| 8 | | Twin data assimilation experiments are a common approach to assess data assimilation methods. In twin experiments one uses the model to generate a 'true' model state. Further one generates synthetic observations by adding random perturbations to the true state. |
| | 8 | Twin data assimilation experiments are a common approach to assess data assimilation methods. In twin experiments one uses the model to generate a ''true'' model state. Further one generates synthetic observations by adding random perturbations to the true state. The, in the actual twin experiment one starts the data assimilation with a state estimate that is different from the true state and assimilates the synthetic observations. One can analyze the assimilation result by comparing the state estimate from the twin experiment with the previously generated true state. |
| | 9 | |
| | 10 | Starting with version 1.14, PDAF provides functionality to generate synthetic observations. The functionality bases on the normal implementation of the assimilation used with PDAF. However, one can run the observation generation with an ensemble of just one member, which should be initialized with the initial true state. PDAF provides the routines `PDAF_generate_obs` and `PDAF_put_state_generate_obs` to perform the observation generation. These routines use the observation operator routines which the user implements e.g. for assimilating real observations. |
| | 11 | |
| | 12 | Here we describes the steps need to generate synthetic obsrvations. |
| | 13 | |
| | 14 | == Initialization == |
| | 15 | |
| | 16 | The implementation of the initialization of PDAF is explained on the [wikiInitPdaf page on `init_pdaf` and `PDAF_init`]. |
| | 17 | |
| | 18 | For the observation generation one just has to set [[BR]] |
| | 19 | `filtertype` = 11 |
| | 20 | |
