LCOV - code coverage report
Current view: top level - physics/clubb/src/CLUBB_core - stats_zt_module.F90 (source / functions) Hit Total Coverage
Test: coverage.info Lines: 0 1771 0.0 %
Date: 2024-12-17 17:57:11 Functions: 0 1 0.0 %

          Line data    Source code
       1             : !---------------------------------------------------------------------------
       2             : ! $Id$
       3             : !===============================================================================
       4             : module stats_zt_module
       5             : 
       6             :   implicit none
       7             : 
       8             :   private ! Default Scope
       9             : 
      10             :   public :: stats_init_zt
      11             : 
      12             :   ! Constant parameters
      13             :   integer, parameter, public :: nvarmax_zt = 800 ! Maximum variables allowed
      14             : 
      15             :   contains
      16             : 
      17             :   !=============================================================================
      18           0 :   subroutine stats_init_zt( hydromet_dim, sclr_dim, edsclr_dim, & ! intent(in)
      19           0 :                             hydromet_list, l_mix_rat_hm,        & ! intent(in)
      20           0 :                             vars_zt,                            & ! intent(in)
      21             :                             l_error,                            & ! intent(inout)
      22             :                             stats_metadata, stats_zt )            ! intent(inout)
      23             : 
      24             :     ! Description:
      25             :     ! Initializes array indices for stats_zt
      26             : 
      27             :     ! Note:
      28             :     ! All code that is within subroutine stats_init_zt, including variable
      29             :     ! allocation code, is not called if l_stats is false.  This subroutine is
      30             :     ! called only when l_stats is true.
      31             : 
      32             :     !-----------------------------------------------------------------------
      33             : 
      34             :     use constants_clubb, only:  &
      35             :         fstderr ! Constant(s)
      36             : 
      37             :     use stats_type_utilities, only: &
      38             :         stat_assign ! Procedure
      39             : 
      40             :     use stats_type, only: &
      41             :         stats ! Type
      42             : 
      43             :     use stats_variables, only: &
      44             :         stats_metadata_type
      45             : 
      46             :     implicit none
      47             : 
      48             :     ! External
      49             :     intrinsic :: trim
      50             : 
      51             :     ! Local Constants
      52             : 
      53             :     !--------------------- Input Variable ---------------------
      54             :     integer, intent(in) :: &
      55             :       hydromet_dim, &
      56             :       sclr_dim, &
      57             :       edsclr_dim
      58             : 
      59             :     character(len=10), dimension(hydromet_dim), intent(in) :: & 
      60             :       hydromet_list
      61             : 
      62             :     logical, dimension(hydromet_dim), intent(in) :: &
      63             :       l_mix_rat_hm   ! if true, then the quantity is a hydrometeor mixing ratio
      64             : 
      65             :     character(len= * ), dimension(nvarmax_zt), intent(in) :: &
      66             :       vars_zt
      67             : 
      68             :     !--------------------- InOut Variables ---------------------      
      69             :     type (stats_metadata_type), intent(inout) :: &
      70             :       stats_metadata
      71             : 
      72             :     type (stats), target, intent(inout) :: &
      73             :       stats_zt
      74             : 
      75             :     logical, intent(inout) :: l_error
      76             :  
      77             :     !--------------------- Local Varables ---------------------
      78             :     integer :: tot_zt_loops
      79             : 
      80             :     integer :: i, j, k
      81             : 
      82             :     integer :: hm_idx, hmx_idx, hmy_idx
      83             : 
      84             :     character(len=10) :: hm_type, hmx_type, hmy_type
      85             : 
      86             :     character(len=50) :: sclr_idx
      87             : 
      88             :     !--------------------- Begin Code ---------------------
      89             : 
      90             :     ! The default initialization for array indices for stats_zt is zero (see module
      91             :     ! stats_variables)
      92             : 
      93             :     ! If any of the index arrays are allocated, then we have called this before
      94             :     ! to set up stats_metadata, so all we want to do is set stats_zt via stats_assign
      95           0 :     if ( .not. allocated(stats_metadata%ihm_1) ) then
      96             : 
      97             :       ! Allocate and initialize hydrometeor statistical variables.
      98           0 :       allocate( stats_metadata%ihm_1(1:hydromet_dim) )
      99           0 :       allocate( stats_metadata%ihm_2(1:hydromet_dim) )
     100           0 :       allocate( stats_metadata%imu_hm_1(1:hydromet_dim) )
     101           0 :       allocate( stats_metadata%imu_hm_2(1:hydromet_dim) )
     102           0 :       allocate( stats_metadata%imu_hm_1_n(1:hydromet_dim) )
     103           0 :       allocate( stats_metadata%imu_hm_2_n(1:hydromet_dim) )
     104           0 :       allocate( stats_metadata%isigma_hm_1(1:hydromet_dim) )
     105           0 :       allocate( stats_metadata%isigma_hm_2(1:hydromet_dim) )
     106           0 :       allocate( stats_metadata%isigma_hm_1_n(1:hydromet_dim) )
     107           0 :       allocate( stats_metadata%isigma_hm_2_n(1:hydromet_dim) )
     108             : 
     109           0 :       allocate( stats_metadata%icorr_w_hm_1(1:hydromet_dim) )
     110           0 :       allocate( stats_metadata%icorr_w_hm_2(1:hydromet_dim) )
     111           0 :       allocate( stats_metadata%icorr_chi_hm_1(1:hydromet_dim) )
     112           0 :       allocate( stats_metadata%icorr_chi_hm_2(1:hydromet_dim) )
     113           0 :       allocate( stats_metadata%icorr_eta_hm_1(1:hydromet_dim) )
     114           0 :       allocate( stats_metadata%icorr_eta_hm_2(1:hydromet_dim) )
     115           0 :       allocate( stats_metadata%icorr_Ncn_hm_1(1:hydromet_dim) )
     116           0 :       allocate( stats_metadata%icorr_Ncn_hm_2(1:hydromet_dim) )
     117           0 :       allocate( stats_metadata%icorr_hmx_hmy_1(1:hydromet_dim,1:hydromet_dim) )
     118           0 :       allocate( stats_metadata%icorr_hmx_hmy_2(1:hydromet_dim,1:hydromet_dim) )
     119             : 
     120           0 :       allocate( stats_metadata%icorr_w_hm_1_n(1:hydromet_dim) )
     121           0 :       allocate( stats_metadata%icorr_w_hm_2_n(1:hydromet_dim) )
     122           0 :       allocate( stats_metadata%icorr_chi_hm_1_n(1:hydromet_dim) )
     123           0 :       allocate( stats_metadata%icorr_chi_hm_2_n(1:hydromet_dim) )
     124           0 :       allocate( stats_metadata%icorr_eta_hm_1_n(1:hydromet_dim) )
     125           0 :       allocate( stats_metadata%icorr_eta_hm_2_n(1:hydromet_dim) )
     126           0 :       allocate( stats_metadata%icorr_Ncn_hm_1_n(1:hydromet_dim) )
     127           0 :       allocate( stats_metadata%icorr_Ncn_hm_2_n(1:hydromet_dim) )
     128           0 :       allocate( stats_metadata%icorr_hmx_hmy_1_n(1:hydromet_dim,1:hydromet_dim) )
     129           0 :       allocate( stats_metadata%icorr_hmx_hmy_2_n(1:hydromet_dim,1:hydromet_dim) )
     130             : 
     131           0 :       allocate( stats_metadata%ihmp2_zt(1:hydromet_dim) )
     132             : 
     133           0 :       allocate( stats_metadata%iwp2hmp(1:hydromet_dim) )
     134             : 
     135           0 :       stats_metadata%ihm_1(:) = 0
     136           0 :       stats_metadata%ihm_2(:) = 0
     137           0 :       stats_metadata%imu_hm_1(:) = 0
     138           0 :       stats_metadata%imu_hm_2(:) = 0
     139           0 :       stats_metadata%imu_hm_1_n(:) = 0
     140           0 :       stats_metadata%imu_hm_2_n(:) = 0
     141           0 :       stats_metadata%isigma_hm_1(:) = 0
     142           0 :       stats_metadata%isigma_hm_2(:) = 0
     143           0 :       stats_metadata%isigma_hm_1_n(:) = 0
     144           0 :       stats_metadata%isigma_hm_2_n(:) = 0
     145             : 
     146           0 :       stats_metadata%icorr_w_hm_1(:) = 0
     147           0 :       stats_metadata%icorr_w_hm_2(:) = 0
     148           0 :       stats_metadata%icorr_chi_hm_1(:) = 0
     149           0 :       stats_metadata%icorr_chi_hm_2(:) = 0
     150           0 :       stats_metadata%icorr_eta_hm_1(:) = 0
     151           0 :       stats_metadata%icorr_eta_hm_2(:) = 0
     152           0 :       stats_metadata%icorr_Ncn_hm_1(:) = 0
     153           0 :       stats_metadata%icorr_Ncn_hm_2(:) = 0
     154           0 :       stats_metadata%icorr_hmx_hmy_1(:,:) = 0
     155           0 :       stats_metadata%icorr_hmx_hmy_2(:,:) = 0
     156             : 
     157           0 :       stats_metadata%icorr_w_hm_1_n(:) = 0
     158           0 :       stats_metadata%icorr_w_hm_2_n(:) = 0
     159           0 :       stats_metadata%icorr_chi_hm_1_n(:) = 0
     160           0 :       stats_metadata%icorr_chi_hm_2_n(:) = 0
     161           0 :       stats_metadata%icorr_eta_hm_1_n(:) = 0
     162           0 :       stats_metadata%icorr_eta_hm_2_n(:) = 0
     163           0 :       stats_metadata%icorr_Ncn_hm_1_n(:) = 0
     164           0 :       stats_metadata%icorr_Ncn_hm_2_n(:) = 0
     165           0 :       stats_metadata%icorr_hmx_hmy_1_n(:,:) = 0
     166           0 :       stats_metadata%icorr_hmx_hmy_2_n(:,:) = 0
     167             : 
     168           0 :       stats_metadata%ihmp2_zt(:) = 0
     169             : 
     170           0 :       stats_metadata%iwp2hmp(:) = 0
     171             : 
     172             :       ! Allocate and then zero out passive scalar arrays
     173           0 :       allocate( stats_metadata%isclrm(1:sclr_dim) )
     174           0 :       allocate( stats_metadata%isclrm_f(1:sclr_dim) )
     175             : 
     176           0 :       stats_metadata%isclrm(:)     = 0
     177           0 :       stats_metadata%isclrm_f(:)   = 0
     178             : 
     179           0 :       allocate( stats_metadata%iedsclrm(1:edsclr_dim) )
     180           0 :       allocate( stats_metadata%iedsclrm_f(1:edsclr_dim) )
     181             : 
     182           0 :       stats_metadata%iedsclrm(:)   = 0
     183           0 :       stats_metadata%iedsclrm_f(:) = 0
     184             : 
     185             :     end if
     186             : 
     187             :     ! Assign pointers for statistics variables stats_zt using stat_assign
     188             : 
     189           0 :     tot_zt_loops = stats_zt%num_output_fields
     190             : 
     191           0 :     if ( any( vars_zt == "hm_i" ) ) then
     192             :        ! Correct for number of variables found under "hm_i".
     193             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     194             :        ! for each hydrometeor.
     195           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     196             :        ! Add 1 for "hm_i" to the loop size.
     197           0 :        tot_zt_loops = tot_zt_loops + 1
     198             :     endif
     199           0 :     if ( any( vars_zt == "mu_hm_i" ) ) then
     200             :        ! Correct for number of variables found under "mu_hm_i".
     201             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     202             :        ! for each hydrometeor.
     203           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     204             :        ! Add 1 for "mu_hm_i" to the loop size.
     205           0 :        tot_zt_loops = tot_zt_loops + 1
     206             :     endif
     207           0 :     if ( any( vars_zt == "mu_Ncn_i" ) ) then
     208             :        ! Correct for number of variables found under "mu_Ncn_i".
     209             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     210           0 :        tot_zt_loops = tot_zt_loops - 2
     211             :        ! Add 1 for "mu_Ncn_i" to the loop size.
     212           0 :        tot_zt_loops = tot_zt_loops + 1
     213             :     endif
     214           0 :     if ( any( vars_zt == "mu_hm_i_n" ) ) then
     215             :        ! Correct for number of variables found under "mu_hm_i_n".
     216             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     217             :        ! for each hydrometeor.
     218           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     219             :        ! Add 1 for "mu_hm_i_n" to the loop size.
     220           0 :        tot_zt_loops = tot_zt_loops + 1
     221             :     endif
     222           0 :     if ( any( vars_zt == "mu_Ncn_i_n" ) ) then
     223             :        ! Correct for number of variables found under "mu_Ncn_i_n".
     224             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     225           0 :        tot_zt_loops = tot_zt_loops - 2
     226             :        ! Add 1 for "mu_Ncn_i_n" to the loop size.
     227           0 :        tot_zt_loops = tot_zt_loops + 1
     228             :     endif
     229           0 :     if ( any( vars_zt == "sigma_hm_i" ) ) then
     230             :        ! Correct for number of variables found under "sigma_hm_i".
     231             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     232             :        ! for each hydrometeor.
     233           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     234             :        ! Add 1 for "sigma_hm_i" to the loop size.
     235           0 :        tot_zt_loops = tot_zt_loops + 1
     236             :     endif
     237           0 :     if ( any( vars_zt == "sigma_Ncn_i" ) ) then
     238             :        ! Correct for number of variables found under "sigma_Ncn_i".
     239             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     240           0 :        tot_zt_loops = tot_zt_loops - 2
     241             :        ! Add 1 for "sigma_Ncn_i" to the loop size.
     242           0 :        tot_zt_loops = tot_zt_loops + 1
     243             :     endif
     244           0 :     if ( any( vars_zt == "sigma_hm_i_n" ) ) then
     245             :        ! Correct for number of variables found under "sigma_hm_i_n".
     246             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     247             :        ! for each hydrometeor.
     248           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     249             :        ! Add 1 for "sigma_hm_i_n" to the loop size.
     250           0 :        tot_zt_loops = tot_zt_loops + 1
     251             :     endif
     252           0 :     if ( any( vars_zt == "sigma_Ncn_i_n" ) ) then
     253             :        ! Correct for number of variables found under "sigma_Ncn_i_n".
     254             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     255           0 :        tot_zt_loops = tot_zt_loops - 2
     256             :        ! Add 1 for "sigma_Ncn_i_n" to the loop size.
     257           0 :        tot_zt_loops = tot_zt_loops + 1
     258             :     endif
     259             : 
     260           0 :     if ( any( vars_zt == "corr_w_hm_i" ) ) then
     261             :        ! Correct for number of variables found under "corr_whm_i".
     262             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     263             :        ! for each hydrometeor.
     264           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     265             :        ! Add 1 for "corr_whm_i" to the loop size.
     266           0 :        tot_zt_loops = tot_zt_loops + 1
     267             :     endif
     268           0 :     if ( any( vars_zt == "corr_w_Ncn_i" ) ) then
     269             :        ! Correct for number of variables found under "corr_wNcn_i".
     270             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     271           0 :        tot_zt_loops = tot_zt_loops - 2
     272             :        ! Add 1 for "corr_wNcn_i" to the loop size.
     273           0 :        tot_zt_loops = tot_zt_loops + 1
     274             :     endif
     275           0 :     if ( any( vars_zt == "corr_chi_hm_i" ) ) then
     276             :        ! Correct for number of variables found under "corr_chi_hm_i".
     277             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     278             :        ! for each hydrometeor.
     279           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     280             :        ! Add 1 for "corr_chi_hm_i" to the loop size.
     281           0 :        tot_zt_loops = tot_zt_loops + 1
     282             :     endif
     283           0 :     if ( any( vars_zt == "corr_chi_Ncn_i" ) ) then
     284             :        ! Correct for number of variables found under "corr_chi_Ncn_i".
     285             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     286           0 :        tot_zt_loops = tot_zt_loops - 2
     287             :        ! Add 1 for "corr_chi_Ncn_i" to the loop size.
     288           0 :        tot_zt_loops = tot_zt_loops + 1
     289             :     endif
     290           0 :     if ( any( vars_zt == "corr_eta_hm_i" ) ) then
     291             :        ! Correct for number of variables found under "corr_eta_hm_i".
     292             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     293             :        ! for each hydrometeor.
     294           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     295             :        ! Add 1 for "corr_eta_hm_i" to the loop size.
     296           0 :        tot_zt_loops = tot_zt_loops + 1
     297             :     endif
     298           0 :     if ( any( vars_zt == "corr_eta_Ncn_i" ) ) then
     299             :        ! Correct for number of variables found under "corr_eta_Ncn_i".
     300             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     301           0 :        tot_zt_loops = tot_zt_loops - 2
     302             :        ! Add 1 for "corr_eta_Ncn_i" to the loop size.
     303           0 :        tot_zt_loops = tot_zt_loops + 1
     304             :     endif
     305           0 :     if ( any( vars_zt == "corr_Ncn_hm_i" ) ) then
     306             :        ! Correct for number of variables found under "corr_Ncnhm_i".
     307             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     308             :        ! for each hydrometeor.
     309           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     310             :        ! Add 1 for "corr_Ncnhm_i" to the loop size.
     311           0 :        tot_zt_loops = tot_zt_loops + 1
     312             :     endif
     313           0 :     if ( any( vars_zt == "corr_hmx_hmy_i" ) ) then
     314             :        ! Correct for number of variables found under "corr_hmxhmy_i".
     315             :        ! Subtract 2 (1st PDF component and 2nd PDF component) multipled by the
     316             :        ! number of correlations of two hydrometeors, which is found by:
     317             :        ! (1/2) * hydromet_dim * ( hydromet_dim - 1 ); from the loop size.
     318           0 :        tot_zt_loops = tot_zt_loops - hydromet_dim * ( hydromet_dim - 1 )
     319             :        ! Add 1 for "corr_hmxhmy_i" to the loop size.
