Journal article
Improving dust aerosol simulation over northern China: Synergy of updated numerical models and machine learning post-processing
Atmospheric environment (1994), Vol.380, 122098
09/2026
DOI: 10.1016/j.atmosenv.2026.122098
Abstract
Frequent dust events in China pose significant challenges to the accurate numerical simulation of dust aerosols. Here, we present a novel framework that synergizes an updated physical parameterization in the Unified Inputs for WRF-Chem (UI-WRF-Chem) model with an ensemble machine learning (ML) post-processor, focusing on Shaanxi Province, a typical Northwest region often affected by dust storms. The key improvement involves a dynamically updated erodibility dataset derived from multi-source satellite observations, thereby better capturing the spatiotemporal heterogeneity of dust sources on a monthly scale. The numerical model provides 5-km resolution dust simulations, which are subsequently bias-corrected using an ensemble ML approach combining extreme gradient Boost (XGBoost), random forest (RF), and neural Net-enhanced LightGBM (NetGBM). All ML models are trained using ground-based dust concentrations calibrated against PM10 observations during spring (March-May) from 2018 to 2022, and evaluated with independent data in spring 2023 and PM10 as the assessment indicator. Results show that the ML post-processor provides more accurate PM10 simulations than the numerical model across the spring months, increasing correlation coefficient (R) by 50∼52% and reducing Normalized Standard Deviation (NSD) and Centered Root Mean Square Error (CRMSE) by 58∼78% and 48∼66%, respectively. The post-processing performance is region-dependent, substantial in northern Shaanxi but limited in the south due to lower dust contribution and uncalibrated non-dust components. Additionally, the ensemble ML model mitigates overestimation of dust-related PM10 by the UI-WRF-Chem model during dust episodes, and better captures dust transport processes. This study demonstrates that a robust framework for synergizing numerical modeling with ML approach can substantially enhance dust simulation accuracy in regions influenced by complex dust dynamics.
Details
- Title: Subtitle
- Improving dust aerosol simulation over northern China: Synergy of updated numerical models and machine learning post-processing
- Creators
- Tong Sha - China Meteorological AdministrationLiangqing Li - China Meteorological AdministrationZipeng Dong - China Meteorological AdministrationQingcai Chen - Shaanxi University of Science and TechnologyShuqi Yan - Jiangsu Institute of Meteorological SciencesHuanxin Zhang - University of IowaJhoon Kim - Yonsei UniversityJun Wang - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Atmospheric environment (1994), Vol.380, 122098
- DOI
- 10.1016/j.atmosenv.2026.122098
- ISSN
- 1352-2310
- Publisher
- Elsevier
- Language
- English
- Date published
- 09/2026
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9985164634702771
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