= PDAF-OMI Observation Diagnostics =

{{{
#!html
<div class="wiki-toc">
<h4>PDAF-OMI Guide</h4>
<ol><li><a href="PDAF_OMI_Overview">Overview</a></li>
<li><a href="OMI_Callback_obs_pdafomi">callback_obs_pdafomi.F90</a></li>
<li><a href="OMI_observation_modules">Observation Modules</a></li>
<li><a href="OMI_observation_operators">Observation operators</a></li>
<li><a href="OMI_error_checking">Checking error status</a></li>
<li><a href="OMI_debugging">Debugging functionality</a></li>
<li><a href="OMI_ImplementationofAnalysisStep">Implementing the analysis step with OMI</a></li>
<ol>
<li> <a href="ImplementFilterAnalysisOverview"> General overview for ensemble filters</a></li>
<ol>
<li><a href="ImplementAnalysisGlobal">Implementation for Global Filters</a></li>
<li><a href="ImplementAnalysisLocal">Implementation for Local Filters</a></li>
<li><a href="ImplementAnalysislenkfOmi">Implementation for LEnKF</a></li>
</ol>
<li> <a href="Implement3DVarAnalysisOverview"> General overview for 3D-Var methods</a></li>
<ol>
<li><a href="ImplementAnalysis_3DVar">Implementation for 3D-Var</a></li>
<li><a href="ImplementAnalysis_3DEnVar">Implementation for 3D Ensemble Var</a></li>
<li><a href="ImplementAnalysis_Hyb3DVar">Implementation for Hybrid 3D-Var</a></li>
</ol>
</ol>
<li><a href="OMI_nondiagonal_observation_error_covariance_matrices">Using nondiagonal R-matrices</a></li>
<li><a href="Porting_to_OMI">Porting an existing implemention to OMI</a></li>
<li><a href="PDAFomi_additional_functionality">Additional OMI Functionality</a></li>
<li>Observation diagnostics</li>
</ol>
</div>
}}}

[[PageOutline(2-3,Contents of this page)]]

The PDAF-OMI observation diagnostics module provides functionality to obtain statistics about the differences between observations and the observed model state. In addition, there are routines that provide the user access to the observations and observed quantities like the observed ensemble mean state. 

Here, we describe the functionalities of the observation diagnostics routines.

A common place to call the PDAFomi_diag diagnostics routines is in `prepoststep_pdaf`, which is the usual place to also analyze the ensemble. Usually, PDAF initializes the observations after `prepoststep_pdaf` was executed after the forecast. To be able to compare the observations and the forecast ensemble, one has to switch the place at which observations are initialized. This is done with 
{{{
  CALL PDAF_set_iparam(9, 0)
}}}
which can be called in `init_pdaf` after the initialization of PDAF with `PDAF_init`.

The routines for observation diagnostics can be organized in four groups
 * Activation of observation diagnostics
   * [PDAFomi_observation_diagnostics#PDAFomi_set_obs_diag PDAFomi_set_obs_diag]
 * Statistics
   * PDAFomi_diag_rmsd
   * PDAFomi_diag_stats
 * Access to observation dimensions
   * PDAFomi_diag_nobs
   * PDAFomi_diag_dimobs
 * Acces to observation arrays
   * PDAFomi_diag_get_obs
   * PDAFomi_diag_get_HXmean
   * PDAFomi_diag_get_HX
   * PDAFomi_diag_get_ivar

== Activation of observation diagnostics ==

=== PDAFomi_set_obs_diag ===

The routine is used to activate or deactivate the observation diagnostics. 

The routine can be called by all processes, but it is sufficient to call it for those processes that handle observations, which usually are the filter processes. A common place is to call the routine in `init_pdaf` afer the initialization of PDAF in `PDAF_init`.

The interface is: 
{{{
  SUBROUTINE PDAFomi_set_obs_diag(diag)

    INTEGER, INTENT(in) :: diag   ! Value for observation diagnostics mode
                                  ! >0 activates observation diagnostics
}}}