The ensemble Kalman filter for combined state and parameter estimation

By Geir Evensen. IEEE Control Systems, 2009

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Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “An efficient and stable numerical implementation of the analysis scheme is discussed in Evensen (2009b), including the case in which C is singular or of low rank, for example, due to the number of measurements being larger than the number of realizations or if measurements are linearly dependent on…”
A Non-Gaussian Ensemble Filter Update for Data Assimilation (2010, methodology): “An ensemble Kalman ﬁlter with a mean-preserving random rotation (Sakov and Oke 2008b) is another mechanism for removing outliers (Evensen 2009) but discards any non-Gaussian information that might exist in the ensemble.”
Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF (2015, background): “By contrast, the EnKF may better retain some of these features as it directly samples its analysis ensemble from the ensemble-based estimated posterior distribution (Frei and Kunsch 2013).”
History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations (2012, background): “Equation 5 pertains to the parameter-state estimation problem [14].”