In general, the EAKF is considered to be one of the state-of-the-art data assimilation methods. However, this particular convective system made the data assimilation a nonlinear and a non-Gaussian likelihood problem. Specifically, the prior states of the ensemble members diverged from each other without data assimilation. In such a case the EAKF may not perform optimally. The RHF, on the other hand, is better able to handle non-Gaussian priors and outlier observations. Therefore, the study case represents an ideal opportunity to test if the RHF can perform better than the EAKF. Comparisons are also made with variations in localization, ensemble perturbations, and the inflation.
Early results show that both filters have considerable value in improving the simulations of the convective system. Overall, the EAKF proved to be superior by most metrics with larger and more realistic spread, but lower root-mean-square (RMS) errors and bias of atmospheric state variables. More detailed studies and additional results, especially the relative strengths and weaknesses of each filter, will be presented at the conference.
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