Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1055-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1055-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Mar 2020
Development and technical paper |
| 10 Mar 2020
| 10 Mar 2020
Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues
Kyunghwa Lee
Environmental Satellite Center, Climate and Air Quality Research Department,
National Institute of Environmental Research (NIER), Incheon, Republic of
Korea
School of Earth Sciences and Environmental Engineering, Gwangju Institute of
Science and Technology (GIST), Gwangju, Republic of Korea
Jinhyeok Yu
School of Earth Sciences and Environmental Engineering, Gwangju Institute of
Science and Technology (GIST), Gwangju, Republic of Korea
Sojin Lee
Department of Earth and Atmospheric Sciences, University of Houston, Texas,
USA
Mieun Park
Air Quality Forecasting Center, Climate and Air Quality Research Department,
National Institute of Environmental Research (NIER), Incheon, Republic of
Korea
Environmental Meteorology Research Division, National Institute of
Meteorological Sciences (NIMS), Jeju, Republic of Korea
Hun Hong
School of Earth Sciences and Environmental Engineering, Gwangju Institute of
Science and Technology (GIST), Gwangju, Republic of Korea
Soon Young Park
School of Earth Sciences and Environmental Engineering, Gwangju Institute of
Science and Technology (GIST), Gwangju, Republic of Korea
Myungje Choi
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of
Korea
Jhoon Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of
Korea
Younha Kim
Department of Advanced Technology Fusion, Konkuk University, Seoul, Republic
of Korea
Jung-Hun Woo
Department of Advanced Technology Fusion, Konkuk University, Seoul, Republic
of Korea
Sang-Woo Kim
School of Earth and Environmental Sciences, Seoul National University,
Seoul, Republic of Korea
Chul H. Song
CORRESPONDING AUTHOR
School of Earth Sciences and Environmental Engineering, Gwangju Institute of
Science and Technology (GIST), Gwangju, Republic of Korea
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Cited
16 citations as recorded by crossref.
- Assessing CALIOP-Derived Planetary Boundary Layer Height Using Ground-Based Lidar M. Kim et al. 10.3390/rs13081496
- Highly resolved mapping of NO2 vertical column densities from GeoTASO measurements over a megacity and industrial area during the KORUS-AQ campaign G. Choo et al. 10.5194/amt-16-625-2023
- Development and Application of the SmartAQ High-Resolution Air Quality and Source Apportionment Forecasting System for European Urban Areas E. Siouti et al. 10.3390/atmos13101693
- Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
- 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
- Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement H. Sun et al. 10.5194/gmd-15-8439-2022
- Optical and chemical properties of long-range transported aerosols using satellite and ground-based observations over seoul, South Korea G. Choo et al. 10.1016/j.atmosenv.2020.118024
- Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5 S. Park et al. 10.5194/gmd-15-2773-2022
- Assessing the assimilation of Himawari-8 observations on aerosol forecasts and radiative effects during pollution transport from South Asia to the Tibetan Plateau M. Zhao et al. 10.5194/acp-24-235-2024
- Generation of High-Resolution Blending Data Using Gridded Visibility Data and GK2A Fog Product M. Suh et al. 10.3390/rs16132350
- Assessment of long-range transboundary aerosols in Seoul, South Korea from Geostationary Ocean Color Imager (GOCI) and ground-based observations S. Lee et al. 10.1016/j.envpol.2020.115924
- Multi-model intercomparisons of air quality simulations for the KORUS-AQ campaign R. Park et al. 10.1525/elementa.2021.00139
- Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
- Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ B. Gaubert et al. 10.5194/acp-20-14617-2020
- Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability J. Yu et al. 10.1038/s41612-023-00363-w
- Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network M. Kim et al. 10.3390/rs15143621
16 citations as recorded by crossref.
- Assessing CALIOP-Derived Planetary Boundary Layer Height Using Ground-Based Lidar M. Kim et al. 10.3390/rs13081496
- Highly resolved mapping of NO2 vertical column densities from GeoTASO measurements over a megacity and industrial area during the KORUS-AQ campaign G. Choo et al. 10.5194/amt-16-625-2023
- Development and Application of the SmartAQ High-Resolution Air Quality and Source Apportionment Forecasting System for European Urban Areas E. Siouti et al. 10.3390/atmos13101693
- Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
- 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
- Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement H. Sun et al. 10.5194/gmd-15-8439-2022
- Optical and chemical properties of long-range transported aerosols using satellite and ground-based observations over seoul, South Korea G. Choo et al. 10.1016/j.atmosenv.2020.118024
- Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5 S. Park et al. 10.5194/gmd-15-2773-2022
- Assessing the assimilation of Himawari-8 observations on aerosol forecasts and radiative effects during pollution transport from South Asia to the Tibetan Plateau M. Zhao et al. 10.5194/acp-24-235-2024
- Generation of High-Resolution Blending Data Using Gridded Visibility Data and GK2A Fog Product M. Suh et al. 10.3390/rs16132350
- Assessment of long-range transboundary aerosols in Seoul, South Korea from Geostationary Ocean Color Imager (GOCI) and ground-based observations S. Lee et al. 10.1016/j.envpol.2020.115924
- Multi-model intercomparisons of air quality simulations for the KORUS-AQ campaign R. Park et al. 10.1525/elementa.2021.00139
- Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
- Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ B. Gaubert et al. 10.5194/acp-20-14617-2020
- Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability J. Yu et al. 10.1038/s41612-023-00363-w
- Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network M. Kim et al. 10.3390/rs15143621
Latest update: 31 Mar 2025
Short summary
For the purpose of providing reliable and robust air quality predictions, an operational air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2) by preparing the initial conditions for model simulations via data assimilation using satellite- and ground-based observations. The performance of the developed air quality prediction system was evaluated using ground in situ data during the KORUS-AQ campaign period.
For the purpose of providing reliable and robust air quality predictions, an operational air...