     320           0 :        tot_zt_loops = tot_zt_loops + 1
     321             :     endif
     322             : 
     323           0 :     if ( any( vars_zt == "corr_w_hm_i_n" ) ) then
     324             :        ! Correct for number of variables found under "corr_whm_i_n".
     325             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     326             :        ! for each hydrometeor.
     327           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     328             :        ! Add 1 for "corr_whm_i_n" to the loop size.
     329           0 :        tot_zt_loops = tot_zt_loops + 1
     330             :     endif
     331           0 :     if ( any( vars_zt == "corr_w_Ncn_i_n" ) ) then
     332             :        ! Correct for number of variables found under "corr_wNcn_i_n".
     333             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     334           0 :        tot_zt_loops = tot_zt_loops - 2
     335             :        ! Add 1 for "corr_wNcn_i_n" to the loop size.
     336           0 :        tot_zt_loops = tot_zt_loops + 1
     337             :     endif
     338           0 :     if ( any( vars_zt == "corr_chi_hm_i_n" ) ) then
     339             :        ! Correct for number of variables found under "corr_chi_hm_i_n".
     340             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     341             :        ! for each hydrometeor.
     342           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     343             :        ! Add 1 for "corr_chi_hm_i_n" to the loop size.
     344           0 :        tot_zt_loops = tot_zt_loops + 1
     345             :     endif
     346           0 :     if ( any( vars_zt == "corr_chi_Ncn_i_n" ) ) then
     347             :        ! Correct for number of variables found under "corr_chi_Ncn_i_n".
     348             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     349           0 :        tot_zt_loops = tot_zt_loops - 2
     350             :        ! Add 1 for "corr_chi_Ncn_i_n" to the loop size.
     351           0 :        tot_zt_loops = tot_zt_loops + 1
     352             :     endif
     353           0 :     if ( any( vars_zt == "corr_eta_hm_i_n" ) ) then
     354             :        ! Correct for number of variables found under "corr_eta_hm_i_n".
     355             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     356             :        ! for each hydrometeor.
     357           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     358             :        ! Add 1 for "corr_eta_hm_i_n" to the loop size.
     359           0 :        tot_zt_loops = tot_zt_loops + 1
     360             :     endif
     361           0 :     if ( any( vars_zt == "corr_eta_Ncn_i_n" ) ) then
     362             :        ! Correct for number of variables found under "corr_eta_Ncn_i_n".
     363             :        ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
     364           0 :        tot_zt_loops = tot_zt_loops - 2
     365             :        ! Add 1 for "corr_eta_Ncn_i_n" to the loop size.
     366           0 :        tot_zt_loops = tot_zt_loops + 1
     367             :     endif
     368           0 :     if ( any( vars_zt == "corr_Ncn_hm_i_n" ) ) then
     369             :        ! Correct for number of variables found under "corr_Ncnhm_i_n".
     370             :        ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
     371             :        ! for each hydrometeor.
     372           0 :        tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
     373             :        ! Add 1 for "corr_Ncnhm_i_n" to the loop size.
     374           0 :        tot_zt_loops = tot_zt_loops + 1
     375             :     endif
     376           0 :     if ( any( vars_zt == "corr_hmx_hmy_i_n" ) ) then
     377             :        ! Correct for number of variables found under "corr_hmxhmy_i_n".
     378             :        ! Subtract 2 (1st PDF component and 2nd PDF component) multipled by the
     379             :        ! number of normal space correlations of two hydrometeors, which is found
     380             :        ! by:  (1/2) * hydromet_dim * ( hydromet_dim - 1 );
     381             :        ! from the loop size.
     382           0 :        tot_zt_loops = tot_zt_loops - hydromet_dim * ( hydromet_dim - 1 )
     383             :        ! Add 1 for "corr_hmxhmy_i_n" to the loop size.
     384           0 :        tot_zt_loops = tot_zt_loops + 1
     385             :     endif
     386             : 
     387           0 :     if ( any( vars_zt == "hmp2_zt" ) ) then
     388             :        ! Correct for number of variables found under "hmp2_zt".
     389             :        ! Subtract 1 from the loop size for each hydrometeor.
     390           0 :        tot_zt_loops = tot_zt_loops - hydromet_dim
     391             :        ! Add 1 for "hmp2_zt" to the loop size.
     392           0 :        tot_zt_loops = tot_zt_loops + 1
     393             :     endif
     394             : 
     395           0 :     if ( any( vars_zt == "wp2hmp" ) ) then
     396             :        ! Correct for number of variables found under "wp2hmp".
     397             :        ! Subtract 1 from the loop size for each hydrometeor.
     398           0 :        tot_zt_loops = tot_zt_loops - hydromet_dim
     399             :        ! Add 1 for "wp2hmp" to the loop size.
     400           0 :        tot_zt_loops = tot_zt_loops + 1
     401             :     endif
     402             : 
     403           0 :     if ( any( vars_zt == "sclrm" ) ) then
     404             :        ! Correct for number of variables found under "sclrm".
     405             :        ! Subtract 1 from the loop size for each scalar.
     406           0 :        tot_zt_loops = tot_zt_loops - sclr_dim
     407             : 
     408             :        ! Add 1 for "sclrm" to the loop size.
     409           0 :        tot_zt_loops = tot_zt_loops + 1
     410             :     endif
     411             : 
     412           0 :     if ( any( vars_zt == "sclrm_f" ) ) then
     413             :        ! Correct for number of variables found under "sclrm_f".
     414             :        ! Subtract 1 from the loop size for each scalar.
     415           0 :        tot_zt_loops = tot_zt_loops - sclr_dim
     416             :        ! Add 1 for "sclrm_f" to the loop size.
     417           0 :        tot_zt_loops = tot_zt_loops + 1
     418             :     endif
     419             : 
     420           0 :     if ( any( vars_zt == "edsclrm" ) ) then
     421             :        ! Correct for number of variables found under "edsclrm".
     422             :        ! Subtract 1 from the loop size for each scalar.
     423           0 :        tot_zt_loops = tot_zt_loops - edsclr_dim
     424             :        ! Add 1 for "edsclrm" to the loop size.
     425           0 :        tot_zt_loops = tot_zt_loops + 1
     426             :     endif
     427             : 
     428           0 :     if ( any( vars_zt == "edsclrm_f" ) ) then
     429             :        ! Correct for number of variables found under "edsclrm_f".
     430             :        ! Subtract 1 from the loop size for each scalar.
     431           0 :        tot_zt_loops = tot_zt_loops - edsclr_dim
     432             :        ! Add 1 for "edsclrm_f" to the loop size.
     433           0 :        tot_zt_loops = tot_zt_loops + 1
     434             :     endif
     435             : 
     436           0 :     k = 1
     437             : 
     438           0 :     do i = 1, tot_zt_loops
     439             : 
     440           0 :       select case ( trim( vars_zt(i) ) )
     441             :       case ('thlm')
     442           0 :         stats_metadata%ithlm = k
     443             :         call stat_assign( var_index=stats_metadata%ithlm, var_name="thlm", &
     444             :              var_description="thlm, Liquid water potential temperature (theta_l)", var_units="K", &
     445           0 :              l_silhs=.false., grid_kind=stats_zt )
     446           0 :         k = k + 1
     447             : 
     448             :       case ('T_in_K')
     449           0 :         stats_metadata%iT_in_K = k
     450             :         call stat_assign( var_index=stats_metadata%iT_in_K, var_name="T_in_K", &
     451             :              var_description="T_in_K, Absolute temperature", var_units="K", l_silhs=.false., &
     452           0 :              grid_kind=stats_zt )
     453           0 :         k = k + 1
     454             : 
     455             :       case ('thvm')
     456           0 :         stats_metadata%ithvm = k
     457             :         call stat_assign( var_index=stats_metadata%ithvm, var_name="thvm", &
     458             :              var_description="thvm, Virtual potential temperature", &
     459             :              var_units="K", l_silhs=.false., &
     460           0 :              grid_kind=stats_zt )
     461           0 :         k = k + 1
     462             : 
     463             :       case ('rtm')
     464           0 :         stats_metadata%irtm = k
     465             : 
     466             :         call stat_assign( var_index=stats_metadata%irtm, var_name="rtm", &
     467             :              var_description="rtm, Total (vapor+liquid) water mixing ratio", &
     468           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
     469             : 
     470           0 :         k = k + 1
     471             : 
     472             :       case ('rcm')
     473           0 :         stats_metadata%ircm = k
     474             :         call stat_assign( var_index=stats_metadata%ircm, var_name="rcm", &
     475             :              var_description="rcm, Cloud water mixing ratio", var_units="kg/kg", &
     476           0 :              l_silhs=.false., grid_kind=stats_zt )
     477           0 :         k = k + 1
     478             : 
     479             :       case ('rfrzm')
     480           0 :         stats_metadata%irfrzm = k
     481             :         call stat_assign( var_index=stats_metadata%irfrzm, var_name="rfrzm", &
     482             :              var_description="rfrzm, Total ice phase water mixing ratio", var_units="kg/kg", &
     483           0 :              l_silhs=.false., grid_kind=stats_zt )
     484           0 :         k = k + 1
     485             : 
     486             :       case ('rvm')
     487           0 :         stats_metadata%irvm = k
     488             :         call stat_assign( var_index=stats_metadata%irvm, var_name="rvm", &
     489             :              var_description="rvm, Vapor water mixing ratio", var_units="kg/kg", &
     490           0 :              l_silhs=.false., grid_kind=stats_zt )
     491           0 :         k = k + 1
     492             :       case ('rel_humidity')
     493           0 :         stats_metadata%irel_humidity = k
     494             :         call stat_assign( var_index=stats_metadata%irel_humidity, var_name="rel_humidity", &
     495             :              var_description="rel_humidity, Relative humidity w.r.t. liquid (between 0 and 1)", &
     496           0 :              var_units="[-]", l_silhs=.false., grid_kind=stats_zt )
     497           0 :         k = k + 1
     498             :       case ('um')
     499           0 :         stats_metadata%ium = k
     500             :         call stat_assign( var_index=stats_metadata%ium, var_name="um", &
     501             :              var_description="u_bar, Grid-mean eastward (u) wind", &
     502             :              var_units="m/s", l_silhs=.false., &
     503           0 :              grid_kind=stats_zt )
     504           0 :         k = k + 1
     505             :       case ('vm')
     506           0 :         stats_metadata%ivm = k
     507             :         call stat_assign( var_index=stats_metadata%ivm, var_name="vm", &
     508             :              var_description="v_bar, Grid-mean northward (v) wind", &
     509             :              var_units="m/s", l_silhs=.false., &
     510           0 :              grid_kind=stats_zt )
     511           0 :         k = k + 1
     512             :       case ('wm_zt')
     513           0 :         stats_metadata%iwm_zt = k
     514             :         call stat_assign( var_index=stats_metadata%iwm_zt, var_name="wm_zt", &
     515             :              var_description="w_bar, Grid-mean upward (w) wind", &
     516             :              var_units="m/s", l_silhs=.false., &
     517           0 :              grid_kind=stats_zt )
     518           0 :         k = k + 1
     519             :       case ('um_ref')
     520           0 :         stats_metadata%ium_ref = k
     521             :         call stat_assign( var_index=stats_metadata%ium_ref, var_name="um_ref", &
     522             :              var_description="um_ref, Reference u wind", var_units="m/s", l_silhs=.false., &
     523           0 :              grid_kind=stats_zt )
     524           0 :         k = k + 1
     525             :       case ('vm_ref')
     526           0 :         stats_metadata%ivm_ref = k
     527             :         call stat_assign( var_index=stats_metadata%ivm_ref, var_name="vm_ref", &
     528             :              var_description="vm_ref, Reference v wind", var_units="m/s", l_silhs=.false., &
     529           0 :              grid_kind=stats_zt )
     530           0 :         k = k + 1
     531             :       case ('ug')
     532           0 :         stats_metadata%iug = k
     533             :         call stat_assign( var_index=stats_metadata%iug, var_name="ug", &
     534             :              var_description="ug, u geostrophic wind", var_units="m/s", l_silhs=.false., &
     535           0 :              grid_kind=stats_zt )
     536           0 :         k = k + 1
     537             :       case ('vg')
     538           0 :         stats_metadata%ivg = k
     539             :         call stat_assign( var_index=stats_metadata%ivg, var_name="vg", &
     540             :              var_description="vg, v geostrophic wind", var_units="m/s", l_silhs=.false., &
     541           0 :              grid_kind=stats_zt )
     542           0 :         k = k + 1
     543             :       case ('cloud_frac')
     544           0 :         stats_metadata%icloud_frac = k
     545             :         call stat_assign( var_index=stats_metadata%icloud_frac, var_name="cloud_frac", &
     546             :              var_description="cloud_frac, Cloud fraction (between 0 and 1)", var_units="-", &
     547           0 :              l_silhs=.false., grid_kind=stats_zt )
     548           0 :         k = k + 1
     549             : 
     550             :       case ('ice_supersat_frac')
     551           0 :         stats_metadata%iice_supersat_frac = k
     552             :         call stat_assign( var_index=stats_metadata%iice_supersat_frac, var_name="ice_supersat_frac", &
     553             :              var_description="ice_supersat_frac, Ice cloud fraction (between 0 and 1)", &
     554             :              var_units="count", &
     555           0 :              l_silhs=.false., grid_kind=stats_zt )
     556           0 :         k = k + 1
     557             : 
     558             :       case ('rcm_in_layer')
     559           0 :         stats_metadata%ircm_in_layer = k
     560             :         call stat_assign( var_index=stats_metadata%ircm_in_layer, var_name="rcm_in_layer", &
     561             :              var_description="rcm_in_layer, rcm in cloud layer", &
     562             :              var_units="kg/kg", l_silhs=.false., &
     563           0 :              grid_kind=stats_zt )
     564           0 :         k = k + 1
     565             : 
     566             :       case ('rcm_in_cloud')
     567           0 :         stats_metadata%ircm_in_cloud = k
     568             :         call stat_assign( var_index=stats_metadata%ircm_in_cloud, var_name="rcm_in_cloud", &
     569             :              var_description="rcm_in_cloud, In-cloud value of rcm (for microphysics)", &
     570           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
     571           0 :         k = k + 1
     572             : 
     573             :       case ('cloud_cover')
     574           0 :         stats_metadata%icloud_cover = k
     575             :         call stat_assign( var_index=stats_metadata%icloud_cover, var_name="cloud_cover", &
     576             :              var_description="cloud_cover, Cloud cover (between 0 and 1)", var_units="count", &
     577           0 :              l_silhs=.false., grid_kind=stats_zt )
     578           0 :         k = k + 1
     579             :       case ('p_in_Pa')
     580           0 :         stats_metadata%ip_in_Pa = k
     581             :         call stat_assign( var_index=stats_metadata%ip_in_Pa, var_name="p_in_Pa", &
     582             :              var_description="p_in_Pa, Pressure", &
     583           0 :              var_units="Pa", l_silhs=.false., grid_kind=stats_zt )
     584           0 :         k = k + 1
     585             :       case ('exner')
     586           0 :         stats_metadata%iexner = k
     587             :         call stat_assign( var_index=stats_metadata%iexner, var_name="exner", &
     588             :              var_description="exner, Exner function = (p/p0)**(rd/cp)", var_units="count", &
     589           0 :              l_silhs=.false., grid_kind=stats_zt )
     590           0 :         k = k + 1
     591             :       case ('rho_ds_zt')
     592           0 :         stats_metadata%irho_ds_zt = k
     593             :         call stat_assign( var_index=stats_metadata%irho_ds_zt, var_name="rho_ds_zt", &
     594             :              var_description="rho_ds_zt, Dry static base-state density", var_units="kg m^{-3}", &
     595           0 :              l_silhs=.false., grid_kind=stats_zt )
     596           0 :         k = k + 1
     597             :       case ('thv_ds_zt')
     598           0 :         stats_metadata%ithv_ds_zt = k
     599             :         call stat_assign( var_index=stats_metadata%ithv_ds_zt, var_name="thv_ds_zt", &
     600             :              var_description="thv_ds_zt, Dry base-state theta_v", &
     601             :              var_units="K", l_silhs=.false., &
     602           0 :              grid_kind=stats_zt )
     603           0 :         k = k + 1
     604             :       case ('Lscale')
     605           0 :         stats_metadata%iLscale = k
     606             :         call stat_assign( var_index=stats_metadata%iLscale, var_name="Lscale", &
     607             :           var_description="L, Turbulent mixing length", &
     608           0 :           var_units="m", l_silhs=.false., grid_kind=stats_zt )
     609           0 :         k = k + 1
     610             :       case ('thlm_forcing')
     611           0 :         stats_metadata%ithlm_forcing = k
     612             :         call stat_assign( var_index=stats_metadata%ithlm_forcing, var_name="thlm_forcing", &
     613             :              var_description="thlm_forcing, thlm budget: thetal forcing " &
     614             :              // "(includes thlm_mc and radht)",&
     615           0 :              var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
     616           0 :         k = k + 1
     617             :       case ('thlm_mc')
     618           0 :         stats_metadata%ithlm_mc = k
     619             :         call stat_assign( var_index=stats_metadata%ithlm_mc, var_name="thlm_mc", &
     620             :              var_description="thlm_mc, Change in thlm due to microphysics (not in budget)", &
     621           0 :              var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
     622           0 :         k = k + 1
     623             :       case ('rtm_forcing')
     624           0 :         stats_metadata%irtm_forcing = k
     625             :         call stat_assign( var_index=stats_metadata%irtm_forcing, var_name="rtm_forcing", &
     626             :              var_description="rtm_forcing, rtm budget: rt forcing (includes rtm_mc)", &
     627           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
     628           0 :         k = k + 1
     629             : 
     630             :       case ('rtm_mc')
     631           0 :         stats_metadata%irtm_mc = k
     632             :         call stat_assign( var_index=stats_metadata%irtm_mc, var_name="rtm_mc", &
     633             :              var_description="rtm_mc, Change in rt due to microphysics (not in budget)", &
     634           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
     635           0 :         k = k + 1
     636             : 
     637             :       case ('rvm_mc')
     638           0 :         stats_metadata%irvm_mc = k
