Constructing An Event Based Aerosol Product Under High Aerosol Loading Conditions

High aerosol loading events, such as the Indonesia’s forest fire in Fall 2015 or the persistent wintertime haze near Beijing, pose large impact on urban environment.  Understanding the optical properties of these events and further being able to simulate and predict these events are critical to understand the urban environment.  However, it is a great challenge to consistently identify and then retrieve aerosol optical depth (AOD) from passive sensors during heavy aerosol events. Some reasons include:1). large differences between optical properties of high-loading aerosols and those under normal conditions, 2) spectral signals of optically thick aerosols can be mistaken with surface depending on aerosol types, and 3) Extremely optically thick aerosol plumes can also be misidentified as clouds due to its high optical thickness.  Thus, even under clear-sky conditions, the global distribution of extreme aerosol events is not well captured in datasets such as the MODIS Dark-Target (DT) aerosol product. In this study, with the synthetic use of OMI Aerosol Index, MODIS cloud product, and operational DT product, the heavy smoke events over the seven sea region are identified and retrieved over the dry season.  An event based aerosol product that focuses on high aerosol loading events over urban areas and would compensate the standard “global” aerosol retrieval will be created and evaluated.  The impact of missing high AOD retrievals on the urban aerosol climatology will be studied using this newly developed research product.