A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

The paper correctly states the EnSF construction, including the forward/reverse SDEs, the α,β parametrization, the prior-score integral identity, the self-normalized Monte Carlo estimator, and the damped posterior-score update, and validates the approach empirically at scale; however, it does not provide a formal convergence theorem or consistency proof. The model solution sketches plausible consistency and convergence arguments (ULLN for the score estimator, a small-noise bridging control, and a pathwise KL bound), but key steps are only outlined and rely on unproven approximations (e.g., the O(βt) bridging mismatch) and unstated regularity conditions. Hence, both are incomplete: the paper on theory, and the model on rigor.