     639             :         call stat_assign( var_index=stats_metadata%irvm_mc, var_name="rvm_mc", &
     640             :              var_description="rvm_mc, Time tendency of vapor mixing ratio due to microphysics", &
     641           0 :              var_units="kg/(kg s)", l_silhs=.false., grid_kind=stats_zt )
     642           0 :         k = k + 1
     643             : 
     644             :       case ('rcm_mc')
     645           0 :         stats_metadata%ircm_mc = k
     646             :         call stat_assign( var_index=stats_metadata%ircm_mc, var_name="rcm_mc", &
     647             :              var_description="rcm_mc, Time tendency of liquid water mixing ratio " &
     648             :              // "due microphysics",&
     649           0 :              var_units="kg/kg/s", l_silhs=.false., grid_kind=stats_zt )
     650           0 :         k = k + 1
     651             : 
     652             :       case ('rcm_sd_mg_morr')
     653           0 :         stats_metadata%ircm_sd_mg_morr = k
     654             :         call stat_assign( var_index=stats_metadata%ircm_sd_mg_morr, var_name="rcm_sd_mg_morr", &
     655             :              var_description="rcm_sd_mg_morr, rcm sedimentation when using morrision or MG " &
     656             :              // "microphysics (not in budget, included in rcm_mc)", &
     657           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.true., grid_kind=stats_zt )
     658           0 :         k = k + 1
     659             : 
     660             :       case ('thlm_mfl_min')
     661           0 :         stats_metadata%ithlm_mfl_min = k
     662             :         call stat_assign( var_index=stats_metadata%ithlm_mfl_min, var_name="thlm_mfl_min", &
     663             :              var_description="thlm_mfl_min, Minimum allowable thlm", var_units="K", &
     664             :              l_silhs=.false., &
     665           0 :              grid_kind=stats_zt )
     666           0 :         k = k + 1
     667             : 
     668             :       case ('thlm_mfl_max')
     669           0 :         stats_metadata%ithlm_mfl_max = k
     670             :         call stat_assign( var_index=stats_metadata%ithlm_mfl_max, var_name="thlm_mfl_max", &
     671             :              var_description="thlm_mfl_max, Maximum allowable thlm", var_units="K", &
     672             :              l_silhs=.false., &
     673           0 :              grid_kind=stats_zt )
     674           0 :         k = k + 1
     675             : 
     676             :       case ('thlm_enter_mfl')
     677           0 :         stats_metadata%ithlm_enter_mfl = k
     678             :         call stat_assign( var_index=stats_metadata%ithlm_enter_mfl, var_name="thlm_enter_mfl", &
     679             :              var_description="thlm_enter_mfl, Thlm before flux-limiter", var_units="K", &
     680             :              l_silhs=.false., &
     681           0 :              grid_kind=stats_zt )
     682           0 :         k = k + 1
     683             : 
     684             :       case ('thlm_exit_mfl')
     685           0 :         stats_metadata%ithlm_exit_mfl = k
     686             :         call stat_assign( var_index=stats_metadata%ithlm_exit_mfl, var_name="thlm_exit_mfl", &
     687             :              var_description="thlm_exit_mfl, Thlm exiting flux-limiter", var_units="K", &
     688             :              l_silhs=.false., &
     689           0 :              grid_kind=stats_zt )
     690           0 :         k = k + 1
     691             : 
     692             :       case ('thlm_old')
     693           0 :         stats_metadata%ithlm_old = k
     694             :         call stat_assign( var_index=stats_metadata%ithlm_old, var_name="thlm_old", &
     695             :              var_description="thlm_old, Thlm at previous timestep", var_units="K", &
     696             :              l_silhs=.false., &
     697           0 :              grid_kind=stats_zt )
     698           0 :         k = k + 1
     699             : 
     700             :       case ('thlm_without_ta')
     701           0 :         stats_metadata%ithlm_without_ta = k
     702             :         call stat_assign( var_index=stats_metadata%ithlm_without_ta, var_name="thlm_without_ta", &
     703             :              var_description="thlm_without_ta, Thlm without turbulent advection contribution", &
     704             :              var_units="K", &
     705           0 :              l_silhs=.false., grid_kind=stats_zt )
     706           0 :         k = k + 1
     707             : 
     708             :       case ('rtm_mfl_min')
     709           0 :         stats_metadata%irtm_mfl_min = k
     710             :         call stat_assign( var_index=stats_metadata%irtm_mfl_min, var_name="rtm_mfl_min", &
     711             :              var_description="rtm_mfl_min, Minimum allowable rtm", var_units="kg/kg", &
     712           0 :              l_silhs=.false., grid_kind=stats_zt )
     713           0 :         k = k + 1
     714             : 
     715             :       case ('rtm_mfl_max')
     716           0 :         stats_metadata%irtm_mfl_max = k
     717             :         call stat_assign( var_index=stats_metadata%irtm_mfl_max, var_name="rtm_mfl_max", &
     718             :              var_description="rtm_mfl_max, Maximum allowable rtm", var_units="kg/kg", &
     719           0 :              l_silhs=.false., grid_kind=stats_zt )
     720           0 :         k = k + 1
     721             : 
     722             :       case ('rtm_enter_mfl')
     723           0 :         stats_metadata%irtm_enter_mfl = k
     724             :         call stat_assign( var_index=stats_metadata%irtm_enter_mfl, var_name="rtm_enter_mfl", &
     725             :              var_description="rtm_enter_mfl, Rtm before flux-limiter", var_units="kg/kg", &
     726           0 :              l_silhs=.false., grid_kind=stats_zt )
     727           0 :         k = k + 1
     728             : 
     729             :       case ('rtm_exit_mfl')
     730           0 :         stats_metadata%irtm_exit_mfl = k
     731             :         call stat_assign( var_index=stats_metadata%irtm_exit_mfl, var_name="rtm_exit_mfl", &
     732             :              var_description="rtm_exit_mfl, Rtm exiting flux-limiter", var_units="kg/kg", &
     733           0 :              l_silhs=.false., grid_kind=stats_zt )
     734           0 :         k = k + 1
     735             : 
     736             :       case ('rtm_old')
     737           0 :         stats_metadata%irtm_old = k
     738             :         call stat_assign( var_index=stats_metadata%irtm_old, var_name="rtm_old", &
     739             :              var_description="rtm_old, Rtm at previous timestep", var_units="kg/kg", &
     740           0 :              l_silhs=.false., grid_kind=stats_zt )
     741           0 :         k = k + 1
     742             : 
     743             :       case ('rtm_without_ta')
     744           0 :         stats_metadata%irtm_without_ta = k
     745             :         call stat_assign( var_index=stats_metadata%irtm_without_ta, var_name="rtm_without_ta", &
     746             :              var_description="rtm_without_ta, Rtm without turbulent advection contribution", &
     747           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
     748           0 :         k = k + 1
     749             : 
     750             :       case ('wp3')
     751           0 :         stats_metadata%iwp3 = k
     752             :         call stat_assign( var_index=stats_metadata%iwp3, var_name="wp3", &
     753             :              var_description="w'^3, Third-order moment of vertical air velocity", &
     754             :              var_units="m^3/s^3", &
     755           0 :              l_silhs=.false., grid_kind=stats_zt )
     756           0 :         k = k + 1
     757             : 
     758             :       case ('wpup2')
     759           0 :         stats_metadata%iwpup2 = k
     760             :         call stat_assign( var_index=stats_metadata%iwpup2, var_name="wpup2", &
     761             :              var_description="w'u'^2, Third-order moment from PDF", &
     762             :              var_units="m^3/s^3", &
     763           0 :              l_silhs=.false., grid_kind=stats_zt )
     764           0 :         k = k + 1
     765             : 
     766             :       case ('wpvp2')
     767           0 :         stats_metadata%iwpvp2 = k
     768             :         call stat_assign( var_index=stats_metadata%iwpvp2, var_name="wpvp2", &
     769             :              var_description="w'v'^2, Third-order moment from PDF", &
     770             :              var_units="m^3/s^3", &
     771           0 :              l_silhs=.false., grid_kind=stats_zt )
     772           0 :         k = k + 1
     773             : 
     774             :       case ('thlp3')
     775           0 :         stats_metadata%ithlp3 = k
     776             :         call stat_assign( var_index=stats_metadata%ithlp3, var_name="thlp3", &
     777             :              var_description="thl'^3, Third-order moment of theta_l", var_units="K^3", &
     778           0 :              l_silhs=.false., grid_kind=stats_zt )
     779           0 :         k = k + 1
     780             : 
     781             :       case ('rtp3')
     782           0 :         stats_metadata%irtp3 = k
     783             :         call stat_assign( var_index=stats_metadata%irtp3, var_name="rtp3", &
     784             :              var_description="rt'^3, Third-order moment of total water, rt", var_units="(kg/kg)^3", &
     785           0 :              l_silhs=.false., grid_kind=stats_zt )
     786           0 :         k = k + 1
     787             : 
     788             :       case ('wpthlp2')
     789           0 :         stats_metadata%iwpthlp2 = k
     790             :         call stat_assign( var_index=stats_metadata%iwpthlp2, var_name="wpthlp2", &
     791             :              var_description="w'theta_l'^2, Vertical turbulent flux of theta_l'^2", &
     792             :              var_units="(m K^2)/s", l_silhs=.false., &
     793           0 :              grid_kind=stats_zt )
     794           0 :         k = k + 1
     795             : 
     796             :       case ('wp2thlp')
     797           0 :         stats_metadata%iwp2thlp = k
     798             :         call stat_assign( var_index=stats_metadata%iwp2thlp, var_name="wp2thlp", &
     799             :              var_description="w'^2theta_l', Vertical turbulent flux of w'theta_l'", &
     800             :              var_units="(m^2 K)/s^2", l_silhs=.false., &
     801           0 :              grid_kind=stats_zt )
     802           0 :         k = k + 1
     803             : 
     804             :       case ('wprtp2')
     805           0 :         stats_metadata%iwprtp2 = k
     806             :         call stat_assign( var_index=stats_metadata%iwprtp2, var_name="wprtp2", &
     807             :              var_description="w'rt'^2, Vertical turbulent flux of rt'^2", &
     808             :              var_units="(m kg)/(s kg)", &
     809           0 :              l_silhs=.false., grid_kind=stats_zt )
     810           0 :         k = k + 1
     811             : 
     812             :       case ('wp2rtp')
     813           0 :         stats_metadata%iwp2rtp = k
     814             :         call stat_assign( var_index=stats_metadata%iwp2rtp, var_name="wp2rtp", &
     815             :              var_description="w'^2rt', Vertical turbulent flux of w'rt'", &
     816             :              var_units="(m^2 kg)/(s^2 kg)", &
     817           0 :              l_silhs=.false., grid_kind=stats_zt )
     818           0 :         k = k + 1
     819             : 
     820             :       case ('Lscale_up')
     821           0 :         stats_metadata%iLscale_up = k
     822             :         call stat_assign( var_index=stats_metadata%iLscale_up, var_name="Lscale_up", &
     823             :              var_description="Lscale_up, Upward mixing length", var_units="m", l_silhs=.false., &
     824           0 :              grid_kind=stats_zt )
     825           0 :         k = k + 1
     826             : 
     827             :       case ('Lscale_down')
     828           0 :         stats_metadata%iLscale_down = k
     829             :         call stat_assign( var_index=stats_metadata%iLscale_down, var_name="Lscale_down", &
     830             :              var_description="Lscale_down, Downward mixing length", var_units="m", &
     831             :              l_silhs=.false.,&
     832           0 :              grid_kind=stats_zt )
     833           0 :         k = k + 1
     834             : 
     835             :       case ('Lscale_pert_1')
     836           0 :         stats_metadata%iLscale_pert_1 = k
     837             :         call stat_assign( var_index=stats_metadata%iLscale_pert_1, var_name="Lscale_pert_1", &
     838             :              var_description="Lscale_pert_1, Mixing length using a perturbed value of rtm/thlm", &
     839           0 :              var_units="m", l_silhs=.false., grid_kind=stats_zt )
     840           0 :         k = k + 1
     841             : 
     842             :       case ('Lscale_pert_2')
     843           0 :         stats_metadata%iLscale_pert_2 = k
     844             :         call stat_assign( var_index=stats_metadata%iLscale_pert_2, var_name="Lscale_pert_2", &
     845             :              var_description="Lscale_pert_2, Mixing length using a perturbed value of rtm/thlm", &
     846           0 :              var_units="m", l_silhs=.false., grid_kind=stats_zt )
     847           0 :         k = k + 1
     848             : 
     849             :       case ('tau_zt')
     850           0 :         stats_metadata%itau_zt = k
     851             :         call stat_assign( var_index=stats_metadata%itau_zt, var_name="tau_zt", &
     852             :              var_description="tau_zt, Dissipation time", var_units="s", l_silhs=.false., &
     853           0 :              grid_kind=stats_zt )
     854           0 :         k = k + 1
     855             : 
     856             :       case ('invrs_tau_zt')
     857           0 :         stats_metadata%iinvrs_tau_zt = k
     858             :         call stat_assign( var_index=stats_metadata%iinvrs_tau_zt, var_name="invrs_tau_zt", &
     859             :              var_description="invrs_tau_zt, Inverse of dissipation time", var_units="s^-1", & 
     860           0 :              l_silhs=.false., grid_kind=stats_zt )
     861           0 :         k = k + 1
     862             : 
     863             :       case ('Kh_zt')
     864           0 :         stats_metadata%iKh_zt = k
     865             :         call stat_assign( var_index=stats_metadata%iKh_zt, var_name="Kh_zt", &
     866             :              var_description="Kh_zt, Eddy diffusivity", var_units="m^2/s", l_silhs=.false., &
     867           0 :              grid_kind=stats_zt )
     868           0 :         k = k + 1
     869             : 
     870             :       case ('wp2thvp')
     871           0 :         stats_metadata%iwp2thvp = k
     872             :         call stat_assign( var_index=stats_metadata%iwp2thvp, var_name="wp2thvp", &
     873             :              var_description="w'^2theta_v', Vertical turbulent flux of w'theta_v'", &
     874             :              var_units="K m^2/s^2", l_silhs=.false., &
     875           0 :              grid_kind=stats_zt )
     876           0 :         k = k + 1
     877             : 
     878             :       case ('wp2rcp')
     879           0 :         stats_metadata%iwp2rcp = k
     880             :         call stat_assign( var_index=stats_metadata%iwp2rcp, var_name="wp2rcp", &
     881             :              var_description="w'^2rc'", var_units="(m^2 kg)/(s^2 kg)", &
     882           0 :              l_silhs=.false., grid_kind=stats_zt )
     883           0 :         k = k + 1
     884             :         
     885             :       case ('w_up_in_cloud')
     886           0 :         stats_metadata%iw_up_in_cloud = k
     887             :         call stat_assign( var_index=stats_metadata%iw_up_in_cloud, var_name="w_up_in_cloud", &
     888             :              var_description="Mean W in saturated updrafts", &
     889           0 :              var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
     890           0 :          k = k + 1
     891             : 
     892             :       case ('w_down_in_cloud')
     893           0 :         stats_metadata%iw_down_in_cloud = k
     894             :         call stat_assign( var_index=stats_metadata%iw_down_in_cloud, var_name="w_down_in_cloud", &
     895             :              var_description="Mean W in saturated downdrafts", &
     896           0 :              var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
     897           0 :          k = k + 1
     898             : 
     899             :       case ('cld_updr_frac')
     900           0 :         stats_metadata%icld_updr_frac = k
     901             :         call stat_assign( var_index=stats_metadata%icld_updr_frac, var_name="cld_updr_frac", &
     902             :              var_description="Cloudy Updraft Fraction", &
     903           0 :              var_units="-", l_silhs=.false., grid_kind=stats_zt )
     904           0 :          k = k + 1
     905             : 
     906             :       case ('cld_downdr_frac')
     907           0 :         stats_metadata%icld_downdr_frac = k
     908             :         call stat_assign( var_index=stats_metadata%icld_downdr_frac, var_name="cld_downdr_frac", &
     909             :              var_description="Cloudy Downdraft Fraction", &
     910           0 :              var_units="-", l_silhs=.false., grid_kind=stats_zt )
     911           0 :          k = k + 1
     912             : 
     913             :       case ('wprtpthlp')
     914           0 :         stats_metadata%iwprtpthlp = k
     915             :         call stat_assign( var_index=stats_metadata%iwprtpthlp, var_name="wprtpthlp", &
     916             :              var_description="w'rt'theta_l', Vertical turbulent flux of rt'theta_l'", &
     917             :              var_units="(m kg K)/(s kg)", &
     918           0 :              l_silhs=.false., grid_kind=stats_zt )
     919           0 :         k = k + 1
     920             : 
     921             :       case ('rc_coef')
     922           0 :         stats_metadata%irc_coef = k
     923             :         call stat_assign( var_index=stats_metadata%irc_coef, var_name="rc_coef", &
     924             :              var_description="rc_coef, Coefficient of X'r_c'", &
     925           0 :              var_units="K/(kg/kg)", l_silhs=.false., grid_kind=stats_zt )
     926           0 :         k = k + 1
     927             : 
     928             :       case ('sigma_sqd_w_zt')
     929           0 :         stats_metadata%isigma_sqd_w_zt = k
     930             :         call stat_assign( var_index=stats_metadata%isigma_sqd_w_zt, var_name="sigma_sqd_w_zt", &
     931             :              var_description="sigma_sqd_w_zt, Nondimensionalized w variance of " &
     932             :              // "Gaussian component",&
     933           0 :              var_units="-", l_silhs=.false., grid_kind=stats_zt )
     934           0 :         k = k + 1
     935             : 
     936             :       case ('rho')
     937           0 :         stats_metadata%irho = k
     938             :         call stat_assign( var_index=stats_metadata%irho, var_name="rho", var_description="rho, Air density", &
     939           0 :              var_units="kg m^{-3}", l_silhs=.false., grid_kind=stats_zt )
     940           0 :         k = k + 1
     941             : 
     942             :       case ('Ncm')           ! Brian
     943           0 :         stats_metadata%iNcm = k
     944             :         call stat_assign( var_index=stats_metadata%iNcm, var_name="Ncm", &
     945             :              var_description="Ncm, Cloud droplet number concentration", var_units="num/kg", &
     946           0 :              l_silhs=.false., grid_kind=stats_zt )
     947           0 :         k = k + 1
     948             : 
     949             :       case ('Nc_in_cloud')
     950           0 :         stats_metadata%iNc_in_cloud = k
     951             : 
     952             :         call stat_assign( var_index=stats_metadata%iNc_in_cloud, var_name="Nc_in_cloud", &
     953             :              var_description="Nc_in_cloud, In cloud droplet concentration", var_units="num/kg", &
     954           0 :              l_silhs=.false., grid_kind=stats_zt )
     955             : 
     956           0 :         k = k + 1
     957             : 
