Version 17 (modified by 15 months ago) (diff)  ,

Implementation of the Analysis step for the ESTKF
Implementation Guide
 Main page
 Adaptation of the parallelization
 Initialization of PDAF
 Modifications for ensemble integration
 Implementation of the analysis step
 Implementation for ESTKF
 Implementation for LESTKF
 Implementation for ETKF
 Implementation for LETKF
 Implementation for SEIK
 Implementation for LSEIK
 Implementation for SEEK
 Implementation for EnKF
 Implementation for LEnKF
 Implementation for NETF
 Implementation for LNETF
 Implementation for PF
 Implementation for 3DVar
 Implementation for 3D Ensemble Var
 Implementation for Hybrid 3DVar
 Memory and timing information
 Ensemble Generation
 Diagnostics
Contents of this page
 Overview

PDAF_assimilate_estkf

PDAF_put_state_estkf

Usersupplied routines

U_collect_state
(collect_state_pdaf.F90) 
U_distribute_state
(distribute_state_pdaf.F90) 
U_init_dim_obs
(init_dim_obs_pdaf.F90) 
U_obs_op
(obs_op_pdaf.F90) 
U_init_obs
(init_obs_pdaf.F90) 
U_prepoststep
(prepoststep_ens_pdaf.F90) 
U_prodRinvA
(prodrinva_pdaf.F90) 
U_init_obsvar
(init_obsvar_pdaf.F90) 
U_next_observation
(next_observation_pdaf.F90)

