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While important for the management of air quality, human health and transportation, surface visibility data currently are only available through ground-based measurements, such as the Automated Surface Observing System (ASOS), and therefore lack spatial coverage. In analogy to the recent work of using satellite-based aerosol optical depth (AOD) to derive surface dry aerosol mass concentration at continental-to-global scale for cloud-free conditions, this study evaluates the potential of AOD retrieved from the MODerate Resolution Imaging Spectroradiometer (MODIS) for deriving surface visibility. For this purpose of evaluation, the truncated and discrete visibility data from daily weather reports are not suitable and the ASOS-measured one-minute raw surface extinction coefficient (bext) values have to be used. Consequently, a method for quality control on the bext data is first developed to eliminate frequent problems such as extraneous points, poor calibration, and bad formatting, after which reliable bext data are obtained to estimate the surface visibility that can be considered as ground truth. Subsequent analysis of the AOD and bext relationship on the East Coast of the United States reveals their average linear correlation coefficient (R) of 0.61 for all 12 (2000-2011) years of data at 32 ASOS stations, with the highest R value in summer and the lowest in winter. Incorporating the Goddard Earth Observing System, Version 5 (GEOS-5) modeled vertical profile of aerosols into the derivation of visibility from AOD is evaluated for five different methods that are commonly used in the estimate of dry aerosol mass from AOD. For three years of available GEOS-5 data, scaling the modeled surface bext with the ratio between MODIS AOD and the modeled AOD is found to produce the best overall estimate of surface visibility that correlates with ASOS-based visibility with an R of 0.72 and a small negative bias of -0.03 km-1. This study is among the first to demonstrate the use of the MODIS aerosol product over land to derive surface visibility.
Advisor: Jun Wang