Pros and cons, 4DVar vs.
ensemble Kalman filter (EnKF)
§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
The choice between going with the fixed-interval versus fixed-lag smoother boils down to a tradeoff between the additional run-time cost of the fixed-lag smoother versus the up-front cost of developing an adjoint for the fixed-interval smoother.