Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2773-2022
© Author(s) 2022. 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-15-2773-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 Apr 2022
Development and technical paper |
| 06 Apr 2022
| 06 Apr 2022
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
Soon-Young Park
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 61005, Republic of
Korea
Institute of Environmental Studies, Pusan National University, Busan,
46241, Republic of Korea
Uzzal Kumar Dash
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 61005, Republic of
Korea
Jinhyeok Yu
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 61005, Republic of
Korea
Keiya Yumimoto
Research Institute for Applied Mechanics, Kyushu University, Fukuoka,
816-8580, Japan
Itsushi Uno
Research Institute for Applied Mechanics, Kyushu University, Fukuoka,
816-8580, Japan
Chul Han Song
CORRESPONDING AUTHOR
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 61005, Republic of
Korea
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Cited
16 citations as recorded by crossref.
- Evaluation of high‐resolution regional CO2 data assimilation–forecast system in East Asia using observing system simulation experiment and effect of observation network on simulated CO2 concentrations M. Seo & H. Kim
- Data assimilation experiments over Europe with the Chemical Transport Model FARM M. Adani & F. Uboldi
- 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.
- A data-assimilative approach to air quality analytics for policy and emergency planning E. Poudel et al.
- Mechanism-Driven vs. Data-Driven: A comparison of physics-informed bayesian estimation and deep learning models for EPB shield chamber pressure forecasting T. Yin et al.
- Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods U. Dash et al.
- A new decomposition-integrated air quality index prediction model X. Sun et al.
- Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions S. Li et al.
- Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability J. Yu et al.
- Deep Learning Emulator Towards Both Forward and Adjoint Modes of Atmospheric Gas-Phase Chemical Process Y. Liu et al.
- PM2.5 assimilation within JEDI for NOAA's regional Air Quality Model (AQMv7): application to the September 2020 Western US wildfires H. Wang et al.
- Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting M. Pang et al.
- Non-uniform downscaling data assimilation algorithm in variational framework Y. Zhao et al.
- Effects of chemical and meteorological data assimilation on air-quality and meteorological forecasts in the Korean Peninsula Y. Cho et al.
- Correlation-split and Recombination-sort Interaction Networks for air quality forecasting Y. Feng et al.
- Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model M. Chang et al.
16 citations as recorded by crossref.
- Evaluation of high‐resolution regional CO2 data assimilation–forecast system in East Asia using observing system simulation experiment and effect of observation network on simulated CO2 concentrations M. Seo & H. Kim
- Data assimilation experiments over Europe with the Chemical Transport Model FARM M. Adani & F. Uboldi
- 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.
- A data-assimilative approach to air quality analytics for policy and emergency planning E. Poudel et al.
- Mechanism-Driven vs. Data-Driven: A comparison of physics-informed bayesian estimation and deep learning models for EPB shield chamber pressure forecasting T. Yin et al.
- Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods U. Dash et al.
- A new decomposition-integrated air quality index prediction model X. Sun et al.
- Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions S. Li et al.
- Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability J. Yu et al.
- Deep Learning Emulator Towards Both Forward and Adjoint Modes of Atmospheric Gas-Phase Chemical Process Y. Liu et al.
- PM2.5 assimilation within JEDI for NOAA's regional Air Quality Model (AQMv7): application to the September 2020 Western US wildfires H. Wang et al.
- Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting M. Pang et al.
- Non-uniform downscaling data assimilation algorithm in variational framework Y. Zhao et al.
- Effects of chemical and meteorological data assimilation on air-quality and meteorological forecasts in the Korean Peninsula Y. Cho et al.
- Correlation-split and Recombination-sort Interaction Networks for air quality forecasting Y. Feng et al.
- Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model M. Chang et al.
Saved (final revised paper)
Latest update: 11 May 2026
Short summary
An EnKF was applied to CMAQ for assimilating ground PM2.5 observations from China and South Korea. The EnKF performed better than that without assimilation and even superior to 3D-Var. The reduced MBs in 24 h predictions were 48 % and 27 % by improving ICs and BCs, respectively.
An EnKF was applied to CMAQ for assimilating ground PM2.5 observations from China and South...
