| 53 | | For the analysis step of the ENSRF and EAKF, different operations related to the observations are needed. These operations are requested by PDAF by calling user-supplied routines. Intentionally, the operations are split into separate routines in order to keep the operations rather elementary. This procedure should simplify the implementation. The names of the required routines are specified in the call to the routine `PDAF_assimilate_ensrf` for the fully-parallel and flexible parallelization implementations (alternatively `PDAF_put_state_lenkf` for the 'flexible' implementation and for the offline-coupled assimilation). With regard to the parallelization, all these routines are executed by the filter processes (`filterpe=.true.`) only. |
| 54 | | |
| 55 | | For completeness we discuss here all user-supplied routines that are specified in the interface to `PDAF_assimilate_ensrf`. Thus, some of the user-supplied routines that are explained on the page explaining the modification of the model code for the ensemble integration are repeated here. |
| 56 | | |
| 57 | | In our study Nerger et al. (2015), we discussed that applying localization can lead to stability issues of the ENSRF. The filter performs a loop over all single observations and with localization the assimilation result depends on the order in which the observations are assimilated. This actually leads to the effect that the assimilation result at some grid point does not only depend on the observations with the localization radius **r**, but also on observations further away. This happens if these observation at larger distance are assimilated before the observations within the radius **r** and if they influence the state close to distance **r**. In this case the innovation for the state close to distance **r** is already change when assimilating the observation within the radius and hence lead to a different result. This effect has implications on the parallelization since keeping the observation order constant over the full model domain leads to a partial serialization of the algorithm. In the implementation in PDAF, we use the parallelization approach that does not guarantee the same order of the observations. Usually, the differences when changing the observation order are small, but the benefit is a better scaling since the serialization is avoided. Nonetheless, we generally recommend using LESTKF or LETKF, or their global variants ESTKF or ETKF, since they no not depend explicitly on the observation order, and they allow for non-diagonal observation error covariance matrices. However, the ENSRF/EAKF might have a good compute performance. |
| | 53 | For the analysis step of the ENSRF and EAKF, different operations related to the observations are needed. These operations are requested by PDAF by calling user-supplied routines. Intentionally, the operations are split into separate routines in order to keep the operations rather elementary. This procedure should simplify the implementation. The names of the required routines are specified in the call to the routine `PDAF_assimilate_ensrf` for the fully-parallel and flexible parallelization implementations (alternatively, `PDAF_put_state_lenkf` for the 'flexible' implementation) and `PDAF_assim_offline_lenkf` for the offline-coupled assimilation. With regard to the parallelization, all these routines are executed by the filter processes (`filterpe=.true.`) only. |
| | 54 | |
| | 55 | For completeness we discuss here all user-supplied routines that are specified in the interface to `PDAF_assimilate_ensrf`. Thus, some of the user-supplied routines that are explained on the page about the modification of the model code for the ensemble integration are repeated here. |
| | 56 | |
| | 57 | In our study, Nerger et al. (2015), we discussed that applying localization can lead to stability issues of the ENSRF. The filter performs a loop over all single observations. With localization the assimilation result depends on the order in which these observations are assimilated. This actually leads to the effect that the assimilation result at some grid point does not only depend on the observations within the localization radius **r**, but also on observations further away. This happens if these observation at larger distance are assimilated before the observations within the radius **r** and if they influence the state close to distance **r**. In this case the innovation for the state close to distance **r** is already changed when assimilating the observation within the radius and hence leads to a different result. This effect has implications on the parallelization since keeping the observation order constant over the full model domain leads to a partial serialization of the algorithm. In the implementation of the serial observation processing in PDAF we use the parallelization approach that does not guarantee the same order of the observations. Usually, the differences when changing the observation order are small, but the benefit is a better scaling because the serialization is avoided. Nonetheless, we generally recommend using LESTKF or LETKF, or their global variants ESTKF or ETKF, since they do not depend explicitly on the observation order, and they allow for non-diagonal observation error covariance matrices. However, the ENSRF/EAKF might have a good compute performance. |
| 61 | | The general aspects of the filter specific routines `PDAF_assimilate_*` have been described on the page [ModifyModelforEnsembleIntegration Modification of the model code for the ensemble integration] and its sub-page on [InsertAnalysisStep inserting the analysis step]. The routine is used in the fully-parallel and the flexible implementation variants of the data assimilation system. When the offline model is used, the routines `PDAF_put_state_*` are used. These have also been used in previous PDAF releases for the 'flexible' implementation variant. Here, we list once more the full interface of the routine. Subsequently, the full set of user-supplied routines specified in the call to `PDAF_assimilate_ensrf` is explained. |
| 62 | | |
| 63 | | The interface when using the LEnKF is the following: |
| | 61 | The general aspects of the filter-specific routines `PDAF_assimilate_*` have been described on the page [ModifyModelforEnsembleIntegration Modification of the model code for the ensemble integration] and its sub-page on [InsertAnalysisStep inserting the analysis step]. The routine is used in the fully-parallel and the flexible implementation variants of the data assimilation system. When the offline model is used, the routines `PDAF_assim_offline_*` are used. (Implementations done for PDAF2 might also use routines `PDAF_put_state_*`, which have also been used in previous PDAF releases for the 'flexible' implementation variant.) Here, we list the full interface of the routine once more. Subsequently, the full set of user-supplied routines specified in the call to `PDAF_assimilate_ensrf` is explained. |
| | 62 | |
| | 63 | The interface for the ENSRF/EAKF: |
| | 81 | |
| | 82 | |
| | 83 | == `PDAF_assim_offline_ensrf` == |
| | 84 | |
| | 85 | For the offline mode of PDAF, the routine `PDAF_assim_offline_ensrf` has to be used instead of `PDAF_assimilate_ensrf`. The general aspects of the filter specific routines `PDAF_assim_offline_*` have been described on the page [wiki:OfflineImplementationGuide Implementation Guide for the offline mode]. The interface of the routine is identical with that of `PDAF_assimilate_ensrf` with the exception that the arguments of the user-supplied routines `U_distribute_state`, `U_collect_state` and `U_next_observation` are not present here. |
| | 86 | |
| | 87 | The interface is the following: |
| | 88 | {{{ |
| | 89 | SUBROUTINE PDAF_assim_offline_ensrf(& |
| | 90 | U_init_dim_obs_f, U_obs_op_f, U_init_obs_f, U_init_obsvars_f, & |
| | 91 | U_localize_covar_serial, & |
| | 92 | U_prepoststep, outflag) |
| | 93 | }}} |
| | 94 | |
| | 95 | |
