Articles | Volume 8, issue 9
https://doi.org/10.5194/gmd-8-2959-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-2959-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
25 Sep 2015
Development and technical paper |
| 25 Sep 2015
Evaluation of modeled surface ozone biases as a function of cloud cover fraction
NOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA
P. Lee
NOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
F. Ngan
NOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA
NOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA
H. L. Yoo
NOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA
L. Pan
NOAA Air Resources Laboratory (ARL), NOAA center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA
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Cited
11 citations as recorded by crossref.
- Improved modeling of cloudy‐sky actinic flux using satellite cloud retrievals Y. Ryu et al. 10.1002/2016GL071892
- Synoptic weather and surface ozone concentration in South Korea H. Kim et al. 10.1016/j.atmosenv.2020.117985
- Gridded global surface ozone metrics for atmospheric chemistry model evaluation E. Sofen et al. 10.5194/essd-8-41-2016
- Regional contributions to particulate matter concentration in the Seoul metropolitan area, South Korea: seasonal variation and sensitivity to meteorology and emissions inventory H. Kim et al. 10.5194/acp-17-10315-2017
- Cloud impacts on photochemistry: building a climatology of photolysis rates from the Atmospheric Tomography mission S. Hall et al. 10.5194/acp-18-16809-2018
- Compound heat and ozone pollution in the urban environment C. Wang et al. 10.1016/j.uclim.2025.102511
- Influence of Cold Fronts on Variability of Daily Surface O3 over the Houston-Galveston-Brazoria Area in Texas USA during 2003–2016 R. Lei et al. 10.3390/atmos9050159
- The influence of cloud cover on the reliability of satellite-based solar resource data Y. Xie et al. 10.1016/j.rser.2024.115070
- Heterogeneous Processes in the Atmosphere of Mars and Impact on H2O2 and O3 Abundances F. Daerden et al. 10.1029/2023JE008014
- CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks A. Sayeed et al. 10.1016/j.atmosenv.2022.118961
- Gridded global surface ozone metrics for atmospheric chemistry model evaluation E. Sofen et al. 10.5194/essdd-8-603-2015
10 citations as recorded by crossref.
- Improved modeling of cloudy‐sky actinic flux using satellite cloud retrievals Y. Ryu et al. 10.1002/2016GL071892
- Synoptic weather and surface ozone concentration in South Korea H. Kim et al. 10.1016/j.atmosenv.2020.117985
- Gridded global surface ozone metrics for atmospheric chemistry model evaluation E. Sofen et al. 10.5194/essd-8-41-2016
- Regional contributions to particulate matter concentration in the Seoul metropolitan area, South Korea: seasonal variation and sensitivity to meteorology and emissions inventory H. Kim et al. 10.5194/acp-17-10315-2017
- Cloud impacts on photochemistry: building a climatology of photolysis rates from the Atmospheric Tomography mission S. Hall et al. 10.5194/acp-18-16809-2018
- Compound heat and ozone pollution in the urban environment C. Wang et al. 10.1016/j.uclim.2025.102511
- Influence of Cold Fronts on Variability of Daily Surface O3 over the Houston-Galveston-Brazoria Area in Texas USA during 2003–2016 R. Lei et al. 10.3390/atmos9050159
- The influence of cloud cover on the reliability of satellite-based solar resource data Y. Xie et al. 10.1016/j.rser.2024.115070
- Heterogeneous Processes in the Atmosphere of Mars and Impact on H2O2 and O3 Abundances F. Daerden et al. 10.1029/2023JE008014
- CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks A. Sayeed et al. 10.1016/j.atmosenv.2022.118961
1 citations as recorded by crossref.
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Latest update: 14 Jul 2025
Short summary
This study focuses on the evaluation of regional air quality model's performance based on the cloud information from satellites. While cloud information is crucial in photochemistry model, the definitions of cloud fraction from model and satellite are not physically consistent. We demonstrate that improper modeling of cloud fraction is correlated with surface ozone bias, and we also show that current model cloud field might be too bright, causing an overestimation of surface ozone level.
This study focuses on the evaluation of regional air quality model's performance based on the...
