Ensemble Data Assimilation without Perturbed Observations

  title={Ensemble Data Assimilation without Perturbed Observations},
  author={Jeffrey S. Whitaker and Thomas M. Hamill},
The ensemble Kalman filter (EnKF) is a data assimilation scheme based on the traditional Kalman filter update equation. An ensemble of forecasts are used to estimate the background-error covariances needed to compute the Kalman gain. It is known that if the same observations and the same gain are used to update each member of the ensemble, the ensemble will systematically underestimate analysis-error covariances. This will cause a degradation of subsequent analyses and may lead to filter… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 21 references

The impact of ensemble forecasts on predictability

  • J. M. Murphy
  • Quart. J. Roy. Meteor. Soc.,
  • 1988
Highly Influential
4 Excerpts

Comment on ‘‘Data assimilation using an ensemble Kalman filter technique.’

  • P. J. van Leeuwen
  • Mon. Wea. Rev.,
  • 1999
Highly Influential
7 Excerpts

Statistical Methods in the Atmospheric Sciences

  • D. S. Wilks
  • 1995
Highly Influential
1 Excerpt

Baroclinic wave packets in models and observations

  • S. Lee, I. M. Held
  • J. Atmos. Sci.,
  • 1993
Highly Influential
3 Excerpts

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