Quality Assessment of FY-4A/AGRI Official Sea Surface Temperature Product

Sea surface temperature (SST) is an important variable in climate and weather research. We utilize two SST datasets to analyze the performance of the Fengyun-4A (FY-4A)/Advanced Geostationary Radiation Imager (AGRI) SST product retrieved from the nonlinear split-window algorithm. Validation results with the in situ SST Quality monitor (iQuam) (version 2.1) show that the overall bias and root mean square error (RMSE) are -0.38 and 1.04 K, respectively, meeting the demand for numerical weather prediction. There is a good agreement between the AGRI SST and the Advanced Himawari Imager (AHI) SST (version 2.0), with an overall bias of 0.09 and an RMSE of 0.95 K. The significant accuracy decreases in AGRI SST since August 2020 could be attributed to an updated operation calibration. This letter will benefit the scientific disciplines that require an SST as input by highlighting the accuracy and uncertainty of the AGRI SST product.