Full-window branch discovery and loss-selected EnKF continuation for data assimilation

Angxiu Ni

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arXiv · 2609.10949

Full-window branch discovery and loss-selected EnKF continuation for data assimilation

Angxiu Ni

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Abstract

We develop a framework for offline full-window branch discovery, optionally followed by online continuation with an ensemble Kalman filter (EnKF). Three mechanisms drive the branch search: adjoint path-kernel (APK) differentiation balances kernel differentiation and correction-stabilized path perturbation, shifting the optimization from exploration to exploitation; an optimized Gaussian initial law broadens the search over initial-state basins; and loss-weighted mixing across independent runs recombines successful path components. We may then select an interior state using a local loss and continue online with an EnKF. In 40-dimensional Lorenz-96 experiments, the mean offline path RMSE of APK is 4.3 times smaller than that of population weak-$\mathrm{4D\text{-}Var}_x$. The resulting APK-EnKF method has a mean online RMSE 64 times smaller than that of ordinary EnKF.

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Angxiu Ni. 2026-09-10. Full-window branch discovery and loss-selected EnKF continuation for data assimilation. https://arxiv.org/abs/2609.10949

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