 Execution order of usersupplied routines
This page describes the implementation of the analysis step without using PDAFOMI. Please see the page on the analysis with OMI for the more modern and efficient implementation variant using PDAFOMI. 
Overview
With Version 1.8 of PDAF, the ESTKF [Error Subspace Transform Kalman Filter] algorithm has been introduced. The usersupplied routines required for the ESTKF are identical to those required for the SEIK filter and amost identical to those required for the ETKF method.
For the analysis step of the ESTKF different operations related to the observations are needed. These operations are requested by PDAF by callback routines supplied by the user. Intentionally, the operations are split into separate routines in order to keep the operations rather elementary and efficient. This procedure should simplify the implementation. The names of the required routines are specified in the call to the routine PDAF_assimilate_estkf
in the fullyparallel implementation (or PDAF_put_state_estkf
for the 'flexible' implementation) that was discussed before. With regard to the parallelization, all these routines are executed by the filter processes (filterpe=.true.
) only.
For completeness we discuss here all usersupplied routines that are specified in the interface to PDAF_assimilate_estkf. Thus, some of the usersupplied routines that are explained on the page describing the modification of the model code for the ensemble integration are repeated here.
The ESTKF and the ETKF (Ensemble Transform Kalman Filter) are very similar. For this reason, the interface to the usersupplied routines is almost identical. Depending on the implementation it can be possible to use identical routines for the ESTKF and the ETKF. Differences are marked in the text below.
PDAF_assimilate_estkf
The general aspects of the filter specific routines PDAF_assimilate_*
have been described on the page Modification of the model code for the ensemble integration and its subpage on inserting the analysis step. The routine is used in the fullyparallel implementation variant of the data assimilation system. When the 'flexible' implementation variant is used, the routines PDAF_put_state_*' is used as described further below. Here, we list once more the full interface of the routine. Subsequently, the full set of usersupplied routines specified in the call to
PDAF_assimilate_estkf` is explained.
The interface when using the ESTKF is the following:
SUBROUTINE PDAF_assimilate_estkf(U_collect_state, U_distribute_state, U_init_dim_obs, & U_obs_op, U_init_obs, U_prepoststep, U_prodRinvA, & U_init_obsvar, U_next_observation, status)
with the following arguments:
 U_collect_state: The name of the usersupplied routine that initializes a state vector from the array holding the ensemble of model states from the model fields. This is basically the inverse operation to
U_distribute_state
used inPDAF_get_state
as well as here.  U_distribute_state: The name of a user supplied routine that initializes the model fields from the array holding the ensemble of model state vectors.
 U_init_dim_obs: The name of the usersupplied routine that provides the size of observation vector
 U_obs_op: The name of the usersupplied routine that acts as the observation operator on some state vector
 U_init_obs: The name of the usersupplied routine that initializes the vector of observations
 U_prepoststep: The name of the pre/poststep routine as in
PDAF_get_state
 U_prodRinvA: 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, ETKF, and ESTKF algorithms.
 U_init_obsvar: The name of the usersupplied routine that provides a mean observation error variance to PDAF (This routine will only be executed, if an adaptive forgetting factor is used)
 U_next_observation: The name of a user supplied routine that initializes the variables
nsteps
,timenow
, anddoexit
. The same routine is also used inPDAF_get_state
. status
: The integer status flag. It is zero, ifPDAF_assimilate_estkf
is exited without errors.
PDAF_put_state_estkf
When the 'flexible' implementation variant is chosen for the assimilation system, the routine PDAF_put_state_estkf
has to be used instead of PDAF_assimilate_estkf
. The general aspects of the filter specific routines PDAF_put_state_*
have been described on the page Modification of the model code for the ensemble integration. The interface of the routine is identical with that of PDAF_assimilate_estkf
with the exception the specification of the usersupplied routines U_distribute_state
and U_next_observation
are missing.
The interface when using the ESTKF is the following:
SUBROUTINE PDAF_put_state_estkf(U_collect_state, U_init_dim_obs, U_obs_op, & U_init_obs, U_prepoststep, U_prodRinvA, U_init_obsvar, status)
Usersupplied routines
Here all usersupplied routines are described that are required in the call to PDAF_assimilate_estkf
. 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 tutorials in tutorial/
and in the template directory templates/
these routines exist without the prefix, but with the extension _pdaf
. The files are named correspondingly. In the section titles below we provide the name of the template file in parentheses.
In the subroutine interfaces some variables appear with the suffix _p
. This suffix indicates that the variable is particular to a model subdomain, if a domain decomposed model is used. Thus, the value(s) in the variable will be different for different model subdomains.
U_collect_state
(collect_state_pdaf.F90)
This routine is independent of the filter algorithm used. See the page on inserting the analysis step for the description of this routine.
U_distribute_state
(distribute_state_pdaf.F90)
This routine is independent of the filter algorithm used. See the page on inserting the analysis step for the description of this routine.
U_init_dim_obs
(init_dim_obs_pdaf.F90)
This routine is used by all global filter algorithms (SEEK, SEIK, EnKF, ETKF, ESTKF).
The interface for this routine is:
SUBROUTINE init_dim_obs(step, dim_obs_p) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(out) :: dim_obs_p ! Dimension of observation vector
The routine is called at the beginning of each analysis step. It has to initialize the size dim_obs_p
of the observation vector according to the current time step. Without parallelization dim_obs_p
will be the size for the full model domain. When a domaindecomposed model is used, dim_obs_p
will be the size of the observation vector for the subdomain of the calling process.
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 locations 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
of the example implementation.
U_obs_op
(obs_op_pdaf.F90)
This routine is used by all global filter algorithms (SEEK, SEIK, EnKF, ETKF, ESTKF).
The interface for this routine is:
SUBROUTINE obs_op(step, dim_p, dim_obs_p, state_p, m_state_p) INTEGER, INTENT(in) :: step ! Currrent time step INTEGER, INTENT(in) :: dim_p ! PElocal dimension of state INTEGER, INTENT(in) :: dim_obs_p ! Dimension of observed state REAL, INTENT(in) :: state_p(dim_p) ! PElocal model state REAL, INTENT(out) :: m_state_p(dim_obs_p) ! PElocal observed state
The routine is called during the analysis step. 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_p
.
For a model using domain decomposition, the operation is on the PElocal subdomain of the model and has to provide the observed substate for the PElocal domain.
