Implications of Stochastic and Deterministic Filters as Ensemble-Based Data Assimilation Methods in Varying Regimes of Error Growth

By W. G. Lawson, James A. Hansen. 2004

137 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/615d69ace4d27c2dd6f21e9566c368d43033922b

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Citations in the corpus

A Non-Gaussian Ensemble Filter Update for Data Assimilation (2010, background): “The sample kurtosis: where x is the ensemble mean and s 2 is the sample variance is a statistic that is especially sensitive to the presence of outliers (Lawson and Hansen 2004).”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, background): “For more details, see Whitaker and Hamill (2002) and Lawson and Hansen (2004).”
Ensemble Kalman filtering (2005, methodology): “In the case of large errors, non-Gaussianity of the distribution and significant curvature of the local balanced space can lead to analyses that are off the attractor (Lawson and Hansen 2004).”
Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations (2005, methodology): “of active research (e.g. Tippett et al. 2003; Hamill and Snyder 2000; Lawson and Hansen 2004).”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, background): “Lawson and Hansen (2004) have used them to assess how differently stochastic and deterministic ensemble-based filters handle non-Gaussianity.”
A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters (2008, methodology): “This model has been used for testing ensemble-based assimilation methods in a number of earlier studies (Anderson, 2001; Whitaker and Hamill, 2002; Ott et al., 2004; Lawson and Hansen, 2004).”
A Moment Matching Ensemble Filter for Nonlinear Non-Gaussian Data Assimilation (2011, background): “This could be a result of its vulnerability to the presence of outliers (Lawson and Hansen 2004; Lei et al. 2010).”
Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF (2015, background): “Lawson and Hansen (2004) argued that the stochastic EnKF has the advantage that the observation perturbations tend to ‘‘re-Gaussianize’’ the ensemble distribution to explain the improved stability compared to ensemble filters without perturbations that slavishly follow the shape of the background…”
Implications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters (2008, methodology): “This particularly applies to many studies using the ensemble transform Kalman filter (ETKF), starting with the original work by Bishop et al. (2001); however, the non-mean-preserving solutions used in these studies have often became associated with the ETKF (e.g., Tippett et al. 2003).”
An Iterative EnKF for Strongly Nonlinear Systems (2012, background): “) The rotations prevent the buildup of non-Gaussianity in the ensemble that is often associated with larger ensemble sizes in nonlinear ESRF-based systems (Lawson and Hansen 2004; Sakov and Oke 2008; Anderson 2010).”
A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation (2015, methodology): “preventing the built-up of ensemble outliers, as similarly found for the ETKF (Lawson and Hansen 2004; Sakov et al. 2012).”