### Advanced Geosynchronous Radiation Imager (AGRI)  
AGRI is one of the most important payloads on the FY-4A GEO meteorological satellite platform. It measures radiances at 14 spectral bands covering the range of wavelengths from 0.47 to 13.5\(\upmu\)m. These spectral band locations are very similar to those planned for other greatly improved imagers on board the new generation of GEO meteorological satellites such as AHI on Himawari-8 [29], ABI on GOES-R [28], and the European MTG Flexible Combined Imager (FCI). The spatial resolution of AGRI at the sub-satellite point is 1 km, 2 km, and 4 km in the visible (VIS), near infrared (NIR), and other IR spectral bands, respectively [32]. FY-4A offers full-disk Earth-view coverage every 15 min with the option for more rapid regional and mesoscale observation modes. The information from AGRI will be used for many applications related to cloud and aerosol properties, severe weather, tropical cyclones and typhoons, aviation, natural hazards, land and ocean surfaces, etc. AGRI will offer the same products and information for applications over China and its adjacent regions [30; 32]. Enhanced calibration accuracy of FY-4A is better than 1 K for the thermal infrared bands and 5% for the reflective solar bands, which can greatly benefit quantitative applications [32].  
AGRI scientific products are generated by the product generation system (PGS) of NSMC/CMA based on FYGAT algorithms. FYGAT algorithms are partially inherited from the FY-2/VISSR algorithm and partially referenced from the GOES-R/ABI algorithm [30]. Operational cloud mask algorithm for AGRI also classifies each pixel as being either clear, probably clear, probably cloudy, or cloudy. This cloud mask algorithm combines 13 spectral and spatial uniformity tests, and two restore tests to produce a cloud mask product for every pixel of data. The restore tests refer to tests that \"restore\" probably cloudy pixels to clear pixels and \"restore\" cloudy pixels to probably cloudy pixels. The purpose of restore tests is to provide a conservative estimate on cloudiness. Specifically, the spectral tests include six infrared independent tests, three solar reflectance tests, two shortwave infrared tests, a reflectance uniformity test, and a thermal uniformity test. These tests have different thresholds over land, ocean, and snow/ice. Some of these thresholds are derived from the statistical analysis from the comparisons with collocated CALIPSO products, and some from simulated data [31].  
The cloud top pressure retrieval algorithm for AGRI combines two IR window band (11 and 12 \(\upmu\)m) observations with a single CO\({}_{2}\) absorption band (13.5 \(\upmu\)m) observation to estimate cloud height without large assumptions on cloud microphysics. This is the well-known CO\({}_{2}\)/Split-Window algorithm, originally designed for ABI flown on the GOES-R series [44; 47]. The split-window approach is based on two window band (11 and 12 \(\upmu\)m) observations. It can provide accurate measurements of cloud emissivity and its spectral variation, but lacks sensitivity to cloud height for optically thin cirrus [47; 64]. However, the CO\({}_{2}\)/Split-Window method benefits from combining the sensitivity to cloud height offered by a CO\({}_{2}\) channel with the sensitivity cloud microphysics offered by IR window channels [44]. First, the retrieval methodology employed the optimal estimation approach (also referred to as a 1 DVAR approach) to retrieve cloud top temperature product at the pixel level; once the cloud top temperature is computed, the Numerical Weather Prediction temperature profiles are used to interpolate the values of cloud top pressure, and cloud top height. For pixels determined to be multi-layer clouds, information about the height of the surrounding low clouds is used to estimate the height of low clouds underneath higher clouds [37]. Table 1 listed the key bands used for cloud mask and cloud top height in MODIS and AGRI.