用神经网络加速二氧化碳反演，提升卫星监测效率。
Efficient Neural-Network-Based High-Resolution Radiative Transfer for CO___ Retrieval, and Application to Interferometric Sensing
- 构建多层感知机替代高精度辐射传输模型，实现快速计算。
- 在纳米碳干涉仪数据上验证，反演误差低于0.5%。
- 适合需要高频次、大范围温室气体监测的卫星项目使用。
研究气候变化需降低二氧化碳和甲烷排放估算的不确定性，以更好区分人为与自然源。为此，欧盟地平线计划下的SCARBOn项目评估了一种低成本卫星星座，其核心传感器为NanoCarb成像干涉仪，用于监测大气中二氧化碳和甲烷排放。然而，高重访率和空间覆盖率下的浓度反演面临挑战：传统全物理反演算法依赖重复的高分辨率辐射传输（RT）模拟，使用逐线计算模型时计算成本极高。本文提出一种前馈多层感知机（MLP）代理模型，通过联合使用辐射亮度和辐射传输雅可比矩阵的平均绝对误差损失函数，精准高效地预测二氧化碳弱带的星顶辐射。将该MLP代理模型与NanoCarb仪器响应结合，形成高效精确的前向模型，对二氧化碳浓度反演表现优异，具备实际应用潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。