Changes between Version 4 and Version 5 of ImplementationConceptOnline

Timestamp:

Feb 25, 2012, 6:23:43 PM (12 years ago)

Author:

lnerger

Comment:

--

ImplementationConceptOnline

@@ -28 +28 @@
 
 The right hand side of Figure 1 shows the extensions required for the assimilation system (marked yellow):
- * Close to the start of the model code the routine `init_parallel_pdaf` as added to the code. If the model itself is parallelized the correct location is directly after the initialization of the parallelization in the model code. `init_parallel_pdaf` creates the parallel environment that allows to perform several time stepping loops at the same time.
- * After the initialization part of the model, a routines `init_pdaf` is added. In this routine, parameters for PDAF can be defined and then the core initialization routine PDAF_init is called. This core routine also initializes the array of ensemble states.
- * In order to allow for the integration of the state ensemble an unconditional loop is added around the time stepping loop of the model. This will allow to compute the time stepping loop multiple time during the model integration. PDAF provide an exit-flag for this loop. (There are some conditions, under which this external loop is not required. Some note on this are given further below.)
- * Inside the external loop the PDAF core routine `PDAF_get_state` is added to the code. This routine initializes model fields form the array of ensemble states and initialized the number of time step that have to be computed and ensured that the ensemble integration is performed correctly.
- * At the end of the external loop, the PDAF core routine `PDAF_put_state` is added to the model code. This routine write the propagated model fields back into a state vector of the ensemble array. Also it checks whether the ensemble integration is complete. If not, the next ensemble member will be integrated. If the ensemble integration is complete, the analysis step (i.e. the actual assimilation of the observations) is computed.
+ * `init_parallel_pdaf`: This routine is inserted close to the start of the model code. If the model itself is parallelized the correct location is directly after the initialization of the parallelization in the model code. `init_parallel_pdaf` creates the parallel environment that allows to perform several time stepping loops ("model tasks") at the same time.
+ * `init_pdaf`: This routine is added after the initialization part of the model. In `init_pdaf`, parameters for PDAF can be defined and then the core initialization routine `PDAF_init` is called. This core routine also initializes the array of ensemble states.
+ * Ensemble loop: In order to allow for the integration of the state ensemble an unconditional loop is added around the time stepping loop of the model. This loop will allow to compute the time stepping loop multiple time during the model integration. PDAF provides an exit-flag for this loop. (There are some conditions, under which this external loop is not required. Some notes on this are given further below.)
+ * `PDAF_get_state`: Inside the ensemble loop, the PDAF core routine `PDAF_get_state` is added to the code. This routine initializes model fields from the array of ensemble states and initializes the number of time steps that have to be computed and ensures that the ensemble integration is performed correctly.
+ * `PDAF_put_state`: At the end of the external loop, the PDAF core routine `PDAF_put_state` is added to the model code. This routine writes the propagated model fields back into a state vector of the ensemble array. Also it checks whether the ensemble integration is complete. If not, the next ensemble member will be integrated. If the ensemble integration is complete, the analysis step (i.e. the actual assimilation of the observations) is computed.
 
 With the implementation strategy of PDAF, four routines and the external loop have to be added to the model code. While this looks like a large change in figure 1, this change does actually only affect the general part of the model code. In addition, the amount of source code of the numerical model will be much longer than the addition for the data assimilation system.
@@ -41 +41 @@
  * Depending on the parallelization, there can be cases in which the model has to jump back in time and cases in which the time always moves forward: 
   * Jumping back in time will be required if the number of model tasks used to evolve the ensemble states is smaller than the number of ensemble members. In this case a model task has integrate more than one model state and will have to jump back in time after the integration of each ensemble member. 
-  * If there are as many model tasks as ensemble members, the model time always moves forward. In this case, one can implement PDAF also without the external ensemble loop. That is, one can add calls to `PDAF_get_state` and `PDAF_put_state` directly into the code of the model's time stepping loop. This strategy might also be called for, if a model uses staggered loops (like a loop over minutes inside a loop over hours).
+  * If there are as many model tasks as ensemble members, the model time always moves forward. In this case, one can implement PDAF also without the external ensemble loop. That is, one can add calls to `PDAF_get_state` and `PDAF_put_state` directly into the code of the model's time stepping loop. This strategy might also be called for, if a model uses nested loops (like a loop over minutes inside a loop over hours).
  * Model-specific operations like the initialization of the array of ensemble states in `PDAF_init` are actually performed by user-supplied routines. These routines are called through the standard interface of `PDAF`. Details on the interface and the required routines are given on the pages describing the implementation steps.
  * The assimilation system is controlled by the user-supplied routines that are called through PDAF.  With this strategy, the assimilation program is essentially driven by the model part of the program. Thus, logically the model is not a sub-component of the assimilation system, but the implementation with PDAF results in a model extended for data assimilation.