     958             :       case ('Nc_activated')
     959           0 :         stats_metadata%iNc_activated = k
     960             : 
     961             :         call stat_assign( var_index=stats_metadata%iNc_activated, var_name="Nc_activated", &
     962             :              var_description="Nc_activated, Droplets activated by GFDL activation", &
     963           0 :              var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
     964             : 
     965           0 :         k = k + 1
     966             : 
     967             :       case ('Nccnm')
     968           0 :         stats_metadata%iNccnm = k
     969             :         call stat_assign( var_index=stats_metadata%iNccnm, var_name="Nccnm", &
     970             :              var_description="Nccnm, Cloud condensation nuclei concentration", &
     971           0 :              var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
     972           0 :         k = k + 1
     973             : 
     974             :       case ('Nim')           ! Brian
     975           0 :         stats_metadata%iNim = k
     976             :         call stat_assign( var_index=stats_metadata%iNim, var_name="Nim", &
     977             :              var_description="Nim, Ice crystal number concentration", var_units="num/kg", &
     978           0 :              l_silhs=.false., grid_kind=stats_zt )
     979           0 :         k = k + 1
     980             : 
     981             :       case ('snowslope')     ! Adam Smith, 22 April 2008
     982           0 :         stats_metadata%isnowslope = k
     983             :         call stat_assign( var_index=stats_metadata%isnowslope, var_name="snowslope", &
     984             :              var_description="snowslope, COAMPS microphysics snow slope parameter", &
     985             :              var_units="1/m", &
     986           0 :              l_silhs=.false., grid_kind=stats_zt )
     987           0 :         k = k + 1
     988             : 
     989             :       case ('Nsm')        ! Adam Smith, 22 April 2008
     990           0 :         stats_metadata%iNsm = k
     991             :         call stat_assign( var_index=stats_metadata%iNsm, var_name="Nsm", &
     992             :              var_description="Nsm, Snow particle number concentration", var_units="num/kg", &
     993           0 :              l_silhs=.false., grid_kind=stats_zt )
     994           0 :         k = k + 1
     995             : 
     996             :       case ('Ngm')
     997           0 :         stats_metadata%iNgm = k
     998             :         call stat_assign( var_index=stats_metadata%iNgm, var_name="Ngm", &
     999             :              var_description="Ngm, Graupel number concentration", var_units="num/kg", &
    1000           0 :              l_silhs=.false., grid_kind=stats_zt )
    1001           0 :         k = k + 1
    1002             : 
    1003             :       case ('sed_rcm')       ! Brian
    1004           0 :         stats_metadata%ised_rcm = k
    1005             :         call stat_assign( var_index=stats_metadata%ised_rcm, var_name="sed_rcm", &
    1006             :              var_description="sed_rcm, d(rcm)/dt due to cloud sedimentation", &
    1007           0 :              var_units="kg / [m^2 s]", l_silhs=.false., grid_kind=stats_zt )
    1008           0 :         k = k + 1
    1009             : 
    1010             :       case ('rsat')           ! Brian
    1011           0 :         stats_metadata%irsat = k
    1012             :         call stat_assign( var_index=stats_metadata%irsat, var_name="rsat", &
    1013             :              var_description="rsat, Saturation mixing ratio over liquid", var_units="kg/kg", &
    1014           0 :              l_silhs=.false., grid_kind=stats_zt )
    1015           0 :         k = k + 1
    1016             : 
    1017             :       case ('rsati')
    1018           0 :         stats_metadata%irsati = k
    1019             :         call stat_assign( var_index=stats_metadata%irsati, var_name="rsati", &
    1020             :              var_description="rsati, Saturation mixing ratio over ice", var_units="kg/kg", &
    1021           0 :              l_silhs=.false., grid_kind=stats_zt )
    1022           0 :         k = k + 1
    1023             : 
    1024             :       case ('rrm')           ! Brian
    1025           0 :         stats_metadata%irrm = k
    1026             :         call stat_assign( var_index=stats_metadata%irrm, var_name="rrm", &
    1027             :              var_description="rrm, Rain water mixing ratio", var_units="kg/kg", &
    1028           0 :              l_silhs=.false., grid_kind=stats_zt )
    1029           0 :         k = k + 1
    1030             : 
    1031             :       case ('rsm')
    1032           0 :         stats_metadata%irsm = k
    1033             :         call stat_assign( var_index=stats_metadata%irsm, var_name="rsm", &
    1034             :              var_description="rsm, Snow water mixing ratio", var_units="kg/kg", &
    1035           0 :              l_silhs=.false., grid_kind=stats_zt )
    1036           0 :         k = k + 1
    1037             : 
    1038             :       case ('rim')
    1039           0 :         stats_metadata%irim = k
    1040             :         call stat_assign( var_index=stats_metadata%irim, var_name="rim", &
    1041             :              var_description="rim, Pristine ice water mixing ratio", var_units="kg/kg", &
    1042           0 :              l_silhs=.false., grid_kind=stats_zt )
    1043           0 :         k = k + 1
    1044             : 
    1045             :       case ('rgm')
    1046           0 :         stats_metadata%irgm = k
    1047             :         call stat_assign( var_index=stats_metadata%irgm, var_name="rgm", &
    1048             :              var_description="rgm, Graupel water mixing ratio", var_units="kg/kg", &
    1049           0 :              l_silhs=.false., grid_kind=stats_zt )
    1050           0 :         k = k + 1
    1051             : 
    1052             :       case ('Nrm')           ! Brian
    1053           0 :         stats_metadata%iNrm = k
    1054             :         call stat_assign( var_index=stats_metadata%iNrm, var_name="Nrm", &
    1055             :              var_description="Nrm, Rain drop number concentration", var_units="num/kg", &
    1056           0 :              l_silhs=.false., grid_kind=stats_zt )
    1057           0 :         k = k + 1
    1058             : 
    1059             :       case ('m_vol_rad_rain')  ! Brian
    1060           0 :         stats_metadata%im_vol_rad_rain = k
    1061             :         call stat_assign( var_index=stats_metadata%im_vol_rad_rain, var_name="mvrr", &
    1062             :              var_description="mvrr, Rain drop mean volume radius", &
    1063             :              var_units="m", l_silhs=.false.,&
    1064           0 :              grid_kind=stats_zt )
    1065           0 :         k = k + 1
    1066             : 
    1067             :       case ('m_vol_rad_cloud')
    1068           0 :         stats_metadata%im_vol_rad_cloud = k
    1069             :         call stat_assign( var_index=stats_metadata%im_vol_rad_cloud, var_name="m_vol_rad_cloud", &
    1070             :              var_description="m_vol_rad_cloud, Cloud drop mean volume radius", var_units="m", &
    1071             :              l_silhs=.false., &
    1072           0 :              grid_kind=stats_zt )
    1073           0 :         k = k + 1
    1074             : 
    1075             :       case ('eff_rad_cloud')
    1076           0 :         stats_metadata%ieff_rad_cloud = k
    1077             :         call stat_assign( var_index=stats_metadata%ieff_rad_cloud, var_name="eff_rad_cloud", &
    1078             :              var_description="eff_rad_cloud, Cloud drop effective volume radius", &
    1079             :              var_units="microns", &
    1080           0 :              l_silhs=.false., grid_kind=stats_zt )
    1081           0 :         k = k + 1
    1082             : 
    1083             :       case ('eff_rad_ice')
    1084           0 :         stats_metadata%ieff_rad_ice = k
    1085             : 
    1086             :         call stat_assign( var_index=stats_metadata%ieff_rad_ice, var_name="eff_rad_ice", &
    1087             :              var_description="eff_rad_ice, Ice effective volume radius", var_units="microns", &
    1088           0 :              l_silhs=.false., grid_kind=stats_zt )
    1089           0 :         k = k + 1
    1090             : 
    1091             :       case ('eff_rad_snow')
    1092           0 :         stats_metadata%ieff_rad_snow = k
    1093             :         call stat_assign( var_index=stats_metadata%ieff_rad_snow, var_name="eff_rad_snow", &
    1094             :              var_description="eff_rad_snow, Snow effective volume radius", &
    1095             :              var_units="microns", &
    1096           0 :              l_silhs=.false., grid_kind=stats_zt )
    1097           0 :         k = k + 1
    1098             : 
    1099             :       case ('eff_rad_rain')
    1100           0 :         stats_metadata%ieff_rad_rain = k
    1101             :         call stat_assign( var_index=stats_metadata%ieff_rad_rain, var_name="eff_rad_rain", &
    1102             :              var_description="eff_rad_rain, Rain drop effective volume radius", &
    1103             :              var_units="microns", &
    1104           0 :              l_silhs=.false., grid_kind=stats_zt )
    1105           0 :         k = k + 1
    1106             : 
    1107             :       case ('eff_rad_graupel')
    1108           0 :         stats_metadata%ieff_rad_graupel = k
    1109             :         call stat_assign( var_index=stats_metadata%ieff_rad_graupel, var_name="eff_rad_graupel", &
    1110             :              var_description="eff_rad_graupel, Graupel effective volume radius", &
    1111             :              var_units="microns", &
    1112           0 :              l_silhs=.false., grid_kind=stats_zt )
    1113           0 :         k = k + 1
    1114             : 
    1115             :       case ('precip_rate_zt')     ! Brian
    1116           0 :         stats_metadata%iprecip_rate_zt = k
    1117             : 
    1118             :         call stat_assign( var_index=stats_metadata%iprecip_rate_zt, var_name="precip_rate_zt", &
    1119             :              var_description="precip_rate_zt, Rain rate", var_units="mm/day", l_silhs=.false., &
    1120           0 :              grid_kind=stats_zt )
    1121           0 :         k = k + 1
    1122             : 
    1123             :       case ('radht')
    1124           0 :         stats_metadata%iradht = k
    1125             : 
    1126             :         call stat_assign( var_index=stats_metadata%iradht, var_name="radht", &
    1127             :              var_description="radht, Total (sw+lw) radiative heating rate", var_units="K/s", &
    1128           0 :              l_silhs=.false., grid_kind=stats_zt )
    1129           0 :         k = k + 1
    1130             : 
    1131             :       case ('radht_LW')
    1132           0 :         stats_metadata%iradht_LW = k
    1133             : 
    1134             :         call stat_assign( var_index=stats_metadata%iradht_LW, var_name="radht_LW", &
    1135             :              var_description="radht_LW, Long-wave radiative heating rate", var_units="K/s", &
    1136           0 :              l_silhs=.false., grid_kind=stats_zt )
    1137             : 
    1138           0 :         k = k + 1
    1139             : 
    1140             :       case ('radht_SW')
    1141           0 :         stats_metadata%iradht_SW = k
    1142             :         call stat_assign( var_index=stats_metadata%iradht_SW, var_name="radht_SW", &
    1143             :              var_description="radht_SW, Short-wave radiative heating rate", var_units="K/s", &
    1144           0 :              l_silhs=.false., grid_kind=stats_zt )
    1145           0 :         k = k + 1
    1146             : 
    1147             :       case ('diam')
    1148           0 :         stats_metadata%idiam = k
    1149             : 
    1150             :         call stat_assign( var_index=stats_metadata%idiam, var_name="diam", &
    1151             :              var_description="diam, Ice crystal diameter", var_units="m", l_silhs=.false., &
    1152           0 :              grid_kind=stats_zt )
    1153           0 :         k = k + 1
    1154             : 
    1155             :       case ('mass_ice_cryst')
    1156           0 :         stats_metadata%imass_ice_cryst = k
    1157             :         call stat_assign( var_index=stats_metadata%imass_ice_cryst, var_name="mass_ice_cryst", &
    1158             :              var_description="mass_ice_cryst, Mass of a single ice crystal", var_units="kg", &
    1159           0 :              l_silhs=.false., grid_kind=stats_zt )
    1160           0 :         k = k + 1
    1161             : 
    1162             :       case ('rcm_icedfs')
    1163           0 :         stats_metadata%ircm_icedfs = k
    1164             :         call stat_assign( var_index=stats_metadata%ircm_icedfs, var_name="rcm_icedfs", &
    1165             :              var_description="rcm_icedfs, Change in liquid due to ice", var_units="kg/kg/s", &
    1166           0 :              l_silhs=.false., grid_kind=stats_zt )
    1167           0 :         k = k + 1
    1168             : 
    1169             :       case ('u_T_cm')
    1170           0 :         stats_metadata%iu_T_cm = k
    1171             :         call stat_assign( var_index=stats_metadata%iu_T_cm, var_name="u_T_cm", &
    1172             :              var_description="u_T_cm, Ice crystal fallspeed", var_units="cm s^{-1}", &
    1173           0 :              l_silhs=.false., grid_kind=stats_zt )
    1174           0 :         k = k + 1
    1175             : 
    1176             :       case ('rtm_bt')
    1177           0 :         stats_metadata%irtm_bt = k
    1178             : 
    1179             :         call stat_assign( var_index=stats_metadata%irtm_bt, var_name="rtm_bt", &
    1180             :              var_description="rtm_bt, rtm budget: rtm time tendency", &
    1181           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1182           0 :         k = k + 1
    1183             : 
    1184             :       case ('rtm_ma')
    1185           0 :         stats_metadata%irtm_ma = k
    1186             : 
    1187             :         call stat_assign( var_index=stats_metadata%irtm_ma, var_name="rtm_ma", &
    1188             :              var_description="rtm_ma, rtm budget: rtm vertical mean advection", &
    1189           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1190           0 :         k = k + 1
    1191             : 
    1192             :       case ('rtm_ta')
    1193           0 :         stats_metadata%irtm_ta = k
    1194             : 
    1195             :         call stat_assign( var_index=stats_metadata%irtm_ta, var_name="rtm_ta", &
    1196             :              var_description="rtm_ta, rtm budget: rtm turbulent advection", &
    1197           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1198           0 :         k = k + 1
    1199             : 
    1200             :       case ('rtm_mfl')
    1201           0 :         stats_metadata%irtm_mfl = k
    1202             : 
    1203             :         call stat_assign( var_index=stats_metadata%irtm_mfl, var_name="rtm_mfl", &
    1204             :          var_description="rtm_mfl, rtm budget: rtm correction due to monotonic flux limiter", &
    1205           0 :          var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt)
    1206           0 :         k = k + 1
    1207             : 
    1208             :       case ('rtm_tacl')
    1209           0 :         stats_metadata%irtm_tacl = k
    1210             : 
    1211             :         call stat_assign( var_index=stats_metadata%irtm_tacl, var_name="rtm_tacl", &
    1212             :           var_description="rtm_tacl, rtm budget: rtm correction due to ta term" &
    1213             :           // " (wprtp) clipping", &
    1214           0 :           var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt)
    1215             : 
    1216           0 :         k = k + 1
    1217             : 
    1218             :       case ('rtm_cl')
    1219           0 :         stats_metadata%irtm_cl = k
    1220             : 
    1221             :         call stat_assign( var_index=stats_metadata%irtm_cl, var_name="rtm_cl", &
    1222             :              var_description="rtm_cl, rtm budget: rtm clipping", &
    1223           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1224             : 
    1225           0 :         k = k + 1
    1226             :       case ('rtm_sdmp')
    1227           0 :         stats_metadata%irtm_sdmp = k
    1228             : 
    1229             :         call stat_assign( var_index=stats_metadata%irtm_sdmp, var_name="rtm_sdmp", &
    1230             :              var_description="rtm_sdmp, rtm budget: rtm correction due to sponge damping", &
    1231           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1232           0 :         k = k + 1
    1233             : 
    1234             : 
    1235             :       case ('rtm_pd')
    1236           0 :         stats_metadata%irtm_pd = k
    1237             : 
    1238             :         call stat_assign( var_index=stats_metadata%irtm_pd, var_name="rtm_pd", &
    1239             :              var_description="rtm_pd, rtm budget: rtm positive definite adjustment", &
    1240           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1241             : 
    1242           0 :         k = k + 1
    1243             : 
    1244             :       case ('thlm_bt')
    1245           0 :         stats_metadata%ithlm_bt = k
    1246             : 
    1247             :         call stat_assign( var_index=stats_metadata%ithlm_bt, var_name="thlm_bt", &
    1248             :              var_description="thlm_bt, thlm budget: thlm time tendency", var_units="K s^{-1}", &
    1249           0 :              l_silhs=.false., grid_kind=stats_zt )
    1250           0 :         k = k + 1
    1251             : 
    1252             :       case ('thlm_ma')
    1253           0 :         stats_metadata%ithlm_ma = k
    1254             : 
    1255             :         call stat_assign( var_index=stats_metadata%ithlm_ma, var_name="thlm_ma", &
    1256             :              var_description="thlm_ma, thlm budget: thlm vertical mean advection", &
    1257           0 :              var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1258           0 :         k = k + 1
    1259             : 
    1260             :       case ('thlm_sdmp')
    1261           0 :         stats_metadata%ithlm_sdmp = k
    1262             : 
    1263             :         call stat_assign( var_index=stats_metadata%ithlm_sdmp, var_name="thlm_sdmp", &
    1264             :              var_description="thlm_sdmp, thlm budget: thlm correction due to sponge damping", &
    1265           0 :              var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1266           0 :         k = k + 1
    1267             : 
    1268             : 
    1269             :       case ('thlm_ta')
    1270           0 :         stats_metadata%ithlm_ta = k
    1271             : 
    1272             :         call stat_assign( var_index=stats_metadata%ithlm_ta, var_name="thlm_ta", &
    1273             :              var_description="thlm_ta, thlm budget: thlm turbulent advection", &
    1274           0 :              var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1275           0 :         k = k + 1
    1276             : 
    1277             :       case ('thlm_mfl')
    1278           0 :         stats_metadata%ithlm_mfl = k
    1279             : 
    1280             :         call stat_assign( var_index=stats_metadata%ithlm_mfl, var_name="thlm_mfl", &
    1281             :              var_description="thlm_mfl, thlm budget: thlm correction due to monotonic " &
    1282             :              // "flux limiter", &
    1283           0 :              var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1284           0 :         k = k + 1
    1285             : 
    1286             :       case ('thlm_tacl')
    1287           0 :         stats_metadata%ithlm_tacl = k
    1288             : 
    1289             :         call stat_assign( var_index=stats_metadata%ithlm_tacl, var_name="thlm_tacl", &
    1290             :              var_description="thlm_tacl, thlm budget: thlm correction due to ta term " &
    1291             :              // "(wpthlp) clipping", &
