EXPLORING THE NEED FOR LOCALIZATION IN ENSEMBLE DATA ASSIMILATION USING A HIERARCHICAL ENSEMBLE FILTER

By Jeffrey L. Anderson. 2007

318 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/de2bde5b0ebf4970748628a2b3c186a09286ae6d

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Citations in the corpus

Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization (2015, methodology): “, requiring several ensembles as in Anderson (2007)] or complex to implement [e.”
Relation between two common localisation methods for the EnKF (2011, methodology): “[2, 3, 14]) and insufficient ensemble rank (e.”
History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations (2012, unknown): “, [2, 17, 44].”
A Moment Matching Ensemble Filter for Nonlinear Non-Gaussian Data Assimilation (2011, methodology): “The Lorenz 96 system (Lorenz 1996) is another common testbed for data assimilation algorithms (Bengtsson et al. 2003; Ott et al. 2004; Anderson 2007).”
Ensemble Kalman filtering without the intrinsic need for inflation (2011, methodology): “…on the inflation issue, they are based on a specific maximum likelihood estimator of the inflation scaling, or of several scalars that parameterize the error covariance matrices (Mitchell and Houtekamer, 1999; Anderson, 2007b; Brankart et al., 2010) essentially following the ideas ofDee (1995).”
Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother (2011, methodology): “oil contact and the layer mean of permeability, are not localized, although the update of them could be regularized using adaptive localization methods (Anderson 2007; Zhang and Oliver 2010).”
Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications (2011, methodology): “There also exist non-distance-based procedures for covariance localization; see, for example, the so-called pseudo-optimal localization taper function and related procedures presented in Furrer and Bengtsson [9 ]a nd the hierarchical ensemble filter in Anderson [ 2 ].”
Spatially and temporally varying adaptive covariance inflation for ensemble filters (2009, methodology): “If localization and/or a hierarchical filter (Anderson, 2007b) are being used to reduce sampling error, it is necessary to introduce a further factor”
Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation (2012, methodology): “This is consistent with results in Anderson (2007) suggesting that localization needs to be applied not only as a function of the spatial relation between an observation and state variable, but also depending on the ‘‘types’’ of the observation and state.”
The ensemble Kalman filter for combined state and parameter estimation (2009, methodology): “The hierarchical approach in [55] uses several small ensembles to explore the need for using localization in the analysis.”
An adaptive covariance inflation error correction algorithm for ensemble filters (2007, background): “Others, like sampling error resulting from small ensembles, are unique to ensemble methods (Anderson, 2007).”
Improving the Ensemble Estimate of the Kalman Gain by Bootstrap Sampling (2010, methodology): “In the hierarchical ensemble filter method of Anderson (2007) , a small number of ensembles is used to”
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, methodology): “Various EnKF algorithms have been proposed such as the ensemble adjustment Kalman filter (EAKF; Anderson 2001), the ensemble transform Kalman filter (ETKF; Bishop et al. 2001), the serial ensemble square root filter (serial EnSRF; Whitaker and Hamill 2002), and the local ensemble transform Kalman…”
Balance and Ensemble Kalman Filter Localization Techniques (2011, unknown): “…such as satellite observations (Campbell et al. 2010), is equally challenging for BH T and R-localization techniques, as both require a distance be-tween an observation and model grid point, and this issue is a motivation for adaptive localization (Anderson 2007; Bishop and Hodyss 2009b).”