World Library  


Add to Book Shelf
Flag as Inappropriate
Email this Book

Multivariate Localization Methods for Ensemble Kalman Filtering : Volume 2, Issue 3 (08/05/2015)

By Roh, S.

Click here to view

Book Id: WPLBN0004020152
Format Type: PDF Article :
File Size: Pages 31
Reproduction Date: 2015

Title: Multivariate Localization Methods for Ensemble Kalman Filtering : Volume 2, Issue 3 (08/05/2015)  
Author: Roh, S.
Volume: Vol. 2, Issue 3
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Szunyogh, I., Genton, M. G., Jun, M., & Roh, S. (2015). Multivariate Localization Methods for Ensemble Kalman Filtering : Volume 2, Issue 3 (08/05/2015). Retrieved from http://hawaiilibrary.net/


Description
Description: Department of Statistics, Texas A&M University, College Station, TX, USA. In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

Summary
Multivariate localization methods for ensemble Kalman filtering

Excerpt
Anderson, J. L.: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111, 2007.; Anderson, J. and Lei, L.: Empirical localization of observation impact in ensemble kalman filters, Mon. Weather Rev., 142, 739–754, 2013.; Askey, R.: Radial Characteristic Functions, technical report no. 1262, Mathematical Research Center, University of Wisconsin-Madison, Madison, 1973.; Bishop, C. H. and Hodyss, D.: Flow adaptive moderation of spurious ensemble correlations and its use in ensemble based data assimilation, Q. J. Roy. Meteor. Soc., 133, 2029–2044, 2007.; Bishop, C. H. and Hodyss, D.: Ensemble covariances adaptively localized with ECO}-RAP. {Part 1: Tests on simple error models, Tellus A, 61, 84–96, 2009a.; Bishop, C. H. and Hodyss, D.: Ensemble covariances adaptively localized with ECO}-RAP. Part 2: {A strategy for the atmosphere, Tellus A, 61, 97–111, 2009b.; Buehner, M. and Charron, M.: Spectral and spatial localization of background-error correlations for data assimilation, Q. J. Roy. Meteor. Soc., 133, 615–630, 2007.; Campbell, W. F., Bishop, C. H., and Hodyss, D.: Vertical covariance localization for satellite radiances in ensemble kalman filters, Mon. Weather Rev., 138, 282–290, 2010.; Du, J. and Ma, C.: Vector random fields with compactly supported covariance matrix functions, J. Stat. Plan. Infer., 143, 457–467, 2013.; Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999.; Genton, M. G. and Kleiber, W.: Cross-covariance functions for multivariate geostatistics, Statist. Sci., in press, 2015.; Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev., 129, 2776–2790, 2001.; Houtekamer, P. L. and Mitchell, H. L.: Data assimilation using an ensemble Kalman filter technique, Mon. Weather Rev., 126, 796–811, 1998.; Houtekamer, P. L. and Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123–137, 2001.; Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter, Physica D, 230, 112–126, 2007.; Jun, M., Szunyogh, I., Genton, M. G., Zhang, F., and Bishop, C. H.: A statistical investigation of the sensitivity of ensemble-based Kalman filters to covariance filtering, Mon. Weather Rev., 139, 3036–3051, 2011.; Kang, J.-S., Kalnay, E., Liu, J., Fung, I., Miyoshi, T., and Ide, K.: Variable localization in an ensemble Kalman filter: application to the carbon cycle data assimilation, J. Geophys. Res., 116, D09110, doi:10.1029/2010JD014673, 2011.; Kleiber, W. and Porcu, E.: Nonstationary Matrix Covariances: Compact Support, Long Range Dependence and Quasi-Arithmetic Constructions, Stochast. Environ. Res. Risk Assess., 29, 193–204, 2015.; Lei, L. and Anderson, J.: Comparison of empirical localization techniques for serial ensemble kalman filters in a simple atmospheric general circulation model, Mon. Weather Rev., 141, 4140–4153, 2014.; Lorenz, E. N.: Predictability – A problem partly solved. Proc. seminar on predictability, Reading, United Kindom, European Center for Medium-Range Weather Forecast., 1–18, 1996; Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corazza, M., Kalnay, E., Patil, D. J., and Yorke, J. A.: A local ensemble Kalman filter for atmospheric data assimilation, Tellus A, 56, 415–428, 2004.; Porcu, E., Daley, D. J., Buhmann, M., and Bevilacqua, M.: Radial basis functions with compact support for multivariate geostatistics, Stoch. Env. Res. Risk A., 27, 909–922, 2012.; Szunyogh, I.: Applicable Atmospheric Dynamics: Techniques for the Exploration of Atmospheric Dynamics, World Scientific, New Jersey, 2014.; Whitaker, J. S. and Hamill, T. M.

 

Click To View

Additional Books


  • Lagrangian Transport in a Circular Lake:... (by )
  • Nonlinear Instability of Baroclinic Atmo... (by )
  • Assimilation of Earth Rotation Parameter... (by )
  • Wavefield Decomposition and Phase Space ... (by )
  • Plankton Blooms in Vortices: the Role of... (by )
  • A Viscoelastic Rivlin-ericksen Material ... (by )
  • Vlasov Simulation of Langmuir Wave Packe... (by )
  • Analysis and Simulation of Bgk Electron ... (by )
  • Representing Model Error in Ensemble Dat... (by )
  • Influence of the Nonlinearity on Statist... (by )
  • A Novel Method for Analyzing the Process... (by )
  • Developing a Dynamically Based Assimilat... (by )
Scroll Left
Scroll Right

 



Copyright © World Library Foundation. All rights reserved. eBooks from Hawaii eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.