Data Assimilation: The Ensemble Kalman Filter

By Geir Evensen. 2006

2,410 citations recorded by the source corpus.

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “One of the long-standing approaches of the scientific community for integrating data in theory-based models is to use data assimilation approaches, which has been widely used in climate science and hydrology [74].”
Localization and regularization for iterative ensemble smoothers (2017, background): “Evensen [11] Chen and Oliver [4] both showed benefit from using TSVD for nonlinear data assimilation problems.”
Surrogate accelerated sampling of reservoir models with complex structures using sparse polynomial chaos expansion (2015, methodology): “Hence the polynomial chaos proxy can be thought as a generalization to efficient linear estimators (such as the Ensemble Kalman Filter [14, 15]) that works for the non-Gaussian posterior distribution as well.”
Data assimilation: making sense of Earth Observation (2014, background): “Although Bayesian estimation defines a systematic and rigorous approach to data assimilation (Rodgers, 2000; Evensen, 2007), its full-scale implementation in many areas, including weather forecasting, is impossible, chiefly due to the size of the problem.”
Asynchronous data assimilation with the EnKF (2010, methodology): “The practical aspects of the EnKS have been further considered in Evensen (2003) and Evensen (2007).”
An iterative ensemble Kalman smoother (2014, methodology): “We will denote by EnKS the ensemble square-root variant of the non-iterative smoother discussed in Evensen and van Leeuwen (2000), Evensen (2003, 2009) and Cosme et al. (2012).”
Ensemble smoother with multiple data assimilation (2013, methodology): “However, C may be poorly conditioned (Evensen, 2007,324 Chap.”
Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications (2011, background): “This loss of variance can make it difficult to properly modify the state component by assimilating later time data, [14, 15, 8, 1].”
A Bayesian approach to state and parameter estimation in a Phytoplankton-Zooplankton model (2010, methodology): “Within the last 15 years there have been significant advances in statistical techniques available for data assimilation (DA), largely driven by a combination of the availability of high performance computing and dense observational data-sets (Lorenc 2003; Evensen 2007).”
An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices (2008, background): “This reconstruction of the state of a system using data is also called data assimilation [6].”
The quiet revolution of numerical weather prediction (2015, methodology): “The variational principle has been implemented in different ‘flavours’ and the next decade is likely to be dominated by either choosing the most effective combination of variational and ensemble elements 86 or by using purely ensemble based methods like ensemble Kalman filters 87 .”
Bayesian inverse problems for functions and applications to fluid mechanics (2009, methodology): “A good overview of these topics, aimed at applications in geophysical fluid dynamics, may be found in the book [23].”
Large sample asymptotics for the ensemble Kalman filter (2009, unknown): “…EN AUTOMATIQUE Large Sample Asymptotics for the Ensemble Kalman Filter François Le Gland — Valérie Monbet — Vu–Duc Tran N° 7014 Août 2009 in ria -0 04 09 06 0, v er si on 1 - 5 Au g 20 09 in ria -0 04 09 06 0, v er si on 1 - 5 Au g 20 09 Centre de recherche INRIA Rennes – Bretagne Atlantique…”