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Validated satellite-derived sea surface temperatures (SSTs) are widely used for climate monitoring and ocean data assimilation systems. In this study, the Fengyun-3A (FY-3A) SST experimental product is evaluated using Advanced Very High Resolution Radiometer (AVHRR)-merged and in situ SSTs. A comparison of AVHRR-merged SSTs reveals a negative bias of more than 2K in FY-3A SSTs in most of the tropical Pacific and low-latitude Indian and Atlantic Oceans. The error variance of FY-3A SSTs is estimated using three-way error analysis. FY-3A SSTs show regional error variance in global oceans with a maximum error variance of 2.2 K in the Pacific Ocean. In addition, a significant seasonal variation of error variance is present in FY-3A SSTs, which indicates that the quality of FY-3A SST could be improved by adjusting the parameters in the SST retrieval algorithm and by applying regional and seasonal algorithms, particularly in key areas such as the tropical Pacific Ocean. An objective analysis method is used to merge FY-3A SSTs with the drifter buoy data. The errors of FY-3A SSTs are decreased to-0.45K comparing with SST observations from GTSPP.
Validated satellite-derived sea surface temperatures (SSTs) are widely used for climate monitoring and ocean data assimilation systems. In this study, the Fengyun-3A (FY-3A) SST experimental product is evaluated using Advanced Very High Resolution Radiometer (AVHRR) - merged and in situ SSTs. A comparison of AVHRR-merged SSTs reveals a negative bias of more than 2K in FY-3A SSTs in the most of the tropical Pacific and low-latitude Indian and Atlantic Oceans. The error variance of FY-3A SSTs is estimated using three-way error analysis. FY-3A SSTs show regional error variance in global oceans with a maximum error variance of 2.2 K in the Pacific Ocean. In addition, a significant seasonal variation of error variance is present in FY-3A SSTs, which indicates that the quality of FY-3A SST could be improved by adjusting the parameters in the SST retrieval algorithm and by applying regional and seasonal algorithms, particularly in key areas such as the tropical Pacific Ocean. An objective analysis The methods of used to merge FY-3A SSTs with the drifter buoy data. The errors of FY-3A SSTs are decreased to-0.45K comparing with SST observations from GTSPP.