DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants
This study addresses the error accumulation problem in existing data assimilation filters, which cannot leverage new observations to correct historical states. To this end, we propose a unified framework integrating filtering and smoothing. Methodologically, efficient state estimation for high-dimensional non-Gaussian systems is achieved via windowed reverse sampling. Furthermore, we introduce a novel multi-task interpolator that assigns independent flow times, seamlessly incorporating forecasting into the assimilation cycle and enabling dynamic correction of historical states without requiring additional predictive models. The technical core relies on flow/diffusion generative models, inference-time guidance, and multi-task stochastic interpolation. Experimental results demonstrate that the proposed method significantly outperforms conventional baselines under nonlinear, sparse, and noisy observation conditions, effectively mitigating long-term error accumulation.