Using the hybrid gain algorithm to sample data assimilation uncertainty

At the Canadian Meteorological Centre (CMC), an ensemble variational (EnVar) data assimilation system is used for the global deterministic prediction system and an ensemble Kalman filter (EnKF) is used for the global ensemble prediction system. These two systems are co-developed and co-evolving at the CMC and in this study we explore how to maximize the impact of having two algorithms. Following earlier work at the European Centre for Medium-Range Weather Forecasts (ECMWF), we perform experiments with a pure EnKF and an EnKF system that is recentered on the EnVar solution, as well as with a hybrid gain configuration, in which the EnKF system is recentered on the mean of the EnKF and EnVar analyses. Encouraged by the results of the hybrid gain algorithm, we modify it to leave half of the members unchanged and to recenter the other half on the EnVar analysis. With this multi-analysis approach, we sample the different design decisions made for the EnKF and EnVar and see corresponding improvements, notably for the stratospheric analysis. An evaluation using humidity-sensitive radiance channels shows more mixed results of the hybrid gain and multi-analysis approaches. An investigation of the spread-skill relation showed that the background ensembles were overdispersive for humidity and this issue was resolved by a reduction of the additive error for humidity. This highlights the fact that diagnostic information from two analysis systems can be used to identify where those systems have room for improvement. Finally, for various aspects of data assimilation systems, we weigh the benefits of algorithmic diversity against the corresponding additional development cost.