A31D-0076
An investigation of a potential low bias in the MODIS aerosol products over Asia
Wednesday, 16 December 2015
Poster Hall (Moscone South)
Yingxi Shi1, Jianglong Zhang2, Jeffrey S. Reid3,4, James R Campbell4, Nai-Yung Christina Hsu5 and Theodore M McHardy2, (1)University of North Dakota, Grand Forks, ND, United States, (2)University of North Dakota, Atmospheric Sceinces, Grand Forks, ND, United States, (3)Naval Research Laboratory, Marine Meteorology Division, Monterey, CA, United States, (4)Naval Research Lab, Monterey, CA, United States, (5)NASA Goddard Space Flight Center, Greenbelt, MD, United States
Abstract:
Heavy aerosol plumes can be misidentified as clouds in passive satellite-based aerosol retrievals due to their relatively high visible reflectivity. Thus, over regions such as China, where a higher frequency of heavy aerosol plumes is expected, regional aerosol optical depth analyses reported from passive satellite-based aerosol products may biased low. This fundamental error can be suppressed under certain conditions. In this study, with a synergistic use of satellite observations from MODIS, OMI and CALIOP, a low bias in the MODIS Dark Target (DT) and Deep Blue (DB) aerosol products is studied over Asia for the influence of dense aerosol plume undersampling. A new scheme has been developed for detecting heavy aerosol plumes by coupling OMI aerosol index retrievals with available CALIOP level 1B and cloud and aerosol profile data. Collocated CALIOP, MODIS and OMI data are then used to further investigate the potential low bias in the MODIS DT and DB aerosol products, in an attempt to quantify the measure of undersampling in the regional DT and DB archive. Our preliminary results show that DT and DB aerosol algorithms detect about half heavy aerosol loading when CALIPSO and OMI AI believe there are heavy absorbing aerosols.
