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In this study, channel selections derived from threshold-based algorithms are embedded as feature-engineering priors into a 1D-CNN, and machine learning (ML) is used to learn latent physical relationships to simplify physical retrievals for operational deployment. The results show that the 1D-CNN achieves a multilayer-cloud probability of detection ($\\mathrm{POD}{\\mathrm{mul}}$) of 0.620 and a false alarm rate ($\\mathrm{FAR}{\\mathrm{mul}}$) of 0.240, outperforming the conventional threshold algorithm ($\\mathrm{POD}{\\mathrm{mul}} = 0.558$, $\\mathrm{FAR}{\\mathrm{mul}} = 0.369$). These results demonstrate that prior physical knowledge derived from radiative transfer theory can serve as an effective feature-engineering prior. Further experiments show that ML-revealed physical mechanisms can also enhance traditional algorithms. Replacing A","title":"Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites","url":"https://arxiv.org/abs/2607.16270","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.16270v1 Announce Type: cross \nAbstract: Multilayer cloud detection from active--passive observation is vital for numerical weather prediction. In this study, channel selections derived from threshold-based algorithms are embedded as feature-engineering priors into a 1D-CNN, and machine learning (ML) is used to learn latent physical relationships to simplify physical retrievals for operational deployment. The results show that the 1D-CNN achieves a multilayer-cloud probability of detection ($\\mathrm{POD}{\\mathrm{mul}}$) of 0.620 and a false alarm rate ($\\mathrm{FAR}{\\mathrm{mul}}$) of 0.240, outperforming the conventional threshold algorithm ($\\mathrm{POD}{\\mathrm{mul}} = 0.558$, $\\mathrm{FAR}{\\mathrm{mul}} = 0.369$). These results demonstrate that prior physical knowledge derived from radiative transfer theory can serve as an effective feature-engineering prior. Further experiments show that ML-revealed physical mechanisms can also enhance traditional algorithms. 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