Improving the condition number of estimated covariance matrices

Tabeart, J. M., Dance, S. L. ORCID: https://orcid.org/0000-0003-1690-3338, Lawless, A. S. ORCID: https://orcid.org/0000-0002-3016-6568, Nichols, N. K. ORCID: https://orcid.org/0000-0003-1133-5220 and Waller, J. A. (2020) Improving the condition number of estimated covariance matrices. Tellus A: Dynamic Meteorology & Oceanography, 72 (1). pp. 1-19. ISSN 1600-0870 doi: 10.1080/16000870.2019.1696646

Abstract/Summary

High dimensional error covariance matrices and their inverses are used to weight the contribution of observation and background information in data assimilation procedures. As observation error covariance matrices are often obtained by sampling methods, estimates are often degenerate or ill-conditioned, making it impossible to invert an observation error covariance matrix without the use of techniques to reduce its condition number. In this paper we present new theory for two existing methods that can be used to ‘recondition’ any covariance matrix: ridge regression, and the minimum eigenvalue method. We compare these methods with multiplicative variance inflation, which cannot alter the condition number of a matrix, but is often used to account for neglected correlation information. We investigate the impact of reconditioning on variances and correlations of a general covariance matrix in both a theoretical and practical setting. Improved theoretical understanding provides guidance to users regarding method selection, and choice of target condition number. The new theory shows that, for the same target condition number, both methods increase variances compared to the original matrix, with larger increases for ridge regression than the minimum eigenvalue method. We prove that the ridge regression method strictly decreases the absolute value of off-diagonal correlations. Theoretical comparison of the impact of reconditioning and multiplicative variance inflation on the data assimilation objective function shows that variance inflation alters information across all scales uniformly, whereas reconditioning has a larger effect on scales corresponding to smaller eigenvalues. We then consider two examples: a general correlation function, and an observation error covariance matrix arising from interchannel correlations. The minimum eigenvalue method results in smaller overall changes to the correlation matrix than ridge regression, but can increase off-diagonal correlations. Data assimilation experiments reveal that reconditioning corrects spurious noise in the analysis but underestimates the true signal compared to multiplicative variance inflation.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/86703
Identification Number/DOI	10.1080/16000870.2019.1696646
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Taylor & Francis
Download/View statistics	View download statistics for this item