Changes between Version 28 and Version 29 of ImplementAnalysislseik
 Timestamp:
 Sep 4, 2010, 6:32:27 PM (10 years ago)
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ImplementAnalysislseik
v28 v29 130 130 Hints: 131 131 * 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 subdomain, the 'full' routine has to include observations that spatially belong to neighboring model subdomains. 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. 132 133 133 134 … … 151 152 Hints: 152 153 * 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. 153 155 154 156 … … 165 167 The interface for this routine is: 166 168 {{{ 167 SUBROUTINE prodRinvA_local(domain_p, step, dim_obs_p, rank, obs_p, A_p, C_p)168 169 SUBROUTINE prodRinvA_local(domain_p, step, dim_obs_l, rank, obs_l, A_l, C_l) 169 170 … … 182 183 183 184 Hints: 184 * the routine does not require that the product is implemented as a real matrixmatrix 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 matrixmatrix 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. 185 187 * 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. 188 186 189 187 190 === `U_init_n_domains` (init_n_domains.F90) === … … 202 205 Hints: 203 206 * 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 subdomain. 207 204 208 205 209 === `U_init_dim_local` (init_dim_local.F90) === … … 241 245 242 246 Some 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 ijgrid, 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 ijgrid, 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. 245 250 246 251 … … 336 341 * For a model with domaindecomposition one might use the mean variance for the model subdomain 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`). 337 342 * 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. 338 344 339 345 … … 354 360 355 361 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. 362 363 Hints: 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. 356 365 357 366 == Execution order of usersupplied routines ==