Hint:
 If the observation operator involves a global operation, e.g. some global integration, while using domaindecomposition one has to gather the information from the other model domains using MPI communication.
U_init_obs
(init_obs_pdaf.F90)
This routine is used by all global filter algorithms (SEEK, SEIK, EnKF, ETKF, ESTKF).
The interface for this routine is:
SUBROUTINE init_obs(step, dim_obs_p, observation_p) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: dim_obs_p ! PElocal dimension of obs. vector REAL, INTENT(out) :: observation_p(dim_obs_p) ! PElocal observation vector
The routine is called during the analysis step.
It has to provide the vector of observations in observation_p
for the current time step.
For a model using domain decomposition, the vector of observations that exist on the model subdomain for the calling process has to be initialized.
U_prepoststep
(prepoststep_ens_pdaf.F90)
The routine has already been described on the page on modifying the model code for the ensemble integration. For completeness, the description is repeated:
The interface of the routine is identical for all filters. However, the particular operations that are performed in the routine can be specific for each filter algorithm.
The interface for this routine is
SUBROUTINE prepoststep(step, dim_p, dim_ens, dim_ens_p, dim_obs_p, & state_p, Uinv, ens_p, flag) INTEGER, INTENT(in) :: step ! Current time step ! (When the routine is called before the analysis step is provided.) INTEGER, INTENT(in) :: dim_p ! PElocal state dimension INTEGER, INTENT(in) :: dim_ens ! Size of state ensemble INTEGER, INTENT(in) :: dim_ens_p ! PElocal size of ensemble INTEGER, INTENT(in) :: dim_obs_p ! PElocal dimension of observation vector REAL, INTENT(inout) :: state_p(dim_p) ! PElocal forecast/analysis state ! The array 'state_p' is not generally not initialized in the case of SEIK/EnKF/ETKF/ESTKF. ! It can be used freely in this routine. REAL, INTENT(inout) :: Uinv(dim_ens1, dim_ens1) ! Inverse of matrix U REAL, INTENT(inout) :: ens_p(dim_p, dim_ens) ! PElocal state ensemble INTEGER, INTENT(in) :: flag ! PDAF status flag
The routine U_prepoststep
is called once at the beginning of the assimilation process. In addition, it is called during the assimilation cycles before the analysis step and after the ensemble transformation. The routine is called by all filter processes (that is filterpe=1
).
The routine provides for the user the full access to the ensemble of model states. Thus, usercontrolled pre and poststep operations can be performed. For example the forecast and the analysis states and ensemble covariance matrix can be analyzed, e.g. by computing the estimated variances. In addition, the estimates can be written to disk.
Hint:
 If a user considers to perform adjustments to the estimates (e.g. for balances), this routine is the right place for it.
 Only for the SEEK filter the state vector (
state_p
) is initialized. For all other filters, the array is allocated, but it can be used freely during the execution ofU_prepoststep
.  The interface has a difference for ETKF and ESTKF: For the ETKF, the array
Uinv
has sizedim_ens
xdim_ens
. In contrast it has sizedim_ens1
xdim_ens1
for the ESTKF. (For most cases, this will be irrelevant, because most usually the ensemble arrayens_p
is used for computations, rather thanUinv
. Only for the SEIK filter with fixed covariance matrix,Uinv
is required to compute the estimate analysis error. The fixed covariance matrix mode is not available for the ETKF or ESTKF.)  The interface through which
U_prepoststep
is called does not include the array of smoothed ensembles. In order to access the smoother ensemble array one has to set a pointer to it using a call to the routinePDAF_get_smootherens
(see page on auxiliary routines)
U_prodRinvA
(prodrinva_pdaf.F90)
This routine is used by all filter algorithms that use the inverse of the observation error covariance matrix (SEEK, SEIK, ETKF, ESTKF).
The interface for this routine is:
SUBROUTINE prodRinvA(step, dim_obs_p, rank, obs_p, A_p, C_p) INTEGER, INTENT(in) :: step ! Current time step INTEGER, INTENT(in) :: dim_obs_p ! PElocal dimension of obs. vector INTEGER, INTENT(in) :: rank ! Rank of initial covariance matrix REAL, INTENT(in) :: obs_p(dim_obs_p) ! PElocal vector of observations REAL, INTENT(in) :: A_p(dim_obs_p,rank) ! Input matrix from analysis routine REAL, INTENT(out) :: C_p(dim_obs_p,rank) ! Output matrix
The routine is called during the analysis step. In the algorithms the product of the inverse of the observation error covariance matrix with some matrix has to be computed. For the ESTKF this matrix holds the observed part of the ensemble perturbations. The matrix is provided as A_p
. The product has to be given as C_p
.
For a model with domain decomposition, A_p
contains the part of the matrix that resides on the model subdomain of the calling process. The product has to be computed for this subdomain, too.
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_p
has to be implemented.  The observation vector
obs_p
is provided through the interface for cases where the observation error variance is relative to the actual value of the observations.  The interface has a difference for ESTKF and ETKF: For ETKF the third argument is the ensemble size (
dim_ens
), while for the ESTKF it is the rank (rank
) of the covariance matrix (usually ensemble size minus one). In addition, the second dimension ofA_p
andC_p
has sizedim_ens
for ETKF, while it isrank
for the ESTKF. (Practically, one can usually ignore this difference as the fourth argument of the interface can be named arbitrarily in the routine.)
U_init_obsvar
(init_obsvar_pdaf.F90)
This routine is used by the global filter algorithms SEIK, ETKF, and ESTKF as well as the local filters LSEIK, LETKF, ad LESTKF. 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 routine for the case that the observation error variance is relative to the value of the observations.
U_next_observation
(next_observation_pdaf.F90)
This routine is independent of the filter algorithm used. See the page on inserting the analysis step for the description of this routine.
Execution order of usersupplied routines
For the ESTKF, the usersupplied routines are essentially executed in the order they are listed in the interface to PDAF_assimilate_estkf
. The order can be important as some routines can perform preparatory work for later routines. For example, U_init_dim_obs
can prepare an index array that provides the information for executing the observation operator in U_obs_op
.
Before the analysis step is called the following routine is executed:
The analysis step is executed when the ensemble integration of the forecast is completed. During the analysis step the following routines are executed in the given order:
 U_prepoststep (Call to act on the forecast ensemble, called with negative value of the time step)
 U_init_dim_obs
 U_obs_op (A single call to operate on the ensemble mean state)
 U_init_obs
 U_obs_op (
dim_ens
calls: one call for each ensemble member)  U_init_obsvar (Only executed, if the adaptive forgetting factor is used (
type_forget=1
in the example implemention))  U_prodRinvA
 U_prepoststep (Call to act on the analysis ensemble, called with (positive) value of the time step)
In case of the routine PDAF_assimilate_estkf
, the following routines are executed after the analysis step: