Implications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters

By Pavel Sakov, Peter R. Oke. 2008

193 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/67b3bdf6d6df95a000f2206bb9ee1297d1b236f9

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Citations in the corpus

Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, methodology): “2000), and the mean-preserving sampling (Sakov and Oke 2008).”
Evaluation of Gaussian approximations for data assimilation in reservoir models (2012, unknown): “Define Since the permeability in the forward reservoir model does not change in time, it follows that Eqs.”
An Iterative EnKF for Strongly Nonlinear Systems (2012, background): “The ensemble size of 25 is the size when an EnKF–L40 system with standard settings6 without localization is expected to show some small, but statistically significant deterioration in performance compared to that with larger ensembles (e.g., Sakov and Oke 2008; Anderson 2010).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “It was shown (Livings, Dance, & Nichols, 2008; Sakov & Oke, 2008b; Wang, Bishop, & Julier, 2004) that the symmetric square root preserves the zero mean in the updated perturbations.”
Ensemble Kalman filtering without the intrinsic need for inflation (2011, methodology): “The experiments follow the configuration of Sakov and Oke(2008). In the first experiment, the time interval between analyzes is1t = 0.”
The ensemble Kalman filter for combined state and parameter estimation (2009, methodology): “As noted in [44] and [47], this condition is not satisfied for the update in (72).”
A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters (2008, background): “A brief description of four different schemes may be found in Tippett et al. (2003); see also Pham et al. (1998), Ott et al. (2004), Evensen (2004) and Sakov and Oke (2008).”
A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation (2015, methodology): “An ensemble filter’s performance in nonlinear scenarios is quite sensitive to the chosen inflation, which is the most important tuning parameter (Sakov and Oke 2008b).”