A Comparison of Hybrid-Gain Versus Hybrid-Covariance Data Assimilation for Global NWP

Two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system for global weather prediction are compared. 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.

Automated summary

This study compares two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system: the hybrid-covariance approach and the hybrid-gain approach. The results show that the simpler and less expensive hybrid-gain approach can achieve similar performance if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.