A Bayesian Approach to Multivariate Adaptive Localization in Ensemble-Based Data Assimilation with Time-Dependent Extensions
This work addresses the need for adaptive localization in ensemble data assimilation, offering a principled Bayesian approach that improves filter accuracy for geophysical models.
The authors propose a Bayesian method for adaptive Schur-product localization in the Deterministic Ensemble Kalman Filter (DEnKF), extending it to multivariate and time-dependent settings. They demonstrate improved filter performance on Lorenz'96 and a 1.5-layer Quasi-Geostrophic model.
Ever since its inception, the Ensemble Kalman Filter has elicited many heuristic methods that sought to correct it. One such method is localization---the thought that `nearby' variables should be highly correlated with `far away' variable not. Recognizing that correlation is a time-dependent property, adaptive localization is a natural extension to these heuristics. We propose a Bayesian approach to adaptive Schur-product localization for the DEnKF, and extend it to support multiple radii of influence. We test both the empirical validity of (multivariate) adaptive localization, and of our approach. We test a simple toy problem (Lorenz'96), extending it to a multivariate model, and a more realistic geophysical problem (1.5 Layer Quasi-Geostrophic). We show that the multivariate approach has great promise on the toy problem, and that the univariate approach leads to improved filter performance for the realistic geophysical problem.