Abstract
The erodibility factor is an essential parameter in the WRF-Chem model that determines the amount of dust emission. However, current models that use erodibility data are assumed static and lack sufficient characterization of dust source regions' highly heterogeneous and dynamic nature. This results in significant errors and uncertainties in the dust simulation. To address this issue, we propose a new approach that involves developing a physically based erodibility dataset using soil moisture, vegetation coverage, soil texture, and land use data. This new dataset covers the entire globe with a 1 km resolution, which can represent dust sources in finer detail. The results of the dust simulations indicated that the new dataset considerably improved the model's overall performance. The evaluation results showed that the root-mean-square error (RMSE) of PM10 simulated with the new erodibility data was reduced by 32.4%, and the correlation coefficient (R) was increased by 82.4% compared to the default data. Additionally, the simulated spatial distribution of aerosol optical depth (AOD) is closer to that of the satellite AOD product. Our new data provide a more accurate description of the erodibility of dust source regions, refine the parameterization of dust emissions in the model, and ultimately improve dust prediction.
Published Version
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