AI 中文总结
针对CO₂反演中高分辨率辐射传输计算成本高的问题，欧盟SCARBOn项目团队提出基于MLP的高效辐射传输代理模型，结合NanoCarb仪器响应构建正演模型，为CO₂浓度反演提供了新方案。
AI 中文摘要
研究气候变化需要降低CO₂和CH₄排放估算的不确定性，以更好地区分人为源与自然源，这推动了具有更高重访频率和空间覆盖的星载测量技术发展。在此背景下，欧盟地平线计划SCARBOn项目评估了一个低成本卫星星座，其核心传感器为NanoCarb成像干涉仪，用于监测大气中的CO₂和CH₄排放。然而，以高重访频率和空间覆盖估算CO₂和CH₄浓度面临重大挑战：常用的全物理反演算法依赖重复的高分辨率辐射传输（RT）模拟，使用逐线RT模型时计算成本极高。作为替代方案，本研究提出一种前馈多层感知器（MLP）代理模型，旨在准确高效地预测CO₂弱带的大气层顶辐射，采用结合辐射和RT雅可比矩阵的平均绝对误差（MAE）损失函数，以同时保持光谱精度和对地球物理参数的敏感性。将基于MLP的RT代理模型与NanoCarb仪器响应耦合，得到用于NanoCarb测量的高效精确正演模型，该模型在CO₂浓度反演中展现出良好的应用前景。
英文摘要
Studying climate change requires reducing uncertainties in CO2 and CH4 emission estimates to better distinguish anthropogenic from natural sources, which motivates spaceborne measurements with improved revisit frequency and spatial coverage. In this context, the Horizon Europe SCARBOn project assesses a low-cost satellite constellation featuring the NanoCarb imaging interferometer as its core sensor for monitoring CO2 and CH4 emissions in the atmosphere. However, estimating CO2 and CH4 concentrations with high revisit and spatial coverage poses significant challenges: full-physics retrieval algorithms commonly used rely on repeated high-resolution radiative transfer (RT) simulations, which are computationally expensive when using line-by-line RT models. As an alternative, we propose in this study a feedforward multilayer perceptron (MLP) surrogate designed to accurately and efficiently predict top-of-atmosphere radiances in the CO2 weak band, using a combined mean absolute error (MAE) loss on radiances and RT Jacobians to preserve both spectral accuracy and sensitivity to geophysical parameters. Coupling the MLP-based RT surrogate with the NanoCarb instrumental response yields an efficient and precise forward model for NanoCarb measurements, which shows promising results for CO2 concentration retrieval.
