用随机掩码和不确定性估计，恢复遥感数据中的精细气象结构
CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction
- 通过渐进式掩码训练视觉变压器，重建缺失的遥感数据
- 可生成像素级不确定度图，同时还原上升/下沉气流等细粒度特征
- 适合气象预报、气候分析与实时数据同化场景
多普勒激光雷达、雷达和辐射计等遥感仪器获取的大气廓线常因信噪比低、距离折叠和虚假间断而受损。传统插值会模糊细尺度结构，深度模型则缺乏置信度估计。本文提出CuMoLoS-MAE，一种基于课程引导的蒙特卡洛随机集成掩码自编码器，旨在（i）恢复上升/下沉气流核心、切变线和小涡旋等细尺度特征，（ii）学习大气场的数据驱动先验，（iii）量化像素级不确定性。训练时采用掩码比例课程策略，迫使ViT解码器从逐步稀疏的上下文重建；推理时通过多次随机掩码采样，对MAE进行蒙特卡洛评估，聚合输出以获得后验预测均值及高分辨率像素级不确定性图。该方法实现高保真重建，支持对流诊断、实时数据同化与长期气候再分析。
原文摘要 · Abstract (English)
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.
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