Cloud Property Retrieval and Uncertainty Estimation from FY-4B AGRI Using Conditional Diffusion

Generative models are increasingly used for quantitative remote-sensing retrievals, yet the physical interpretability and reliability of their ensemble-based uncertainty estimates remain insufficiently assessed. We introduce RTMDiff, a retrieval framework that couples a conditional diffusion model with radiative transfer model (RTM) simulations to retrieve cloud properties and associated uncertainties from multi-channel thermal infrared (TIR) observations of FY-4B AGRI, enabling consistent day–night retrievals. RTMDiff is evaluated against a Bayesian optimal-estimation (OE) baseline using the same forward RTM, showing that the diffusion-based ensembles yield stable uncertainty estimates while preserving physical consistency. Comparisons with independent MODIS and CALIPSO products further support the realism of the retrieved cloud fields, with particularly clear improvements for low-level, optically thick clouds where pixel-wise OE is constrained by limited spectral sensitivity in TIR.