Sea Surface Temperature (SST) products at NOAA are produced from multiple polar-orbiting and geostationary sensors using the Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise system. Data of several high-resolution (~1 km) sensors onboard US and EUMETSAT polar-orbiting platforms are processed, including two VIIRSs (onboard NPP and N20), three AVHRR FRAC (onboard Metop-A, -B, and -C), and two MODISs (onboard Terra and Aqua). L1b data of each platform/sensor are processed independently, and two SST products generated: swath (L2P) and 0.02° mapped equal-grid L3U. All L2P and L3U are consistently reported in 10-min granules in a Group for High-Resolution SST (GHRSST) Data Specification Version 2 (GDS2) format, and assimilated in several gridded gap-free L4 analyses, which reconcile data from individual platforms, sensors and overpasses, and fill in cloud obscured regions by optimal interpolation. The L4 feature resolution is degraded, compared to input L2P/3U, with no measure provided of this degradation or identification which grids contain clear-sky observations versus those created by estimation (modeling). There is currently no global SST product based on real observations from all available platforms/sensors, without modeled data. As a result, users either have to rely on L4 products (without knowing what data come from real observations versus those modeled, and how much the satellite data have been smoothed), or deal with huge and ever growing data volumes from multiple L2P/3U data files, and learn how to fuse/aggregate those, themselves. In response to multiple users’ requests, NOAA started developing a new multi-sensor, high-resolution, sensor-agnostic gridded L3 SST product, with no modeled data added, which maximally preserves the original sensors’ resolution. In creating such collated and super-collated (L3C/S) products, several issues must be addressed, including minimizing the effect of residual cloud leakages, which are always present in the L2P/3U data, on the L3C/S product, while maximally preserving the feature-resolution present in the original satellite imagery. This aspect of data fusion is the focus of this study.
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