Journal article
A statistical investigation of the sensitivity of ensemble-based kalman filters to covariance filtering
M Jun, I Szunyogh, MG Genton, F Zhang, CH Bishop
Monthly Weather Review | AMER METEOROLOGICAL SOC | Published : 2011
Abstract
This paper investigates the effects of spatial filtering on the ensemble-based estimate of the background error covariance matrix in an ensemble-based Kalman filter (EnKF). In particular, a novel kernel smoothing method with variable bandwidth is introduced and its performance is compared to that of the widely used Gaspari-Cohn filter, which uses a fifth-order kernel function with a fixed localization length. Numerical experiments are carried out with the 40-variable Lorenz-96 model. The results of the experiments show that the nonparametric approach provides a more accurate estimate of the background error covariance matrix than the Gaspari-Cohn filter with any localization length. It is al..
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Awarded by National Science Foundation
Awarded by NSF
Awarded by ONR
Funding Acknowledgements
Mikyoung Jun, Marc G. Genton, and Fuqing Zhang acknowledge the support from the National Science Foundation (ATM 0620624). Mikyoung Jun's research is also supported by NSF Grant DMS-0906532. Istvan Szunyogh acknowledges the support from NSF (ATM 0935538) and ONR (N000140910589). Marc Genton's research is supported by NSF DMS-1007504. Fuqing Zhang acknowledges the support from ONR Grant N000140410471. Craig H. Bishop acknowledges support from ONR Project Element 0602435N, Project Number BE-435-003. The authors are grateful to Herschel Mitchell (the Editor), Andrew Tangborn, and one anonymous reviewer for valuable comments.