An ensemble Kalman smoother for nonlinear dynamics

By Geir Evensen, P. V. Leeuwen. 2000

619 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4bb38fa3cfd67ce9047bbf85fec3f5dc3fa18165

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

1. Geir Evensen
2. Laurent Bertino
3. Marc Bocquet
4. Pavel Sakov
5. Q. Xiao
6. Chengsi Liu
7. Bin Wang
8. Takemasa Miyoshi

In Global and Planetary Change

1. Laurent Bertino
2. Geir Evensen
3. Marc Bocquet
4. Pavel Sakov
5. Takemasa Miyoshi
6. Q. Xiao
7. Chengsi Liu
8. Bin Wang

Citations in the corpus

• Inverse methods in hydrogeology: Evolution and recent trends (2014, background): “For this reason, the EnKF is optimal when param-492 eter and states are linearly related and follow a multiGaussian distribution (Evensen and Leeuwen, 2000).493 However, in hydrogeology, hydraulic conductivity is likely not to be properly modeled as multiGaussian, and494 even if it were, the states…”
• Combining geostatistics and Kalman filtering for data assimilation in an estuarine system (2002, methodology): “Formulating this problem using Bayesian statistics, one can derive traditional methods such as the representer solution of the weak constraint variational problem by assuming Gaussian priors, the strong constraint adjoint method by additionally assuming the model is perfect, and the Kalman filter me”
• Ensemble smoother with multiple data assimilation (2013, background): “Evensen and van Leeuwen (2000) compared51 ES and EnKF with Lorenz equations and concluded that EnKF outperforms52 ES because the recursive updates in the EnKF keep the ensemble of states53 “on track” and closer to the true solution.”
• Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter (2005, result): “This approach is similar to that of an ensemble Kalman smoother [10,8], but over a much shorter time interval.”
• Stochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems (2001, neutral): “…shall consider the particle filter (Del Moral and Salut 1995; Del Moral et al. 1995), the ensemble Kalman filter (Evensen 1994, 1997a,b; Evensen and van Leeuwen 1996, 2000; Burgers et al. 1998), and some new filters that we introduce: the second-order-exact ensemble Kalman filter and the singular…”
• A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96 (2007, background): “In this way, Evensen and van Leeuwen (2000), Anderson (2001) and Hunt et al. (2004) extend EnKF to accurately assimilate asynchronous observations at the correct time.”
• A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, methodology): “Both the PF and EAKF use a Gaspari and Cohn (1999) correlation function for localization with a shape that is modified by specifying a half-width r for its decay to 0.”
• An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, background): “Evensen and van Leeuwen (2000) presented an ensemble Kalman smoother, using only forward-intime model integrations.”
• Nonlinearity in Data Assimilation Applications: A Practical Method for Analysis (2001, methodology): “Several extensions for Kalman filtering and Kalman smoothing algorithms have been proposed to overcome the problems related to the linear time propagation of the error covariance in traditional Kalman filters (Evensen and van Leeuwen 1995, 2000; Evensen 1997; Kushner 1967a,b; Cohn 1993).”
• Hierarchical Models in the Brain (2008, background): “Although, computationally intensive (see Discussion), particle filtering can be efficient [79]; Furthermore, it can be combined with local linearised sequential methods (e.g., the Ensemble Kalman Filter; [80]) to provide data assimilation methods for huge data sets.”
• Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “(1) in which all the dipole–dipole couplings and chemical shielding parameters are varied.”
• Data assimilation: making sense of Earth Observation (2014, methodology): “The Ensemble Kalman smoother, EnKS (Evensen and van Leeuwen, 2000; Evensen, 2003), is an extension of the EnKF where information at assimilation times is propagated backward in time, i.e., estimates at times t ≤ tn, contain information from all data up to and including tn.”
• Asynchronous data assimilation with the EnKF (2010, neutral): “Unlike 4D-Var, the asynchronous EnKF requires no tangent linear or adjoint model.”