    1292           0 :              var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1293           0 :         k = k + 1
    1294             : 
    1295             :       case ('thlm_cl')
    1296           0 :         stats_metadata%ithlm_cl = k
    1297             : 
    1298             :         call stat_assign( var_index=stats_metadata%ithlm_cl, var_name="thlm_cl", &
    1299             :              var_description="thlm_cl, thlm budget: thlm_cl", var_units="K s^{-1}", &
    1300           0 :              l_silhs=.false., grid_kind=stats_zt )
    1301           0 :         k = k + 1
    1302             : 
    1303             :       case ('wp3_bt')
    1304           0 :         stats_metadata%iwp3_bt = k
    1305             : 
    1306             :         call stat_assign( var_index=stats_metadata%iwp3_bt, var_name="wp3_bt", &
    1307             :              var_description="wp3_bt, wp3 budget: wp3 time tendency", &
    1308           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1309           0 :         k = k + 1
    1310             : 
    1311             :       case ('wp3_ma')
    1312           0 :         stats_metadata%iwp3_ma = k
    1313             : 
    1314             :         call stat_assign( var_index=stats_metadata%iwp3_ma, var_name="wp3_ma", &
    1315             :              var_description="wp3_ma, wp3 budget: wp3 vertical mean advection", &
    1316           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1317           0 :         k = k + 1
    1318             : 
    1319             :       case ('wp3_ta')
    1320           0 :         stats_metadata%iwp3_ta = k
    1321             : 
    1322             :         call stat_assign( var_index=stats_metadata%iwp3_ta, var_name="wp3_ta", &
    1323             :              var_description="wp3_ta, wp3 budget: wp3 turbulent advection", &
    1324           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1325             : 
    1326           0 :         k = k + 1
    1327             : 
    1328             :       case ('wp3_tp')
    1329           0 :         stats_metadata%iwp3_tp = k
    1330             :         call stat_assign( var_index=stats_metadata%iwp3_tp, var_name="wp3_tp", &
    1331             :              var_description="wp3_tp, wp3 budget: wp3 turbulent transport", &
    1332           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1333           0 :         k = k + 1
    1334             : 
    1335             :       case ('wp3_ac')
    1336           0 :         stats_metadata%iwp3_ac = k
    1337             :         call stat_assign( var_index=stats_metadata%iwp3_ac, var_name="wp3_ac", &
    1338             :              var_description="wp3_ac, wp3 budget: wp3 accumulation term", &
    1339           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1340           0 :         k = k + 1
    1341             : 
    1342             :       case ('wp3_bp1')
    1343           0 :         stats_metadata%iwp3_bp1 = k
    1344             :         call stat_assign( var_index=stats_metadata%iwp3_bp1, var_name="wp3_bp1", &
    1345             :              var_description="wp3_bp1, wp3 budget: wp3 buoyancy production", &
    1346           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1347           0 :         k = k + 1
    1348             : 
    1349             :       case ('wp3_pr_tp')
    1350           0 :         stats_metadata%iwp3_pr_tp = k
    1351             :         call stat_assign( var_index=stats_metadata%iwp3_pr_tp, var_name="wp3_pr_tp", &
    1352             :              var_description= &
    1353             :                "wp3_pr_tp, wp3 budget: wp3 pressure damping of turbulent production", &
    1354           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1355           0 :         k = k + 1
    1356             : 
    1357             :       case ('wp3_pr_turb')
    1358           0 :         stats_metadata%iwp3_pr_turb = k
    1359             :         call stat_assign( var_index=stats_metadata%iwp3_pr_turb, var_name="wp3_pr_turb", &
    1360             :              var_description="wp3_pr_turb, wp3 budget: wp3 pressure-turbulence correlation term", &
    1361           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1362           0 :         k = k + 1
    1363             : 
    1364             :       case ('wp3_pr_dfsn')
    1365           0 :         stats_metadata%iwp3_pr_dfsn = k
    1366             :         call stat_assign( var_index=stats_metadata%iwp3_pr_dfsn, var_name="wp3_pr_dfsn", &
    1367             :              var_description="wp3_pr_dfsn, wp3 budget: wp3 pressure diffusion term", &
    1368           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1369           0 :         k = k + 1
    1370             : 
    1371             :       case ('wp3_pr1')
    1372           0 :         stats_metadata%iwp3_pr1 = k
    1373             :         call stat_assign( var_index=stats_metadata%iwp3_pr1, var_name="wp3_pr1", &
    1374             :              var_description="wp3_pr1, wp3 budget: wp3 pressure term 1", &
    1375           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1376           0 :         k = k + 1
    1377             : 
    1378             :       case ('wp3_pr2')
    1379           0 :         stats_metadata%iwp3_pr2 = k
    1380             :         call stat_assign( var_index=stats_metadata%iwp3_pr2, var_name="wp3_pr2", &
    1381             :              var_description="wp3_pr2, wp3 budget: wp3 pressure term 2", &
    1382           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1383             : 
    1384           0 :         k = k + 1
    1385             : 
    1386             :       case ('wp3_pr3')
    1387           0 :         stats_metadata%iwp3_pr3 = k
    1388             :         call stat_assign( var_index=stats_metadata%iwp3_pr3, var_name="wp3_pr3", &
    1389             :              var_description="wp3_pr3, wp3 budget: wp3 pressure term 3", &
    1390           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1391             : 
    1392           0 :         k = k + 1
    1393             : 
    1394             :       case ('wp3_dp1')
    1395           0 :         stats_metadata%iwp3_dp1 = k
    1396             :         call stat_assign( var_index=stats_metadata%iwp3_dp1, var_name="wp3_dp1", &
    1397             :              var_description="wp3_dp1, wp3 budget: wp3 dissipation term 1", &
    1398           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1399           0 :         k = k + 1
    1400             : 
    1401             :       case ('wp3_sdmp')
    1402           0 :         stats_metadata%iwp3_sdmp = k
    1403             :         call stat_assign( var_index=stats_metadata%iwp3_sdmp, var_name="wp3_sdmp", &
    1404             :              var_description="wp3_sdmp, wp3 budget: wp3 sponge damping term", &
    1405           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1406           0 :         k = k + 1
    1407             : 
    1408             :       case ('wp3_cl')
    1409           0 :         stats_metadata%iwp3_cl = k
    1410             :         call stat_assign( var_index=stats_metadata%iwp3_cl, var_name="wp3_cl", &
    1411             :              var_description="wp3_cl, wp3 budget: wp3 clipping term", &
    1412           0 :              var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
    1413           0 :         k = k + 1
    1414             : 
    1415             :       case ('wp3_splat')
    1416           0 :         stats_metadata%iwp3_splat = k
    1417             :         call stat_assign( var_index=stats_metadata%iwp3_splat, var_name="wp3_splat", &
    1418             :              var_description="wp3_splat, wp3 budget: wp3 splatting term", &
    1419           0 :              var_units="m^3 s^-4", l_silhs=.false., grid_kind=stats_zt )
    1420           0 :         k = k + 1
    1421             : 
    1422             :       case ('rtp3_bt')
    1423           0 :         stats_metadata%irtp3_bt = k
    1424             : 
    1425             :         call stat_assign( var_index=stats_metadata%irtp3_bt, var_name="rtp3_bt", &
    1426             :              var_description="rtp3_bt, rtp3 budget: rtp3 time tendency" &
    1427             :                              // "[kg^{3} kg^{-3} s^{-1}]", &
    1428             :              var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
    1429           0 :              grid_kind=stats_zt )
    1430           0 :         k = k + 1
    1431             : 
    1432             :       case ('rtp3_tp')
    1433           0 :         stats_metadata%irtp3_tp = k
    1434             : 
    1435             :         call stat_assign( var_index=stats_metadata%irtp3_tp, var_name="rtp3_tp", &
    1436             :              var_description="rtp3_tp, rtp3 budget: rtp3 turbulent production" &
    1437             :                              // "[kg^{3} kg^{-3} s^{-1}]", &
    1438             :              var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
    1439           0 :              grid_kind=stats_zt )
    1440           0 :         k = k + 1
    1441             : 
    1442             :       case ('rtp3_ac')
    1443           0 :         stats_metadata%irtp3_ac = k
    1444             : 
    1445             :         call stat_assign( var_index=stats_metadata%irtp3_ac, var_name="rtp3_ac", &
    1446             :              var_description="rtp3_ac, rtp3 budget: rtp3 accumulation" &
    1447             :                              // "[kg^{3} kg^{-3} s^{-1}]", &
    1448             :              var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
    1449           0 :              grid_kind=stats_zt )
    1450           0 :         k = k + 1
    1451             : 
    1452             :       case ('rtp3_dp')
    1453           0 :         stats_metadata%irtp3_dp = k
    1454             : 
    1455             :         call stat_assign( var_index=stats_metadata%irtp3_dp, var_name="rtp3_dp", &
    1456             :              var_description="rtp3_dp, rtp3 budget: rtp3 dissipation" &
    1457             :                              // "[kg^{3} kg^{-3} s^{-1}]", &
    1458             :              var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
    1459           0 :              grid_kind=stats_zt )
    1460           0 :         k = k + 1
    1461             : 
    1462             :       case ('thlp3_bt')
    1463           0 :         stats_metadata%ithlp3_bt = k
    1464             : 
    1465             :         call stat_assign( var_index=stats_metadata%ithlp3_bt, var_name="thlp3_bt", &
    1466             :              var_description="thlp3_bt, thlp3 budget: thlp3 time tendency" &
    1467             :                              // "[K^{3} s^{-1}]", &
    1468           0 :              var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1469           0 :         k = k + 1
    1470             : 
    1471             :       case ('thlp3_tp')
    1472           0 :         stats_metadata%ithlp3_tp = k
    1473             : 
    1474             :         call stat_assign( var_index=stats_metadata%ithlp3_tp, var_name="thlp3_tp", &
    1475             :              var_description="thlp3_tp, thlp3 budget: thlp3 turbulent production" &
    1476             :                              // "[K^{3} s^{-1}]", &
    1477           0 :              var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1478           0 :         k = k + 1
    1479             : 
    1480             :       case ('thlp3_ac')
    1481           0 :         stats_metadata%ithlp3_ac = k
    1482             : 
    1483             :         call stat_assign( var_index=stats_metadata%ithlp3_ac, var_name="thlp3_ac", &
    1484             :              var_description="thlp3_ac, thlp3 budget: thlp3 accumulation" &
    1485             :                              // "[K^{3} s^{-1}]", &
    1486           0 :              var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1487           0 :         k = k + 1
    1488             : 
    1489             :       case ('thlp3_dp')
    1490           0 :         stats_metadata%ithlp3_dp = k
    1491             : 
    1492             :         call stat_assign( var_index=stats_metadata%ithlp3_dp, var_name="thlp3_dp", &
    1493             :              var_description="thlp3_dp, thlp3 budget: thlp3 dissipation", &
    1494           0 :              var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1495           0 :         k = k + 1
    1496             : 
    1497             :       case ('rrm_bt')
    1498           0 :         stats_metadata%irrm_bt = k
    1499             :         call stat_assign( var_index=stats_metadata%irrm_bt, var_name="rrm_bt", &
    1500             :              var_description="rrm_bt, rrm budget: rrm time tendency", &
    1501           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1502           0 :         k = k + 1
    1503             : 
    1504             :       case ('rrm_ma')
    1505           0 :         stats_metadata%irrm_ma = k
    1506             : 
    1507             :         call stat_assign( var_index=stats_metadata%irrm_ma, var_name="rrm_ma", &
    1508             :              var_description="rrm_ma, rrm budget: rrm vertical mean advection", &
    1509           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1510           0 :         k = k + 1
    1511             : 
    1512             :       case ('rrm_sd')
    1513           0 :         stats_metadata%irrm_sd = k
    1514             : 
    1515             :         call stat_assign( var_index=stats_metadata%irrm_sd, var_name="rrm_sd", &
    1516             :              var_description="rrm_sd, rrm budget: rrm sedimentation", &
    1517           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1518           0 :         k = k + 1
    1519             : 
    1520             :       case ('rrm_ts')
    1521           0 :         stats_metadata%irrm_ts = k
    1522             : 
    1523             :         call stat_assign( var_index=stats_metadata%irrm_ts, var_name="rrm_ts", &
    1524             :              var_description="rrm_ts, rrm budget: rrm turbulent sedimentation", &
    1525           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1526           0 :         k = k + 1
    1527             : 
    1528             :       case ('rrm_sd_morr')
    1529           0 :         stats_metadata%irrm_sd_morr = k
    1530             : 
    1531             :         call stat_assign( var_index=stats_metadata%irrm_sd_morr, var_name="rrm_sd_morr", &
    1532             :              var_description="rrm_sd_morr, rrm sedimentation when using morrision microphysics &
    1533             :              &(not in budget, included in rrm_mc)", &
    1534           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.true., grid_kind=stats_zt )
    1535           0 :         k = k + 1
    1536             : 
    1537             :       case ('rrm_ta')
    1538           0 :         stats_metadata%irrm_ta = k
    1539             : 
    1540             :         call stat_assign( var_index=stats_metadata%irrm_ta, var_name="rrm_ta", &
    1541             :              var_description="rrm_ta, rrm budget: rrm turbulent advection", &
    1542           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1543           0 :         k = k + 1
    1544             : 
    1545             :       case ('rrm_evap')
    1546           0 :         stats_metadata%irrm_evap = k
    1547             : 
    1548             :         call stat_assign( var_index=stats_metadata%irrm_evap, var_name="rrm_evap", &
    1549             :              var_description="rrm_evap, rrm evaporation rate", &
    1550           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1551           0 :         k = k + 1
    1552             : 
    1553             :       case ('rrm_auto')
    1554           0 :         stats_metadata%irrm_auto = k
    1555             : 
    1556             :         call stat_assign( var_index=stats_metadata%irrm_auto, var_name="rrm_auto", &
    1557             :              var_description="rrm_auto, rrm autoconversion rate", &
    1558           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1559           0 :         k = k + 1
    1560             : 
    1561             :       case ('rrm_accr')
    1562           0 :         stats_metadata%irrm_accr = k
    1563             :         call stat_assign( var_index=stats_metadata%irrm_accr, var_name="rrm_accr", &
    1564             :              var_description="rrm_accr, rrm accretion rate", &
    1565           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1566           0 :         k = k + 1
    1567             : 
    1568             :       case ('rrm_evap_adj')
    1569           0 :         stats_metadata%irrm_evap_adj = k
    1570             : 
    1571             :         call stat_assign( var_index=stats_metadata%irrm_evap_adj, var_name="rrm_evap_adj", &
    1572             :              var_description="rrm_evap_adj, rrm evaporation adjustment due to over-evaporation", &
    1573           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1574           0 :         k = k + 1
    1575             : 
    1576             :       case ('rrm_src_adj')
    1577           0 :         stats_metadata%irrm_src_adj = k
    1578             : 
    1579             :         call stat_assign( var_index=stats_metadata%irrm_src_adj, var_name="rrm_src_adj", &
    1580             :              var_description="rrm_src_adj, rrm source term adjustment due to over-depletion", &
    1581           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1582           0 :         k = k + 1
    1583             : 
    1584             :       case ('rrm_mc_nonadj')
    1585           0 :         stats_metadata%irrm_mc_nonadj = k
    1586             : 
    1587             :         call stat_assign( var_index=stats_metadata%irrm_mc_nonadj, var_name="rrm_mc_nonadj", &
    1588             :              var_description="rrm_mc_nonadj, Value of rrm_mc tendency before adjustment", &
    1589           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1590           0 :         k = k + 1
    1591             : 
    1592             :       case ('rrm_hf')
    1593           0 :         stats_metadata%irrm_hf = k
    1594             :         call stat_assign( var_index=stats_metadata%irrm_hf, var_name="rrm_hf", &
    1595             :              var_description="rrm_hf, rrm budget: rrm hole-filling term", &
    1596           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1597           0 :         k = k + 1
    1598             : 
    1599             :       case ('rrm_wvhf')
    1600           0 :         stats_metadata%irrm_wvhf = k
    1601             :         call stat_assign( var_index=stats_metadata%irrm_wvhf, var_name="rrm_wvhf", &
    1602             :              var_description="rrm_wvhf, rrm budget: rrm water vapor hole-filling term", &
    1603           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1604           0 :         k = k + 1
    1605             : 
    1606             :       case ('rrm_cl')
    1607           0 :         stats_metadata%irrm_cl = k
    1608             :         call stat_assign( var_index=stats_metadata%irrm_cl, var_name="rrm_cl", &
    1609             :              var_description="rrm_cl, rrm budget: rrm clipping term", &
    1610           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1611           0 :         k = k + 1
    1612             : 
    1613             :       case ('rrm_mc')
    1614           0 :         stats_metadata%irrm_mc = k
    1615             : 
    1616             :         call stat_assign( var_index=stats_metadata%irrm_mc, var_name="rrm_mc", &
    1617             :              var_description="rrm_mc, rrm budget: Change in rrm due to microphysics", &
    1618           0 :              var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
    1619           0 :         k = k + 1
    1620             : 
    1621             :       case ('Nrm_bt')
    1622           0 :         stats_metadata%iNrm_bt = k
    1623             :         call stat_assign( var_index=stats_metadata%iNrm_bt, var_name="Nrm_bt", &
    1624             :              var_description="Nrm_bt, Nrm budget: Nrm time tendency", &
    1625           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1626             : 
    1627           0 :         k = k + 1
    1628             : 
    1629             :       case ('Nrm_ma')
    1630           0 :         stats_metadata%iNrm_ma = k
    1631             : 
    1632             :         call stat_assign( var_index=stats_metadata%iNrm_ma, var_name="Nrm_ma", &
    1633             :              var_description="Nrm_ma, Nrm budget: Nrm vertical mean advection", &
