Articles | Volume 9, issue 1
https://doi.org/10.5194/gmd-9-17-2016
© 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-9-17-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
15 Jan 2016
Development and technical paper |
| 15 Jan 2016
GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia
School of Environmental Science and Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 500-712,
South Korea
C. H. Song
CORRESPONDING AUTHOR
School of Environmental Science and Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 500-712,
South Korea
R. S. Park
School of Environmental Science and Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 500-712,
South Korea
Numerical Model Team, Korea Institute of Atmospheric
Prediction Systems (KIAPS), Seoul, 156-849, South Korea
M. E. Park
School of Environmental Science and Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 500-712,
South Korea
Asian Dust Research Division, National Institute of
Meteorological Research (NIMR), Jeju-do, 697-845, South Korea
K. M. Han
School of Environmental Science and Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 500-712,
South Korea
Department of Atmospheric Sciences, Yonsei University,
Seoul, 120-749, South Korea
Department of Atmospheric Sciences, Yonsei University,
Seoul, 120-749, South Korea
Y. S. Ghim
Department of Environmental Science, Hankuk University of
Foreign Studies, Yongin, 449-791, South Korea
J.-H. Woo
Department of Advanced Technology Fusion, Konkuk
University, Seoul, 143-701, South Korea
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Cited
27 citations as recorded by crossref.
- 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. 10.5194/amt-16-3039-2023
- Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD S. Lee et al. 10.1029/2021GL096066
- Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite K. Yumimoto et al. 10.1002/2016GL069298
- Long-term variations of aerosol optical depth according to satellite data and its effects on radiation and temperature in the Moscow megacity A. Poliukhov et al. 10.1016/j.atmosres.2024.107398
- Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues K. Lee et al. 10.5194/gmd-13-1055-2020
- Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic K. Han et al. 10.3390/app11041959
- 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. 10.1038/s41598-024-76223-1
- 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. 10.5194/amt-12-4619-2019
- Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China J. Pang et al. 10.1016/j.atmosenv.2018.02.011
- Satellite retrieval of aerosol combined with assimilated forecast M. Yoshida et al. 10.5194/acp-21-1797-2021
- Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD B. Tang et al. 10.1016/j.atmosenv.2024.120603
- Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
- Simultaneous assimilation of Fengyun-4A and Himawari-8 aerosol optical depth retrieval to improve air quality simulations during one storm event over East Asia X. Xia et al. 10.3389/feart.2023.1057299
- Hourly and Daily PM2.5 Estimations Using MERRA‐2: A Machine Learning Approach A. Sayeed et al. 10.1029/2022EA002375
- New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS) J. Kim et al. 10.1175/BAMS-D-18-0013.1
- Spatial–Temporal Fusion of 10-Min Aerosol Optical Depth Products with the GEO–LEO Satellite Joint Observations X. Xia et al. 10.3390/rs15082038
- Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models S. Park et al. 10.1016/j.scitotenv.2020.136516
- Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. 10.1029/2018MS001475
- Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ P. Saide et al. 10.5194/acp-20-6455-2020
- Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system U. Shin et al. 10.1016/j.atmosenv.2020.118141
- 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. 10.5194/amt-11-385-2018
- Development of a Reactive Plume Model for the Consideration of Power-Plant Plume Photochemistry and Its Applications Y. Kim et al. 10.1021/acs.est.6b03919
- Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite M. Kikuchi et al. 10.1109/TGRS.2018.2800060
- A Development of Air Quality Forecasting System with Data Assimilation using Surface Measurements in East Asia D. Choi et al. 10.5572/KOSAE.2019.35.1.060
- Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network Y. Lops et al. 10.1007/s00521-023-08558-1
- 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
- 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. 10.1029/2019JD030641
27 citations as recorded by crossref.
- 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. 10.5194/amt-16-3039-2023
- Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD S. Lee et al. 10.1029/2021GL096066
- Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite K. Yumimoto et al. 10.1002/2016GL069298
- Long-term variations of aerosol optical depth according to satellite data and its effects on radiation and temperature in the Moscow megacity A. Poliukhov et al. 10.1016/j.atmosres.2024.107398
- Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues K. Lee et al. 10.5194/gmd-13-1055-2020
- Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic K. Han et al. 10.3390/app11041959
- 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. 10.1038/s41598-024-76223-1
- 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. 10.5194/amt-12-4619-2019
- Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China J. Pang et al. 10.1016/j.atmosenv.2018.02.011
- Satellite retrieval of aerosol combined with assimilated forecast M. Yoshida et al. 10.5194/acp-21-1797-2021
- Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD B. Tang et al. 10.1016/j.atmosenv.2024.120603
- Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
- Simultaneous assimilation of Fengyun-4A and Himawari-8 aerosol optical depth retrieval to improve air quality simulations during one storm event over East Asia X. Xia et al. 10.3389/feart.2023.1057299
- Hourly and Daily PM2.5 Estimations Using MERRA‐2: A Machine Learning Approach A. Sayeed et al. 10.1029/2022EA002375
- New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS) J. Kim et al. 10.1175/BAMS-D-18-0013.1
- Spatial–Temporal Fusion of 10-Min Aerosol Optical Depth Products with the GEO–LEO Satellite Joint Observations X. Xia et al. 10.3390/rs15082038
- Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models S. Park et al. 10.1016/j.scitotenv.2020.136516
- Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. 10.1029/2018MS001475
- Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ P. Saide et al. 10.5194/acp-20-6455-2020
- Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system U. Shin et al. 10.1016/j.atmosenv.2020.118141
- 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. 10.5194/amt-11-385-2018
- Development of a Reactive Plume Model for the Consideration of Power-Plant Plume Photochemistry and Its Applications Y. Kim et al. 10.1021/acs.est.6b03919
- Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite M. Kikuchi et al. 10.1109/TGRS.2018.2800060
- A Development of Air Quality Forecasting System with Data Assimilation using Surface Measurements in East Asia D. Choi et al. 10.5572/KOSAE.2019.35.1.060
- Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network Y. Lops et al. 10.1007/s00521-023-08558-1
- 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
- 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. 10.1029/2019JD030641
Saved (preprint)
Latest update: 23 Nov 2024
Short summary
We developed an integrated air quality modeling system using AOD data retrieved from a geostationary satellite sensor, GOCI (Geostationary Ocean Color Imager), over Northeast Asia with an application of the spatiotemporal-kriging (STK) method and conducted short-term hindcast runs using the developed system. It appears that the STK approach can greatly reduce not only the errors and biases of AOD and PM10 predictions but also the computational burden of a chemical weather forecast (CWF).
We developed an integrated air quality modeling system using AOD data retrieved from a...
