AI 中文总结
该研究提出基于潜在孪生方法的深度学习架构，平衡模型复杂度等并量化数据质量，将其应用于IASI光谱反演大气廓线，经合成数据和真实IASI观测验证了方法的有效性。
AI 中文摘要
近年来，数据驱动方法作为传统基于物理的反演方法的替代方案出现，利用了可学习伪逆、随机森林或深度学习架构等机器学习技术。经典数据驱动模型对样本外区域的泛化能力较差，因为它们在有限数据集上优化，未纳入潜在物理定律，这往往需要大型模型和海量数据才能达到可靠性。物理信息神经网络通过在学习过程中嵌入物理约束来解决这一问题，实现了更好的外推能力，但由于需要在每个训练步骤求解控制方程，其计算成本很高。本研究引入了一种基于潜在孪生方法的新型深度学习架构，该架构平衡了模型复杂度、数据集规模和训练成本，同时提供了数据质量的量化衡量。该架构应用于IASI光谱，目标是评估该方法在真实晴空条件下反演大气廓线的鲁棒性，包括温度、水汽、臭氧、表面发射率和表面温度。该算法首先应用于由NWP SAF数据库通过快速辐射传输代码sigma-IASI/F2N生成的合成辐射数据。在合成数据上验证该架构后，将算法应用于IASI Level 1C观测数据，同时利用其对应的Level 2产品作为参考，评估基于自动编码器的反演的重建精度。本文讨论了反演性能，以及为重建的热力学廓线提供误差分析的可能策略。
英文摘要
In recent years, data-driven approaches emerged as alternatives to traditional physics-based retrievals, taking advantage of machine learning techniques such as learnable pseudoinverse, random forests, or deep learning architectures. Classical data-driven models generalize poorly to out-of-sample regimes, as they optimize over finite datasets without incorporating underlying physical laws. This often requires large models and extensive data to achieve reliability. Physics-Informed Neural Networks address this by embedding physical constraints into the learning process, enabling improved extrapolation. However, they requires substantial computational cost due to the need to solve governing equations at each training step. In this work, we introduce a novel deep learning architecture, based on latent twin approach, that balances model complexity, dataset size, and training cost, while providing a quantitative measure of data quality. This architecture is applied to IASI spectra, with the goal to assess the robustness of this method for retrieving atmospheric profiles, including temperature, water vapor, ozone, surface emissivity, and surface temperature, in real-world clear-sky conditions. The algorithm is first applied on synthetic radiances derived from the NWP SAF database using the fast radiative transfer code sigma-IASI/F2N. After validating the architecture on synthetic data, the algorithm is applied to IASI Level 1C observations, along with their corresponding Level 2 products which serve as reference to evaluate the reconstruction accuracy of the autoencoder-based retrieval. The retrieval performances are discussed along with possible strategies to provide an error analysis for the reconstructed thermodynamical profiles.
Comments11 pages
