An Ensemble Adjustment Kalman Filter for Data Assimilation

By Jeffrey L. Anderson. 2001

1,817 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/0efa80c5a4a525d3fc3ff3da13ebba9f94ae78c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

In Oceanography

Citations in the corpus

An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, positive): “There are several formulations of EnKF that have been developed in recent years, such as a double EnKF (Houtekamer and Mitchell 1998), an ensemble square root filter (EnSRF; Whitaker and Hamill 2002), an ensemble adjusted Kalman filter (EAKF; Anderson 2001), and an ensemble transform Kalman filter (”
A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96 (2007, methodology): “x Mitchell, 1998; Anderson, 2001; Bishop et al., 2001; Whitaker and Hamill, 2002; Ott et al., 2004; Zupanski, 2005). These schemes evolve an ensemble of model trajectories to estimate background uncertainty. Though not yet operational, experiments such as those of Houtekamer et al. (2005), Szunoygh ”
Enhanced Adaptive Inflation Algorithm for Ensemble Filters (2018, background): “Inflation has been introduced into stochastic (e.g., EnKF) and deterministic (e.g., Anderson 2001; Hoteit et al. 2002; Whitaker and Hamill 2002; Hunt et al. 2007; Sakov and Oke 2008) ensemble filters as a way to counteract the underestimation of the true variance.”
Data assimilation for transient flow in geologic formations via ensemble Kalman filter (2006, methodology): “Anderson [1] used an ensemble adjustment Kalman filer (EAKF) to achieve the same purpose.”
Sequential State and Variance Estimation within the Ensemble Kalman Filter (2007, methodology): “We also investigate whether the algorithm can be combined with the state augmentation approach of Anderson (2001) to estimate an unknown forcing parameter.”
An economical approach to four-dimensional variational data assimilation (2010, methodology): “Many researchers have reported encouraging results with this approach (e.g. Houtekamer and Mitchell, 1998; Anderson, 2001; Bishop et al., 2001; Whitaker et al., 2004; Houtekamer et al., 2005; Szunoygh et al., 2005; Pu and Hacker, 2009).”
On the Theoretical Equivalence of Differently Proposed Ensemble 3DVAR Hybrid Analysis Schemes (2007, methodology): “Hybrid schemes present a possible alternative to more conventional ensemble data assimilation schemes (e.g., Evensen 1994; Burgers et al. 1998; Anderson 2001; Bishop et al. 2001; Whitaker and Hamill 2002, 2005; Snyder and Zhang 2003; Zhang et al. 2004; Ott et al. 2004; Szunyogh et al. 2005;…”
A Dual-Weighted Approach to Order Reduction in 4DVAR Data Assimilation (2008, methodology): “Ensemble Kalman filter (EnKF) methods build on the original work of Evensen (1994) to provide the analysis state and error covariance using an ensemble of model forecasts (Molteni et al. 1996; Burgers et al. 1998; Anderson 2001).”
An exploration of the equivalent weights particle filter (2013, background): “One is to make Gaussian and linear assumptions that lead to a Gaussian posterior, as is done in the ensemble Kalman filter (EnKF) and its variants (Evensen, 1994; Burgers et al., 1998; Anderson, 2001; Bishop et al., 2001; Whitaker and Hamill, 2002).”
A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment (2008, methodology): “, Houtekamer and Mitchell 1998, 2001; Anderson 2001; Whitaker and Hamill 2002; Szunyogh et al. 2005; Torn et al. 2006) and experiments with real numerical weather prediction (NWP) models and observations (e.g., Houtekamer et al. 2005; Whitaker et al. 2008; Szunyogh et al. 2008). Studies more recentl”
System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies (2007, methodology): “Tippett et al. (2003) analyzed existing ensemble-based filters (Anderson 2001; Bishop et al. 2001; Whitaker and Hamill 2002) and pointed out that these methods are roughly equivalent and suggested that the deterministic square-root filter as a unified family name may be appropriate.”
Ensemble Data Assimilation without Perturbed Observations (2002, methodology): “This expression for AaPi is identical to the one derived in the appendix of Anderson (2001).”
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, methodology): “Various EnKF algorithms have been proposed such as the ensemble adjustment Kalman filter (EAKF; Anderson 2001), the ensemble transform Kalman filter (ETKF; Bishop et al. 2001), the serial ensemble square root filter (serial EnSRF; Whitaker and Hamill 2002), and the local ensemble transform Kalman…”
Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF (2015, methodology): “This same ensemble framework is also used by other ensemble-based Kalman filters, including the singular evolutive interpolated Kalman (SEIK) filter (Pham 2001), the ensemble transform Kalman filter (ETKF) (Bishop et al. 2001), the ensemble adjustment Kalman filter (EAKF) (Anderson 2001), the ensemb”
Ensemble Kalman filtering (2005, methodology): “This can be done by performing the analysis in a joint state-observation space, following Tarantola (1987) and Anderson (2001).”