用深度学习从气象卫星数据中独立反演大气温湿廓线，无需依赖预报场。
Tropospheric temperature and humidity profile retrieval from Meteosat Flexible Combined Imager based on deep learning
- 基于16通道图像设计空间感知深度网络，融合多尺度空间信息
- 温度偏差低于0.4K，湿度标准差12-20%，云下性能下降有限
- 不依赖数值预报场，适合快速自主的大气监测应用
新一代静止气象卫星Meteosat Third Generation的灵活组合成像仪（FCI）在时空分辨率和光谱覆盖范围上均优于前代。传统宽带成像仪垂直反演存在挑战，现有业务算法通常依赖数值天气预报（NWP）背景场以弥补红外光谱分辨率不足，削弱了反演独立性。本文提出一种空间感知的深度学习框架，直接从FCI数据中反演全天空下的对流层温湿廓线，无需输入预报场。采用残差U-Net模型，利用全部16个通道的空间上下文，在欧洲地区基于14个月的同步FCI观测与CERRA再分析数据进行训练。验证结果表明，温度偏差低于0.4 K，标准差为1.5–1.9 K；湿度标准差为12–20%，略高于CERRA的9–19%。云下性能略有下降，但温度标准差增加不超过0.4 K，湿度增加低于3%相对湿度。消融实验显示空间上下文显著提升反演效果，尤其在云顶以下区域。特征敏感性分析表明模型响应与各波段辐射传输特性一致，可见光与近红外通道亦具贡献。结果证明，空间感知深度学习模型可从地球静止卫星图像中提取统计可靠的温湿廓线，摆脱对NWP的依赖，实现更快速、自主的大气监测。
原文摘要 · Abstract (English)
The Meteosat Third Generation (MTG) Flexible Combined Imager (FCI) offers new opportunities for tropospheric temperature and humidity profiling, at higher spatio-temporal resolutions and expanded spectral coverage relative to its predecessor. Vertically resolved retrievals from broadband imagers are inherently challenging, and operational retrieval algorithms typically rely on numerical weather prediction (NWP) background fields to compensate for limited infrared spectral resolution, reducing the retrievals' independence. We develop a spatially aware deep learning framework to retrieve all-sky tropospheric temperature and humidity profiles from FCI, without forecast profiles as input. A Residual U-Net that exploits spatial context across all 16 FCI channels was trained on 14 months of collocated FCI observations and CERRA reanalysis targets over Europe. Validated against independent radiosondes, retrieved temperatures show biases below 0.4 K and standard deviations of 1.5-1.9 K. Retrieved relative humidity standard deviations range from 12-20 %, compared to 9-19 % for CERRA. Performance degrades modestly under clouds, with standard deviation increases below 0.4 K and 3 % RH beneath cloud tops despite limited direct radiative information. Ablation experiments show that spatial context improves retrievals, with the largest gains below cloud tops. Feature sensitivity analysis indicates broad consistency with FCI bands' established radiative transfer characteristics. Visible and near-infrared channels contribute despite not being commonly used in physics-based profile inversions. These results demonstrate that spatially aware deep learning models can extract statistically reliable tropospheric profiles from geostationary imager observations, independent of NWP forecast fields, enabling more rapid autonomous monitoring of the atmosphere.
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