RISDA: Reliability-Informed Score-Based Data Assimilation of Multi-Source Atmospheric Observations

Score-based atmospheric data assimilation separates a learned atmospheric prior from observation guidance, making it naturally flexible to changing observations. However, this flexibility breaks down for real-world heterogeneous multi-source observations, where the main bottleneck shifts to the guidance itself: different observations cannot always be directly matched to atmospheric states, and the estimated guidance can vary greatly in reliability across sources, regions, and latent channels. Therefore, we introduce RISDA (Reliability-Informed Score-based Data Assimilation), a framework designed for the assimilation of real-world multi-source observations. First, an atmospheric encoder and source-specific observation encoders map atmospheric states and heterogeneous observations into a shared latent space, bridging variable mismatches and enabling direct guidance for posterior sampling. Second, empirical diagnostics of latent errors motivate Variance-Calibrated Latent Guidance (VCLG), which constructs more accurate observation guidance by accounting for channel-specific uncertainties within the shared latent space. Third, to reasonably combine guidance from multiple sources during diffusion, we introduce Priority-Preserving Gradient Fusion (PPGF), which merges source-specific gradients according to reliability-informed priorities, ensuring that guidance from more reliable sources is preserved during sampling. Assimilation experiments incorporating five types of satellite observations with the surface observation demonstrate that RISDA achieves superior analysis accuracy and higher inference efficiency compared to baseline methods.