High-quality reconstruction of Aerosol Optical Depth (AOD) fields is critical for Atmosphere monitoring, yet current models remain constrained by the scarcity of complete training data and a lack of uncertainty quantification.To address these limitations, we propose AODDiff, a probabilistic reconstruction framework based on diffusion-based Bayesian inference. By leveraging the learned spatiotemporal probability distribution of the AOD field as a generative prior, this framework can be flexibly adapted to various reconstruction tasks without requiring task-specific retraining. We first introduce a corruption-aware training strategy to learns a spatiotemporal AOD prior solely from naturally incomplete data. Subsequently, we employ a decoupled annealing posterior sampling strategy that enables the more effective and integration of heterogeneous observations as constraints to guide the generation process. We validate the proposed framework through extensive experiments on Reanalysis data. Results across downscaling and inpainting tasks confirm the efficacy and robustness of AODDiff, specifically demonstrating its advantage in maintaining high spatial spectral fidelity. Furthermore, as a generative model, AODDiff inherently enables uncertainty quantification via multiple sampling, offering critical confidence metrics for downstream applications.

翻译：高质量的气溶胶光学厚度（AOD）场重建对于大气监测至关重要，然而现有模型仍受限于完整训练数据的稀缺以及不确定性量化能力的缺乏。为应对这些局限，本文提出AODDiff——一种基于扩散贝叶斯推断的概率重建框架。该框架通过利用学习到的AOD场时空概率分布作为生成先验，能够灵活适应多种重建任务，且无需针对特定任务进行重新训练。我们首先提出一种退化感知训练策略，使其仅从自然不完整数据中学习时空AOD先验。随后采用解耦退火后验采样策略，该策略能更有效地整合异构观测数据作为约束条件以指导生成过程。我们通过再分析数据上的大量实验验证了所提框架的有效性。在降尺度和修复任务中的实验结果表明，AODDiff具有卓越的效能与鲁棒性，特别在保持高空间频谱保真度方面展现出显著优势。此外，作为生成模型，AODDiff通过多重采样天然具备不确定性量化能力，为下游应用提供了关键的可信度度量指标。