Assessing the Challenges of Surface-Level Aerosol Mass Estimates From Remote...

Kaku, K. C., J. S. Reid, J. L. Hand, E. S. Edgerton, B. N. Holben, J. Zhang, and R. E. Holz (2018), Assessing the Challenges of Surface-Level Aerosol Mass Estimates From Remote Sensing During the SEAC4RS and SEARCH Campaigns: Baseline Surface Observations and Remote Sensing in the Southeastern United States, J. Geophys. Res., 123, 7530-7562, doi:10.1029/2017JD028074.

The Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign conducted in the southeast United States (SEUS) during the summer of 2013 provided a singular opportunity to study local aerosol chemistry and investigate aerosol radiative properties and PM2.5 relationships, focusing on the complexities involved in simplifying the relationship into a linear regression. We utilize three Southeastern Aerosol Research and Characterization network sites and one Environmental Protection Agency Chemical Speciation Network station that afforded simultaneous Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and aerosol mass, chemistry, and light scattering monitoring. Prediction of AERONET AOD using linear regression of daily-mean PM2.5 during the SEAC4RS campaign yielded r2 of 0.36–0.53 and highly variable slopes across four sites. There were further reductions in PM2.5 predictive skill using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpetroRadiometer (MISR) AOD data, which have shorter correlation lengths and times relative to surface PM2.5. Long-term trends in aerosol chemistry and optical properties in the SEUS are also investigated and compared to SEAC4RS period data, establishing that the SEUS experienced significant reduction in aerosol mass, corresponding with changes in both aerosol chemistry and optical properties. These changes have substantial impact on the PM2.5-AOD linear regression relationship and reinforce the need for long-term aerosol observation stations in addition to concentrated field campaigns.

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Atmospheric Composition Modeling and Analysis Program (ACMAP)