Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation
Description
<jats:title>Abstract</jats:title><jats:p>Ensemble Kalman filters use the sample covariance of an observation and a model state variable to update a prior estimate of the state variable. The sample covariance can be suboptimal as a result of small ensemble size, model error, model nonlinearity, and other factors. The most common algorithms for dealing with these deficiencies are inflation and covariance localization. A statistical model of errors in ensemble Kalman filter sample covariances is described and leads to an algorithm that reduces ensemble filter root-mean-square error for some applications. This sampling error correction algorithm uses prior information about the distribution of the correlation between an observation and a state variable. Offline Monte Carlo simulation is used to build a lookup table that contains a correction factor between 0 and 1 depending on the ensemble size and the ensemble sample correlation. Correction factors are applied like a traditional localization for each pair of observations and state variables during an ensemble assimilation. The algorithm is applied to two low-order models and reduces the sensitivity of the ensemble assimilation error to the strength of traditional localization. When tested in perfect model experiments in a larger model, the dynamical core of a general circulation model, the sampling error correction algorithm produces analyses that are closer to the truth and also reduces sensitivity to traditional localization strength.</jats:p>
Journal
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- Monthly Weather Review
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Monthly Weather Review 140 (7), 2359-2371, 2012-07
American Meteorological Society
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Details 詳細情報について
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- CRID
- 1364233270848605056
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- ISSN
- 15200493
- 00270644
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- Data Source
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- Crossref