Articles | Volume 18, issue 7
https://doi.org/10.5194/gmd-18-2303-2025
https://doi.org/10.5194/gmd-18-2303-2025
Model evaluation paper
 | 
14 Apr 2025
Model evaluation paper |  | 14 Apr 2025

Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem

Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park

Data sets

Model code and various datasets for evaluation Ji Won Yoon https://doi.org/10.5281/zenodo.11649488

NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999 NCEP https://doi.org/10.5065/D6M043C6

The Chemistry Mechanism in the Community Earth System Model Version 2 (CESM2) (https://www.acom.ucar.edu/cam-chem/cam-chem.shtml) L. K. Emmons et al. https://doi.org/10.1029/2019MS001882

AERONET - A federated instrument network and data archive for aerosol characterization (https://ladsweb.modaps.eosdis.nasa.gov/search) B. N. Holben et al. https://doi.org/10.1016/S0034-4257(98)00031-5

Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer (https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_aod.html) Y. J. Kaufman et al. https://doi.org/10.1029/96jd03988

The modern-era retrospective analysis for research and applications, version 2 (MERRA-2) (https://disc.gsfc.nasa.gov/datasets?project=MERRA-2) R. Gelaro et al. https://doi.org/10.1175/JCLI-D-16-0758.1

Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products (https://asdc.larc.nasa.gov/project/CALIPSO?level=2) M. A. Vaughan et al. https://doi.org/10.1117/12.572024

Model code and software

mozbc utility NCAR/ACOM https://www.acom.ucar.edu/wrf-chem/download.shtml

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Short summary
This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
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