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.

Further information: The PDAF3 interface.

• Revision of the initialization of observations: Now the observations are initialized before the actual analysis step is computed. Also the observation operator is called before the actual analysis step. This chane allows for the option to initialize observations before prepoststep_pdaf is called for the forecast ensemble. Related to this the screen output for the observations was revised and is displayed at a different place from before.
• New PDAF3 interface: The selection of possible calls to PDAF*_put_state and PDAF*_assimilate has grown over the years since new functionality, in particular PDAF-OMI and PDAFlocal, required a new interfaces. We now defined a new standard interface in which a single routine (PDAF3_assimilate) can execute all available filter methods for the case of a diagonal observation error covariance matrix. The routine leverages the latest features that were introduced in previous PDAF releases to allow for a minimum number of arguments. There is also the routine PDAF3_put_state, which is now mainly recommended when the offline mode is used.
• New routine PDAF_get_fcst_info: This routine allows to retrieve the return values of PDAF_get_state (nsteps, time, doexit) for the case that PDAF_assimilate routines, which do not return these values, are used.
• Change for flexible parallelization variant: It is now possible to use PDAF_assimilate for the flexible parallelization, while PDAF_put_state routines had to be called before. Since PDAF_assimilate is called at each time step during the forecast phase this allows to easy use of the IAU functionality.
• New routine PDAFomi_set_localize_covar: This routine is called in the OMI observation module in init_dim_obs_pdafomi. It sets the information for localization in the LEnKF and ENSRF/EAKF methods. (optional for LEnKF)
• Calling LEnKF with the universal routine PDAF3_assimilate: In PDAF 2.3 and before, the LEnKF had its own interface due to the additional call-back routine localize_covar_pdafomi required by this filter. With the new routine PDAFomi_set_localize_covar that is called in the OMI observation module in init_dim_obs_pdafomi the additional user-supplied subroutine localize_covar_pdafomi is no longer required which lowers the amount of implementation work for the user.
• New routine PDAF_set_seedset: This routine can be used to seed the random number seed for PDAF's random number generating routine.
• Introduced named variables for choosing the filter type: The filter type can now also be specified by a named parameter of the form PDAF_DA_X, where 'X' is the DA method, thus, e.g. 'PDAF_DA_ESTKF'. With this, one does not need to look up the number of the filter type.
• Added routine 'PDAF_print_filter_types': This routine writes a list of the available filter types and named parameters PDAF_DA_X.
• Added Fortran function PDAF_localfilter as alternative to subroutine PDAF_get_localfilter to make code more compact
• Added subroutine PDAF_get_local_type and Fortran function PDAF_local_type. These return information which of the different localization variants a filter uses (no localization, local analysis, covariance localization, covariance localization with serial observation processing)
• The screen output of the configration of the DA method was revised and provides now a complete configuration overview