发表机构
Delft University of Technology; Bern University of Applied Sciences; Meteomatics AG（代尔夫特理工大学; 伯尔尼应用科学大学; 美迪奥马蒂克斯公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种空间感知深度学习框架，利用Meteosat灵活组合成像仪数据反演对流层温湿廓线，无需数值天气预报预报场，经探空数据验证性能良好，可支持快速大气自主监测。
AI 中文摘要
第三代Meteosat（MTG）灵活组合成像仪（FCI）相比前代产品，在时空分辨率和光谱覆盖范围上有所扩展，为对流层温湿廓线反演提供了新机遇。宽带成像仪的垂直分辨反演本就存在固有挑战，业务反演算法通常依赖数值天气预报（NWP）背景场来弥补红外光谱分辨率的不足，这降低了反演结果的独立性。我们开发了一种空间感知的深度学习框架，可从FCI观测中反演全天空条件下的对流层温湿廓线，无需将预报廓线作为输入。我们训练了一个残差U-Net模型，该模型利用FCI全部16个通道的空间上下文信息，训练数据为欧洲地区14个月的同步FCI观测数据与CERRA再分析目标数据。通过独立探空数据验证，反演得到的温度偏差低于0.4K，标准差为1.5-1.9K；反演得到的相对湿度标准差为12-20%，而CERRA的对应值为9-19%。在云区下性能略有下降，尽管直接辐射信息有限，云顶下方的标准差增幅仍低于0.4K和3%RH。消融实验表明，空间上下文可提升反演性能，云顶下方的提升最为显著。特征敏感性分析显示，该结果与FCI波段已确立的辐射传输特性大致一致；可见光和近红外通道虽在基于物理的廓线反演中不常使用，但仍有贡献。这些结果表明，空间感知深度学习模型可从静止轨道成像仪观测数据中提取统计可靠的对流层廓线，且不依赖NWP预报场，支持更快速的大气自主监测。
英文摘要
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.
Comments27 pages, 11 figures. Supporting information has 12 pages and 17 figures. Submitted for publication in Journal of Geophysical Research: Atmospheres
