A comparison of the hybrid ensemble-variational and the ensemble Kalman filter data assimilation schemes for hurricane initialization

A new hybrid ensemble-variational data assimilation system is recently developed for the Weather Research and Forecasting (WRF) model. The system incorporates the flow-dependent ensemble covariance using the extended control variable method in a variational framework. The ensemble is generated by the computationally efficient ensemble transform Kalman filter (ETKF). Experiments with WRF over the Continental USA domain have demonstrated the hybrid method provided significantly more accurate analyses than the three-dimensional variational method, which assumes an isotropic static forecast error covariance model.

The potential of the hybrid data assimilation system for hurricane initialization will be explored and compared with the ensemble Kalman filter. We will conduct the comparison for Hurricane Rita 2005 running WRF. Standard conventional observations will be assimilated. For both methods, we will also test if including multiple physics ensemble will further improve the analyses.