Articles | Volume 18, issue 10
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, Alma Hodzic, Jerome Barre, Gael Descombes, and Patrick Minnis


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (27 Mar 2018)  Author's response
ED: Referee Nomination & Report Request started (28 Mar 2018) by Bryan N. Duncan
RR by Anonymous Referee #3 (03 Apr 2018)
ED: Publish as is (05 Apr 2018) by Bryan N. Duncan
AR by Young-Hee Ryu on behalf of the Authors (16 Apr 2018)  Author's response    Manuscript
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.
Final-revised paper