameters quantified using A‐Train data Conversion faster in unstable environments with lower aerosol concentrations Technique is shown to be useful to examine relative trends in a global sense