Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 7
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.
https://doi.org/10.5194/gmd-15-2773-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 7
Development and technical paper
 | 
06 Apr 2022
Development and technical paper |  | 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, Uzzal Kumar Dash, Jinhyeok Yu, Keiya Yumimoto, Itsushi Uno, and Chul Han Song

Supplement

https://doi.org/10.5194/gmd-15-2773-2022-supplement

Data sets

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: Model Simulation Outputs Soon-Young Park https://doi.org/10.5281/zenodo.5566441

Model code and software

WRF Model Users' Page WRF Users Page https://doi.org/10.5065/D6MK6B4K

CMAQv5.1 (5.1) US EPA Office of Research and Development https://doi.org/10.5281/zenodo.1079909

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: Data Assimilation Codes Soon-Young Park https://doi.org/10.5281/zenodo.5376214

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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.
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