用于地球静止卫星多层云检测的物理信息特征工程1D-CNN

中文摘要 AI 辅助

从主动-被动观测进行多层云检测对数值天气预报至关重要。本研究中，将基于阈值算法得出的通道选择作为特征工程先验嵌入1D-CNN，利用机器学习学习潜在物理关系以简化物理反演用于业务部署。结果表明1D-CNN的多层云检测概率（PODmul）为0.620，误报率（FARmul）为0.240，优于传统阈值算法。这表明辐射传输理论得出的先验物理知识可作为有效的特征工程先验。进一步实验表明机器学习揭示的物理机制也能增强传统算法。更换通道对检测概率有不同影响，除了光谱响应函数不匹配，轨道辐射稳定性也是因素。物理信息机器学习方法有望推动遥感人工智能发展，但业务转移时要考虑传感器特性。

英文摘要

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. Replacing AGRI channel 12 (C12, centered at $10.8~μ\mathrm{m}$) with channel 13 (C13, centered at $12.0~μ\mathrm{m}$) increased $\mathrm{POD}{\mathrm{mul}}$ from 0.558 to 0.609 without materially affecting $\mathrm{FAR}{\mathrm{mul}}$. However, for AHI, substituting the $11.2~μ\mathrm{m}$ channel with the $12.3~μ\mathrm{m}$ channel yielded negligible improvement. In addition to spectral response function (SRF) mismatches, a primary contributing factor is the channels' on-orbit radiometric stability. Hence, physics-informed machine-learning methods appear promising for advancing remote-sensing AI, while sensor-specific characteristics must be considered during operational transfer.

发表机构

• State Key Laboratory of Severe Weather Meteorological Science and Technology, CMA Earth System Modeling and Prediction Centre and Key Laboratory of Earth System Modeling and Prediction, China Meteorological Administration（国家 severe weather 气象科学与技术重点实验室，中国气象局地球系统模拟与预测中心及地球系统模拟与预测关键实验室，中国气象局）
• School of Computer Science and Engineering, Sichuan University of Science and Engineering（四川工程学院计算机科学与工程学院）
• School of Electronics and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）
• National Satellite Meteorological Center (NSMC) and the Innovation Center for FengYun Meteorological Satellite (FYSIC), China Meteorological Administration (CMA)（国家卫星气象中心（NSMC）和风云气象卫星创新中心（FYSIC），中国气象局）

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