The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var

By A. Lorenc. 2003

777 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c9811ae47d63b9386b27076cebab6cc161da464f

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Citations in the corpus

An Ensemble Multiscale Filter for Large Nonlinear Data Assimilation Problems (2008, background): “A number of researchers have observed and commented on imbalances introduced by localization in meteorological applications (Mitchell et al. 2002; Lorenc 2003).”
Particle Filtering in Geophysical Systems (2009, background): “For instance, Lorenc (2003) shows how particles can be used to bring flow-dependent information in the background covariance in strong-constraint applications.”
Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction (2013, background): “Several past studies have compared these approaches from both theoretical (Lorenc, 2003; Kalnay et al., 2007; Gustafsson, 2007) and empirical (Caya et al., 2005; Published by Copernicus Publications on behalf of the European Geosciences Union & the American Geophysical Union.”
Estimating surface CO 2 fluxes from space-borne CO 2 dry air mole fraction observations using an ensemble Kalman Filter (2008, methodology): “…and complementary approach to variational assimilation, has been developed in the physical oceanography and meteorology communities (e.g., Evensen, 1994; Houtekamer and Mitchell, 1998; Lorenc, 2003), and recently applied to carbon cycle research (Peters et al., 2005; Bruhwiler et al., 2005).”
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter (2005, background): “A cost function equivalent to (15) appears in [33].”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “In this context, it may be convenient to enforce CL via techniques such as the α control variable trick (Buehner, 2005; Lorenc, 2003; Wang, Snyder, & Hamill, 2007)—which is mathematically equivalent to CL—or using wavelet truncations (Berre et al.”
Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization (2015, methodology): “The optimal localization is known to depend on the ensemble size, the variable, and the vertical and horizontal location, which are often poorly taken into account because of a lack of theory (Lorenc 2003).”
Balance and Ensemble Kalman Filter Localization Techniques (2011, neutral): “Lorenc (2003) provides an example of how localization produces imbalance.”
Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system (2008, methodology): “In practice a covariance localization would have to be applied to ensemble before it is used in DA (Lorenc, 2003).”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “Dedicated comparisons of four-dimensional variational data assimilation (4D-VAR) and ensemble Kalman ﬁlter (EnKF) approaches can be found in [184]–[186].”
A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics (2008, background): “…the closer the B-matrix is to the ‘true’ forecast error covariance matrix (e.g. Pf from the Kalman filter if the system is linear), the closer the analysis is to that produced from the optimal combination of background and observational information (e.g. Daley, 1991; Lorenc, 2003a; Fisher, 2007).”
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): “See Evensen (2003), Lorenc (2003), and Hamill (2006) for a recent review.”
Ensemble‐derived stationary and flow‐dependent background‐error covariances: Evaluation in a quasi‐operational NWP setting (2005, methodology): “The approach is simpler than that proposed by Dee (1995) and examined by Mitchell and Houtekamer (2000) in the EnKF context.”
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments (2006, background): “More background on ensemble-based data assimilation can be found in recent reviews of Evensen (2003), Lorenc (2003) , and Hamill (2006).”
Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses (2011, methodology): “Typical formulations of EnKF methods are considered optimal when the following assumptions are satisfied: 1) the observations are linearly related to the true model state, 2) observation and forecast errors have Gaussian distributions, 3) observation and forecast errors are unbiased, and 4) ensemble”
Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation (2011, methodology): “There is a growing in-terestin the use of localized ensemble covariances in three-dimensional variational data assimilation (3D-VAR) and 4D-VAR schemes evident in papers by Lorenc (2003), Buehner (2005), Buehner et al. (2010a,b), Wang et al. (2007), Liu et al. (2009), and others.”
An extended specification of flow‐dependent background error variances in the Météo‐France global 4D‐Var system (2011, methodology): “Studies have been conducted on hybrid schemes (Hamill and Snyder, 2000; Lorenc, 2003; Wang et al., 2008) which combine the static 3D-Var B matrix with the flowdependent EnKF B matrix.”