“Initialization” using an iterative Matsuno style scheme in the Eta Model adjustment stage

Summary Following a recent approach of Fox-Rabinovitz an iterative Matsuno or a “Super-Matsuno” style scheme is applied as a filter in the Eta Model. In contrast to Fox-Rabinovitz, we however apply the scheme not for all of the model’s time-differencing but for its adjustment terms only. These distinctions compared to the original Fox-Rabinovitz’-method are made easy to implement by the split time differencing approach of the Eta, and at the same time would appear clearly appropriate for the “initialization” purpose. In addition, while Fox-Rabinovitz emphasizes the use of the method within a long time-scale data assimilation framework, we are focusing on the impact of the method in a short-range forecasting environment/time-scale.After a short one hour “initialization” procedure is completed, standard model integration is continued, now very much free of noise. The Super-Matsuno style scheme is found to balance initially unbalanced external and internal modes and to significantly reduce the high-frequency noise during the first 6 time steps. In a control case noise also reduces in amplitude as integration proceeds, but at a much slower rate. The model integration results with and without “initialization” after 6 hours are however very similar. Even so, it is to be expected that small differences, given that they have resulted from the removal of spurious initial noise, have to be beneficial.