A cloud optical and microphysical property product for the advanced geosynchronous radiation imager onboard China's Fengyun-4 satellites: The first version

Abstract

Fengyun-4 (FY-4), the latest collection of Chinese geostationary meteorological satellites, monitors the Eastern Hemisphere with high spatiotemporal resolutions. This study developed a cloud optical and microphysical property product for the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4 satellites. The product focuses on cloud optical thickness (COT) and cloud effective radius (CER) using a bi-spectral retrieval algorithm, and also includes cloud mask and phase using machine learning (ML) algorithms as prerequisites for COT and CER retrievals. The ML-based algorithm develops four independent models using Random Forest methods for cloud mask, liquid water, ice, and mixed-phase/multi-layer clouds, respectively. A two-habit ice and sphere water cloud model are employed to give their optical properties. Look-up tables of cloud reflectance in the COT and CER sensitive channels are built for efficient forward simulations, and the retrieval is performed by an optimal estimation algorithm. Compared with collocated active observations, the cloud mask and phase results give true positive rates of ∼95% and ∼85% and are more sensitive to mixed-phase clouds. Meanwhile, the AGRI-based COT and CER agree closely with those given by the collocated MODIS and AHI cloud products, and the correlation coefficients between MODIS and the AGRI results are 0.76 and 0.63 for COT and CER, respectively. The COT and CER retrievals will be persistently maintained and improved as the operational product for FY-4/AGRI.摘要风云四号作为中国新一代静止气象卫星, 提供了高时空分辨率的监测产品. 本文介绍风云四号搭载的先进地球同步轨道辐射成像仪AGRI的云光学和微物理特性产品. 该产品包含了基于双光谱通道反演的云光学厚度和云粒子有效半径产品, 以及基于机器学习的云识别和云相态产品. 与时空匹配的主动卫星观测结果对比显示, 该产品的云识别和云相态的准确率分别在95%和85%; 该产品提供的云光学厚度和云有效粒径与经典的MODIS产品的相关系数达到0.76和0.63. 团队将持续优化和更新该云光学和微物理特性定量产品, 服务风云四号卫星定量应用.