Interpolating irregularly spaced observations for filtering turbulent complex systems

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

We present a numerically fast reduced filtering strategy, the Fourier domain Kalman filter with appropriate interpolations to account for irregularly spaced observations of complex turbulent signals. The design of such a reduced filter involves: (i) interpolating irregularly spaced observations to the model regularly spaced grid points, (ii) understanding under which situation the small scale oscillatory artifact from such interpolation won't degrade the filtered solutions, (iii) understanding when the interpolated covariance structure can be approximated by its diagonal terms when observations are corrupted by independent Gaussian noise, and (iv) applying a scalar Kalman filter formula on each Fourier component independently with an approximate diagonal interpolated covariance matrix. From the practical point of view, there is an emerging need to understand the effect of (i) toward the filtered solutions, for example, in utilizing the data produced from various interpolation techniques that merge multiple satellite measurements of atmospheric and ocean dynamical quantities. To understand point (iii) above and to see how many of nondiagonal terms are effectively ignored in (iv), we compute a ratio ? between the largest nondiagonal components and the smallest diagonal components of the interpolated covariance matrix. We find that for piecewise linear interpolation, this ratio, ?, is always smaller than that of the alternative interpolation schemes such as trigonometric and cubic spline for any irregularly spaced observation networks. When observations are not so sparse with small noise, we find that the small scale oscillatory artifact in (ii) above is negligible when piecewise linear interpolation is used whereas for the other schemes such as the nearest neighbor, trigonometric, and cubic spline interpolation, the oscillatory artifact degrades the filtered solutions significantly. Finally, we also find that the reduced filtering strategy with piecewise linear interpolation produces more accurate filtered solutions than conventional approaches when observations are extremely irregularly spaced (such that the ratio ? is not so small) and very sparse.

Original languageEnglish (US)
Pages (from-to)2620-2640
Number of pages21
JournalSIAM Journal on Scientific Computing
Volume33
Issue number5
DOIs
StatePublished - Nov 24 2011

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Large scale systems
Complex Systems
Interpolation
Filtering
Linear Interpolation
Interpolate
Piecewise Linear
Kalman Filter
Covariance matrix
Kalman filters
Splines
Cubic Spline Interpolation
Cubic Spline
Covariance Structure
Diagonal matrix
Gaussian Noise
Term
Ocean
Observation
Nearest Neighbor

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

Cite this

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title = "Interpolating irregularly spaced observations for filtering turbulent complex systems",
abstract = "We present a numerically fast reduced filtering strategy, the Fourier domain Kalman filter with appropriate interpolations to account for irregularly spaced observations of complex turbulent signals. The design of such a reduced filter involves: (i) interpolating irregularly spaced observations to the model regularly spaced grid points, (ii) understanding under which situation the small scale oscillatory artifact from such interpolation won't degrade the filtered solutions, (iii) understanding when the interpolated covariance structure can be approximated by its diagonal terms when observations are corrupted by independent Gaussian noise, and (iv) applying a scalar Kalman filter formula on each Fourier component independently with an approximate diagonal interpolated covariance matrix. From the practical point of view, there is an emerging need to understand the effect of (i) toward the filtered solutions, for example, in utilizing the data produced from various interpolation techniques that merge multiple satellite measurements of atmospheric and ocean dynamical quantities. To understand point (iii) above and to see how many of nondiagonal terms are effectively ignored in (iv), we compute a ratio ? between the largest nondiagonal components and the smallest diagonal components of the interpolated covariance matrix. We find that for piecewise linear interpolation, this ratio, ?, is always smaller than that of the alternative interpolation schemes such as trigonometric and cubic spline for any irregularly spaced observation networks. When observations are not so sparse with small noise, we find that the small scale oscillatory artifact in (ii) above is negligible when piecewise linear interpolation is used whereas for the other schemes such as the nearest neighbor, trigonometric, and cubic spline interpolation, the oscillatory artifact degrades the filtered solutions significantly. Finally, we also find that the reduced filtering strategy with piecewise linear interpolation produces more accurate filtered solutions than conventional approaches when observations are extremely irregularly spaced (such that the ratio ? is not so small) and very sparse.",
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Interpolating irregularly spaced observations for filtering turbulent complex systems. / Harlim, John.

In: SIAM Journal on Scientific Computing, Vol. 33, No. 5, 24.11.2011, p. 2620-2640.

Research output: Contribution to journalArticle

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