1887

Abstract

Summary

Ensemble-based data assimilation methods like the ensemble Kalman filter must estimate covariances between state variables and observed variables to update ensemble members. In high-dimensional, geostatistical estimation settings where the system state consists of spatial random fields, spurious entries in estimated covariance matrices can degrade the predictive performance of posterior ensembles. We propose to avoid spurious correlations by specifying a parametric form for the state covariance, and fitting this model to the forecast ensemble. The idea is demonstrated on a partially synthetic North Sea test case involving forward stratigraphic modeling.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201902172
2019-09-02
2020-02-18
Loading full text...

Full text loading...

References

  1. Anderson, J.L.
    [2007] Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D: Nonlinear Phenomena, 230(1–2), 99–111.
    [Google Scholar]
  2. Charvin, K., Gallagher, K., Hampson, G.L. and Labourdette, R.
    [2009] A Bayesian approach to inverse modelling of stratigraphy, part 1: Method. Basin Research, 21(1), 5–25.
    [Google Scholar]
  3. Fuglstad, G.A., Lindgren, F., Simpson, D. and Rue, H.
    [2015] Exploring a new class of non-stationary spatial Gaussian random fields with varying local anisotropy. Statistica Sinica, 115–133.
    [Google Scholar]
  4. Nychka, D.W. and Anderson, J.L.
    [2010] Data assimilation. In: Handbook of spatial statistics, CRC Press, 476–491.
    [Google Scholar]
  5. Skauvold, J. and Eidsvik, J.
    [2018] Data assimilation fora geological processmodel using the ensemble Kalman filter. Basin Research, 30(4), 730–745.
    [Google Scholar]
  6. [2019] Parametric spatial covariance models in the ensemble Kalman filter. Spatial statistics, 29, 226–242.
    [Google Scholar]
  7. Stroud, J.R., Stein, M.L., Lesht, B.M., Schwab, D.J. and Beletsky, D.
    [2010] An ensemble Kalman filter and smoother for satellite data assimilation. Journal of the American Statistical Association, 105(491), 978–990.
    [Google Scholar]
  8. Whitaker, J.S. and Hamill, T.M.
    [2012] Evaluating methods to account for system errors in ensemble data assimilation. Monthly Weather Review, 140(9), 3078–3089.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201902172
Loading
/content/papers/10.3997/2214-4609.201902172
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error