# Ensemble Data Assimilation without Perturbed Observations

@article{Whitaker2002EnsembleDA, title={Ensemble Data Assimilation without Perturbed Observations}, author={Jeffrey S. Whitaker and Thomas M. Hamill}, journal={Monthly Weather Review}, year={2002}, volume={130}, pages={1913-1924} }

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…

## 1,395 Citations

Properties of the Ensemble Kalman Filter

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This dissertation investigates two problems exhibited by the Ensemble Kalman Filter: ensemble collapse and bias and develops an unbiased filter with random rotations that shows that it does not exhibit the ensemble collapse problem, and that it is unbiased.

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The use of perturbed observations in the traditional ensemble Kalman filter (EnKF) results in a suboptimal filter behaviour, particularly for small ensembles. In this work, we propose a simple…

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This paper develops efficient ensemble Kalman filter (EnKF) implementations based on shrinkage covariance estimation. The forecast ensemble members at each step are used to estimate the background…

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- Environmental ScienceQuarterly Journal of the Royal Meteorological Society
- 2020

Ensemble Kalman Filters are used extensively in all geoscience areas. Often a stochastic variant is used, in which each ensemble member is updated via the Kalman Filter equation with an extra…

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- Environmental Science
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AbstractThe stochastic ensemble Kalman filter (EnKF) updates its ensemble members with observations perturbed with noise sampled from the distribution of the observational errors. This was shown to…

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This paper develops efficient ensemble Kalman filter (EnKF) implementations based on shrinkage covariance estimation. The forecast ensemble members at each step are used to estimate the background…

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