Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.699 IF 1.699
  • IF 5-year value: 1.559 IF 5-year
    1.559
  • CiteScore value: 1.61 CiteScore
    1.61
  • SNIP value: 0.884 SNIP 0.884
  • IPP value: 1.49 IPP 1.49
  • SJR value: 0.648 SJR 0.648
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 52 Scimago H
    index 52
  • h5-index value: 21 h5-index 21
Volume 22, issue 6
Nonlin. Processes Geophys., 22, 723–735, 2015
https://doi.org/10.5194/npg-22-723-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 22, 723–735, 2015
https://doi.org/10.5194/npg-22-723-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Dec 2015

Research article | 03 Dec 2015

Multivariate localization methods for ensemble Kalman filtering

S. Roh1, M. Jun1, I. Szunyogh2, and M. G. Genton3 S. Roh et al.
  • 1Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA
  • 2Department of Atmospheric Sciences, Texas A&M University, College Station, TX 77843-3148, USA
  • 3CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia

Abstract. 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 (element-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 that exist at the same locations 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.

Publications Copernicus
Download
Short summary
This paper shows how statistical methods can be applied to the ensemble Kalman filtering technique, a widely used method in atmospheric science and other related fields. Traditional methods for covariance localization, commonly done in the field with multiple variables, are compared to the newly proposed method with the use of a parametric localization function (proposed in statistics as a class of multivariate covariance functions) in the ensemble Kalman filter system with multiple variables.
This paper shows how statistical methods can be applied to the ensemble Kalman filtering...
Citation