A comparison of hybrid-gain versus hybrid-covariance data assimilation for global NWP

The hybrid-covariance approach linearly combines the static and ensemble-based covariance estimate in a four-dimensional variational solver, whereas the hybrid-gain approach blends analysis increments computed separately using a three-dimensional variational solution and an ensemble Kalman filter solution.

Results show that the simpler and less expensive hybrid-gain approach performs similarly if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.