Abstract 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.

中文翻译：

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使用下一代地球静止气象卫星风云四号A数据进行气溶胶数据同化

摘要 风云四号A（FY-4A）气象卫星是下一代地球静止气象卫星，于2016年12月11日发射。例如，风云四号A（AGRI/FY-4A）上的先进地球同步辐射成像仪（AGRI） ) 在 14 个光谱带中以 15 分钟的间隔拍摄全盘图像，分辨率为 0.5-4 公里。在这里，我们开发了基于网格点统计插值（GSI）系统的数据同化系统，其中首次成功同化了来自 FY-4A 数据的气溶胶光学深度（AOD）。然后通过 2019 年 5 月 12 日至 14 日期间东亚上空的沙尘暴评估了以每小时循环配置吸收 FY-4A 气溶胶光学深度 (AOD) 的能力。该分析初始化了天气研究和预测化学 (WRF-Chem) 模型预测。该系统在实验中使用 FY-4 AOD、Himawari-8 AOD 进行测试，然后将结果与气溶胶机器人网络 (AERONET) AOD 观测进行比较，作为独立观测。结果表明，同化 FY-4 AOD 与观察结果的一致性比来自对照的要好得多。此外，偏差和均方根误差在预测范围内普遍降低了约 20%。本研究表明，利用风云四号A数据进行气溶胶数据同化，可以提高预报模型的性能。结果表明，同化 FY-4 AOD 与观察结果的一致性比来自对照的要好得多。此外，偏差和均方根误差在预测范围内普遍降低了约 20%。本研究表明，利用风云四号A数据进行气溶胶数据同化，可以提高预报模型的性能。结果表明，同化 FY-4 AOD 与观察结果的一致性比来自对照的要好得多。此外，偏差和均方根误差在预测范围内普遍降低了约 20%。本研究表明，利用风云四号A数据进行气溶胶数据同化，可以提高预报模型的性能。