Ensemble size, balance, and model-error representation in an ensemble Kalman filter

By H. Mitchell, P. Houtekamer, G. Pellerin. 2002

311 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/cdc3fd15bceb3f4852a67cedbca42dfd4eadf3c1

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Citations in the corpus

An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, background): “Although many encouraging research results of EnKF have been obtained in either global data assimilation (Mitchell et al. 2002; Houtekamer and Mitchell 2005) or regional mesoscale data assimilation (Snyder and Zhang 2003), there is not much evidence to indicate that EnKF outperforms variational…”
Model Error Estimation Employing an Ensemble Data Assimilation Approach (2006, methodology): “A similar approach is used in Mitchell et al. (2002), where a balanced random noise, generated from a multivariate probability distribution function, is included into the model.”
The Ensemble Kalman Filter: theoretical formulation and practical implementation (2003, methodology): “Mitchell et al. (2002) examined the EnKF with a global atmospheric general circulation model with simulated data resembling realistic operational observations.”
Ensemble‐derived stationary and flow‐dependent background‐error covariances: Evaluation in a quasi‐operational NWP setting (2005, methodology): “Several NWP centres, including CMC, employ variational assimilation systems with stationary background-error covariances estimated using the NMC method (Parrish and Derber 1992; Gauthier et al. 1998; Rabier et al. 1998; Derber and Bouttier 1999).”
A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses (2009, methodology): “For covariance localization (Mitchell et al. 2002; Hamill et al. 2001), a compactly supported fifth-order correlation function following Gaspari and Cohn (1999) is employed.”
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study (2008, methodology): “Since the first application of an ensemble Kalman filter (EnKF; Evensen 1994) in the atmospheric sciences field (Houtekamer and Mitchell 1998), the EnKF has been widely examined with different models at different scales and to different realistic extents (Houtekamer and Mitchell 1998; Hamill and Sny”
Efficient Ensemble-Based Closed-Loop Production Optimization (2009, methodology): “The EnKF has been found suitable for sequential data assimilation in large-scale nonlinear dynamics (Houtekamer and Mitchell, 2001; Mitchell et al., 2002; Bertino et al., 2003).”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, methodology): “Using such techniques, ensemble-based data assimilation algorithms have proven useful in either global atmospheric data assimilation (Mitchell et al. 2002; Houtekamer and Mitchell 2005; Buehner 2005) or regional mesoscale atmospheric data assimilation (Snyder and Zhang 2003; Xue et al. 2006).”
Model Error Representation in an Operational Ensemble Kalman Filter (2009, methodology): “A third-order autoregressive function is used for the horizontal correlations [Mitchell et al. 2002, their Eq.”
The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model (2006, methodology): “…one may mention that with the ensemble approach the model errors em can be simulated, either by using different NWP models for the different ensemble members (Houtekamer et al. 1996), or by drawing random realizations from an estimate of the model error covariance matrix (Mitchell et al. 2002).”
A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, background): “Mitchell et al. (2002) show that imbalance issues are made worse for EnKFs as the localization length scale decreases, which is equally true in a PF framework.”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, methodology): “To improve the modeling of background-error cova- riances, the covariances estimated from the ensemble are subject to modification by “localization” (e.g., Houtekamer and Mitchell 2001; Hamill et al. 2001; Mitchell et al. 2002; Lorenc 2003).”