The initial release of near real-time (NRT) atmospheric and oceanic science products from Japanese Himawari-8/9 (H8/9) geostationary (GEO) satellites over the South China Sea (SCS) was unveiled in 2024. The primary objective behind crafting these NRT H8/9 satellite products is to facilitate weather and marine environment monitoring, enhance maritime security, and aid ocean navigation, among other purposes. As part of this investigation, a novel NRT data processing system was devised to generate a variety of regional H8/9 GEO satellite science products within a temporal resolution of 10 minutes and a gridded resolution of 0.05° × 0.05° from November 3, 2022 to the present. This algorithm system was built upon the preceding FengYun (FY) geostationary satellite algorithm testbed (FYGAT), which was the prototype of FY-4 GEO meteorological satellite science product operational processing system. These regional H8/9 GEO satellite science products encompass a range of crucial data such as cloud mask, fraction, height, phase, optical and microphysical properties, layered precipitable water, sea surface temperature, etc. We subjected these products to rigorous evaluations against high-quality analogous satellite products and reanalysis data spanning four months in 2023. The validations underscore a strong consistency between the H8/9 GEO satellite atmospheric and oceanic science products over the SCS and the referenced products. Nevertheless, slight discrepancies in these satellite science products were identified, primarily stemming from variations in sensor/dataset characteristics, retrieval algorithms, and geometric conditions. These outcomes demonstrate the suitability of the first edition of NRT atmospheric and oceanic science products of H8/9 satellites over the SCS in supporting the intended quantitative applications. This NRT GEO satellite data record is publicly accessible through the File Transfer Protocol (FTP) provided by the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) in China. Free access to the dataset can be found at https://doi.org/10.6084/m9.figshare.25015853 (Liu, 2024).
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