Abstract.

Motivated by the spurious variance loss encountered during covariance propagation in atmospheric and other large-scale data assimilation systems, we consider the problem for state dynamics governed by the continuity and related hyperbolic partial differential equations. This loss of variance has been attributed to reduced-rank representations of the covariance matrix, as in ensemble methods for example, or else to the use of dissipative numerical methods. Through a combination of analytical work and numerical experiments, we demonstrate that significant variance loss, as well as gain, typically occurs during covariance propagation, even at full rank. The cause of this unusual behavior is a discontinuous change in the continuum covariance dynamics as correlation lengths become small, for instance in the vicinity of sharp gradients in the velocity field. This discontinuity in the covariance dynamics arises from hyperbolicity: the diagonal of the kernel of the covariance operator is a characteristic surface for advective dynamics. Our numerical experiments demonstrate that standard numerical methods for evolving the state are not adequate for propagating the covariance, because they do not capture the discontinuity in the continuum covariance dynamics as correlations lengths tend to zero. Our analytical and numerical results show that this leads to significant, spurious variance loss in certain regions and gain in others. The results suggest that developing local covariance propagation methods designed specifically to capture covariance evolution near the diagonal may prove a useful alternative to current methods of covariance propagation.

Keywords

  1. covariance propagation
  2. variance loss
  3. data assimilation
  4. advective systems

MSC codes

  1. 35L65
  2. 47B38
  3. 65M22
  4. 86A10
  5. 65M75
  6. 65M32
  7. 65M80

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Information & Authors

Information

Published In

cover image SIAM/ASA Journal on Uncertainty Quantification
SIAM/ASA Journal on Uncertainty Quantification
Pages: 886 - 914
ISSN (online): 2166-2525

History

Submitted: 26 August 2021
Accepted: 24 February 2022
Published online: 5 August 2022

Keywords

  1. covariance propagation
  2. variance loss
  3. data assimilation
  4. advective systems

MSC codes

  1. 35L65
  2. 47B38
  3. 65M22
  4. 86A10
  5. 65M75
  6. 65M32
  7. 65M80

Authors

Affiliations

Shay Gilpin Contact the author
Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO 80303-0429 USA ([email protected]).
Tomoko Matsuo
Ann and H. J. Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO 80303-0429 USA ([email protected]).
Stephen E. Cohn
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA ([email protected]).

Funding Information

This work was supported by the National Science Foundation (NSF) Graduate Research Fellowship Program under grant DGE-1650115. The work of the second author was supported by the NSF CAREER Program under grant AGS-1848544. The work of the third author was supported by the National Aeronautics and Space Administration (NASA) under Modeling, Analysis and Prediction (MAP) program Core funding to the Global Modeling and Assimilation Office (GMAO) at Goddard Space Flight Center (GSFC). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF or of NASA.

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