Validation and Application Potential of the Sea Surface Temperature Fusion Data from Reprocessed Fengyun-3 Infrared and Microwave Products

Global sea surface temperature (SST) is a crucial climate element owing to its significant heat capacity and extensive global ocean coverage. This study evaluates an SST fusion dataset derived from Fengyun-3 satellite observations (FY-3_fusion_SST; 2011–2019), with emphasis on its accuracy and potential applications in climate monitoring and operational services. Validation against the operational SST and sea ice analysis (OSTIA_SST) dataset revealed that the accuracy of daily FY-3_fusion_SST data was 0.03±0.55 ℃, while that of monthly FY-3_fusion_SST data was 0.02±0.20 ℃. Spatially, FY-3_fusion_SST corresponded well with OSTIA_SST in mid-latitude regions and the tropical central-eastern Pacific region, whereas significant differences were observed in the tropical regions of the Atlantic and Indian Oceans and the western Pacific warm pool. In addition, FY-3_fusion_SST and OSTIA_SST data varied in different seasons. The Niño 3.4 index calculated using the FY-3_fusion_SST data effectively characterizes the onset, termination, and intensity of El Niño–Southern Oscillation cycles, although it exhibits a systematic warm bias relative to the index calculated from the Optimal Interpolation SST data. In the tropical regions of the Atlantic and Indian Oceans, the SST indices derived from the FY-3_fusion_SST data exhibited large biases that were comparable to the amplitudes of the SST indices for certain months. These larger SST biases may be due to the influence of strong convective clouds and weak precipitation in tropical regions. In addition, these larger biases may be related to the definitions of the SST indices. When the areas defined by the indices are small, the indices are more prone to large deviations, limiting the application of the FY-3_fusion_SST dataset in climate monitoring and prediction operations.