发表机构
CIRA; ECE（大气研究合作研究所; 电气与计算机工程）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对被动卫星成像仪，开发C3DIR深度学习模型估计三维云属性。采用体素级搭配方法，经定性和定量评估，其在水凝物检测等方面表现出色，虽有不足，但证明有潜力为多领域提供云结构三维输出。
AI 中文摘要
我们开发了云三维成像仪反演（C3DIR），这是一种深度学习模型，用于为多个被动卫星成像仪估计三维云属性，其经过训练以匹配来自地球云气溶胶和辐射探测器（EarthCARE）ACM-CAP产品的反演结果。这项工作旨在使人工智能/机器学习三维云算法更接近实际应用。C3DIR预测沿成像仪视线方向的冰、云水和雨的出现含水量，并使用体素级搭配方法来考虑被动成像仪和主动剖面仪器的观测几何不匹配。定性案例研究表明，C3DIR能准确描绘多个不同的重叠云层，不过有一些平滑处理。定量评估表明，C3DIR在水凝物检测方面总体表现出色，水含量估计较为准确，在冰云中效果最佳，但液体和雨水含量仍存在不确定性。柱积分水路径与EarthCARE的一致性更强。与当前美国国家海洋和大气管理局（NOAA）业务产品所基于的算法比较，凸显了C3DIR可改进之处。总体而言，这些结果证明了C3DIR提供灵活三维输出描绘垂直分辨云结构的潜力，可为航空应用、数值天气预报和气候研究提供更广泛的用途。
英文摘要
We develop the Cloud 3-Dimensional Imager Retrieval (C3DIR), a deep learning model that estimates 3-D cloud properties for multiple passive satellite imagers trained to match retrievals from the Earth Cloud Aerosol and Radiation Explorer(EarthCARE) ACM-CAP product. This work is aimed towards moving AI/ML 3-D cloud algorithms closer towards operational use. C3DIR predicts the occurrence water content of ice, cloud liquid, and rain along the imager line-of-sight and uses a voxel-level collocation approach to account for the misaligned viewing geometries of passive imagers and active profiling instruments. This precise collocation methodology allows for constructing vertical profiles using voxels contained by multiple imager pixels to facilitate comparisons with active profiling instruments. Qualitative case studies show that C3DIR can accurately depict multiple distinct overlapping cloud layers, albeit with some smoothing. Quantitative evaluations illustrate that C3DIR overall excels at hydrometeor detection which intuitively tends to be a function of water content. However, detection of voxels classified as liquid cloud remains difficult due to the their small geometric thickness, finer horizontal scale, and frequent tendency to be obscured or embedded within ice clouds. In general, water content estimation is reasonably accurate, yielding the best results in ice clouds but uncertainties remain for liquid and rain water content. Column-integrated water paths are in tighter agreement with EarthCARE. Comparisons with the algorithms underpinning current NOAA operational products highlight several areas where C3DIR may offer improvement. Overall, these results demonstrate the potential for C3DIR to provide flexible 3-D output depicting vertically resolved cloud structure which can offer broader utility for aviation applications, numerical weather modeling, and climate research.
