Fengyun-4A (FY-4A), the first of the Chinese next-generation geostationary meteorological satellites, launched in 2016, offers several advances over the FY-2: more spectral bands, faster imaging, and infrared hyperspectral measurements. To support the major objective of developing the prototypes of FY-4 science algorithms, two science product algorithm testbeds for imagers and sounders have been developed by the scientists in the FY-4 Algorithm Working Group (AWG). Both testbeds, written in FORTRAN and C programming languages for Linux or UNIX systems, have been tested successfully by using Intel/g compilers. Some important FY-4 science products, including cloud mask, cloud properties, and temperature profiles, have been retrieved successfully through using a proxy imager, Himawari-8/Advanced Himawari Imager (AHI), and sounder data, obtained from the Atmospheric InfraRed Sounder, thus demonstrating their robustness. In addition, in early 2016, the FY-4 AWG was developed based on the imager testbed—a near real-time processing system for Himawari-8/AHI data for use by Chinese weather forecasters. Consequently, robust and flexible science product algorithm testbeds have provided essential and productive tools for popularizing FY-4 data and developing substantial improvements in FY-4 products.

Random forests (RFs), an advanced machine learning (ML) method, was used here to develop a robust and rapid quantitative precipitation estimates (QPEs) algorithm for the new-generation geostationary satellite of Himawari-8. In this algorithm, the global precipitation measurement (GPM) product has been employed to train QPE prediction model. The real-time multiband infrared brightness temperature from Himawari-8, combined with the spatiotemporally matched numerical weather prediction (NWP) data from the global forecast system, have been used as predictor variables for QPE. Among the variables used in RF learning model, total precipitable water and K-index from NWP data have the highest rankings, indicating the importance of atmospheric environment for QPE. To enhance the accuracy of RF models or to optimize model training, a sample-balance technique has been utilized to adjust the ratios of samples in nonprecipitation/precipitation classification and quantitative precipitation regression data sets. Further sensitivity and validation analyses help determine the optimal RF classification and regression models for predicting nonprecipitation/precipitation pixel and rain rate. The selected RF classification model is found to predict precipitation area with an accuracy of 0.87. For predicted QPE product, the mean-absolute-error and root-mean-square error of RF regression model are 0.51 and 2.0 mm/h, respectively. Overall, the RF ML algorithm has a higher detection rate over homogenous ocean surface as compared with over land. Meanwhile, this RF algorithm tends to underestimate rain rate, especially in the presence of heavy rainfall. Despite this, it still produces a reasonable pattern of rainfall area and intensity, which are highly consistent with GPM observations.

In this paper, we developed a unified and operational cloud mask algorithm for the new-generation geostationary (GEO) meteorological satellite imagers of the Advanced Geostationary Radiation Imager (AGRI) aboard Fengyun-4A (FY-4A) and the Advanced Himawari Imager (AHI) aboard Himawari-8 (H08). We investigated the all-round performance of the cloud mask algorithm. Spatiotemporally, the algorithm matches the official Collection-6 cloud mask products of a Moderate Resolution Imaging Spectroradiometer (MODIS) from both the Terra and Aqua platforms, which we employed as the benchmark for performing intercomparisons and validations. The robust cloud mask algorithm can show high consistency between FY-4A/AGRI and H08/AHI. The MODIS-based validation results suggest that cloudy scene identification is better than that observed for clear skies for both FY-4A/AGRI and H08/AHI; there is also a relatively low false-alarm ratio (FAR). Moreover, the algorithm is more reliable during daytime hours, with a hit rate (HR) of approximately 92% for both FY-4A/AGRI and H08/AHI. We found slightly higher accuracy in cloud-masking results over water than those over land. Furthermore, we found that more than 67% of the matched pixels for both advanced GEO imagers had no bias when taking MODIS as the benchmark. Overall, HR values were approximately 91.04% and 91.82% for FY-4A/AGRI and H08/AHI, respectively. These results confirm the high quality of the algorithm for retrieving real-time cloud mask products.

The knowledge of mesoscale convective system (MCS) in the Asian monsoon region remains still deficient due to the limited available data and less powerful algorithms. Here, using the data from Advanced Himawari Imager onboard Himawari‐8 (HW8), an improved algorithm combining the area overlapping with the Kalman filter is developed, which captures much smaller MCSs that are unavailable otherwise. Several influential factors like the overlapping rate and splitting/merging in the area overlapping method, and the initial state variable in the Kalman filter method, all of which were less appreciated, are handled explicitly. The occurrence frequency, and moving trajectory of two types of MCS, including the ordinary MCS and superconvective system, has been comprehensively examined in the Asian monsoon region for the warm season (April to September) of 2016. Comparison analyses with ground precipitation and radar measurements confirm the good performance of our algorithm. In particular, the moving direction of MCS strongly depends on latitudes, so does the horizontal velocity. Compared with over ocean, the frequency of MCSs dominates over land or along coasts in the tropics, where strong moisture flux convergence is frequently observed in the low troposphere. In addition, the MCSs detected in eastern China can roughly capture the meridional propagation over time, which corresponds well to the precipitation belts linked to Meiyu front systems. The superconvective systems dominate over the Bay of Bengal and South China Sea due to the large‐scale circulation. Our findings provide new insights to spatiotemporal patterns of MCSs during warm season in the Asian monsoon region.