Changes between Version 4 and Version 5 of ModifyModelforEnsembleIntegrationFullpar


Ignore:
Timestamp:
Apr 28, 2014, 9:45:04 PM (6 years ago)
Author:
lnerger
Comment:

--

Legend:

Unmodified
Added
Removed
Modified
  • ModifyModelforEnsembleIntegrationFullpar

    v4 v5  
    2020
    2121Numerical models are typically implemented for normal integration of some initial state. For the data assimilation with a filter algorithm, an ensemble of model states has to be integrated for limited time until observations are available and an analysis step of the filter is computed. Subsequently, the updated ensemble has to be integrated further. To allow for the interruption of the integrations by the analysis step, the model code has to be extended. As described on the page on the [ImplementationConceptOnline implementation concept of the online mode], there are two options for the ensemble integration:
    22  * `fully parallel`: For this implementation one needs to use a parallel computed with a sufficient number of processes such that the data assimilation program run be run with a concurrent time stepping of all ensemble states. Thus, if one runs each mode task with '''n''' processes and the ensemble has '''m''' members, the program has to run with '''n''' times '''m''' processes. This parallelism allows for a simplified implementation as each model task integrated only one model state and the model is always going forward in time.
    23  * `fully flexible`: This variant allows to run the assimilation program in a way so that a model task (set of processors running one model integration) can propagate several ensemble states successively. In the extreme case, this could mean that one only a a single model task that is successively performing the integration of all ensemble states. The implementation for this variant is a bit more complicated, because one has to ensure that the model can jump back in time.
     22 * '''fully parallel''': For this implementation one needs to use a parallel computed with a sufficient number of processes such that the data assimilation program run be run with a concurrent time stepping of all ensemble states. Thus, if one runs each mode task with '''n''' processes and the ensemble has '''m''' members, the program has to run with '''n''' times '''m''' processes. This parallelism allows for a simplified implementation as each model task integrated only one model state and the model is always going forward in time.
     23 * '''fully flexible''': This variant allows to run the assimilation program in a way so that a model task (set of processors running one model integration) can propagate several ensemble states successively. In the extreme case, this could mean that one only a a single model task that is successively performing the integration of all ensemble states. The implementation for this variant is a bit more complicated, because one has to ensure that the model can jump back in time.
    2424
    2525The extension to the model code for both cases is depicted in the figure below (See also the page on the [ImplementationConceptOnline implementation concept of the online mode.)