  The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems
with a large number of variables, such as discretizations of partial
differential equations in geophysical models. The EnKF originated as a version
of the Kalman filter for large problems (essentially, the covariance matrix is
replaced by the sample covariance), and it is now an important data
assimilation component of ensemble forecasting. EnKF is related to the particle
filter (in this context, a particle is the same thing as an ensemble member)
but the EnKF makes the assumption that all probability distributions involved
are Gaussian. This article briefly describes the derivation and practical
implementation of the basic version of EnKF, and reviews several extensions.
