Changes between Version 28 and Version 29 of ImplementAnalysislseik


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Timestamp:
Sep 4, 2010, 6:32:27 PM (10 years ago)
Author:
lnerger
Comment:

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  • ImplementAnalysislseik

    v28 v29  
    130130Hints:
    131131 * As for the other 'full' routines: While the global counterpart of this routine (`init_obs`) has to initialize the observation vector for the local model sub-domain, the 'full' routine has to include observations that spatially belong to neighboring model sub-domains. As an easy choice one can simply initialize a vector of all globally available observations.
     132 * If the adaptive forgetting factor is not used, this routine only has to exist. However, no functionality is required.
    132133
    133134
     
    151152Hints:
    152153 * For parallel efficiency the LSEIK algorithm is implemented in a way that first the full vectors are initialized. Thus, if `observation_f` has been initialized before `U_init_obs_local` is executed (e.g. by `U_init_dim_obs_full`), the operations performed in this routine will be to select the part of the full observation vector that is relevant for the current local analysis domain.
     154 * The routine `U_init_dim_obs_local` is executed before this routine. Thus, if that routine already prepares the information which elements of `observation_f` are need for `observation_l` this information can be used efficiently here.
    153155
    154156
     
    165167The interface for this routine is:
    166168{{{
    167 SUBROUTINE prodRinvA_local(domain_p, step, dim_obs_p, rank, obs_p, A_p, C_p)
    168169SUBROUTINE prodRinvA_local(domain_p, step, dim_obs_l, rank, obs_l, A_l, C_l)
    169170
     
    182183
    183184Hints:
    184  * the routine does not require that the product is implemented as a real matrix-matrix product. Rather, the product can be implemented in its most efficient form. For example, if the observation error covariance matrix is diagonal, only the multiplication of the diagonal with matrix `A_l` has to be implemented.
     185 * The routine is a local variant of the routine `U_prodRinvA`. Thus if that routine has been implemented before. IF can be modified here for the local filter.
     186 * The routine does not require that the product is implemented as a real matrix-matrix product. Rather, the product can be implemented in its most efficient form. For example, if the observation error covariance matrix is diagonal, only the multiplication of the diagonal with matrix `A_l` has to be implemented.
    185187 * The observation vector `obs_l` is provided through the interface for cases where the observation error variance is relative to the actual value of the observations.
     188
    186189
    187190=== `U_init_n_domains` (init_n_domains.F90) ===
     
    202205Hints:
    203206 * As a simple case, if the localization is only performed horizontally, the local analysis domain can be single vertical columns of the model grid. In this case `n_domains_p` is simply the number of vertical columns in the local model sub-domain.
     207
    204208
    205209=== `U_init_dim_local` (init_dim_local.F90) ===
     
    241245
    242246Some hints:
    243  * Usually, the observation to be considered for a local analysis are those which reside within some distance from the local analysis domain. Thus, if the local analysis domain is a single vertical column of the model grid and if the model grid is a regular ij-grid, then one could use some range of i/j indices to select the observations and determine the local number of them. More generally, one can compute the physical distance of an observation from the local analysis domain and decide on this basis, if the observation has to be considered.
    244  * In the loop over the local analysis domains, the routine is always called before `init_obs_local` is executed. Thus, as `init_dim_obs_local` has to check which observations should be used for the local analysis domain, one can already initialize an integer array that stores the index of observations to be considered. This index should be the position of the observation in the array `observation_f`. With this, the initialization of the local observation vector in `init_obs_local` can be sped up.
     247 * Usually, the observations to be considered for a local analysis are those which reside within some distance from the local analysis domain. Thus, if the local analysis domain is a single vertical column of the model grid and if the model grid is a regular ij-grid, then one could use some range of i/j indices to select the observations and determine the local number of them. More generally, one can compute the physical distance of an observation from the local analysis domain and decide on this basis, if the observation has to be considered.
     248 * In the loop over the local analysis domains, the routine is always called before `U_init_obs_local` is executed. Thus, as `U_init_dim_obs_local` has to check which observations should be used for the local analysis domain, one can already initialize an integer array that stores the index of observations to be considered. This index should be the position of the observation in the array `observation_f`. With this, the initialization of the local observation vector in `U_init_obs_local` can be sped up.
     249 * For PDAF, we could not join the routines `U_init_dim_obs_local` and `U_init_obs_local`, because the array for the local observations is allocated internally to PDAF after U_init_dim_obs_local` is executed.
    245250
    246251
     
    336341 * For a model with domain-decomposition one might use the mean variance for the model sub-domain of the calling process. Alternatively one can compute a mean variance for the full model domain using MPI communication (e.g. the function `MPI_allreduce`).
    337342 * The observation vector `obs_p` is provided to the rotine for the case that the observation error variance is relative to the value of the observations.
     343 * If the adaptive forgetting factor is not used, this routine has only to exist for the compilation, but it does not need functionality.
    338344
    339345
     
    354360
    355361The routine is called in the local filters during the loop over all local analysis domains by the by the routine that computes a local adaptive forgetting factor (PDAF_set_forget_local). The routine has to initialize a local mean observation error variance for all observations used for the analysis in the current local analysis domain.
     362
     363Hints:
     364 * If the local adaptive forgetting factor is not used, this routine has only to exist for the compilation, but it does not need functionality.
    356365
    357366== Execution order of user-supplied routines ==