Preserving linear invariants in ensemble filtering methods

arXiv:2404.14328v1 Announce Type: cross
Abstract: Formulating dynamical models for physical phenomena is essential for understanding the interplay between the different mechanisms and predicting the evolution of physical states. However, a dynamical model alone is often insufficient to address these fundamental tasks, as it suffers from model errors and uncertainties. One common remedy is to rely on data assimilation, where the state estimate is updated with observations of the true system. Ensemble filters sequentially assimilate observations by updating a set of …

abstract arxiv ensemble errors evolution filtering fundamental however linear physics.ao-ph physics.data-an stat.co stat.me stat.ml tasks type understanding