Version 15 (modified by 10 years ago) (diff)  ,

Implementation of the Analysis step for the LSEIK filter
Implementation Guide
Contents of this page
 Overview

PDAF_put_state_lseik

Usersupplied routines

U_collect_state
(collect_state.F90) 
U_init_dim_obs_full
(init_dim_obs_full.F90) 
U_obs_op_full
(obs_op_full.F90) 
U_init_obs_full
(init_obs_full.F90) 
U_init_obs_local
(init_obs_local.F90) 
U_prepoststep
(prepoststep_seik.F90) 
U_prodRinvA_local
(prodrinva_local.F90) 
U_init_n_domains
(init_n_domains.F90) 
U_init_dim_local
(init_dim_local.F90) 
U_init_dim_obs_local
(init_dim_obs_local.F90) 
U_global2local_state
(global2local_state.F90) 
U_init_obsvar
(init_obsvar.F90)

Overview
For the analysis step of the LSEIK filter several operations related to the observations are needed. These operations are requested by PDAF by calling usersupplied routines. Intentionally, the operations are split into separate routines in order to keep the operations rather elementary as this procedure should simplify the implementation. The names of the required routines are specified in the call to the routine PDAF_put_state_lseik
described below. With regard to the parallelization, all these routines are executed by the filter processes (filterpe=1
) only.
For completeness we discuss here all usersupplied routines that are specified in the interface to PDAF_put_state_lseik
.
PDAF_put_state_lseik
The general espects of the filter specific routines PDAF_put_state_*
have been described on the page Modification of the model core for the ensemble integration.
The interface for the routine PDAF_put_state_lseik
contains several routine names for routines that operate on the local analysis domains (marked by _l
at then end of the routine name). In addition, there are names for routines that consider all available observations required to perform local analyses with LSEIK within some subdomain of a domaindecomposed model (marked by _full
at then end of the routine name). In case of a serial execution of the assimilation program, this will be all globally available observations. However, if the program is executed with parallelization, this might be a smaller set of observations. To explain the difference it is assumed for simplicity that a local analysis domain consists of a single vertical column of the model grid. In addition, we assume that the domain decomposition splits the global model domain by vertical boundaries as is typical for ocean models and that the observations are spatially distributed observations of model fields that are part of the state vector. Under these assumptions, the situation is the following: When a model uses domain decomposition, the LSEIK filter is executed such that for each model subdomain a loop over all local analysis domains (e.g. vertical columns) that belong to the model subdomain is performed. For the update of each single vertical column observations from some larger domain surrounding the vertical column are considered. If the influence radius for the observations is sufficiently small there will be vertical columns for which all relevant observations reside inside the model subdomain of the process. However, if a vertical column is considered that is located close to the boundary to the model subdomain, there will be some observations that don't belong spatially to the local model subdomain, but to a neighboring model subdomain. These observations nonetheless are required on the local model subdomain. Thus, a simple way to handle this situation is to initialize for each process all globally available observations, together with their coordinates. While this method is simple, it can also become inefficient if the assimilation program is executed using a large number of processes. As for an initial implementation, it is usually not the concern to obtain the highest parallel efficiency, the description below assumes that 'full' refers to the global model domain.
The interface when using the LSEIK filter is the following:
SUBROUTINE PDAF_put_state_lseik(U_collect_state, U_init_dim_obs_full, U_obs_op_full, U_init_obs_full, & U_init_obs_local, U_prepoststep, U_prodRinvA_local, U_init_n_domains, & U_init_dim_local, U_init_dim_obs_local, & U_global2local_state, U_local2glocal_state, U_glocal2local_obs, & U_init_obsvar, U_init_obsvar_local, status)
with the following arguments:
U_collect_state
: The name of the usersupplied routine that initializes a state vector from the array holding the ensembel of model states from the model fields. This is basically the inverse operation toU_distribute_state
used inPDAF_get_state
U_init_dim_obs_full
: The name of the usersupplied routine that provides the size of observation vectorU_obs_op_full
: The name of the usersupplied routine that acts as the observation operator on some state vectorU_init_obs_full
: The name of the usersupplied routine that initializes the vector of observationsU_init_obs_local
: The name of the usersupplied routine that initializes the vector of observations for a local analysis domainU_prepoststep
: The name of the pre/poststep routine as inPDAF_get_state
U_prodRinvA_local
: The name of the usersupplied routine that computes the product of the inverse of the observation error covariance matrix with some matrix provided to the routine by PDAF. This operation occurs during the analysis step of the SEIK filter.U_init_n_domains
: The name of the routine that provides the number of local analysis domainsU_init_dim_local
: The name of the routine that provides the state domains for a local analysis domainU_init_dim_obs_local
: The name of the routine that initializes the size of the observation vector for a local analysis domainU_g2l_state
: The name of the routine that initializes a local state vector from the global state vectorU_l2g_state
: The name of the routine that initializes the part of the global state vector corresponding to the provided local state vectorU_g2l_obs
: The name of the routine that initialized a local observation vector from a full observation vectorU_init_obsvar
