An integrated analysis of aerosol above clouds from A-Train multi-sensor measurements

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• 作者：Hongbin Yu^a ; ^b ; ^{Hongbin.Yu@nasa.gov} ; Yan Zhang^c ; ^b ; Mian Chin^d ; Zhaoyan Liu^e ; ^f ; Ali Omar^f ; Lorraine A. Remer^b ; Yuekui Yang^g ; ^b ; Tianle Yuan^h ; ^b ; Jianglong Zhangⁱ
• 关键词：Aerosols ; Clouds
• 刊名：Remote Sensing of Environment
• 出版年：2012
• 期刊代码：79_00344257
• 类别：ear
• 出版时间：June, 2012
• 卷：121
• 期：Complete
• 页码：125-131
• 文件大小：854 K

摘要

Quantifying above-cloud aerosol can help improve the assessment of aerosol intercontinental transport and climate impacts. In this study we conduct an integrated analysis of aerosols above clouds by using multi-sensor A-Train measurements, including above-cloud aerosol optical depth at 532 nm (AOD₅₃₂) from CALIPSO lidar, the UV aerosol index (AI) from OMI, and cloud fraction and cloud optical depth (COD) from MODIS. The analysis of Saharan dust outflow and Southwest African smoke outflow regions shows that the above-cloud AOD correlates positively with AI in an approximately linear manner, and that the AOD₅₃₂/AI ratio decreases with increasing COD. The dependence of AOD₅₃₂/AI ratio on COD doesn't depend on aerosol type when potential biases in the CALIOP AOD measurements are empirically accounted for. Our results may suggest the potential of combining OMI AI and MODIS cloud measurements to empirically derive above-cloud AOD with a spatial coverage much more extensive than CALIPSO measurements, which needs to be further explored in the future.