The Ensemble Kalman Filter: theoretical formulation and practical implementation

By Geir Evensen. 2003

4,364 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/f9f470b813c10ce27f18f1d1f95bdc35287f3e7f

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Citations in the corpus

An Ensemble Multiscale Filter for Large Nonlinear Data Assimilation Problems (2008, methodology): “This issue is discussed in more detail in citations provided in Evensen (2003) as well as in Zhou et al. (2006).”
A Non-Gaussian Ensemble Filter Update for Data Assimilation (2010, background): “These algorithms are also insufﬁcient to assimilate observations available at different times that have dependent observational error distributions (Evensen 2003).”
A comparison of error subspace Kalman filters (2005, methodology): “It is also common practice to follow this approach and perform a set of local analyses (see, for example, Evensen, 2003).”
State-space models for bio-loggers: A methodological road map (2013, methodology): “, 2011; de Valpine and Hastings, 2002), and marine ecosystem dynamics (Evensen, 2003).”
The ensemble Kalman filter for combined state and parameter estimation (2009, methodology): “It is shown in [36] and [37] that, in the case of time- correlated model errors, it is possible to reformulate the problem as a first-order Markov process by augmenting the model errors to the model-state vector.”
A two-stage ensemble Kalman filter for smooth data assimilation (2008, background): “The Kalman filter is popular in areas as diverse as medicine (Jones 1984), economics (Shumway and Stoffer 1982) and geosciences (Evensen 2003).”
Data assimilation for transient flow in geologic formations via ensemble Kalman filter (2006, methodology): “Kalman filter and its variants provide an alternative, which is explored in this study for dynamic data assimilation of hydrologic models.”
An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics (2015, methodology): “Another method that is suitable for problems with a large number of variables is the Ensemble Kalman Filter [43] (EnKF).”
An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation (2007, methodology): “As a result, the computation of the product G CMG is not as straightforward as in the EnKF where it is only necessary to compute (H∆Y )(H∆Y )T (Evensen 2003).”
Ensemble of metamodels with optimized weight factors (2008, background): “In a related field, the recent development of ensemble of Kalman filters ( Evensen 2003 ) is also a good example.”
Enhanced Adaptive Inflation Algorithm for Ensemble Filters (2018, background): “It operates sequentially by combining information from a prior state of a system and its associated variance with noisy observations to produce an analyzed posterior statewith an updated uncertainty (Evensen 2003).”
Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere (2017, neutral): “The ensemble Kalman filter (EnKF) finds the best estimate of the numerical model state based on the observations and model forecasts with their error covariance (Evensen, 1994, 2003).”
Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results (2013, methodology): “The ensemble Kalman filter (EnKF), which we choose as our data assimilation method, replaces the costly computation of model and error covariances by a Monte Carlo approach (Evensen 2009). It is thus easy to implement and suitable for parallelization. The EnKF can accommodate for optimal estimation ”
Best unbiased ensemble linearization and the quasi‐linear Kalman ensemble generator (2009, methodology): “[18] The EnKF has been proposed by Evensen [1994], later clarified by Burgers et al. [1998] and extensively reviewed by Evensen [2003] .”
Relation between two common localisation methods for the EnKF (2011, background): “The initial formulations of LA [1, 9, 17] did not modify observations and ensemble members within local windows.”