Ensemble Kalman filter methods are typically used in combination with one of two
localization techniques. One technique is covariance localization, or direct forecast error …

Abstract In ensemble Kalman filter (EnKF) data assimilation, localization modifies the error
covariance matrices to suppress the influence of distant observations, removing spurious …

Localization is an essential element of ensemble‐based Kalman filters in large‐scale
systems. Two localization methods are commonly used: covariance localization and domain …

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced
that involve expanding the ensemble size. Such expansions can incorporate model space …

Ensemble Kalman filter techniques are widely used to assimilate observations into
dynamical models. The phase‐space dimension is typically much larger than the number of …

Ensemble‐based Kalman smoother algorithms extend ensemble Kalman filters to reduce
the estimation error of past model states utilizing observational information from the future …

In data assimilation applications using ensemble Kalman filter methods, localization is
necessary to make the method work with high-dimensional geophysical models. For …

The various implementations of the ensemble Kalman filter (EnKF) differ from each other in
several ways. The effects of these differences are not yet well and completely explored and …

One aspect of implementing a limited-area ensemble Kalman filter (EnKF) involves the
specification of a suitable ensemble of lateral boundary conditions. Two classes of methods …

The use of discrete-time stochastic parameterization to account for model error due to
unresolved scales in ensemble Kalman filters is investigated by numerical experiments. The …