Changes between Version 28 and Version 29 of ImplementAnalysislseik

Timestamp:

Sep 4, 2010, 6:32:27 PM (14 years ago)

Author:

lnerger

Comment:

--

ImplementAnalysislseik

@@ -130 +130 @@
 Hints:
  * 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.
+ * If the adaptive forgetting factor is not used, this routine only has to exist. However, no functionality is required.
 
 
@@ -151 +152 @@
 Hints:
  * 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.
+ * 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. 
 
 
@@ -165 +167 @@
 The interface for this routine is:
 {{{
-SUBROUTINE prodRinvA_local(domain_p, step, dim_obs_p, rank, obs_p, A_p, C_p)
 SUBROUTINE prodRinvA_local(domain_p, step, dim_obs_l, rank, obs_l, A_l, C_l)
 
@@ -182 +183 @@
 
 Hints:
- * 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.
+ * 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.
+ * 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.
  * 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.
+
 
 === `U_init_n_domains` (init_n_domains.F90) ===
@@ -202 +205 @@
 Hints: 
  * 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.
+
 
 === `U_init_dim_local` (init_dim_local.F90) ===
@@ -241 +245 @@
 
 Some hints:
- * 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.
- * 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.
+ * 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.
+ * 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.
+ * 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.
 
 
@@ -336 +341 @@
  * 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`).
  * 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.
+ * If the adaptive forgetting factor is not used, this routine has only to exist for the compilation, but it does not need functionality.
 
 
@@ -354 +360 @@
 
 The 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. 
+
+Hints:
+ * If the local adaptive forgetting factor is not used, this routine has only to exist for the compilation, but it does not need functionality.
 
 == Execution order of user-supplied routines ==