Articles | Volume 17, issue 12
https://doi.org/10.5194/acp-17-7291-2017
https://doi.org/10.5194/acp-17-7291-2017
Research article
 | 
17 Jun 2017
Research article |  | 17 Jun 2017

WRF-Chem simulation of aerosol seasonal variability in the San Joaquin Valley

Longtao Wu, Hui Su, Olga V. Kalashnikova, Jonathan H. Jiang, Chun Zhao, Michael J. Garay, James R. Campbell, and Nanpeng Yu

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Cited articles

AERONET (AErosol RObotic NETwork): AERONET observation, NASA and PHOTONS, available at: https://aeronet.gsfc.nasa.gov/, last access: 8 June 2017.
AIRS Science Team/Joao Texeira: AIRS/Aqua L3 Monthly Support Product (AIRS-only) 1 degree x 1 degree V006, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/AQUA/AIRS/DATA324, 2013.
Ångström, A.: On the atmospheric transmission of Sun radiation and on dust in the air, Geogr. Ann., 11, 156–166, 1929.
Archer-Nicholls, S., Lowe, D., Darbyshire, E., Morgan, W. T., Bela, M. M., Pereira, G., Trembath, J., Kaiser, J. W., Longo, K. M., Freitas, S. R., Coe, H., and McFiggans, G.: Characterising Brazilian biomass burning emissions using WRF-Chem with MOSAIC sectional aerosol, Geosci. Model Dev., 8, 549–577, https://doi.org/10.5194/gmd-8-549-2015, 2015.
ASDC: CALIPSO Data and Information, available at: https://eosweb.larc.nasa.gov/project/calipso/calipso_table, last access: 8 June 2017.
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Short summary
The WRF-Chem simulation successfully captures aerosol variations in the cold season in the San Joaquin Valley (SJV) but has poor performance in the warm season. High-resolution model simulation can better resolve nonhomogeneous distribution of anthropogenic emissions in urban areas, resulting in better simulation of aerosols in the cold season in the SJV. Poor performance of the WRF-Chem model in the warm season in the SJV is mainly due to misrepresentation of dust emission and vertical mixing.
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