Maximum Likelihood Ensemble Filter: Theoretical Aspects

By Milija Zupanski. 2005

364 citations recorded by the source corpus.

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Citations in the corpus

Model Error Estimation Employing an Ensemble Data Assimilation Approach (2006, methodology): “…variants of EnsDA (e.g., Houtekamer and Mitchell 1998; Hamill and Snyder 2000; Keppenne 2000; Mitchell and Houtekamer 2000; Anderson 2001; Bishop et al. 2001; van Leeuwen 2001; Reichle et al. 2002a,b; Whitaker and Hamill 2002; Tippett et al. 2003; Zhang et al. 2004; Ott et al. 2004; Zupanski 2005).”
Incorporating Ensemble Covariance in the Gridpoint Statistical Interpolation Variational Minimization: A Mathematical Framework (2010, methodology): “A mathematical derivation is therefore provided to demonstrate how to incorporate the flow-dependent ensemble covariance in the GSI variational minimization.”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, background): “2007), the variational formulation to treat the non-Gaussian error structure in observation (Zupanski 2005) and background (Harlim and Hunt 2007), and the time interpolation of the background forecasts to the observations so as to produce timecoherent assimilations of all the observations available ”
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter (2005, methodology): “Keywords: Data assimilation; Spatiotemporal chaos; State estimation; Ensemble Kalman filtering”
Direct Assimilation of Radar Reflectivity without Tangent Linear and Adjoint of the Nonlinear Observation Operator in the GSI-Based EnVar System: Methodology and Experiment with the 8 May 2003 Oklahoma City Tornadic Supercell (2017, methodology): “Independent studies by Zupanski (2005) and Liu et al. (2008) suggested ways of using an ensemble in the variational framework to avoid the use of the TLA of the observation operator.”
Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system (2008, methodology): “Another choice is to use ensemble DA like ETKF which is in fact one of the ensemble-based Kalman square-root filters (Anderson, 2001; Whitaker and Hamill, 2002; Tippett et al., 2003; Ott et al., 2004; Zupanski, 2005).”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “MLEF, which preconditions the variational problem on the Hessian, has been demonstrated in a 3D context.”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, methodology): “Unlike Unauthenticated | Downloaded 09/18/23 10:17 AM UTC MLEF (Zupanski 2005) or 4DEnKF (Hunt et al. 2004; Fertig et al. 2007), our approach (En4DVAR) adopts the incremental and preconditional scheme in the variational algorithm so that it can be more easily incorporated into many of the operationa”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “Examples include the ensemble transform kalman filter (ETKF) by Bishop, Etherton, and Majumdar (2001); Whitaker and Hamill (2002), the ensemble adjustment kalman filter (EAKF) by Anderson (2001), and the maximum likelihood ensemble filter (MLEF) by Zupanski (2005).”
Estimating surface CO 2 fluxes from space-borne CO 2 dry air mole fraction observations using an ensemble Kalman Filter (2008, background): “where we have absorbed the normalization factor 1/(Ne−1) into ∆X f (Zupansi, 2005).”
Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF) (2007, methodology): “It is defined as a symmetric positive semi-definite square root [ Zupanski , 2005].”
Ensemble Kalman filtering without the intrinsic need for inflation (2011, unknown): “BecauseJb andJ altb are not quadratic, it is clear that the analysis should be variational, in a similar flavor as the maximum likelihood ensemble filter (Zupanski, 2005; Carrassi et al., 2009).”
A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96 (2007, methodology): “We remark that a cost function similar to (5), but without the linear approximation (4) to the observation operator, is used in the Maximum Likelihood Ensemble Filter (Zupanski, 2005).”
GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments (2013, methodology): “A hybrid analysis method has been proposed (e.g., Hamill and Snyder 2000; Lorenc 2003; Etherton and Bishop 2004; Zupanski 2005; Wang et al. 2007b; Wang Corresponding author address: Dr. Xuguang Wang, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072.”
An Iterative EnKF for Strongly Nonlinear Systems (2012, background): “The maximum likelihood ensemble filter (MLEF; Zupanski 2005) represents an ensemble based gradient method.”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, methodology): “In recent years, approaches that couple both features of the two data assimilation algorithms have been proposed, including the ensemble Kalman smoother (Evensen and van Leeuwen 2000), the maximum likelihood ensemble filter (Zupanski 2005), 4DEnKF (Hunt et al.”