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

Jin, Xiaomeng; Fiore, Arlene M.; Curci, Gabriele; Lyapustin, Alexei; Civerolo, Kevin; Ku, Michael; van Donkelaar, Aaron; Martin, Randall V.

doi:https://doi.org/10.5194/acp-19-295-2019

Articles | Volume 19, issue 1

https://doi.org/10.5194/acp-19-295-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-19-295-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 1

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, Arlene M. Fiore, Gabriele Curci, Alexei Lyapustin, Kevin Civerolo, Michael Ku, Aaron van Donkelaar, and Randall V. Martin

Supplement

https://doi.org/10.5194/acp-19-295-2019-supplement

Data sets

Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite observed aerosol optical depth: Data and code X. Jin https://doi.org/10.7916/d8-x9h1-cg54

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Short summary

We use a forward geophysical approach to derive surface PM_2.5 distribution from satellite AOD over the northeastern US by applying relationships between surface PM_2.5 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 PM_2.5.