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 (EnKF) has elicited many heuristic approaches that sought to improve it. One such method is covariance localization, which alleviates spurious correlations due to finite ensemble sizes by using relevant spatial correlation information. Adaptive localization techniques account for how correlations change in time and space, in order to obtain improved covariance estimates. This work develops a Bayesian approach to adaptive Schur-product localization for the deterministic ensemble Kalman filter (DEnKF) and extends it to support multiple radii of influence. We test the proposed adaptive localization using the toy Lorenz'96 problem and a more realistic 1.5-layer quasi-geostrophic model. Results with the toy problem show that the multivariate approach informs us that strongly observed variables can tolerate larger localization radii. The univariate approach leads to markedly improved filter performance for the realistic geophysical model, with a reduction in error by as much as 33 %.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 14, 2019
- Source ID
- 10.5194/npg-26-109-2019
Entities
People
- Adrian Sandu
- Andrey A. Popov
Organizations
- Air Force Office of Scientific Research
- Division of Advanced Cyberinfrastructure
- Division of Computing and Communication Foundations