Earth and Atmospheric Sciences, Department of

 

Date of this Version

2011

Citation

Remote Sensing of Environment 115 (2011) 508–513

Comments

This article is a U.S. government work, and is not subject to copyright in the United States.

Abstract

The aerosol vertical distribution is an important factor in determining the relationship between satellite retrieved aerosol optical depth (AOD) and ground-level fine particle pollution concentrations. We evaluate how aerosol profiles measured by ground-based lidar and simulated by models can help improve the association between AOD retrieved by the Multi-angle Imaging Spectroradiometer (MISR) and fine particle sulfate (SO4) concentrations using matched data at two lidar sites. At the Goddard Space Flight Center (GSFC) site, both lidar and model aerosol profiles marginally improve the association between SO4 concentrations and MISR fractional AODs, as the correlation coefficient between cross-validation (CV) and observed SO4 concentrations changes from 0.87 for the no-scaling model to 0.88 for models scaled with aerosol vertical profiles. At the GSFC site, a large amount of urban aerosols resides in the well-mixed boundary layer so the column fractional AODs are already excellent indicators of ground-level particle pollution. In contrast, at the Atmospheric Radiation Measurement Program (ARM) site with relatively low aerosol loadings, scaling substantially improves model performance. The correlation coefficient between CV and observed SO4 concentrations is increased from 0.58 for the no-scaling model to 0.76 in the GEOS-Chem scaling model, and the model bias is reduced from 17% to 9%. In summary, despite the inaccuracy due to the coarse horizontal resolution and the challenges of simulating turbulent mixing in the boundary layer, GEOS-Chem simulated aerosol profiles can still improve methods for estimating surface aerosol (SO4) mass from satellite-based AODs, particularly in rural areas where aerosols in the free troposphere and any long-range transport of aerosols can significantly contribute to the column AOD.

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