New Functionality of PDAF V3

PDAF V3.0 comes with several new features. Here we provide an overview of them and provide links to more detailed descriptions:

Incremental analysis updates (IAU)

Incremental analysis updating (IAU) is used to avoid shocks in the model states due to the changes of the model state by the assimilation. PDAF V3.0 contains new functionality to apply IAU.

Further information: Using IAU in PDAF.

New filters ENSRF and EAKF

PDAF V3 includes the ensemble square root filter (ENSRF, Whitaker and Hamill, 2002) and the Ensemble Adjustment Filter (EAKF, Anderson, 2003). These filters use serial processing of single observations, which is computationally very efficient. The filters are implemented as different subtypes of what is called ENSRF in PDAF. Both filters can apply covariance localization.

Further information: Implementing the analysis step of the ENSRF & EAKF.

Additional diagnostic routines

We added more diagnostics routines. The new routines provide functionality for:

• Computing ensemble mean (PDAF_diag_ensmean)
• Compute ensemble mean standard deviation (PDAF_diag_stddev)
• Compute variance state vector (PDAF_diag_variance)
• Compute root mean square difference between two vectors (PDAF_diag_rmsd)
• Compute statistical moments (mean, variance, skewness, kurtosis) (PDAF_diag_compute_moments)
• Ensemble reliability budget (PDAF_diag_reliability_budget)

Further information on all diagnostic routines: Data Assimilation Diagnostics.

PDAF-OMI observation diagnostics

We added a module for observation diagnostics in PDAF-OMI. With this, there are routines that provide the user-code - usually prepoststep - direct access to the observation information (observation vector, observed ensemble, observed ensemble mean, observation coordinates, observation error variances). There are also routines that compute statistics between the observation vector and the observed ensemble mean for example, for the root mean square difference, correlation, bias, and mean absolute error. The statistics can be used, e.g., to plot a Taylor diagram.

Further information: PDAFomi observation diagnostics

New routines to set parameters for PDAF

The routines PDAF_set_iparam and PDAF_set_rparam can be used to specify selected integer or real-valued parameters. They can be used in init_pdaf to set initial parameters as an alternative to specifying them in the call to PDAF_init. The routine can also be used during the assimilation process to modify parameters.

Further information: The tutorial examples, e.g. tutorial/offline_2D_serial/init_pdaf.F90 show the implementation. Documentation is provided at PDAF_set_iparam and PDAF_set_rparam.

New universal PDAF3 interface

In PDAF3 it is possible to call any of the different ensemble filter methods using the single routine PDAF3_assimilate for online coupled assimilation, or PDAF3_assim_offline for offline (file-based) coupled programs. Also all 3D-Var methods can be called by PDAF3_assimilate_3dvar_all or PDAF3_assim_offline_3dvar_all.

Further information: The PDAF3 interface.

Using PDAF3_assimilate in flexible parallelization mode

In the flexible parallelization mode of previous PDAF versions, one had to use PDAF_put_state for the flexible parallelization mode. With PDAF V3 it is possible, and recommended, to use PDAF3_assimilate instead. This functionality is used in combination with calling the new routine PDAF_get_fcst_info.

Further information: Modification of the model code for the 'flexible' ensemble integration.

Mandatory module PDAF for all use-included routines

It is now required to include USE PDAF in the subroutines that call PDAF routines. The module PDAF is also recommend to be used to include routines from PDAFomi or PDAFlocal, e.g. in the observation modules and in init_dim_l_pdaf. One does no longer need to distingish the modules, but can use PDAF universally.

Further information: Module PDAF

Covariance localization with PDAF_set_localize_covar

In PDAF2 using PDAF-OMI, the localization in the LEnKF required an additional call-back routine localize_covar_pdafomi and a coresponding routine in each OMI observation module. With PDAF V3 it is recommended to use PDAF_set_localize_covar instead. This routine is called in the observation initialization routine init_dim_obs_pdafomi of each observation module. The routine is used for the LEnKF and also the ENSRF/EAKF methods. Using this routine also enables that the LEnKF can be called using the new universal PDAF3 interface.

Further information: PDAFomi_set_localize_covar.

Initializing observations before prepoststep_pdaf

In PDAF V3, one can choose to initialize observations after an forecast phase before the routine prepoststep_pdaf is called. This is set using the integer parameter with index 9 (see Options for PDAF_init). This option allows the to assess observation information in prepostep_pdaf to compare the forecast ensemble with the observations that will be assimilated in the following analysis step, e.g. using the new PDAFomi observation diagnostics.

Further information: Options for PDAF_init and PDAFomi observation diagnostics

Calling the analysis step in offline coupling with assim_offline

For the offline coupled mode in PDAF2, one used PDAF_put_state routines in combination with PDAF_set_offline_mode, which switched off the ensemble integration functionlity (in older code version one used subtype=5). In PDAF3, there are now PDAF_assim_offline routines, e.g. PDAF3_assim_offline, which can be directly called after the PDAF initialization. This simplifies the code for the offline mode, since only a call to PDAF_init followed by PDAF3_assim_offline are required.

Further information on the offline coupling: Implementation Guide for Offline Mode

Setting random number seed in PDAF

The new routine PDAF_set_seedset provides the possibility to select a set of random number seeds out of 20 choices. This allows to perform, e.g., sensitivity tests by rerunning a data assimilation case with different random numbers.

Further information: PDAF_set_seedset

Named variable for type of assimilation method

The module PDAF provides now a named variable for each type of assimilation method (variable 'filtertype' in tutorial and template codes). The names are of the structure PDAF_DA_X, where 'X' is the assimilation method. For example, one can specify the type of the DA method in the call to PDAF_init as PDAF_DA_LESTKF instead of the number 7.

The named types of DA methods (PDAF_DA_X) can be listed by calling the routine PDAF_print_DA_types.

Further information: PDAF_print_DA_types.

Checking for type of localization

Checking whether a filter uses localization can now be done using the Fortran function PDAF_localfilter (instead of the subroutine PDAF_get_localfilter). The return value will be =1 for domain localized filters (e.g. LESTKF, LETKF) and ENSRF/EAKF, and 0 otherwise.

The function PDAF_get_local type allows to distinguish global filters, domain localization and covariance localization (e.g. LEnKF and ENSRF/EAKF).

Further information: PDAF_localfilter and PDAF_get_local_type.