Articles | Volume 18, issue 10
https://doi.org/10.5194/acp-18-7509-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-18-7509-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 May 2018
Research article |
| 30 May 2018
| 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
National Center for Atmospheric Research, Boulder, CO, USA
Alma Hodzic
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Boulder, CO, USA
Laboratoire d'Aérologie, Université de Toulouse, CNRS, UPS, Toulouse, France
Jerome Barre
National Center for Atmospheric Research, Boulder, CO, USA
now at: European Centre for Medium-Range Weather Forecasts, Reading, UK
Gael Descombes
National Center for Atmospheric Research, Boulder, CO, USA
Patrick Minnis
NASA Langley Research Center, Hampton, VA, USA
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23 citations as recorded by crossref.
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- Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
- Exploring the Potential of Statistical Modeling to Retrieve the Cloud Base Height from Geostationary Satellites: Applications to the ABI Sensor on Board of the GOES-R Satellite Series P. Jiménez & T. McCandless 10.3390/rs13030375
- Sensitivity of Meteorological Skill to Selection of WRF‐Chem Physical Parameterizations and Impact on Ozone Prediction During the Lake Michigan Ozone Study (LMOS) M. Abdi‐Oskouei et al. 10.1029/2019JD031971
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- Absorbing Aerosol Optical Properties and Radiative Effects on Near-Surface Photochemistry in East Asia H. Chen et al. 10.3390/rs15112779
- Improvement of summertime surface ozone prediction by assimilating Geostationary Operational Environmental Satellite cloud observations P. Cheng et al. 10.1016/j.atmosenv.2021.118751
- Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China M. Ma et al. 10.3390/su16010123
- Role of Upwind Precipitation in Transboundary Pollution and Secondary Aerosol Formation: A Case Study during the KORUS-AQ Field Campaign Y. Ryu et al. 10.1175/JAMC-D-21-0162.1
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- Analysis of the Effect of Optical Properties of Black Carbon on Ozone in an Urban Environment at the Yangtze River Delta, China J. An et al. 10.1007/s00376-021-0367-9
- Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network S. Sun-Mack et al. 10.5194/amt-17-3323-2024
- Response of surface ozone over the continental United States to UV radiation declines from the expected recovery of stratospheric ozone A. Hodzic & S. Madronich 10.1038/s41612-018-0045-5
- Evaluation of PM2.5 fluxes in the “2+26” cities: Transport pathways and intercity contributions M. Qi et al. 10.1016/j.apr.2021.03.011
- A Novel Air Pollutant Concentration Prediction System Based on Decomposition-Ensemble Mode and Multi-Objective Optimization for Environmental System Management Y. Hao et al. 10.3390/systems10050139
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- Comparison and evaluation of updates to WRF-Chem (v3.9) biogenic emissions using MEGAN M. Morichetti et al. 10.5194/gmd-15-6311-2022
- High spatial resolution WRF-Chem model over Asia: Physics and chemistry evaluation P. Sicard et al. 10.1016/j.atmosenv.2020.118004
23 citations as recorded by crossref.
- Comparison of CAMS and CMAQ analyses of surface-level PM2.5 and O3 over the conterminous United States (CONUS) J. Lee et al. 10.1016/j.atmosenv.2024.120833
- Learning Calibration Functions on the Fly: Hybrid Batch Online Stacking Ensembles for the Calibration of Low-Cost Air Quality Sensor Networks in the Presence of Concept Drift E. Bagkis et al. 10.3390/atmos13030416
- Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
- Exploring the Potential of Statistical Modeling to Retrieve the Cloud Base Height from Geostationary Satellites: Applications to the ABI Sensor on Board of the GOES-R Satellite Series P. Jiménez & T. McCandless 10.3390/rs13030375
- Sensitivity of Meteorological Skill to Selection of WRF‐Chem Physical Parameterizations and Impact on Ozone Prediction During the Lake Michigan Ozone Study (LMOS) M. Abdi‐Oskouei et al. 10.1029/2019JD031971
- Evaluating the Impact of Chemical Complexity and Horizontal Resolution on Tropospheric Ozone Over the Conterminous US With a Global Variable Resolution Chemistry Model R. Schwantes et al. 10.1029/2021MS002889
- Impacts of maritime shipping on air pollution along the US East Coast M. Golbazi & C. Archer 10.5194/acp-23-15057-2023
- Enhancing Air Quality Forecasts Across the Contiguous United States (CONUS) During Wildfires Using Analog-Based Post-Processing Methods M. Golbazi et al. 10.1016/j.atmosenv.2023.120165
- Absorbing Aerosol Optical Properties and Radiative Effects on Near-Surface Photochemistry in East Asia H. Chen et al. 10.3390/rs15112779
- Improvement of summertime surface ozone prediction by assimilating Geostationary Operational Environmental Satellite cloud observations P. Cheng et al. 10.1016/j.atmosenv.2021.118751
- Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China M. Ma et al. 10.3390/su16010123
- Role of Upwind Precipitation in Transboundary Pollution and Secondary Aerosol Formation: A Case Study during the KORUS-AQ Field Campaign Y. Ryu et al. 10.1175/JAMC-D-21-0162.1
- Meteorological, chemical and biological evaluation of the coupled chemistry-climate WRF-Chem model from regional to urban scale. An impact-oriented application for human health A. Anav et al. 10.1016/j.envres.2024.119401
- Comprehensive isoprene and terpene gas-phase chemistry improves simulated surface ozone in the southeastern US R. Schwantes et al. 10.5194/acp-20-3739-2020
- Analysis of the Effect of Optical Properties of Black Carbon on Ozone in an Urban Environment at the Yangtze River Delta, China J. An et al. 10.1007/s00376-021-0367-9
- Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network S. Sun-Mack et al. 10.5194/amt-17-3323-2024
- Response of surface ozone over the continental United States to UV radiation declines from the expected recovery of stratospheric ozone A. Hodzic & S. Madronich 10.1038/s41612-018-0045-5
- Evaluation of PM2.5 fluxes in the “2+26” cities: Transport pathways and intercity contributions M. Qi et al. 10.1016/j.apr.2021.03.011
- A Novel Air Pollutant Concentration Prediction System Based on Decomposition-Ensemble Mode and Multi-Objective Optimization for Environmental System Management Y. Hao et al. 10.3390/systems10050139
- A Novel Ensemble Design for Probabilistic Predictions of Fine Particulate Matter Over the Contiguous United States (CONUS) R. Kumar et al. 10.1029/2020JD032554
- Improving Wet and Dry Deposition of Aerosols in WRF‐Chem: Updates to Below‐Cloud Scavenging and Coarse‐Particle Dry Deposition Y. Ryu & S. Min 10.1029/2021MS002792
- Comparison and evaluation of updates to WRF-Chem (v3.9) biogenic emissions using MEGAN M. Morichetti et al. 10.5194/gmd-15-6311-2022
- High spatial resolution WRF-Chem model over Asia: Physics and chemistry evaluation P. Sicard et al. 10.1016/j.atmosenv.2020.118004
Latest update: 23 Nov 2024
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.
We investigate whether errors in cloud predictions can significantly impact the ability of air...
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