Abstract
Dust events significantly impact the Earth’s system, making accurate predictions crucial for minimizing their negative effects. This study evaluates the predictive capabilities of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) driven by the Climate Forecast System version 2 (CFSv2) dataset in simulating dust events. Three dust emission schemes in WRF-Chem were used to simulate a major dust event in China from March 20 to 24, 2023. The model successfully reproduced the occurrence and transmission of this event, along with variations in PM10 concentrations. Evaluation at urban sites showed that the GOCART and AFWA schemes demonstrated good simulation performance in both the dust source and affected regions. Additionally, the National Center for Environmental Prediction (NCEP) CFSv2 datasets were used to drive the WRF-Chem model for predicting a dust event in 2024. Model validation shows that different start times impact the accuracy of the dust event prediction in northern China, with the October 2023 experiment using the AFWA scheme yielding the best results, achieving an urban forecast accuracy greater than 69%. This study demonstrates the effectiveness of the CFSv2-driven WRF-Chem model in predicting major dust events months in advance, particularly when using the AFWA scheme. This predictive capability aids in the development of early warning systems for regions prone to dust storms, thereby reducing potential impacts on human health and the environment.
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