| 59 | | PDAF adds the possibility to perform parallel ensemble forecasts, even for models that by themselves do not use parallelization. The structure of the parallelized data assimilation program is displayed in figure 2. In the forecast phase of the data assimilation application, several model state integrations can be performed at the same time by several model tasks. If the numerical model it parallelized by itself, the parallel ensemble forecast adds a second level of parallelization. For the analysis step, in which the filter combines the ensemble of model states with the observations, PDAF provides several parallelized filter algorithms. If the model uses domain decomposition for the parallelization, the same decomposition is typically used in the filter. Before the analysis step, all ensemble members are gathered by the processes that compute the filter analysis. Subsequently to the analysis step, the ensemble members are distributed to all model tasks to enable the next parallel ensemble forecast. These operations are performed within PDAF, so that a user can directly benefit from the second level of parallelization. For the required extension of the parallelization configuration of the model a fully implemented template routine is provided with PDAF. The adaptation of the parallelization is described in the [ImplementationGuide Implementation Guide]. |
| | 59 | PDAF adds the possibility to perform parallel ensemble forecasts, even for models that by themselves do not use parallelization. The structure of the parallelized data assimilation program is displayed in Figure 3. In the forecast phase of the data assimilation application, several model state integrations can be performed at the same time by several model tasks. If the numerical model it parallelized by itself, the parallel ensemble forecast adds a second level of parallelization. For the analysis step, in which the DA method combines the ensemble of model states with the observations, PDAF provides several parallelized methods. If the model uses domain decomposition for the parallelization, the same decomposition is typically used in the filter. Before the analysis step, all ensemble members are gathered by the processes that compute the filter analysis. Subsequently to the analysis step, the ensemble members are distributed to all model tasks to enable the next parallel ensemble forecast. These operations are performed within PDAF, so that a user can directly benefit from the second level of parallelization. For the required extension of the parallelization configuration, a fully implemented template routine is provided with PDAF. The adaptation of the parallelization is described in the [ImplementationGuide Implementation Guide]. |
