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Estimation of sea surface temperature (SST) is one of the key products generated from the new generation of geostationary (GEO) sensors such as Advanced Baseline Imager (ABI), onboard the NOAA Geostationary Operational Environmental Satellite “R” series (GOES-R; including G16/17/18), and Advanced Himawari Imager (AHI) onboard Himawari-8 and -9 (H08/09). NOAA generates a consistent line of SST products from these GEO platforms using Non-Linear SST (NLSST) retrieval algorithms implemented in its enterprise Advanced Clear Sky Processor for Ocean (ACPSO) system. The ACSPO NLSST algorithm performs well overall but shows occasional unstable performance under some selected atmospheric conditions, cross-platform biases between G16, G17 and H08 SSTs in the corresponding overlap zones, and increased noise in SST imagery. These issues proved not easy to address in the current complex regression formulation, with many regressors and high degree of multicollinearity among them. This paper performs additional analyses of the ACSPO GEO NLSST algorithm and evaluates contributions of the available IR bands as well as the information content of various predictors. The new NLSST formulation is simpler and more stable and interpretable for understanding and mitigation of the observed SST anomalies. It provides performance comparable with the current more complex ACSPO formulation, and improves SST imagery
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