Abstract
The current global operational four-dimensional ensemble-variational (4DEnVar) data assimilation (DA) system at NCEP adopts a background ensemble at a reduced resolution, which restricts the range of spatial scales that the ensemble background error covariance can resolve. A prior study developed a multi-resolution ensemble 4DEnVar method and determined that this approach can provide a comparable forecast to an approach using solely high-resolution members, while substantially reducing the computational cost. This study further develops the multi-resolution ensemble 4DEnVar approach to allow for a flexible number of low- and high-resolution ensemble members as well as varying localization length scales between the high- and low-resolution ensembles.
Three 4DEnVar experiments with the same computational costs are compared. The first has an 80-member high-resolution background ensemble with single-scale optimally-tuned localization (SR-High). The second and third utilize the multi-resolution background ensembles. One has 130 low-resolution and 40 high-resolution members (MR170) while the other has 180 low-resolution and 24 high-resolution members (MR204). Both multi-resolution ensemble experiments utilize differing localization radii with ensemble resolution. Despite having the same costs, both MR170 and MR204 improves global forecasts and decreases tropical cyclone track errors for up to five days in lead time compared to SR-High. Improvements are most apparent in larger-scale features, such as jet streams and the environmental steering flow of tropical cyclones. Additionally, MR170 outperforms MR204 in terms of global and tropical cyclone track forecasts, demonstrating the value of both increasing sampling at large scales and retaining substantial information at small scales.
