标题： CuMoLoS-MAE：用于遥感数据重建的掩码自编码器 显示英文标题

标题： CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction

摘要： 从多普勒激光雷达、雷达和辐射计等遥感仪器获得的准确大气剖面经常受到低信噪比（SNR）门、距离折叠和虚假不连续性的影响。传统的填补方法会模糊小尺度结构，而深度模型缺乏置信度估计。我们提出了CuMoLoS-MAE，这是一种课程引导的蒙特卡洛随机集合掩码自编码器，旨在（i）恢复小尺度特征，如上升气流和下降气流核心、剪切线和小涡旋，（ii）学习大气场的数据驱动先验，以及（iii）量化像素级不确定性。在训练过程中，CuMoLoS-MAE采用掩码比率课程，迫使ViT解码器从逐渐稀疏的上下文中进行重建。在推理时，我们通过对随机掩码实现进行蒙特卡洛近似来获得后验预测，多次评估MAE并聚合输出以获得后验预测均值重建以及精细分辨的每个像素不确定性图。结合高保真重建，这种新颖的基于深度学习的工作流程实现了增强的对流诊断，支持实时数据同化，并提高了长期气候再分析。 显示英文摘要

摘要： Accurate atmospheric profiles from remote sensing instruments such as Doppler Lidar, Radar, and radiometers are frequently corrupted by low-SNR (Signal to Noise Ratio) gates, range folding, and spurious discontinuities. Traditional gap filling blurs fine-scale structures, whereas deep models lack confidence estimates. We present CuMoLoS-MAE, a Curriculum-Guided Monte Carlo Stochastic Ensemble Masked Autoencoder designed to (i) restore fine-scale features such as updraft and downdraft cores, shear lines, and small vortices, (ii) learn a data-driven prior over atmospheric fields, and (iii) quantify pixel-wise uncertainty. During training, CuMoLoS-MAE employs a mask-ratio curriculum that forces a ViT decoder to reconstruct from progressively sparser context. At inference, we approximate the posterior predictive by Monte Carlo over random mask realisations, evaluating the MAE multiple times and aggregating the outputs to obtain the posterior predictive mean reconstruction together with a finely resolved per-pixel uncertainty map. Together with high-fidelity reconstruction, this novel deep learning-based workflow enables enhanced convection diagnostics, supports real-time data assimilation, and improves long-term climate reanalysis.