Articles | Volume 9, issue 10
https://doi.org/10.5194/gmd-9-3671-2016
© Author(s) 2016. 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-9-3671-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model evaluation paper
| 
17 Oct 2016
Model evaluation paper |
| 17 Oct 2016
Computationally efficient air quality forecasting tool: implementation of STOPS v1.5 model into CMAQ v5.0.2 for a prediction of Asian dust
Wonbae Jeon
Department of Earth and Atmospheric Sciences, University of Houston,
312 Science & Research Building 1, Houston, TX 77204, USA
Department of Earth and Atmospheric Sciences, University of Houston,
312 Science & Research Building 1, Houston, TX 77204, USA
Peter Percell
Department of Earth and Atmospheric Sciences, University of Houston,
312 Science & Research Building 1, Houston, TX 77204, USA
Amir Hossein Souri
Department of Earth and Atmospheric Sciences, University of Houston,
312 Science & Research Building 1, Houston, TX 77204, USA
Chang-Keun Song
National Institute of Environmental Research, Incheon, Republic of
Korea
Soon-Tae Kim
Division of Environmental Engineering, Ajou University, Suwon,
Republic of Korea
Jhoon Kim
Department of Atmosphere Sciences, Yonsei University, Seoul, Republic
of Korea
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Cited
14 citations as recorded by crossref.
- The Impact of Springtime‐Transported Air Pollutants on Local Air Quality With Satellite‐Constrained NOx Emission Adjustments Over East Asia J. Jung et al. https://doi.org/10.1029/2021JD035251
- Evaluating deep learning models for PM2.5 bias correction in Seoul, South Korea: Forecast trade-offs and observational health impacts R. Islam et al. https://doi.org/10.1016/j.apr.2026.102958
- Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy J. Park et al. https://doi.org/10.5194/amt-16-3039-2023
- Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign M. Choi et al. https://doi.org/10.5194/amt-12-4619-2019
- Development of a Prediction Model for Daily PM2.5 in Republic of Korea by Using an Artificial Neutral Network J. Huh et al. https://doi.org/10.3390/app13063575
- The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign J. Jung et al. https://doi.org/10.1029/2019JD030641
- GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia M. Choi et al. https://doi.org/10.5194/amt-11-385-2018
- Study on the Impact of Various Observations Data Assimilation on the Meteorological Predictions over Eastern Part of the Korean Peninsula J. Kim et al. https://doi.org/10.5322/JESI.2018.27.11.1141
- Relative effects of open biomass burning and open crop straw burning on haze formation over central and eastern China: modeling study driven by constrained emissions K. Mehmood et al. https://doi.org/10.5194/acp-20-2419-2020
- Improved estimation of particulate matter in China based on multisource data fusion S. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.161552
- Investigating the regional difference of aerosol feedback effects over South Korea using the WRF-CMAQ two-way coupled modeling system J. Yoo et al. https://doi.org/10.1016/j.atmosenv.2019.116968
- Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2) A. Pouyaei et al. https://doi.org/10.5194/gmd-13-3489-2020
- Behavior of sulfate on the sea surface during its transport from Eastern China to South Korea W. Jeon et al. https://doi.org/10.1016/j.atmosenv.2018.05.017
- First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns J. Park et al. https://doi.org/10.1038/s41598-024-76223-1
14 citations as recorded by crossref.
- The Impact of Springtime‐Transported Air Pollutants on Local Air Quality With Satellite‐Constrained NOx Emission Adjustments Over East Asia J. Jung et al. https://doi.org/10.1029/2021JD035251
- Evaluating deep learning models for PM2.5 bias correction in Seoul, South Korea: Forecast trade-offs and observational health impacts R. Islam et al. https://doi.org/10.1016/j.apr.2026.102958
- Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy J. Park et al. https://doi.org/10.5194/amt-16-3039-2023
- Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign M. Choi et al. https://doi.org/10.5194/amt-12-4619-2019
- Development of a Prediction Model for Daily PM2.5 in Republic of Korea by Using an Artificial Neutral Network J. Huh et al. https://doi.org/10.3390/app13063575
- The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign J. Jung et al. https://doi.org/10.1029/2019JD030641
- GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia M. Choi et al. https://doi.org/10.5194/amt-11-385-2018
- Study on the Impact of Various Observations Data Assimilation on the Meteorological Predictions over Eastern Part of the Korean Peninsula J. Kim et al. https://doi.org/10.5322/JESI.2018.27.11.1141
- Relative effects of open biomass burning and open crop straw burning on haze formation over central and eastern China: modeling study driven by constrained emissions K. Mehmood et al. https://doi.org/10.5194/acp-20-2419-2020
- Improved estimation of particulate matter in China based on multisource data fusion S. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.161552
- Investigating the regional difference of aerosol feedback effects over South Korea using the WRF-CMAQ two-way coupled modeling system J. Yoo et al. https://doi.org/10.1016/j.atmosenv.2019.116968
- Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2) A. Pouyaei et al. https://doi.org/10.5194/gmd-13-3489-2020
- Behavior of sulfate on the sea surface during its transport from Eastern China to South Korea W. Jeon et al. https://doi.org/10.1016/j.atmosenv.2018.05.017
- First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns J. Park et al. https://doi.org/10.1038/s41598-024-76223-1
Saved (final revised paper)
Latest update: 10 Aug 2026
Short summary
This study suggests a new hybrid Lagrangian–Eulerian modeling tool (the Screening Trajectory Ozone Prediction System, STOPS) for an accurate/fast prediction of Asian dust events. The STOPS is a moving nest (Lagrangian approach) between the source and the receptor inside Eulerian model. We run STOPS, instead of running a time-consuming Eulerian model, using constrained PM concentration from remote sensing aerosol optical depth, reflecting real-time dust particles. STOPS is for unexpected events.
This study suggests a new hybrid Lagrangian–Eulerian modeling tool (the Screening Trajectory...
