§4DVar requires an adjoint model to back-propagate information --
this can
be a royal pain to
develop!
§The EnKF can get around needing an adjoint by using a filter-lag
rather
than a fixed-interval Kalman smoother.
However, the need to
propagate multiple time steps in the state makes it less efficient than the
4DVar method
§Both give a low-rank estimate of the a posteriori covariance matrix
§Both can account for dynamic errors
§Both calculate time-evolving correlations between the state and the
measurements