Relation between two common localisation methods for the EnKF

By Pavel Sakov, Laurent Bertino. 2011

213 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e8406b7736d826f972560535d81519af3a3098b5

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Citations in the corpus

Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “There are ways to improve this update, such as using a left-transform update (Sakov and Bertino, 2011; Bocquet et al., 2015b), or Lanczos vectors (Auligné et al.”
An iterative ensemble Kalman smoother (2014, background): “While local analysis is characterized in the literature as scheme-independent (Sakov and Bertino, 2011), this is less obvious in the context of iterative schemes.”
Ensemble Kalman filtering without the intrinsic need for inflation (2011, background): “Though vaguely connected, the two approaches still require common grounds to be understood (Sakov and Bertino, 2010).”
Localization and regularization for iterative ensemble smoothers (2017, methodology): “The same localization range, however, was used for covariance localization and local analysis in Sakov and Bertino [29].”
History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations (2012, result): “Sakov and Bertino [38] showed that if we apply a correlation function to taper the updates in the local analysis procedure, then local analysis gives similar results to covariance localization.”
Towards a dynamically balanced eddy-resolving ocean reanalysis: BRAN3 (2013, background): “Both187 methods are known to be fundamentally similar (Sakov and Bertino, 2011),188 except that the local analysis has some significant practical advantages.”
Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation (2015, background): “(51) When ζa =N − 1 and z= 0, one recovers the ensemble square root Kalman update formula written in state space: Xa = [ IM+PH TR−1H ]− 1 2 X (Sakov and Bertino, 2011).”