A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme

By Thomas M. Hamill, C. Snyder. 2000

650 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/b63b3ba822940fa7125473fc6c82549a126a5278

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Citations in the corpus

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.”
The Ensemble Kalman Filter: theoretical formulation and practical implementation (2003, methodology): “Hamill and Snyder (2000) constructed a hybrid assimilation scheme by combining 3DVAR and the EnKF.”
The evolution of the ECMWF hybrid data assimilation system (2016, methodology): “This procedure can be implemented either by direct manipulation of the backgrounderror covariance (Hamill and Snyder, 2000; Etherton and Bishop, 2004; Wang et al., 2007b) or, more commonly, by augmentation of the variational control variable (‘extended control variable’ method; Barker, 1999;…”
Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry (2007, background): “The recent advent of ensemble Kalman filter techniques in atmospheric data assimilation (Houtekamer and Mitchell 1998; Hamill and Snyder 2000) marks another promising avenue to nonstationary space-time covariance modeling based on multiple realizations of an underlying spatio-temporal random field.”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, methodology): “In perfect-model tests, the ensemble filters produced simulations with dramatically lower errors than were achieved with competing methods (e.g., Hamill and Snyder 2000, 2002; Anderson 2001).”
Ensemble‐derived stationary and flow‐dependent background‐error covariances: Evaluation in a quasi‐operational NWP setting (2005, methodology): “The third strategy is to form hybrid covariances by using a weighted average of the covariances estimated using the first two strategies, similar to Hamill and Snyder (2000).”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, methodology): “…approaches, such as the Schur product (Houtekamer and Mitchell 2001; Lorenc 2003; Buehner 2005), local truncation (Houtekamer and Mitchell 1998), an inflation factor (Anderson and Anderson 1999), and a hybrid scheme (Hamill and Snyder 2001; Lorenc 2003) have been proposed to relax this problem.”
Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model (2006, neutral): “…background on the application of the EnKF in atmospheric sciences, see, for example, Mitchell and Houtekamer (2000), Keppenne (2000), Hamill and Snyder (2000), Anderson (2001, 2003), Keppenne and Rienecker (2002), Snyder and Zhang (2003), Zhang et al. (2004, 2006), and Houtekamer et al. (2005).”
The Hybrid Local Ensemble Transform Kalman Filter (2014, background): “1 that the standard LETKF algorithm performs well with a large ensemble size (e.g., 20, given that 40 would be full rank), but it fails due to catastrophic filter divergence (Harlim and Majda 2010) when using smaller ensembles (e.g., k 5 5).”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, background): “This result agrees with previous studies using EDA systems in a perfect-model context (Hamill and Snyder 2000) and using real observations characteristic of observing networks of the early 20th century (Whitaker et al. 2004) which have shown that the flow-dependent background-error covariances…”
Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF) (2007, methodology): “…of atmospheric, oceanic, hydrological, chemical and carbon transport sciences [e.g., Houtekamer and Mitchell , 1998; Lermusiaux and Robinson , 1999; Hamill and Snyder , 2000; Keppenne , 2000; Mitchell and Houtekamer , 2000; Anderson , 2001; Bishop et al. , 2001; van Leeuwen , 2001; Reichle et al.…”
Ensemble Square Root Filters (2003, methodology): “Hybrid methods represent forecast error covariances with a combination of ensemble and parameterized correlation models (Hamill and Snyder 2000).”
Hydrologic Data Assimilation with the Ensemble Kalman Filter (2002, background): “Ultimately, a hybrid filter that combines empirical forecast error covariances with dynamic error propagation via the EnKF ( Hamill and Snyder 2000 ) may be the best approach.”
Model Error Estimation Employing an Ensemble Data Assimilation Approach (2006, methodology): “Starting from pioneering work of Evensen (1994), many techniques have been proposed as different 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; Wh”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, methodology): “Although the ensemble-based B matrix has been widely used for flow-dependent analysis in data assimilation algorithms, reducing the effects of sampling errors is a challenge (Houtekamer and Mitchell 1998; Hamill and Snyder 2000; Lorenc 2003).”
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.”