    1634           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1635           0 :         k = k + 1
    1636             : 
    1637             :       case ('Nrm_sd')
    1638           0 :         stats_metadata%iNrm_sd = k
    1639             : 
    1640             :         call stat_assign( var_index=stats_metadata%iNrm_sd, var_name="Nrm_sd", &
    1641             :              var_description="Nrm_sd, Nrm budget: Nrm sedimentation", &
    1642           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1643             : 
    1644           0 :         k = k + 1
    1645             : 
    1646             :       case ('Nrm_ts')
    1647           0 :         stats_metadata%iNrm_ts = k
    1648             : 
    1649             :         call stat_assign( var_index=stats_metadata%iNrm_ts, var_name="Nrm_ts", &
    1650             :              var_description="Nrm_ts, Nrm budget: Nrm turbulent sedimentation", &
    1651           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1652           0 :         k = k + 1
    1653             : 
    1654             :       case ('Nrm_ta')
    1655           0 :         stats_metadata%iNrm_ta = k
    1656             :         call stat_assign( var_index=stats_metadata%iNrm_ta, var_name="Nrm_ta", &
    1657             :              var_description="Nrm_ta, Nrm budget: Nrm turbulent advection", &
    1658           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1659             : 
    1660           0 :         k = k + 1
    1661             : 
    1662             :       case ('Nrm_evap')
    1663           0 :         stats_metadata%iNrm_evap = k
    1664             : 
    1665             :         call stat_assign( var_index=stats_metadata%iNrm_evap, var_name="Nrm_evap", &
    1666             :              var_description="Nrm_evap, Nrm evaporation rate", var_units="(num/kg)/s", &
    1667           0 :              l_silhs=.false., grid_kind=stats_zt )
    1668           0 :         k = k + 1
    1669             : 
    1670             :       case ('Nrm_auto')
    1671           0 :         stats_metadata%iNrm_auto = k
    1672             : 
    1673             :         call stat_assign( var_index=stats_metadata%iNrm_auto, var_name="Nrm_auto", &
    1674             :              var_description="Nrm_auto, Nrm autoconversion rate", var_units="(num/kg)/s", &
    1675           0 :              l_silhs=.false., grid_kind=stats_zt )
    1676             : 
    1677           0 :         k = k + 1
    1678             : 
    1679             :       case ('Nrm_evap_adj')
    1680           0 :         stats_metadata%iNrm_evap_adj = k
    1681             : 
    1682             :         call stat_assign( var_index=stats_metadata%iNrm_evap_adj, var_name="Nrm_evap_adj", &
    1683             :              var_description="Nrm_evap_adj, Nrm evaporation adjustment due to over-evaporation", &
    1684           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1685           0 :         k = k + 1
    1686             : 
    1687             :       case ('Nrm_src_adj')
    1688           0 :         stats_metadata%iNrm_src_adj = k
    1689             : 
    1690             :         call stat_assign( var_index=stats_metadata%iNrm_src_adj, var_name="Nrm_src_adj", &
    1691             :              var_description="Nrm_src_adj, Nrm source term adjustment due to over-depletion", &
    1692           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1693           0 :         k = k + 1
    1694             : 
    1695             :       case ('Nrm_cl')
    1696           0 :         stats_metadata%iNrm_cl = k
    1697             :         call stat_assign( var_index=stats_metadata%iNrm_cl, var_name="Nrm_cl", &
    1698             :              var_description="Nrm_cl, Nrm budget: Nrm clipping term", &
    1699           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1700           0 :         k = k + 1
    1701             : 
    1702             :       case ('Nrm_mc')
    1703           0 :         stats_metadata%iNrm_mc = k
    1704             :         call stat_assign( var_index=stats_metadata%iNrm_mc, var_name="Nrm_mc", &
    1705             :              var_description="Nrm_mc, Nrm budget: Change in Nrm due to microphysics " &
    1706             :              // "(Not in budget)", &
    1707           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1708           0 :         k = k + 1
    1709             : 
    1710             :       case ('rsm_bt')
    1711           0 :         stats_metadata%irsm_bt = k
    1712             :         call stat_assign( var_index=stats_metadata%irsm_bt, var_name="rsm_bt", &
    1713             :              var_description="rsm_bt, rsm budget: rsm time tendency", &
    1714           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1715             : 
    1716           0 :         k = k + 1
    1717             : 
    1718             :       case ('rsm_ma')
    1719           0 :         stats_metadata%irsm_ma = k
    1720             : 
    1721             :         call stat_assign( var_index=stats_metadata%irsm_ma, var_name="rsm_ma", &
    1722             :              var_description="rsm_ma, rsm budget: rsm vertical mean advection", &
    1723           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1724           0 :         k = k + 1
    1725             : 
    1726             :       case ('rsm_sd')
    1727           0 :         stats_metadata%irsm_sd = k
    1728             :         call stat_assign( var_index=stats_metadata%irsm_sd, var_name="rsm_sd", &
    1729             :              var_description="rsm_sd, rsm budget: rsm sedimentation", &
    1730           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1731           0 :         k = k + 1
    1732             : 
    1733             :       case ('rsm_sd_morr')
    1734           0 :         stats_metadata%irsm_sd_morr = k
    1735             :         call stat_assign( var_index=stats_metadata%irsm_sd_morr, var_name="rsm_sd_morr", &
    1736             :              var_description="rsm_sd_morr, rsm sedimentation when using morrison microphysics &
    1737             :              &(Not in budget, included in rsm_mc)", &
    1738           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    1739           0 :         k = k + 1
    1740             : 
    1741             :       case ('rsm_ta')
    1742           0 :         stats_metadata%irsm_ta = k
    1743             : 
    1744             :         call stat_assign( var_index=stats_metadata%irsm_ta, var_name="rsm_ta", &
    1745             :              var_description="rsm_ta, rsm budget: rsm turbulent advection", &
    1746           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1747           0 :         k = k + 1
    1748             : 
    1749             :       case ('rsm_mc')
    1750           0 :         stats_metadata%irsm_mc = k
    1751             : 
    1752             :         call stat_assign( var_index=stats_metadata%irsm_mc, var_name="rsm_mc", &
    1753             :              var_description="rsm_mc, rsm budget: Change in rsm due to microphysics", &
    1754           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1755           0 :         k = k + 1
    1756             : 
    1757             :       case ('rsm_hf')
    1758           0 :         stats_metadata%irsm_hf = k
    1759             : 
    1760             :         call stat_assign( var_index=stats_metadata%irsm_hf, var_name="rsm_hf", &
    1761             :              var_description="rsm_hf, rsm budget: rsm hole-filling term", &
    1762           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1763           0 :         k = k + 1
    1764             : 
    1765             :       case ('rsm_wvhf')
    1766           0 :         stats_metadata%irsm_wvhf = k
    1767             : 
    1768             :         call stat_assign( var_index=stats_metadata%irsm_wvhf, var_name="rsm_wvhf", &
    1769             :              var_description="rsm_wvhf, rsm budget: rsm water vapor hole-filling term", &
    1770           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1771           0 :         k = k + 1
    1772             : 
    1773             :       case ('rsm_cl')
    1774           0 :         stats_metadata%irsm_cl = k
    1775             : 
    1776             :         call stat_assign( var_index=stats_metadata%irsm_cl, var_name="rsm_cl", &
    1777             :              var_description="rsm_cl, rsm budget: rsm clipping term", &
    1778           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1779           0 :         k = k + 1
    1780             : 
    1781             :       case ('Nsm_bt')
    1782           0 :         stats_metadata%iNsm_bt = k
    1783             :         call stat_assign( var_index=stats_metadata%iNsm_bt, var_name="Nsm_bt", &
    1784             :              var_description="Nsm_bt, Nsm budget", var_units="(num/kg)/s", &
    1785           0 :              l_silhs=.false., grid_kind=stats_zt )
    1786             : 
    1787           0 :         k = k + 1
    1788             : 
    1789             :       case ('Nsm_ma')
    1790           0 :         stats_metadata%iNsm_ma = k
    1791             : 
    1792             :         call stat_assign( var_index=stats_metadata%iNsm_ma, var_name="Nsm_ma", &
    1793             :              var_description="Nsm_ma, Nsm budget: Nsm mean advection", &
    1794           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1795           0 :         k = k + 1
    1796             : 
    1797             :       case ('Nsm_sd')
    1798           0 :         stats_metadata%iNsm_sd = k
    1799             : 
    1800             :         call stat_assign( var_index=stats_metadata%iNsm_sd, var_name="Nsm_sd", &
    1801             :              var_description="Nsm_sd, Nsm budget: Nsm sedimentation", &
    1802           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1803             : 
    1804           0 :         k = k + 1
    1805             : 
    1806             :       case ('Nsm_ta')
    1807           0 :         stats_metadata%iNsm_ta = k
    1808             :         call stat_assign( var_index=stats_metadata%iNsm_ta, var_name="Nsm_ta", &
    1809             :              var_description="Nsm_ta, Nsm budget: Nsm turbulent advection", &
    1810           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1811             : 
    1812           0 :         k = k + 1
    1813             : 
    1814             :       case ('Nsm_mc')
    1815           0 :         stats_metadata%iNsm_mc = k
    1816             :         call stat_assign( var_index=stats_metadata%iNsm_mc, var_name="Nsm_mc", &
    1817             :              var_description="Nsm_mc, Nsm budget: Nsm microphysics", &
    1818           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1819             : 
    1820           0 :         k = k + 1
    1821             : 
    1822             :       case ('Nsm_cl')
    1823           0 :         stats_metadata%iNsm_cl = k
    1824             : 
    1825             :         call stat_assign( var_index=stats_metadata%iNsm_cl, var_name="Nsm_cl", &
    1826             :              var_description="Nsm_cl, Nsm budget: Nsm clipping term", &
    1827           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1828           0 :         k = k + 1
    1829             : 
    1830             :       case ('rim_bt')
    1831           0 :         stats_metadata%irim_bt = k
    1832             : 
    1833             :         call stat_assign( var_index=stats_metadata%irim_bt, var_name="rim_bt", &
    1834             :              var_description="rim_bt, rim budget: rim time tendency", &
    1835           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1836             : 
    1837           0 :         k = k + 1
    1838             : 
    1839             :       case ('rim_ma')
    1840           0 :         stats_metadata%irim_ma = k
    1841             : 
    1842             :         call stat_assign( var_index=stats_metadata%irim_ma, var_name="rim_ma", &
    1843             :              var_description="rim_ma, rim budget: rim vertical mean advection", &
    1844           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1845           0 :         k = k + 1
    1846             : 
    1847             :       case ('rim_sd')
    1848           0 :         stats_metadata%irim_sd = k
    1849             : 
    1850             :         call stat_assign( var_index=stats_metadata%irim_sd, var_name="rim_sd", &
    1851             :              var_description="rim_sd, rim budget: rim sedimentation", &
    1852           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1853           0 :         k = k + 1
    1854             : 
    1855             :       case ('rim_sd_mg_morr')
    1856           0 :         stats_metadata%irim_sd_mg_morr = k
    1857             : 
    1858             :         call stat_assign( var_index=stats_metadata%irim_sd_mg_morr, var_name="rim_sd_mg_morr", &
    1859             :              var_description="rim_sd_mg_morr, rim sedimentation when using morrison or MG " &
    1860             :              // "microphysics" &
    1861             :              // "(not in budget, included in rim_mc)", &
    1862           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    1863           0 :         k = k + 1
    1864             : 
    1865             :       case ('rim_ta')
    1866           0 :         stats_metadata%irim_ta = k
    1867             : 
    1868             :         call stat_assign( var_index=stats_metadata%irim_ta, var_name="rim_ta", &
    1869             :              var_description="rim_ta, rim budget: rim turbulent advection", &
    1870           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1871           0 :         k = k + 1
    1872             : 
    1873             :       case ('rim_mc')
    1874           0 :         stats_metadata%irim_mc = k
    1875             : 
    1876             :         call stat_assign( var_index=stats_metadata%irim_mc, var_name="rim_mc", &
    1877             :              var_description="rim_mc, rim budget: Change in rim due to microphysics", &
    1878           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1879           0 :         k = k + 1
    1880             : 
    1881             :       case ('rim_hf')
    1882           0 :         stats_metadata%irim_hf = k
    1883             : 
    1884             :         call stat_assign( var_index=stats_metadata%irim_hf, var_name="rim_hf", &
    1885             :              var_description="rim_hf, rim budget: rim hole-filling term", &
    1886           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1887           0 :         k = k + 1
    1888             : 
    1889             :       case ('rim_wvhf')
    1890           0 :         stats_metadata%irim_wvhf = k
    1891             : 
    1892             :         call stat_assign( var_index=stats_metadata%irim_wvhf, var_name="rim_wvhf", &
    1893             :              var_description="rim_wvhf, rim budget: rim water vapor hole-filling term", &
    1894           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1895           0 :         k = k + 1
    1896             : 
    1897             :       case ('rim_cl')
    1898           0 :         stats_metadata%irim_cl = k
    1899             : 
    1900             :         call stat_assign( var_index=stats_metadata%irim_cl, var_name="rim_cl", &
    1901             :              var_description="rim_cl, rim budget: rim clipping term", &
    1902           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1903           0 :         k = k + 1
    1904             : 
    1905             :       case ('rgm_bt')
    1906           0 :         stats_metadata%irgm_bt = k
    1907             : 
    1908             :         call stat_assign( var_index=stats_metadata%irgm_bt, var_name="rgm_bt", &
    1909             :              var_description="rgm_bt, rgm budget: rgm time tendency", &
    1910           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1911           0 :         k = k + 1
    1912             : 
    1913             :       case ('rgm_ma')
    1914           0 :         stats_metadata%irgm_ma = k
    1915             : 
    1916             :         call stat_assign( var_index=stats_metadata%irgm_ma, var_name="rgm_ma", &
    1917             :              var_description="rgm_ma, rgm budget: rgm vertical mean advection", &
    1918           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1919           0 :         k = k + 1
    1920             : 
    1921             :       case ('rgm_sd')
    1922           0 :         stats_metadata%irgm_sd = k
    1923             : 
    1924             :         call stat_assign( var_index=stats_metadata%irgm_sd, var_name="rgm_sd", &
    1925             :              var_description="rgm_sd, rgm budget: rgm sedimentation", &
    1926           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1927           0 :         k = k + 1
    1928             : 
    1929             :       case ('rgm_sd_morr')
    1930           0 :         stats_metadata%irgm_sd_morr = k
    1931             : 
    1932             :         call stat_assign( var_index=stats_metadata%irgm_sd_morr, var_name="rgm_sd_morr", &
    1933             :              var_description="rgm_sd_morr, rgm sedimentation when using morrison microphysics &
    1934             :              &(not in budget, included in rgm_mc)", &
    1935           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    1936           0 :         k = k + 1
    1937             : 
    1938             :       case ('rgm_ta')
    1939           0 :         stats_metadata%irgm_ta = k
    1940             : 
    1941             :         call stat_assign( var_index=stats_metadata%irgm_ta, var_name="rgm_ta", &
    1942             :              var_description="rgm_ta, rgm budget: rgm turbulent advection", &
    1943           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1944           0 :         k = k + 1
    1945             : 
    1946             :       case ('rgm_mc')
    1947           0 :         stats_metadata%irgm_mc = k
    1948             : 
    1949             :         call stat_assign( var_index=stats_metadata%irgm_mc, var_name="rgm_mc", &
    1950             :              var_description="rgm_mc, rgm budget: Change in rgm due to microphysics", &
    1951           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1952           0 :         k = k + 1
    1953             : 
    1954             :       case ('rgm_hf')
    1955           0 :         stats_metadata%irgm_hf = k
    1956             : 
    1957             :         call stat_assign( var_index=stats_metadata%irgm_hf, var_name="rgm_hf", &
    1958             :              var_description="rgm_hf, rgm budget: rgm hole-filling term", &
    1959           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1960           0 :         k = k + 1
    1961             : 
    1962             :       case ('rgm_wvhf')
    1963           0 :         stats_metadata%irgm_wvhf = k
    1964             : 
    1965             :         call stat_assign( var_index=stats_metadata%irgm_wvhf, var_name="rgm_wvhf", &
    1966             :              var_description="rgm_wvhf, rgm budget: rgm water vapor hole-filling term", &
    1967           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1968           0 :         k = k + 1
    1969             : 
    1970             :       case ('rgm_cl')
    1971           0 :         stats_metadata%irgm_cl = k
    1972             : 
    1973             :         call stat_assign( var_index=stats_metadata%irgm_cl, var_name="rgm_cl", &
    1974             :              var_description="rgm_cl, rgm budget: rgm clipping term", &
    1975           0 :              var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1976           0 :         k = k + 1
    1977             : 
    1978             :       case ('Ngm_bt')
    1979           0 :         stats_metadata%iNgm_bt = k
    1980             :         call stat_assign( var_index=stats_metadata%iNgm_bt, var_name="Ngm_bt", &
    1981             :              var_description="Ngm_bt, Ngm budget:", var_units="(num/kg)/s", &
    1982           0 :              l_silhs=.false., grid_kind=stats_zt )
    1983             : 
    1984           0 :         k = k + 1
    1985             : 
    1986             :       case ('Ngm_ma')
    1987           0 :         stats_metadata%iNgm_ma = k
    1988             : 
    1989             :         call stat_assign( var_index=stats_metadata%iNgm_ma, var_name="Ngm_ma", &
    1990             :              var_description="Ngm_ma, Ngm budget: Ngm mean advection", &
    1991           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    1992           0 :         k = k + 1
    1993             : 
