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The Fengyun-4A (FY-4A) meteorological satellite, a next-generation geostationary meteorological satellite, was launched on December 11, 2016. For instance, the Advanced Geosynchronous Radiation Imager (AGRI) aboard FY-4A (AGRI/FY-4A) takes full-disk images at a 15-min interval in 14 spectral bands with the 0.5–4-km resolution. Here we developed data assimilation system based on the Gridpoint Statistical Interpolation (GSI) system in which the Aerosol Optical Depth (AOD) derived from FY-4A data were successfully assimilated for the first time. The capability to assimilate FY-4A Aerosol optical depth (AOD) with an hourly cycling configuration was then evaluated by a dust storm over East Asia during 12–14 May 2019. The analyses initialized Weather Research and Forecasting-Chemistry (WRF-Chem) model forecasts. The system is tested with FY-4 AOD, Himawari-8 AOD in experiments and then the results are compared to the Aerosol Robotic Network (AERONET) AOD observations, which serving as the independent observations. The results indicated that assimilating FY-4 AOD substantially showed much better agreement with observations than those from the control. Furthermore, the Bias and RMSE generally reduced about 20% with forecast range. This study indicates that the aerosol data assimilation using data from FY-4A can be used to improve the performance of forecast model. • It is the first attempt to assimilate FY-4A AOD with a rapid-update DA system. • It was investigated for the dust storm occurred over East Asia on 12–14 May 2019. • General positive impacts were achieved from assimilating high-frequency data. [ABSTRACT FROM AUTHOR]

Himawari-8, a next-generation geostationary meteorological satellite, is equipped with the Advanced Himawari Imager (AHI) that provides full-disk images of Earth every 10 min for 16 observation bands from visible to infrared. In this study, the capability to assimilate AHI Aerosol optical depth (AOD) has been developed within the Gridpoint Statistical Interpolation (GSI) system with an hourly cycling configuration and application to a dust storm over East Asia during 5–7 May 2017. Analyses were produced hourly, and 24 h forecasts were produced every 6 h within the Weather Research and Forecasting with Chemistry model. It was found that the mean bias and root-mean-square error (RMSE) after data assimilation is obviously reduced (by about 30%) when compared to the control experiment which didn't assimilate any observations during the dust storm period, which verified the positive effects of AOD data assimilation systems. In addition, aerosol analyses and forecasts with AOD data assimilation were substantially improved when compared to independent AOD observations from AERONET sites. Therefore, the AOD data assimilation system developed here could be used as a tool to generate better dust storm forecasts. • It is the first attempt to assimilate AHI AOD with a rapid-update DA system. • It was investigated for the dust storm occurred over East Asia on 5–7 May 2017. • General positive impacts were achieved from assimilating high-frequency data. [ABSTRACT FROM AUTHOR]