Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter

By Thomas M. Hamill, J. Whitaker, C. Snyder. 2001

1,136 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/3abcd059c800ca2bd5f16bf4c75e5fabb2adff9f

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Citations in the corpus

Covariance localisation and balance in an Ensemble Kalman Filter (2009, methodology): “However, while such strategies are useable in an OI analysis, in an EnKF they introduce noise into the analysis as the covariances do not approach zero at large separation (Houtekamer and Mitchell, 2001; Hamill et al., 2001).”
The Twentieth Century Reanalysis Project (2009, methodology): “Covariance localization (Houtekamer and Mitchell, 2001; Hamill et al., 2001) is a spatial filter that smoothly sets the ensemble covariances to zero beyond a specified distance.”
Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations (2002, methodology): “Previous work has shown that in a perfect-model context, such ensembles have desirable sampling characteristics when used either with a 3DVAR assimilation (Hamill et al. 2000) or in the context of the EnKF (Houtekamer and Mitchell 1998, 2001; Hamill and Snyder 2000; Hamill et al. 2001).”
Maximum Likelihood Ensemble Filter: Theoretical Aspects (2005, methodology): “Note that error covariance localization, not employed in the current MLEF algorithm, is an important component of most ensemble-based data assimilation algorithms (e.g., Houtekamer and Mitchell 1998; Hamill et al. 2001; Whitaker and Hamill 2002).”
Ensemble Data Assimilation without Perturbed Observations (2002, methodology): “Sequential processing of observations also makes it much easier to implement covariance localization, which improves the analysis while preventing filter divergence in small ensembles (Houtekamer and Mitchell 2001; Hamill et al. 2001).”
Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter (2003, background): “Recent results from different algorithms, as well as earlier references, can be found in Xu et al. (2001), Montmerle et al.”
Localization and regularization for iterative ensemble smoothers (2017, background): “[17] assimilate data one by one and Houtekamer and Mitchell [18] assimilate data in batches.”
Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants (2007, methodology): “…effects of sampling variability on filter performance are given by squareroot enKf approaches (Anderson, 2001; Bishop et al., 2001; Tippett et al., 2003), double enKf (Houtekamer and Mitchell, 1998; Leeuwen, 1999), and covariance localization approaches (Hamill et al., 2001; Anderson, 2006).”
The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var (2003, result): “Hamill et al. (2001) reach a similar conclusion that the use of covariance localization is bene cial when the ensemble size is small.”
A local ensemble Kalman filter for atmospheric data assimilation (2002, positive): “] Another approach toward reducing the cost of the Kalman filter is to use a relatively small ensemble of forecasts to estimate the forecast error covariance (e.g. Evensen, 1994; Houtekamer and Mitchell, 1998, 2001; Anderson, 2001; Bishop et al., 2001; Hamill et al., 2001; Whitaker and Hamill, 2002;”
Obstacles to High-Dimensional Particle Filtering (2008, background): “Experience has shown, however, that this requirement does not hold for the EnKF if localization of the sample covariance matrix is employed (Houtekamer and Mitchell 1998, 2001; Hamill et al. 2001).”
Ensemble Kalman filtering (2005, background): “Therefore, unless the ensemble is truly large, with a size O(10 000), it is necessary to localize the impact of the observations (Houtekamer and Mitchell 1998; Hamill et al. 2001; Lorenc 2003).”
Efficient Ensemble-Based Closed-Loop Production Optimization (2009, methodology): “Usually localization is used to reduce the effect of the spurious correlation in the application of the EnKF (Hamill and Whitaker, 2001; Arroyo-Negrete et al., 2006; Agbalaka and Oliver, 2007).”
A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters (2008, methodology): “…a repetitive application of the analysis equation to each ensemble member; and it readily permits the traditional Schur-product-based localization (Hamill and Whitaker, 2001; Houtekamer and Mitchell, 2001), which may be required for realistic applications when the system dimension exceeds the…”
Asynchronous data assimilation with the EnKF (2010, methodology): “Harlim and Hunt (2007) argued the AEnKF (4D-LETKF) is ‘mathematically equivalent’ to 4D-EnKF but ‘computationally more efficient since the analysis needs no calculations of eigenvectors’.”
Ensemble kalman filter assimilation of doppler radar data with a compressible nonhydrostatic model : OSS experiments (2005, background): “Therefore covariance localization is necessary (Houtekamer and Mitchell 1998; Anderson 2001; Hamill et al. 2001; Houtekamer and Mitchell 2001).”