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RSS FeedsRemote Sensing, Vol. 11, Pages 2396: New Global View of Above-Cloud Absorbing Aerosol Distribution Based on CALIPSO Measurements (Remote Sensing)

 
 

16 october 2019 23:00:51

 
Remote Sensing, Vol. 11, Pages 2396: New Global View of Above-Cloud Absorbing Aerosol Distribution Based on CALIPSO Measurements (Remote Sensing)
 


Above-low-level-cloud aerosols (ACAs) have gradually gained more interest in recent years; however, the combined aerosol–cloud radiation effects are not well understood. The uncertainty about the radiative effects of aerosols above cloud mainly stems from the lack of comprehensive and accurate retrieval of aerosols and clouds for ACA scenes. In this study, an improved ACA identification and retrieval methodology was developed to provide a new global view of the ACA distribution by combining three-channel CALIOP (The Cloud–Aerosol Lidar with Orthogonal Polarization) observations. The new method can reliably identify and retrieve both thin and dense ACA layers, providing consistent results between the day- and night-time retrieval of ACAs. Then, new four-year (2007 to 2010) global ACA datasets were built, and new seasonal mean views of global ACA occurrence, optical depth, and geometrical thickness were presented and analyzed. Further discussion on the relative position of ACAs to low clouds showed that the mean distance between the ACA layer and the low cloud deck over the tropical Atlantic region is less than 0.2 km. This indicates that the ACAs over this region are more likely to be mixed with low-level clouds, thereby possibly influencing the cloud microphysics over this region, contrary to findings reported from previous studies. The results not only help us better understand global aerosol transportation and aerosol–cloud interactions but also provide useful information for model evaluation and improvements.


 
151 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2397: Extracting Irrigation Structure Networks from Pre-Landsat 4 Satellite Imagery Using Vegetation Indices (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2395: Deep Self-Learning Network for Adaptive Pansharpening (Remote Sensing)
 
 
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