Impact of Assimilating GEMS Aerosol Optical Depth on Asian Dust Storm Prediction: Comparative Assessment with MODIS Observation

Abstract

Asian dust storms (ADSs), originating from the Gobi and Taklamakan deserts, have widespread impacts on air quality, climate, ecosystems, and public health across East Asia due to the large-scale aerosol transport. Accurate prediction of ADS is essential for developing effective mitigation strategies and reducing their public health and ecological repercussions. We investigated the impact of assimilating aerosol optical depth (AOD) from the Geostationary Environment Monitoring Spectrometer (GEMS) on predicting ADS and made a comparative assessment with Moderate Resolution Imaging Spectroradiometer (MODIS) AOD assimilation, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) interfaced with the Maximum Likelihood Ensemble Filter (MLEF). The ADS event, occurred from 10 to 14 April 2023, was selected for the prediction and assimilation experiments. Our results indicate that the AOD assimilation generally improves the AOD forecast fields, with a high temporal resolution (three times a day) of GEMS AOD leading to better performance than once-a-day MODIS AOD. Although more frequent assimilation of GEMS AOD did not result in the lowest mean bias (MB) or root mean square error (RMSE) in PM₁₀ validation, it still outperformed assimilation of once-a-day GEMS AOD. This highlights the importance of frequent assimilation, using GEMS AOD, for PM₁₀ simulations. These findings underscore the significance of observation frequency in improving ADS prediction and emphasize the critical role of geostationary satellite observations in regional prediction.