Variational particle smoothers and their localization
Abstract
Given the success of 4D‐variational methods (4D‐Var) in numerical weather prediction, and recent efforts to merge ensemble Kalman filters with 4D‐Var, we revisit how one can use importance sampling and particle filtering ideas within a 4D‐Var framework. This leads us to variational particle smoothers (varPS) and we study how weight‐localization can prevent the collapse of varPS in high‐dimensional problems. We also discuss the relevance of (localized) weights in near‐Gaussian problems. We test our ideas on the Lorenz'96 model of dimensions n = 40, n = 400, and n = 2,000. In our numerical experiments the localized varPS does not collapse and yields results comparable to ensemble formulations of 4D‐Var, while tuned EnKFs and the local particle filter lead to larger estimation errors. Additional numerical experiments suggest that using localized weights may not yield significant advantages over unweighted or linearized solutions in near‐Gaussian problems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 01, 2018
- Source ID
- 10.1002/qj.3256
Entities
People
- Daniel Hodyss
- Jonathan Poterjoy
- M. Morzfeld
Organizations
- Alfred P. Sloan Foundation
- National Research Council
- National Science Foundation
- Office of Naval Research Global
- United States Naval Research Laboratory
- University of Arizona