    1994             :       case ('Ngm_sd')
    1995           0 :         stats_metadata%iNgm_sd = k
    1996             : 
    1997             :         call stat_assign( var_index=stats_metadata%iNgm_sd, var_name="Ngm_sd", &
    1998             :              var_description="Ngm_sd, Ngm budget: Ngm sedimentation", &
    1999           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2000             : 
    2001           0 :         k = k + 1
    2002             : 
    2003             :       case ('Ngm_ta')
    2004           0 :         stats_metadata%iNgm_ta = k
    2005             :         call stat_assign( var_index=stats_metadata%iNgm_ta, var_name="Ngm_ta", &
    2006             :              var_description="Ngm_ta, Ngm budget: Ngm turbulent advection", &
    2007           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2008             : 
    2009           0 :         k = k + 1
    2010             : 
    2011             :       case ('Ngm_mc')
    2012           0 :         stats_metadata%iNgm_mc = k
    2013             : 
    2014             :         call stat_assign( var_index=stats_metadata%iNgm_mc, var_name="Ngm_mc", &
    2015             :              var_description="Ngm_mc, Ngm budget: Ngm microphysics term", &
    2016           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2017           0 :         k = k + 1
    2018             : 
    2019             :       case ('Ngm_cl')
    2020           0 :         stats_metadata%iNgm_cl = k
    2021             : 
    2022             :         call stat_assign( var_index=stats_metadata%iNgm_cl, var_name="Ngm_cl", &
    2023             :              var_description="Ngm_cl, Ngm budget: Ngm clipping term", &
    2024           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2025           0 :         k = k + 1
    2026             : 
    2027             :       case ('Nim_bt')
    2028           0 :         stats_metadata%iNim_bt = k
    2029             :         call stat_assign( var_index=stats_metadata%iNim_bt, var_name="Nim_bt", &
    2030             :              var_description="Nim_bt, Nim budget", var_units="(num/kg)/s", l_silhs=.false., &
    2031           0 :              grid_kind=stats_zt )
    2032             : 
    2033           0 :         k = k + 1
    2034             : 
    2035             :       case ('Nim_ma')
    2036           0 :         stats_metadata%iNim_ma = k
    2037             : 
    2038             :         call stat_assign( var_index=stats_metadata%iNim_ma, var_name="Nim_ma", &
    2039             :              var_description="Nim_ma, Nim budget: Nim mean advection", &
    2040           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2041           0 :         k = k + 1
    2042             : 
    2043             :       case ('Nim_sd')
    2044           0 :         stats_metadata%iNim_sd = k
    2045             : 
    2046             :         call stat_assign( var_index=stats_metadata%iNim_sd, var_name="Nim_sd", &
    2047             :              var_description="Nim_sd, Nim budget: Nim sedimentation", &
    2048           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2049             : 
    2050           0 :         k = k + 1
    2051             : 
    2052             :       case ('Nim_ta')
    2053           0 :         stats_metadata%iNim_ta = k
    2054             :         call stat_assign( var_index=stats_metadata%iNim_ta, var_name="Nim_ta", &
    2055             :              var_description="Nim_ta, Nim budget: Nim turbulent advection", &
    2056           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2057             : 
    2058           0 :         k = k + 1
    2059             : 
    2060             :       case ('Nim_mc')
    2061           0 :         stats_metadata%iNim_mc = k
    2062             : 
    2063             :         call stat_assign( var_index=stats_metadata%iNim_mc, var_name="Nim_mc", &
    2064             :              var_description="Nim_mc, Nim budget: Nim microphysics term", &
    2065           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2066           0 :         k = k + 1
    2067             : 
    2068             :       case ('Nim_cl')
    2069           0 :         stats_metadata%iNim_cl = k
    2070             : 
    2071             :         call stat_assign( var_index=stats_metadata%iNim_cl, var_name="Nim_cl", &
    2072             :              var_description="Nim_cl, Nim budget: Nim clipping term", &
    2073           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2074           0 :         k = k + 1
    2075             : 
    2076             :       case ('Ncm_bt')
    2077           0 :         stats_metadata%iNcm_bt = k
    2078             :         call stat_assign( var_index=stats_metadata%iNcm_bt, var_name="Ncm_bt", &
    2079             :              var_description="Ncm_bt, Ncm budget: Cloud droplet number concentration budget", &
    2080           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2081             : 
    2082           0 :         k = k + 1
    2083             : 
    2084             :       case ('Ncm_ma')
    2085           0 :         stats_metadata%iNcm_ma = k
    2086             : 
    2087             :         call stat_assign( var_index=stats_metadata%iNcm_ma, var_name="Ncm_ma", &
    2088             :              var_description="Ncm_ma, Ncm budget: Ncm vertical mean advection", &
    2089           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2090           0 :         k = k + 1
    2091             : 
    2092             :       case ('Ncm_act')
    2093           0 :         stats_metadata%iNcm_act = k
    2094             : 
    2095             :         call stat_assign( var_index=stats_metadata%iNcm_act, var_name="Ncm_act", &
    2096             :              var_description="Ncm_act, Ncm budget: Change in Ncm due to activation", &
    2097           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2098             : 
    2099           0 :         k = k + 1
    2100             : 
    2101             :       case ('Ncm_ta')
    2102           0 :         stats_metadata%iNcm_ta = k
    2103             :         call stat_assign( var_index=stats_metadata%iNcm_ta, var_name="Ncm_ta", &
    2104             :              var_description="Ncm_ta, Ncm budget: Ncm turbulent advection", &
    2105           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2106             : 
    2107           0 :         k = k + 1
    2108             : 
    2109             :       case ('Ncm_mc')
    2110           0 :         stats_metadata%iNcm_mc = k
    2111             : 
    2112             :         call stat_assign( var_index=stats_metadata%iNcm_mc, var_name="Ncm_mc", &
    2113             :              var_description="Ncm_mc, Ncm budget: Change in Ncm due to microphysics", &
    2114           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2115           0 :         k = k + 1
    2116             : 
    2117             :       case ('Ncm_cl')
    2118           0 :         stats_metadata%iNcm_cl = k
    2119             : 
    2120             :         call stat_assign( var_index=stats_metadata%iNcm_cl, var_name="Ncm_cl", &
    2121             :              var_description="Ncm_cl, Ncm budget: Ncm clipping term", &
    2122           0 :              var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2123           0 :         k = k + 1
    2124             : 
    2125             :       case ('PSMLT')
    2126           0 :         stats_metadata%iPSMLT = k
    2127             : 
    2128             :         call stat_assign( var_index=stats_metadata%iPSMLT, var_name="PSMLT", &
    2129             :              var_description="PSMLT, Freezing of rain to form snow, +rsm, -rrm", &
    2130           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2131           0 :         k = k + 1
    2132             : 
    2133             :       case ('EVPMS')
    2134           0 :         stats_metadata%iEVPMS = k
    2135             : 
    2136             :         call stat_assign( var_index=stats_metadata%iEVPMS, var_name="EVPMS", &
    2137             :              var_description="EVPMS, Evaporation of melted snow, +rsm, -rvm", &
    2138           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2139           0 :         k = k + 1
    2140             : 
    2141             :       case ('PRACS')
    2142           0 :         stats_metadata%iPRACS = k
    2143             : 
    2144             :         call stat_assign( var_index=stats_metadata%iPRACS, var_name="PRACS", &
    2145             :              var_description="PRACS, Collection of rain by snow, +rsm, -rrm", &
    2146           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2147           0 :         k = k + 1
    2148             : 
    2149             :       case ('EVPMG')
    2150           0 :         stats_metadata%iEVPMG = k
    2151             : 
    2152             :         call stat_assign( var_index=stats_metadata%iEVPMG, var_name="EVPMG", &
    2153             :              var_description="EVPMG, Evaporation of melted graupel, +rgm, -rvm", &
    2154           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2155           0 :         k = k + 1
    2156             : 
    2157             :       case ('PRACG')
    2158           0 :         stats_metadata%iPRACG = k
    2159             : 
    2160             :         call stat_assign( var_index=stats_metadata%iPRACG, var_name="PRACG", &
    2161             :              var_description="PRACG, Negative of collection of rain by graupel, +rrm, -rgm", &
    2162           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2163           0 :         k = k + 1
    2164             : 
    2165             :       case ('PGMLT')
    2166           0 :         stats_metadata%iPGMLT = k
    2167             : 
    2168             :         call stat_assign( var_index=stats_metadata%iPGMLT, var_name="PGMLT", &
    2169             :              var_description="PGMLT, Negative of melting of graupel, +rgm, -rrm", &
    2170           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2171           0 :         k = k + 1
    2172             : 
    2173             :       case ('MNUCCC')
    2174           0 :         stats_metadata%iMNUCCC = k
    2175             : 
    2176             :         call stat_assign( var_index=stats_metadata%iMNUCCC, var_name="MNUCCC", &
    2177             :              var_description="MNUCCC, Contact freezing of cloud droplets, +rim, -rcm", &
    2178           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2179           0 :         k = k + 1
    2180             : 
    2181             :       case ('PSACWS')
    2182           0 :         stats_metadata%iPSACWS = k
    2183             : 
    2184             :         call stat_assign( var_index=stats_metadata%iPSACWS, var_name="PSACWS", &
    2185             :              var_description="PSACWS, Collection of cloud water by snow, +rsm, -rcm", &
    2186           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2187           0 :         k = k + 1
    2188             : 
    2189             :       case ('PSACWI')
    2190           0 :         stats_metadata%iPSACWI = k
    2191             : 
    2192             :         call stat_assign( var_index=stats_metadata%iPSACWI, var_name="PSACWI", &
    2193             :              var_description="PSACWI, Collection of cloud water by cloud ice, +rim, -rcm", &
    2194           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2195           0 :         k = k + 1
    2196             : 
    2197             :       case ('QMULTS')
    2198           0 :         stats_metadata%iQMULTS = k
    2199             : 
    2200             :         call stat_assign( var_index=stats_metadata%iQMULTS, var_name="QMULTS", &
    2201             :              var_description="QMULTS, Splintering from cloud droplets accreted onto snow, " &
    2202             :              // "+rim, -rcm", &
    2203           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2204           0 :         k = k + 1
    2205             : 
    2206             :       case ('QMULTG')
    2207           0 :         stats_metadata%iQMULTG = k
    2208             : 
    2209             :         call stat_assign( var_index=stats_metadata%iQMULTG, var_name="QMULTG", &
    2210             :              var_description="QMULTG, Splintering from droplets accreted onto graupel, " &
    2211             :              // "+rim, -rcm", &
    2212           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2213           0 :         k = k + 1
    2214             : 
    2215             :       case ('PSACWG')
    2216           0 :         stats_metadata%iPSACWG = k
    2217             : 
    2218             :         call stat_assign( var_index=stats_metadata%iPSACWG, var_name="PSACWG", &
    2219             :              var_description="PSACWG, Collection of cloud water by graupel, +rgm, -rcm", &
    2220           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2221           0 :         k = k + 1
    2222             : 
    2223             :       case ('PGSACW')
    2224           0 :         stats_metadata%iPGSACW = k
    2225             : 
    2226             :         call stat_assign( var_index=stats_metadata%iPGSACW, var_name="PGSACW", &
    2227             :              var_description="PGSACW, Reclassification of rimed snow as graupel, +rgm, -rcm", &
    2228           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2229           0 :         k = k + 1
    2230             : 
    2231             :       case ('PRD')
    2232           0 :         stats_metadata%iPRD = k
    2233             : 
    2234             :         call stat_assign( var_index=stats_metadata%iPRD, var_name="PRD", &
    2235             :              var_description="PRD, Depositional growth of cloud ice, +rim, -rvm", &
    2236           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2237           0 :         k = k + 1
    2238             : 
    2239             :       case ('PRCI')
    2240           0 :         stats_metadata%iPRCI = k
    2241             : 
    2242             :         call stat_assign( var_index=stats_metadata%iPRCI, var_name="PRCI", &
    2243             :              var_description="PRCI, Autoconversion of cloud ice to snow, +rsm, -rim", &
    2244           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2245           0 :         k = k + 1
    2246             : 
    2247             :       case ('PRAI')
    2248           0 :         stats_metadata%iPRAI = k
    2249             : 
    2250             :         call stat_assign( var_index=stats_metadata%iPRAI, var_name="PRAI", &
    2251             :              var_description="PRAI, Collection of cloud ice by snow, +rsm, -rim", &
    2252           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2253           0 :         k = k + 1
    2254             : 
    2255             :       case ('QMULTR')
    2256           0 :         stats_metadata%iQMULTR = k
    2257             : 
    2258             :         call stat_assign( var_index=stats_metadata%iQMULTR, var_name="QMULTR", &
    2259             :              var_description="QMULTR, Splintering from rain droplets accreted onto snow, " &
    2260             :              // "+rim, -rrm", &
    2261           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2262           0 :         k = k + 1
    2263             : 
    2264             :       case ('QMULTRG')
    2265           0 :         stats_metadata%iQMULTRG = k
    2266             : 
    2267             :         call stat_assign( var_index=stats_metadata%iQMULTRG, var_name="QMULTRG", &
    2268             :              var_description="QMULTRG, Splintering from rain droplets accreted onto graupel, " &
    2269             :              // "+rim, -rrm", &
    2270           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2271           0 :         k = k + 1
    2272             : 
    2273             :       case ('MNUCCD')
    2274           0 :         stats_metadata%iMNUCCD = k
    2275             : 
    2276             :         call stat_assign( var_index=stats_metadata%iMNUCCD, var_name="MNUCCD", &
    2277             :              var_description="MNUCCD, Freezing of aerosol, +rim, -rvm", &
    2278           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2279           0 :         k = k + 1
    2280             : 
    2281             :       case ('PRACI')
    2282           0 :         stats_metadata%iPRACI = k
    2283             : 
    2284             :         call stat_assign( var_index=stats_metadata%iPRACI, var_name="PRACI", &
    2285             :              var_description="PRACI, Collection of cloud ice by rain, +rgm, -rim", &
    2286           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2287           0 :         k = k + 1
    2288             : 
    2289             :       case ('PRACIS')
    2290           0 :         stats_metadata%iPRACIS = k
    2291             : 
    2292             :         call stat_assign( var_index=stats_metadata%iPRACIS, var_name="PRACIS", &
    2293             :              var_description="PRACIS, Collection of cloud ice by rain, +rsm, -rim", &
    2294           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2295           0 :         k = k + 1
    2296             : 
    2297             :       case ('EPRD')
    2298           0 :         stats_metadata%iEPRD = k
    2299             : 
    2300             :         call stat_assign( var_index=stats_metadata%iEPRD, var_name="EPRD", &
    2301             :              var_description="EPRD, Negative of sublimation of cloud ice, +rim, -rvm", &
    2302           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2303           0 :         k = k + 1
    2304             : 
    2305             :       case ('MNUCCR')
    2306           0 :         stats_metadata%iMNUCCR = k
    2307             : 
    2308             :         call stat_assign( var_index=stats_metadata%iMNUCCR, var_name="MNUCCR", &
    2309             :              var_description="MNUCCR, Contact freezing of rain droplets, +rgm, -rrm", &
    2310           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2311           0 :         k = k + 1
    2312             : 
    2313             :       case ('PIACR')
    2314           0 :         stats_metadata%iPIACR = k
    2315             : 
    2316             :         call stat_assign( var_index=stats_metadata%iPIACR, var_name="PIACR", &
    2317             :              var_description="PIACR, Collection of cloud ice by rain, +rgm, -rrm", &
    2318           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2319           0 :         k = k + 1
    2320             : 
    2321             :       case ('PIACRS')
    2322           0 :         stats_metadata%iPIACRS = k
    2323             : 
    2324             :         call stat_assign( var_index=stats_metadata%iPIACRS, var_name="PIACRS", &
    2325             :              var_description="PIACRS, Collection of cloud ice by rain, +rsm, -rrm", &
    2326           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2327           0 :         k = k + 1
    2328             : 
    2329             :       case ('PGRACS')
    2330           0 :         stats_metadata%iPGRACS = k
    2331             : 
    2332             :         call stat_assign( var_index=stats_metadata%iPGRACS, var_name="PGRACS", &
    2333             :              var_description="PGRACS, Collection of rain by snow, +rgm, -rrm", &
    2334           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2335           0 :         k = k + 1
    2336             : 
    2337             :       case ('PRDS')
    2338           0 :         stats_metadata%iPRDS = k
    2339             : 
    2340             :         call stat_assign( var_index=stats_metadata%iPRDS, var_name="PRDS", &
    2341             :              var_description="PRDS, Depositional growth of snow, +rsm, -rvm", &
    2342           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2343           0 :         k = k + 1
    2344             : 
    2345             :       case ('EPRDS')
    2346           0 :         stats_metadata%iEPRDS = k
    2347             : 
    2348             :         call stat_assign( var_index=stats_metadata%iEPRDS, var_name="EPRDS", &
    2349             :              var_description="EPRDS, Negative of sublimation of snow, +rsm, -rvm", &
    2350           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2351           0 :         k = k + 1
    2352             : 
    2353             :       case ('PSACR')
    2354           0 :         stats_metadata%iPSACR = k
    2355             : 
    2356             :         call stat_assign( var_index=stats_metadata%iPSACR, var_name="PSACR", &
