A Bayesian Approach to Multivariate Adaptive Localization in Ensemble-Based Data Assimilation with Time-Dependent Extensions
Abstract
Abstract. 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 11, 2018
- Source ID
- 10.5194/npg-2018-45
Entities
People
- Adrian Sandu
- Andrey A. Popov
Organizations
- Air Force Office of Scientific Research
- Division of Advanced Cyberinfrastructure
- Division of Computing and Communication Foundations