Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/86572
Citations
Scopus Web of ScienceĀ® Altmetric
?
?
Type: Journal article
Title: Nonglobal parameter estimation using local ensemble Kalman filtering
Author: Bellsky, T.
Berwald, J.
Mitchell, L.
Citation: Monthly Weather Review, 2014; 142(6):2150-2164
Publisher: American Meteorological Society
Issue Date: 2014
ISSN: 0027-0644
1520-0493
Statement of
Responsibility: 
Thomas Bellsky, Jesse Berwald, Lewis Mitchell
Abstract: The authors study parameter estimation for nonglobal 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, they 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 a numerical test bed, the authors show that this parameter estimation methodology accurately estimates parameters that vary in both space and time, as well as parameters representing physics absent from the model.
Keywords: Nonlinear dynamics; Kalman filters; Data assimilation; Parameterization
Rights: Copyright status unknown
DOI: 10.1175/MWR-D-13-00200.1
Published version: http://dx.doi.org/10.1175/MWR-D-13-00200.1
Appears in Collections:Aurora harvest 2
Mathematical Sciences publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.