FY-3C/VIRR Sea Surface Temperature Products and Quality Validation

Sea surface temperature (SST) products are generated at National Satellite Meteorological Center (NSMC) of China Meteorological Administration (CMA) from the visible and infrared radiometer (VIRR) on board FY-3C polar orbiting satellite. The production chain is based on FY-3C/VIRR cloud mask products and a classical multichannel SST (MCSST) algorithm is applied to derive SST in cloud-free zones. Operational MCSST procedures and products are described in detail. FY-3C/VIRR SST products are generated in satellite projection at full resolution in 5-minute granule, and in synthetic fields remapped onto a regular world grid at 0.05 degree resolution (5 km).The quality index (QI) information is delivered with each pixel to provide information about the conditions of the processing. They include in particular a quality level in the last two bits of QI (saved in a 8-bit CHAR) for each pixel defined as follows: Excellent, good, bad and unprocessed (cloud, land, no satellite data etc.). Users can select the SST data with certain quality level according to their application purposes (e.g., for climate-related studies, only the SST data with excellent quality level in the time series are used, and for identifying and tracking specific ocean features, users may be more tolerant of lower-quality SST data). The matchup database (MDB) combining FY-3C/VIRR measurements and buoy measurements is built on a routine basis. Validation methods and results are described in detail. The performance of SST retrievals is characterized with bias and root mean square error (RMSE) with respect to Reynolds L4 daily analysis (OISST). The validation bias and RMSE for FY-3C/VIRR operational granule SST with excellent quality level between January 2015 and December 2019 is found to be -0.18℃ and 0.85℃ in day-time, -0.06℃ and 0.8℃ in night-time, respectively. For day-time, the RMSE fluctuates seasonally. Some monthly RMSE is greater than 1℃ in summer. The bias at night is found fluctuating seasonally highly correlated to the black body temperature on board FY-3C since January 2016, and the SST regression coefficient (SST_COEF_V3) is used ever since then. Causes of FY-3C/VIRR SST products anomaly is analyzed, such as L1 data abnormal (e.g., single event upset), navigation error and operational running environmental error. Above all, some important reference information are provided to users for using FY-3C/VIRR SST products and FY-3C/VIRR data re-geolocation, re-calibration and products reprocessing.