Articles | Volume 18, issue 10
Atmos. Chem. Phys., 18, 7509–7525, 2018
https://doi.org/10.5194/acp-18-7509-2018
Atmos. Chem. Phys., 18, 7509–7525, 2018
https://doi.org/10.5194/acp-18-7509-2018

Research article 30 May 2018

Research article | 30 May 2018

Quantifying errors in surface ozone predictions associated with clouds over the CONUS: a WRF-Chem modeling study using satellite cloud retrievals

Young-Hee Ryu et al.

Viewed

Total article views: 1,861 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
988 747 126 1,861 201 32 34
  • HTML: 988
  • PDF: 747
  • XML: 126
  • Total: 1,861
  • Supplement: 201
  • BibTeX: 32
  • EndNote: 34
Views and downloads (calculated since 03 Nov 2017)
Cumulative views and downloads (calculated since 03 Nov 2017)

Viewed (geographical distribution)

Total article views: 1,863 (including HTML, PDF, and XML) Thereof 1,852 with geography defined and 11 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Sep 2021
Download
Short summary
We investigate whether errors in cloud predictions can significantly impact the ability of air quality models to predict surface ozone over the US during summer 2013. The comparison with satellite data shows that the model predicts ~ 55 % of clouds in the right locations and underpredicts cloud thickness. The error in daytime ozone is estimated to be 1–5 ppb and represents ~ 40 % of the ozone bias. The accurate predictions of clouds particularly benefits ozone predictions in urban areas.
Altmetrics
Final-revised paper
Preprint