= New Functionality of PDAF3 = [[PageOutline(2-3,Contents of this page)]] 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: [wiki:IncrementalAnalysisUpdates 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: [wiki:ImplementAnalysisENSRF_EAKF 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: [wiki:DataAssimilationDiagnostics 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: [wiki:PDAFomi_observation_diagnostics 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 [wiki:PDAF_set_iparam] and [wiki: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: [wiki:PDAF3_interface 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: [wiki:ExternalModelLoop 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: [wiki: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 [wiki:AvailableOptionsforInitPDAFinPDAF3 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: [wiki:AvailableOptionsforInitPDAFinPDAF3 Options for PDAF_init] and [wiki:PDAFomi_observation_diagnostics 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: [wiki: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: [wiki: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.[[BR]] The function `PDAF_get_local type` allows to distinguish global filters, domain localization and covariance localization (e.g. LEnKF and ENSRF/EAKF). Further information: [wiki:PDAF_localfilter] and [wiki:PDAF_get_local_type].