Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2471-2024
https://doi.org/10.5194/gmd-17-2471-2024
Model evaluation paper
 | 
02 Apr 2024
Model evaluation paper |  | 02 Apr 2024

Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China

Chao Gao, Xuelei Zhang, Aijun Xiu, Qingqing Tong, Hongmei Zhao, Shichun Zhang, Guangyi Yang, Mengduo Zhang, and Shengjin Xie

Data sets

FNL data used for producing meteorological ICs/BCs of WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1 C. Gao et al. https://doi.org/10.5281/zenodo.7925012

Chemical initial and boundary conditions for WRF-CMAQ C. Gao et al. https://doi.org/10.5281/zenodo.7932390

Chemical initial and boundary conditions for WRF-Chem C. Gao et al. https://doi.org/10.5281/zenodo.7932936

Chemical initial and boundary conditions for WRF-CHIMERE C. Gao et al. https://doi.org/10.5281/zenodo.7933641

Emission input data for WRF-CMAQ C. Gao et al. https://doi.org/10.5281/zenodo.7932430

Emission input data for WRF-Chem C. Gao et al. https://doi.org/10.5281/zenodo.7932734

Emission input data for WRF-CHMIERE C. Gao et al. https://doi.org/10.5281/zenodo.7931614

Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China" C. Gao et al. https://doi.org/10.5281/zenodo.7750907

Model code and software

.: Source codes of WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1 C. Gao et al. https://doi.org/10.5281/zenodo.7901682

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Short summary
A comprehensive comparison study is conducted targeting the performances of three two-way coupled meteorology and air quality models (WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) for eastern China during 2017. The impacts of aerosol–radiation–cloud interactions on these models’ results are evaluated against satellite and surface observations. Further improvements to the calculation of aerosol–cloud interactions in these models are crucial to ensure more accurate and timely air quality forecasts.