Articles | Volume 19, issue 1
Atmos. Chem. Phys., 19, 295–313, 2019
https://doi.org/10.5194/acp-19-295-2019
Atmos. Chem. Phys., 19, 295–313, 2019
https://doi.org/10.5194/acp-19-295-2019

Research article 09 Jan 2019

Research article | 09 Jan 2019

Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth

Xiaomeng Jin et al.

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Cited articles

Adams, P. J.: Predicting global aerosol size distributions in general circulation models, J. Geophys. Res., 107, 4370, https://doi.org/10.1029/2001JD001010, 2002. 
Appel, K. W., Pouliot, G. A., Simon, H., Sarwar, G., Pye, H. O. T., Napelenok, S. L., Akhtar, F., and Roselle, S. J.: Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0, Geosci. Model Dev., 6, 883–899, https://doi.org/10.5194/gmd-6-883-2013, 2013. 
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q. B., Liu, H., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. 
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We use a forward geophysical approach to derive surface PM2.5 distribution from satellite AOD over the northeastern US by applying relationships between surface PM2.5 and column AOD from a regional air quality model (CMAQ). We use multi-platform surface, aircraft, and radiosonde measurements to quantify different sources of uncertainties. We highlight model representation of aerosol vertical distribution and speciation as major sources of uncertainties for satellite-derived PM2.5.
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