###### Abstract
Fengyun-4 (FY-4) introduced a new generation of Chinese GEO meteorological satellites, with the first FY-4A launched in December 2016. The satellite's highly improved Advanced Geosynchronous Radiation Imager (AGRI) has capabilities comparable to those of state-of-the-art imagers onboard the new generation GEO meteorological satellites such as Advanced Baseline Imager (ABI) on board the U.S. GEO Operational Environmental Satellite (GOES-R) [28] and Japan's Advanced Himawari Imager (AHI) on board Himawari-8 [29]. Combining much higher radiation, spectrum, and spatial resolution with shortened revisit time than its predecessor, FY-4A/AGRI can provide a new level of ability to identify and track rapidly changing weather phenomena and derive quantitative products [30; 31]. FY-4 will become an important component of the space-based Global Observing System (GOS) [32]. To promote better applications and popularization of FY-4 series GEO satellite data, the Fengyun GEO Algorithm Testbed (FYGAT) was developed by scientists in the FY-4 Algorithm Working Group (AWG) of the National Satellite Meteorological Center of the China Meteorological Administration (NSMC/CMA). FYGAT is a near real-time processing system that can process four types of GEO imaging sensor data including FY-4A/AGRI, Himawari-8/AHI, Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI), and FY-2/Visible Infrared Spin Scan Radiometer (VISSR) for weather and climate research and applications [30; 31]. This system has the ability to produce a variety of operational quantitative products including pixel-level cloud products such as cloud mask and cloud top height. These cloud products are not only critical for weather services and nowcasting applications, but are also used in the retrieval of other key products such as \"clear sky radiation (CSR)\" and \"atmospheric motion vector (AMV)\" for numerical weather forecasting. In addition to severe weather monitoring,forecasting, and NWP services [30], these science products generated by FYGAT are also used in the research of cloud climatology and cloud and precipitation microphysics [33; 34]. Both weather forecast and climatology research require an understanding of the uncertainties of cloud records [35]. In order to recognize the uncertainties and limitations of the AHI, AGRI, and their cloud products based on the FYGAT algorithm in different geographical contexts, comparison and verification studies are needed. Some quantitative evaluations have been conducted. [PERSON] et al. [31] employed MODIS as the benchmark for AGRI and AHI cloud mask validation during the 21-day period. Their results suggest that they had high consistency, and cloudy scene identification was better than that observed for clear skies for both AGRI and AHI. [PERSON] et al. [36] compared the cloud properties from AGRI with MODIS cloud retrievals from four months. Their comparison indicates that the cloud mask and cloud phase of these two instruments were consistent, while clear differences were noticed for cloud optical thickness and cloud effective radius results. [PERSON] et al. [37] used CALIPSO to verify the AHI cloud top height for one year. Their results indicated that samples with cloud top height higher than 12.0 km were significantly underestimated, while the average underestimation of high layer clouds was about 5 km. [PERSON] et al. [38] compared the AGRI cloud fraction and cloud top pressure products with MODIS over East Asia for a month. The results showed that the two products from AGRI and MODIS generally agreed well. From these verifications, it can be seen that cloud products based on the FYGAT algorithm generally had good performance, and were consistent with the cloud products retrieved from MODIS and CALIPSO. Although these comparisons have provided important overall insight, they have been limited by the lack of specific evaluation of the cloud products' performance in the TP region. [PERSON] et al.