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• W4387623915 abstract "Clouds play an important role in the Earth’s climate system; however, various observational methods describe clouds differently, leading to cloud products being described with different characteristics, and affecting our understanding of cloud effects. To address this problem, this study integrates different cloud products into the transfer-learning procedure of a deep learning model and determined the Cloud Effective Radius (CER), Cloud Optical Thickness (COT), and Cloud Top Height (CTH) from Himawari-8 thermal infrared measurements. The retrieval results were independently evaluated against the Moderate-resolution Imaging Spectroradiometer cloud products and further compared with Himawari-8 cloud products during the day. The Root Mean Squared Errors (RMSE) of the model for the CER, COT, and CTH were 4.490 μm, 11.198, and 1.904 km, respectively, which are lower than those of Himawari-8 cloud products (RmSe:11.172 μm, 14.755, and 2.860 km). Moreover, validation results against active sensors show that the model performs slightly better during the day than at night, and both are generally better than the Himawari-8 cloud product. Overall, the model maintains stable performance during both day and night, and its accuracy is higher than that of Himawari-8 cloud products." @default.
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• W4387623915 date "2023-01-01" @default.
• W4387623915 modified "2023-10-15" @default.
• W4387623915 title "Transfer-learning-based approach to retrieve the cloud properties using diverse remote sensing datasets" @default.
• W4387623915 doi "https://doi.org/10.1109/tgrs.2023.3318374" @default.
• W4387623915 hasPublicationYear "2023" @default.
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