Changes between Version 17 and Version 18 of ImplementAnalysislseik


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Timestamp:
Sep 3, 2010, 12:11:26 PM (10 years ago)
Author:
lnerger
Comment:

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  • ImplementAnalysislseik

    v17 v18  
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    317315=== `U_init_obsvar` (init_obsvar.F90) ===
    318316
     
    337335 * 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`).
    338336 * 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.
     337
     338
     339=== `U_init_obsvar_local` (init_obsvar_local.F90) ===
     340
     341This routine is used by the local filters LSEIK and LETKF. The routine is only called if the local adaptive forgetting factor is used (`type_forget=2` in the example impementation).
     342
     343The interface for this routine is:
     344{{{
     345SUBROUTINE init_obsvar_local(domain_p, step, dim_obs_l, obs_l, meanvar_l)
     346
     347  INTEGER, INTENT(in) :: domain_p         ! Current local analysis domain
     348  INTEGER, INTENT(in) :: step             ! Current time step
     349  INTEGER, INTENT(in) :: dim_obs_l        ! Local dimension of observation vector
     350  REAL, INTENT(in)    :: obs_l(dim_obs_l) ! Local observation vector
     351  REAL, INTENT(out)   :: meanvar_l        ! Mean local observation error variance
     352}}}
     353
     354The 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 sued for the analysis in the current local analysis domain.
     355
     356Hints:
     357 * 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`).
     358 * 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.