Displacement Data Assimilation
Data Analysis, Statistics and Probability
2016-12-06 v1 Atmospheric and Oceanic Physics
Abstract
We show that modifying a Bayesian data assimilation scheme by incorporating kinematically-consistent displacement corrections produces a scheme that is demonstrably better at estimating partially observed state vectors in a setting where feature information important. While the displacement transformation is not tied to any particular assimilation scheme, here we implement it within an ensemble Kalman Filter and demonstrate its effectiveness in tracking stochastically perturbed vortices.
Keywords
Cite
@article{arxiv.1602.02209,
title = {Displacement Data Assimilation},
author = {W. Steven Rosenthal and Shankar C. Venkataramani and Arthur J. Mariano and Juan M. Restrepo},
journal= {arXiv preprint arXiv:1602.02209},
year = {2016}
}
Comments
26 Pages, 9 figures, 5 tables