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 due to the enormous heat capacity and extensive global coverage of oceans. In this study, an SST fusion dataset from the Fengyun-3 satellite observations (FY-3_fusion_SST) in 2011–2019, is assessed in terms of its accuracy and the potential applications in climate operations. The findings of this research suggest that compared with the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA_SST) data, the accuracy of daily FY-3_fusion_SST data is 0.03℃±0.55℃, the accuracy of monthly FY-3_fusion_SST data is 0.02℃±0.20℃. Spatially, the FY-3_fusion_SST correspond well with the OSTIA_SST in mid-latitude regions and the tropical central-eastern Pacific region, while remarkable differences can be found in the tropical regions of the Atlantic and Indian Oceans and the western Pacific warm pool. In addition, the FY-3_fusion_SST and the OSTIA_SST data also varies in different seasons. The Niño 3.4 index calculated by the FY-3_fusion_SST data can effectively characterize the onset, end and intensity of El Niño-Southern Oscillation cycles, although it is generally warmer than that calculated by the Optimal Interpolation SST data. In the tropical regions of the Atlantic and Indian Oceans, the SST indexes derived from the FY-3_fusion_SST data exhibit large biases, which are even comparable to the amplitudes of the SST indexes in some months. These larger SST biases may be due to the influences of strong convective clouds and weak precipitation in tropical areas. In addition, these larger biases may be related to the definitions of the SST indexes. When the areas defined by the indexes are small, the indexes are more prone to large deviations, limiting the application of the FY-3_fusion_SST dataset in climate monitoring and prediction operations.