An Adaptive Ensemble Kalman Filter

By H. Mitchell, P. Houtekamer. 2000

290 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5f23d8b95dca43ec8ffa51657a149b1aa1a23631

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Citations in the corpus

The Ensemble Kalman Filter: theoretical formulation and practical implementation (2003, background): “Mitchell and Houtekamer (2000) introduced an adaptive formulation of the EnKF, where the model error parameterization was updated by incorporating information from the innovations during the integration.”
A Comparison of Probabilistic Forecasts from Bred, Singular-Vector, and Perturbed Observation Ensembles (2000, methodology): “If model error cannot be rendered unimportant, then perhaps stochastic forcing will need to be added to the model equations (e.g., Buizza et al. 1999), or different plausible model configurations used among the ensemble members (Stensrud and Fritsch 1994; Houtekamer et al. 1996), and/or model error ”
Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations (2005, methodology): “To account for model error, we use a model-error covariance matrix from which we sample different random fields for different members of the ensemble (Mitchell and Houtekamer 2000, hereafter MH2000).”
Model Error Representation in an Operational Ensemble Kalman Filter (2009, methodology): “In view of the difficulty of quantifying the impact of the entire range of possible problems in atmospheric forecasting, one can instead adaptively estimate some parameters of a statistical description of model error (Dee 1995; Mitchell and Houtekamer 2000).”
Ensemble Kalman filtering without the intrinsic need for inflation (2011, background): “…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).”
A Local Least Squares Framework for Ensemble Filtering (2003, methodology): “A variety of ensemble Kalman filters have been described in the literature (see, e.g., Evensen and van Leeuwen 1996; Keppenne 2000; Mitchell and Houtekamer 2000; Pham 2001).”
Accounting for Model Errors in Ensemble Data Assimilation (2009, methodology): “…this problem, approaches include inﬂating the background error covariance with multiplicative inﬂation (Anderson and Anderson 1999), additive inﬂation (Mitchell and Houtekamer 2000; Mitchell et al. 2002; Hamill and Whitaker 2005; Corazza et al. 2007; Whitaker et al. 2008) and the covariance…”
Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model (2006, methodology): “Mitchell and Houtekamer (2000) developed a method of accounting for model error in an ensemble Kalman filter context by adding an ensemble of realizations of model error to the ensemble of model predictions by a three-level quasigeostrophic model.”
Enhanced Adaptive Inflation Algorithm for Ensemble Filters (2018, background): “Additive inflation (Mitchell and Houtekamer 2000) adds a random perturbation drawn from a specific error distribution to each ensemble member during the forecast step.”