: The name of the usersupplied routine that provides a global mean observation error variance to PDAF (This routine will only be executed, if an adaptive forgetting factor is used)U_init_obsvar_local
: The name of the usersupplied routine that provides a mean observation error variance for the local analysis domain to PDAF (This routine will only be executed, if an adaptive forgetting factor is used)status
: The integer status flag. It is zero, if PDAF_get_state is exited without errors.
Usersupplied routines
Here all usersupplied routines are described that are required in the call to PDAF_put_state_lseik
. For some of the generic routines, we link to the page on modifying the model code for the ensemble integration.
To indicate usersupplied routines we use the prefix U_
. In the template directory templates/
these routines are provided in files with the routines name without this prefix. In the example implementation in testsuite/src/dummymodel_1D
the routines exist without the prefix, but with the extension _dummy_D.F90
. In the section titles below we provide the name of the template file in parentheses.
U_collect_state
(collect_state.F90)
This routine is independent from the filter algorithm used. See here for the description of this routine.
U_init_dim_obs_full
(init_dim_obs_full.F90)
This routine is used by all local filter algorithms (LSEIK, LETKF).
The interface for this routine is:
SUBROUTINE init_dim_obs_full(step, dim_obs_f) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(out) :: dim_obs_f ! Dimension of full observation vector
The routine is called at the beginning of each analysis step, before the loop over all local analysis domains is entered. It has to initialize the size dim_obs_f
of the full observation vector according to the current time step. For simplicity, dim_obs_f
can be the size for the global model domain.
Some hints:
 It can be useful to not only determine the size of the observation vector at this point. One can also already gather information about the location of the observations, which will be used later, e.g. to implement the observation operator. An array for the locations can be defined in a module like
mod_assimilation
.  The routine is similar to
init_dim_obs
used in the global filters. However, if the global filter is used with a domaindecomposed model, it only initializes the size of the observation vector for the local model subdomain. This is different for the local filters, as the local analysis also requires observational data from neighboring model subdomains. Anyway, one can base on an implemented routineinit_dim_obs
to implementinit_dim_obs_full
.
U_obs_op_full
(obs_op_full.F90)
This routine is used by all local filter algorithms (LSEIK, LETKF).
The interface for this routine is:
SUBROUTINE obs_op_full(step, dim_p, dim_obs_f, state_p, m_state_f) INTEGER, INTENT(in) :: step ! Currrent time step INTEGER, INTENT(in) :: dim_p ! PElocal dimension of state INTEGER, INTENT(in) :: dim_obs_f ! Dimension of the full observed state REAL, INTENT(in) :: state_p(dim_p) ! PElocal model state REAL, INTENT(out) :: m_state_f(dim_obs_f) ! Full observed state
The routine is called during the analysis step, before the loop over the local analysis domain is entered. It has to perform the operation of the observation operator acting on a state vector that is provided as state_p
. The observed state has to be returned in m_state_f
. It is the observed state corresponding to the 'full' observation vector.
Hint:
 If the observation operator involves a global operation, e.g. some global integration, while using domaindecompostion one has to gather the information from the other model domains using MPI communication.
 Analogously to the situation with
init_dim_obs_full
, the routine is similar toinit_dim_obs
used for the global filters. However, with a domaindecompoared model also herem_state_f
will contain parts of the state vector from neighboring model subdomains. To make these parts accessible, some parallel communication will be necessary (The state information for a neighboring model subdomain, will be in the memory of the process that handles that subdomain). The example implementation intestsuite/dummymodel_1d
uses the functionMPI_AllGatherV
for this communication.
U_init_obs_full
(init_obs_full.F90)
This routine is used by all local filter algorithms (LSEIK, LETKF).
The routine is only called if the globally adaptive forgetting factor is used (type_forget=1
in the example implementation). For the local filters there is also the alternative to use locally adaptive forgetting factors (type_forget=2
in the example implementation)
The interface for this routine is:
SUBROUTINE init_obs_full(step, dim_obs_f, observation_f) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: dim_obs_f ! Dimension of full observation vector REAL, INTENT(out) :: observation_f(dim_obs_f) ! Full observation vector
The routine is called during the analysis step before the loop over the local analysis domains is entered. The caller is the routine that computes an adaptive forgetting factor (PDAF_set_forget). It has to provide the full vector of observations in observation_f
for the current time step.
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 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.
U_init_obs_local
(init_obs_local.F90)
This routine is used by all local filter algorithms (LSEIK, LETKF).
The interface for this routine is:
SUBROUTINE init_obs_local(domain_p, step, dim_obs_l, observation_l) INTEGER, INTENT(in) :: domain_p ! Current local analysis domain INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: dim_obs_l ! Local dimension of observation vector REAL, INTENT(out) :: observation_l(dim_obs_l) ! Local observation vector
The routine is called during the analysis step during the loop over the local analysis domain.
It has to provide the vector of observations for analysis of the local analysis domain of index domain_p
in observation_l
for the current time step.
Hints:
 For parallel efficiency the LSEIK is implemented in a way that first the full vectors are initialized. Thus, as
observation_f
has been initialized beforeinit_obs_local
