New Functionality of PDAF3

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 avail shocks in the model states due to the changes of the model state by the assimilation. PDAF V3 contains new functionality to apply IAU.

Further information: Using IAU in PDAF.

New filters ENSRF and EAKF

PDAF V3 includes the ensemble quare 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 statistic moments (mean, variance, skewness, kurtosis) (PDAF_diag_compute_moments)

Further information on all diagnostic routines: Data Assimilation Diagnostics.

PDAF-OMI observation diagnostics

We added a module for observation diagnostics to 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 ensmeble mean, observation coordinates, observation error variance). There are also routines that compute statistics between the observation vector and the observation ensemble mean for example for the root mean square difference, correlation, bias, and mean absolute error). The statistics can be used to e.g. 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-values parameters. They can be used in init_pdaf to set initial parameters as an alternative to specify 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_put_state for offline (file-based) coupled programs. Also all 3D-Var methods can be called by PDAF3_assimilate_3dvar_all or PDAF3_put_state_3dvar_all.

Further information: The PDAF3 interface.

Using PDAF3_assimilate in flexible parallelization

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

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

Covariance localization with PDAF_set_localize_covar

In PDAF2 the localization in the LEnKF required an additional routine localize_covar_pdafomi in each OMI obsevation module. With PDAF V3 one can instead use PDAF_set_localize_covar. This routine is called in the observation initialization routine. It is used for the LEnKF and also the ENSRF/EAKF methods. Using this routine also ensemble 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 analysis 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 code to assess observation information in prepostep_pdaf to compute the forecast ensemble with the observations that will be assimilated in the following analysis step.

Further information: Options for PDAF_init and PDAFomi observation diagnostics

Setting random number seed in PDAF

The new routine PDAF_set_seedset provided 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 dta assimilation case with different random numbers.

Further information: PDAF_set_seedset

Named variable for filtertype

The module PDAF provides now a names variable for each filter type. The names are of the structure PDAF_DA_X, where 'X' is the assimilation method. Thus, one can specify a filtertype in the call to PDAF_init as PDAF_DA_LESTKF instead of the number 7.

Printing filtertype names and numbers

The named filtertypes (PDAF_DA_X) can be listed by calling the routine PDAF_print_filter_types.

Further information: PDAF_print_filter_types.

Checking for type of localization

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