= Offline Mode: Implementation Guide = {{{ #!html

Offline Mode: Implementation Guide

  1. Main page
  2. Adaptation of the parallelization
  3. Initialization of PDAF
  4. Implementation of the analysis step
  5. Memory and timing information
  6. Ensemble Generation
  7. Ensemble Diagnostics
  8. Auxiliary routines
  9. Filter-specific options
}}} || We recommend to check first the **[wiki:PdafTutorial PDAF Tutorials]**, which provide an easy step-by-step description for assimilation program in the offline and oneline modes utilizing example tutorial codes. [[BR]][[BR]]The Implementation Guide provides more details, e.g. descriptions of the defined interfaces, but is less applied. || This page contains the Implementation Guide for the offline mode of PDAF. The [ImplementationGuide implementation guide for the online mode], which uses a direct coupling of the model and PDAF, is provided on a separate page. The differences between the online and offline modes is described on the page on the [GeneralImplementationConcept Implementation concept of PDAF]. == The implementation of PDAF in offline mode == For the offline mode of data assimilation, the ensemble integrations by the numerical model are executed separately from the assimilation program, which computes the analysis step. Thus, the model code can remain unchanged in the offline mode. The calls to functions of PDAF are only contained in the assimilation program. An example of the implementation is given in the tutorial provided with PDAF in `tutorial/offline_2D_serial` (or `tutorial/offline 2D_parallel` for a parallelized case). These examples base on the template code at `templates/offline`. Several subroutines that contain specific operations for the model and observations are called by PDAF through its defined interface as call-back routines. These subroutines need to be implemented by the user. The operations in all these subroutines are rather elementary like filling the array of the ensemble of model states from model output files and the initialization of the vector of observations. The tutorial code includes examples for these routines for a simple case. We recommend to base your own implementation on the tutorial example or the template by adding to or adapting the functionality, adding reading and writing routines for model files. Figure 1 provide an overview of the call structure of the offline code. [[Image(//pics/PDAFstructure_offline_V3.png)]] [[BR]]'''Figure 1:''' Overall call structure in PDAF's offline mode. The code structure is as follows from left to right * **column 1 'User code'**: The main program calls the three routines shown in this column. These are interface routines, which define variables for PDAF and then call core-subroutines of PDAF * **column 2 'PDAF'**: These are the PDAF core routines which are called by the routines in column 1 to the left. * **column 3 'Call-back routines'**: This column shows the call-back routines, which are called by the PDAF routines in column 2 to the left. The routines in the purple box `callback_obs_pdafomi.F90` are the subroutines that handle observations. The three red routines are used only for localized ensemble filters. * **column 4 'OMI observation modules'**: This column shows observation modules that are used for the specific handling of each observation type. This handling is arranged by the PDAF Observation Module Interface (OMI). The observation-related call-back routines in column 3 to the left call routines in each of the observation modules. * **column 5 'PDAF'**: Some of the call-back routines and the routines in the observation modules call PDAF core routines, e.g. for diagnostics, localization, and to intialize OMI. The implementation of the assimilation program for PDAF in offline mode can be performed in several steps. A possible sequence is the following: 1. [OfflineAdaptParallelization Adaptation of the parallelization] 2. [OfflineInitPdaf Initialization of PDAF and the ensemble by PDAF_init] 3. [OfflineImplementationofAnalysisStep Implementation of the analysis step] 4. [OfflineAddingMemoryandTimingInformation Adding memory and timing information] == Generating initial ensembles == To perform ensemble data assimilation one has to create an initial ensemble, which is then used in the data assimilation process. There are various different ways to generate an ensemble (like random picking of model states, breeding, short-term integrations of perturbed model fields, second-order exact sampling from EOFs). PDAF provides routines to use snapshots from a model integration to first compute EOFs (empirical orthogonal functions) and then to use the EOFs to perform a constrained random transformation to obtain ensemble perturbations to which a central state (i.e. ensemble mean) is added. This second-order exact sampling (Pham, 2001) showed good performance in our own data assimilation applications. The ensemble generation is described on the separate page: 1. [EnsembleGeneration Ensemble Generation]