Non-global parameter estimation using local ensemble Kalman filtering
Data Analysis, Statistics and Probability
2014-09-03 v2 Chaotic Dynamics
Abstract
We study parameter estimation for non-global parameters in a low-dimensional chaotic model using the local ensemble transform Kalman filter (LETKF). By modifying existing techniques for using observational data to estimate global parameters, we present a methodology whereby spatially-varying parameters can be estimated using observations only within a localized region of space. Taking a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics as our numerical testbed, we show that this parameter estimation methodology accurately estimates parameters which vary in both space and time, as well as parameters representing physics absent from the model.
Cite
@article{arxiv.1306.3488,
title = {Non-global parameter estimation using local ensemble Kalman filtering},
author = {Thomas Bellsky and Jesse Berwald and Lewis Mitchell},
journal= {arXiv preprint arXiv:1306.3488},
year = {2014}
}