… For large-scale models, the filters are typically applied with a … interaction of serial observation
processing and localization can … ensemble Kalman filters, except for the classical ensemble …

… , serial processing of observations, … ensemble-based Kalman filtering forms the central
topic of this article, that starts out with a brief overview of Bayesian updating and Kalman filtering …

… The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman
filter (KF) for extremely high-dimensional, possibly nonlinear, and non-Gaussian state …

… We show how the EnKF relates to the Kalman filter. In particular, to make the ensemble …
2003 Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. …

… the use of an ensemble Kalman filter to correct satellite radiance observations for state …
estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate …

… The top panel (a) shows the state, observations, Kalman filter, and the N = 5 EnKF ensemble
members for the first 10 time points. For the Kalman filter, we show the filtering mean and 75…

Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation
errors caused by technical or human errors during the data collection process. In this paper, …

… In Section 3, we show how to evaluate formulas without an observation matrix, involving
observation functions. Sec. 4 contains the discussion of several implementations of the EnKF …

… used to derive an easy-to-implement localized ensemble Kalman filter, which can process
observations simultaneously and can be extended to nonlinear observation operators. …

… An ensemble Kalman filter may be considered for the 4D assimilation of atmospheric data.
In … sequential processing of observations works in the context of the ensemble Kalman filter. …