is executed, 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.
U_prepoststep
(prepoststep_seik.F90)
This routine can be identical to that used for the global SEIK filter. See here for the description of this routine.
U_prodRinvA_local
(prodrinva_local.F90)
This routine is used by the local filters (LSEIK and LETKF).
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) INTEGER, INTENT(in) :: domain_p ! Current local analysis domain INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: dim_obs_l ! Dimension of local observation vector INTEGER, INTENT(in) :: rank ! Rank of initial covariance matrix REAL, INTENT(in) :: obs_l(dim_obs_l) ! Local vector of observations REAL, INTENT(inout) :: A_l(dim_obs_l, rank) ! Input matrix from analysis routine REAL, INTENT(out) :: C_l(dim_obs_l, rank) ! Output matrix
The routine is called during the loop over the local analysis domains in the analysis step. In the algorithm the product of the inverse of the observation error covariance matrix with some matrix has to be computed. For the SEIK filter this matrix holds the observed part of the ensemble perturbations for the local analysis domain of index domain_p
. The matrix is provided as A_l
. The product has to be given as C_l
.
This routine is also the place to perform observation localization. To initialize a vector of weights, the routine PDAF_local_weights
can be called. The procedure is used in the example implementation and also demonstrated in the template routine.
Hints:
 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.  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)
This routine is used by all local filter algorithms (LSEIK, LETKF).
The interface for this routine is:
SUBROUTINE init_n_domains(step, n_domains_p) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(out) :: n_domains_p ! number of analysis domains for local model subdomain
The routine is called during the analysis step before the loop over the local analysis domains is entered. It has to provide the number of local analysis domains. In case of a domaindecomposed model the number of local analysis domain for the model subdmain of the calling process has to be initialized.
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 subdomain.
U_init_dim_local
(init_dim_local.F90)
This routine is used by all local filter algorithms (LSEIK, LETKF).
The interface for this routine is:
SUBROUTINE init_dim_local(step, domain_p, dim_l) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: domain_p ! Current local analysis domain INTEGER, INTENT(out) :: dim_l ! Local state dimension
The routine is called during the loop over the local analysis domains in the analysis step.
It has to provide in dim_l
the dimension of the state vector for the local analysis domain with index domain_p
.
Hints:
 If a local analysis domain is a single vertical column of the model grid, the size of the state in the local analysis domain, will be just the number of vertical grid points at this location.
U_init_dim_obs_local
(init_dim_obs_local.F90)
This routine is used by all local filter algorithms (LSEIK, LETKF).
The interface for this routine is:
SUBROUTINE init_dim_obs_local(domain_p, step, dim_obs_f, dim_obs_l) INTEGER, INTENT(in) :: domain_p ! Current local analysis domain INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: dim_obs_f ! Full dimension of observation vector INTEGER, INTENT(out) :: dim_obs_l ! Local dimension of observation vector
The routine is called during the loop over the local analysis domains in the analysis step.
It has to initialize in dim_obs_l
the size of the observation vector used for the local analysis domain with index domain_p
.
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 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.
 In the loop over the local analysis domains, the routine is always called before
init_obs_local
is executed. Thus, asinit_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 arrayobservation_f
. With this, the initialization of the local observation vector ininit_obs_local
can be sped up.
U_global2local_state
(global2local_state.F90)
This routine is used by all local filter algorithms (LSEIK, LETKF).
The interface for this routine is:
SUBROUTINE global2local_state(step, domain_p, dim_p, state_p, dim_l, state_l) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: domain_ ! Current local analysis domain INTEGER, INTENT(in) :: dim_p ! PElocal full state dimension INTEGER, INTENT(in) :: dim_l ! Local state dimension REAL, INTENT(in) :: state_p(dim_p) ! PElocal full state vector REAL, INTENT(out) :: state_l(dim_l) ! State vector on local analysis domain
The routine is called during the loop over the local analysis domains in the analysis step. It has to provide the local state vector state_l
that corresponds to the local analysis domain with index domain_p
. With a domain decomposed model, the state vector state_p
for the local model subdomain is provided to the routine.
Hints:
 In the simple case that a local analysis domain is a single vertical column of the model grid, the operation in this routine would be to take out the data for the vertical column indexed by
domain_p
.
U_init_obsvar
(init_obsvar.F90)
This routine is used by the global filter algorithms SEIK and ETKF as well as the local filters LSEIK and LETKF. The routine is only called if the adaptive forgetting factor is used (type_forget=1
in the example impementation).
The interface for this routine is:
SUBROUTINE init_obsvar(step, dim_obs_p, obs_p, meanvar) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: dim_obs_p ! PElocal dimension of observation vector REAL, INTENT(in) :: obs_p(dim_obs_p) ! PElocal observation vector REAL, INTENT(out) :: meanvar ! Mean observation error variance
The routine is called in the global filters during the analysis or by the routine that computes an adaptive forgetting factor (PDAF_set_forget). The routine has to initialize the mean observation error variance. For the global filters this should be the global mean.
Hints:
 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
).  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.