    2357             :              var_description="PSACR, Collection of snow by rain, +rgm, -rsm", &
    2358           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2359           0 :         k = k + 1
    2360             : 
    2361             :       case ('PRDG')
    2362           0 :         stats_metadata%iPRDG = k
    2363             : 
    2364             :         call stat_assign( var_index=stats_metadata%iPRDG, var_name="PRDG", &
    2365             :              var_description="PRDG, Depositional growth of graupel, +rgm, -rvm", &
    2366           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2367           0 :         k = k + 1
    2368             : 
    2369             :       case ('EPRDG')
    2370           0 :         stats_metadata%iEPRDG = k
    2371             : 
    2372             :         call stat_assign( var_index=stats_metadata%iEPRDG, var_name="EPRDG", &
    2373             :              var_description="EPRDG, Negative of sublimation of graupel, +rgm, -rvm", &
    2374           0 :              var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
    2375           0 :         k = k + 1
    2376             : 
    2377             :       case ('NGSTEN')
    2378           0 :         stats_metadata%iNGSTEN = k
    2379             : 
    2380             :         call stat_assign( var_index=stats_metadata%iNGSTEN, var_name="NGSTEN", &
    2381             :              var_description="NGSTEN, Graupel sedimentation tendency", var_units="(#/kg/s)", &
    2382           0 :              l_silhs=.true., grid_kind=stats_zt )
    2383           0 :         k = k + 1
    2384             : 
    2385             :       case ('NRSTEN')
    2386           0 :         stats_metadata%iNRSTEN = k
    2387             : 
    2388             :         call stat_assign( var_index=stats_metadata%iNRSTEN, var_name="NRSTEN", &
    2389             :              var_description="NRSTEN, Rain sedimentation tendency", var_units="(#/kg/s)", &
    2390           0 :              l_silhs=.true., grid_kind=stats_zt )
    2391           0 :         k = k + 1
    2392             : 
    2393             :       case ('NISTEN')
    2394           0 :         stats_metadata%iNISTEN = k
    2395             : 
    2396             :         call stat_assign( var_index=stats_metadata%iNISTEN, var_name="NISTEN", &
    2397             :              var_description="NISTEN, Cloud ice sedimentation tendency", var_units="(#/kg/s)", &
    2398           0 :              l_silhs=.true., grid_kind=stats_zt )
    2399           0 :         k = k + 1
    2400             : 
    2401             :       case ('NSSTEN')
    2402           0 :         stats_metadata%iNSSTEN = k
    2403             : 
    2404             :         call stat_assign( var_index=stats_metadata%iNSSTEN, var_name="NSSTEN", &
    2405             :              var_description="NSSTEN, Snow sedimentation tendency", var_units="(#/kg/s)", &
    2406           0 :              l_silhs=.true., grid_kind=stats_zt )
    2407           0 :         k = k + 1
    2408             : 
    2409             :       case ('NCSTEN')
    2410           0 :         stats_metadata%iNCSTEN = k
    2411             : 
    2412             :         call stat_assign( var_index=stats_metadata%iNCSTEN, var_name="NCSTEN", &
    2413             :              var_description="NCSTEN, Cloud water sedimentation tendency", &
    2414           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2415           0 :         k = k + 1
    2416             : 
    2417             :       case ('NPRC1')
    2418           0 :         stats_metadata%iNPRC1 = k
    2419             : 
    2420             :         call stat_assign( var_index=stats_metadata%iNPRC1, var_name="NPRC1", &
    2421             :              var_description="NPRC1, Change in Nrm due to autoconversion of droplets, +Nrm", &
    2422           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2423           0 :         k = k + 1
    2424             : 
    2425             :       case ('NRAGG')
    2426           0 :         stats_metadata%iNRAGG = k
    2427             : 
    2428             :         call stat_assign( var_index=stats_metadata%iNRAGG, var_name="NRAGG", &
    2429             :              var_description="NRAGG, Change in Nrm due to self-collection of raindrops, +Nrm", &
    2430           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2431           0 :         k = k + 1
    2432             : 
    2433             :       case ('NPRACG')
    2434           0 :         stats_metadata%iNPRACG = k
    2435             : 
    2436             :         call stat_assign( var_index=stats_metadata%iNPRACG, var_name="NPRACG", &
    2437             :              var_description="NPRACG, Collection of rainwater by graupel, -Nrm", &
    2438           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2439           0 :         k = k + 1
    2440             : 
    2441             :       case ('NSUBR')
    2442           0 :         stats_metadata%iNSUBR = k
    2443             : 
    2444             :         call stat_assign( var_index=stats_metadata%iNSUBR, var_name="NSUBR", &
    2445             :              var_description="NSUBR, Loss of Nrm by evaporation, +Nrm", var_units="(#/kg/s)", &
    2446           0 :              l_silhs=.true., grid_kind=stats_zt )
    2447           0 :         k = k + 1
    2448             : 
    2449             :       case ('NSMLTR')
    2450           0 :         stats_metadata%iNSMLTR = k
    2451             : 
    2452             :         call stat_assign( var_index=stats_metadata%iNSMLTR, var_name="NSMLTR", &
    2453             :              var_description="NSMLTR, Melting of snow to form rain, -Nrm", &
    2454           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2455           0 :         k = k + 1
    2456             : 
    2457             :       case ('NGMLTR')
    2458           0 :         stats_metadata%iNGMLTR = k
    2459             : 
    2460             :         call stat_assign( var_index=stats_metadata%iNGMLTR, var_name="NGMLTR", &
    2461             :              var_description="NGMLTR, Melting of graupel to form rain, -Nrm", &
    2462           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2463           0 :         k = k + 1
    2464             : 
    2465             :       case ('NPRACS')
    2466           0 :         stats_metadata%iNPRACS = k
    2467             : 
    2468             :         call stat_assign( var_index=stats_metadata%iNPRACS, var_name="NPRACS", &
    2469             :              var_description="NPRACS, Collection of rainwater by snow, -Nrm", &
    2470           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2471           0 :         k = k + 1
    2472             : 
    2473             :       case ('NNUCCR')
    2474           0 :         stats_metadata%iNNUCCR = k
    2475             : 
    2476             :         call stat_assign( var_index=stats_metadata%iNNUCCR, var_name="NNUCCR", &
    2477             :              var_description="NNUCCR, Contact freezing of rain, +Ngm, -Nrm", &
    2478           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2479           0 :         k = k + 1
    2480             : 
    2481             :       case ('NIACR')
    2482           0 :         stats_metadata%iNIACR = k
    2483             : 
    2484             :         call stat_assign( var_index=stats_metadata%iNIACR, var_name="NIACR", &
    2485             :              var_description="NIACR, Collection of cloud ice by rain, +Ngm, -Nrm, -Nim", &
    2486           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2487           0 :         k = k + 1
    2488             : 
    2489             :       case ('NIACRS')
    2490           0 :         stats_metadata%iNIACRS = k
    2491             : 
    2492             :         call stat_assign( var_index=stats_metadata%iNIACRS, var_name="NIACRS", &
    2493             :              var_description="NIACRS, Collection of cloud ice by rain, +Nsm, -Nrm, -Nim", &
    2494           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2495           0 :         k = k + 1
    2496             : 
    2497             :       case ('NGRACS')
    2498           0 :         stats_metadata%iNGRACS = k
    2499             : 
    2500             :         call stat_assign( var_index=stats_metadata%iNGRACS, var_name="NGRACS", &
    2501             :              var_description="NGRACS, Collection of rain by snow, +Ngm, -Nrm, -Nsm", &
    2502           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2503           0 :         k = k + 1
    2504             : 
    2505             :       case ('NSMLTS')
    2506           0 :         stats_metadata%iNSMLTS= k
    2507             : 
    2508             :         call stat_assign( var_index=stats_metadata%iNSMLTS, var_name="NSMLTS", &
    2509             :              var_description="NSMLTS, Melting  of snow, +Nsm", var_units="(#/kg/s)", &
    2510           0 :              l_silhs=.true., grid_kind=stats_zt )
    2511           0 :         k = k + 1
    2512             : 
    2513             :       case ('NSAGG')
    2514           0 :         stats_metadata%iNSAGG= k
    2515             : 
    2516             :         call stat_assign( var_index=stats_metadata%iNSAGG, var_name="NSAGG", &
    2517             :              var_description="NSAGG, Self collection of snow, +Nsm", var_units="(#/kg/s)", &
    2518           0 :              l_silhs=.true., grid_kind=stats_zt )
    2519             : 
    2520           0 :         k = k + 1
    2521             : 
    2522             :       case ('NPRCI')
    2523           0 :         stats_metadata%iNPRCI= k
    2524             : 
    2525             :         call stat_assign( var_index=stats_metadata%iNPRCI, var_name="NPRCI", &
    2526             :              var_description="NPRCI, Autoconversion of cloud ice to snow, -Nim, +Nsm", &
    2527           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2528           0 :         k = k + 1
    2529             : 
    2530             :       case ('NSCNG')
    2531           0 :         stats_metadata%iNSCNG= k
    2532             : 
    2533             :         call stat_assign( var_index=stats_metadata%iNSCNG, var_name="NSCNG", &
    2534             :              var_description="NSCNG, Conversion of snow to graupel, +Ngm, -Nsm", &
    2535           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2536           0 :         k = k + 1
    2537             : 
    2538             :       case ('NSUBS')
    2539           0 :         stats_metadata%iNSUBS= k
    2540             : 
    2541             :         call stat_assign( var_index=stats_metadata%iNSUBS, var_name="NSUBS", &
    2542             :              var_description="NSUBS, Loss of snow due to sublimation, +Nsm", &
    2543           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2544           0 :         k = k + 1
    2545             : 
    2546             :       case ('PRC')
    2547           0 :         stats_metadata%iPRC= k
    2548             : 
    2549             :         call stat_assign( var_index=stats_metadata%iPRC, var_name="PRC", &
    2550             :              var_description="PRC, Autoconversion +rrm -rcm", var_units="(kg/kg/s)", &
    2551           0 :              l_silhs=.true., grid_kind=stats_zt )
    2552           0 :         k = k + 1
    2553             : 
    2554             :       case ('PRA')
    2555           0 :         stats_metadata%iPRA= k
    2556             : 
    2557             :         call stat_assign( var_index=stats_metadata%iPRA, var_name="PRA", &
    2558             :              var_description="PRA, Accretion +rrm -rcm", var_units="(kg/kg/s)", &
    2559           0 :              l_silhs=.true., grid_kind=stats_zt )
    2560           0 :         k = k + 1
    2561             : 
    2562             :       case ('PRE')
    2563           0 :         stats_metadata%iPRE= k
    2564             : 
    2565             :         call stat_assign( var_index=stats_metadata%iPRE, var_name="PRE", &
    2566             :              var_description="PRE, Evaporation of rain -rrm", var_units="(kg/kg/s)", &
    2567           0 :              l_silhs=.true., grid_kind=stats_zt )
    2568           0 :         k = k + 1
    2569             : 
    2570             :       case ('PCC')
    2571           0 :         stats_metadata%iPCC= k
    2572             : 
    2573             :         call stat_assign( var_index=stats_metadata%iPCC, var_name="PCC", &
    2574             :              var_description="PCC, Satuation adjustment -rvm +rcm", var_units="(kg/kg/s)", &
    2575           0 :              l_silhs=.true., grid_kind=stats_zt )
    2576           0 :         k = k + 1
    2577             : 
    2578             :       case ('NNUCCC')
    2579           0 :         stats_metadata%iNNUCCC= k
    2580             : 
    2581             :         call stat_assign( var_index=stats_metadata%iNNUCCC, var_name="NNUCCC", &
    2582             :              var_description="NNUCCC, Contact freezing of drops, -Ncm + Nim", &
    2583           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2584           0 :         k = k + 1
    2585             : 
    2586             :       case ('NPSACWS')
    2587           0 :         stats_metadata%iNPSACWS= k
    2588             : 
    2589             :         call stat_assign( var_index=stats_metadata%iNPSACWS, var_name="NPSACWS", &
    2590             :              var_description="NPSACWS, Droplet accretion by snow, -Ncm", var_units="(#/kg/s)", &
    2591           0 :              l_silhs=.true., grid_kind=stats_zt )
    2592           0 :         k = k + 1
    2593             : 
    2594             :       case ('NPRA')
    2595           0 :         stats_metadata%iNPRA= k
    2596             : 
    2597             :         call stat_assign( var_index=stats_metadata%iNPRA, var_name="NPRA", &
    2598             :              var_description="NPRA, Droplet accretion by rain, -Ncm", var_units="(#/kg/s)", &
    2599           0 :              l_silhs=.true., grid_kind=stats_zt )
    2600           0 :         k = k + 1
    2601             : 
    2602             :       case ('NPRC')
    2603           0 :         stats_metadata%iNPRC= k
    2604             : 
    2605             :         call stat_assign( var_index=stats_metadata%iNPRC, var_name="NPRC", &
    2606             :              var_description="NPRC, Autoconversion of cloud drops, -Ncm", &
    2607           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2608           0 :         k = k + 1
    2609             : 
    2610             :       case ('NPSACWI')
    2611           0 :         stats_metadata%iNPSACWI= k
    2612             : 
    2613             :         call stat_assign( var_index=stats_metadata%iNPSACWI, var_name="NPSACWI", &
    2614             :              var_description="NPSACWI, Droplet accretion by cloud ice, -Ncm", &
    2615           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2616           0 :         k = k + 1
    2617             : 
    2618             :       case ('NPSACWG')
    2619           0 :         stats_metadata%iNPSACWG= k
    2620             : 
    2621             :         call stat_assign( var_index=stats_metadata%iNPSACWG, var_name="NPSACWG", &
    2622             :              var_description="NPSACWG, Collection of cloud droplets by graupel, -Ncm", &
    2623           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2624           0 :         k = k + 1
    2625             : 
    2626             :       case ('NPRAI')
    2627           0 :         stats_metadata%iNPRAI= k
    2628             : 
    2629             :         call stat_assign( var_index=stats_metadata%iNPRAI, var_name="NPRAI", &
    2630             :              var_description="NPRAI, Accretion of cloud ice by snow, -Nim", &
    2631           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2632           0 :         k = k + 1
    2633             : 
    2634             :       case ('NMULTS')
    2635           0 :         stats_metadata%iNMULTS= k
    2636             : 
    2637             :         call stat_assign( var_index=stats_metadata%iNMULTS, var_name="NMULTS", &
    2638             :              var_description="NMULTS, Ice multiplication due to riming of cloud droplets " &
    2639             :              // "by snow, +Nim", &
    2640           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2641           0 :         k = k + 1
    2642             : 
    2643             :       case ('NMULTG')
    2644           0 :         stats_metadata%iNMULTG= k
    2645             : 
    2646             :         call stat_assign( var_index=stats_metadata%iNMULTG, var_name="NMULTG", &
    2647             :              var_description="NMULTG, Ice multiplication due to accretion of droplets " &
    2648             :              // "by graupel, +Nim", &
    2649           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2650           0 :         k = k + 1
    2651             : 
    2652             :       case ('NMULTR')
    2653           0 :         stats_metadata%iNMULTR= k
    2654             : 
    2655             :         call stat_assign( var_index=stats_metadata%iNMULTR, var_name="NMULTR", &
    2656             :              var_description="Ice multiplication due to riming of rain by snow, +Nim", &
    2657           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2658           0 :         k = k + 1
    2659             : 
    2660             :       case ('NMULTRG')
    2661           0 :         stats_metadata%iNMULTRG= k
    2662             : 
    2663             :         call stat_assign( var_index=stats_metadata%iNMULTRG, var_name="NMULTRG", &
    2664             :              var_description="NMULTR, Ice multiplication due to accretion of rain by " &
    2665             :              // "graupel, +Nim", &
    2666           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2667           0 :         k = k + 1
    2668             : 
    2669             :       case ('NNUCCD')
    2670           0 :         stats_metadata%iNNUCCD= k
    2671             : 
    2672             :         call stat_assign( var_index=stats_metadata%iNNUCCD, var_name="NNUCCD", &
    2673             :              var_description="NNUCCD, Primary ice nucleation, freezing of aerosol, +Nim", &
    2674           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2675           0 :         k = k + 1
    2676             : 
    2677             :       case ('NSUBI')
    2678           0 :         stats_metadata%iNSUBI= k
    2679             : 
    2680             :         call stat_assign( var_index=stats_metadata%iNSUBI, var_name="NSUBI", &
    2681             :              var_description="NSUBI, Loss of ice due to sublimation, -Nim", &
    2682           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2683           0 :         k = k + 1
    2684             : 
    2685             :       case ('NGMLTG')
    2686           0 :         stats_metadata%iNGMLTG= k
    2687             : 
    2688             :         call stat_assign( var_index=stats_metadata%iNGMLTG, var_name="NGMLTG", &
    2689             :              var_description="NGMLTG, Loss of graupel due to melting, -Ngm", &
    2690           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2691           0 :         k = k + 1
    2692             : 
    2693             :       case ('NSUBG')
    2694           0 :         stats_metadata%iNSUBG= k
    2695             : 
    2696             :         call stat_assign( var_index=stats_metadata%iNSUBG, var_name="NSUBG", &
    2697             :              var_description="NSUBG, Loss of graupel due to sublimation, -Ngm", &
    2698           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2699           0 :         k = k + 1
    2700             : 
    2701             :       case ('NACT')
    2702           0 :         stats_metadata%iNACT= k
    2703             : 
    2704             :         call stat_assign( var_index=stats_metadata%iNACT, var_name="NACT", &
    2705             :              var_description="NACT, Cloud drop formation by aerosol activation, +Ncm", &
    2706           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2707           0 :         k = k + 1
    2708             : 
    2709             :       case ('SIZEFIX_NR')
    2710           0 :         stats_metadata%iSIZEFIX_NR= k
    2711             : 
    2712             :         call stat_assign( var_index=stats_metadata%iSIZEFIX_NR, var_name="SIZEFIX_NR", &
    2713             :              var_description="SIZEFIX_NR, Adjust rain # conc. for large/small drops, +Nrm", &
    2714           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2715           0 :         k = k + 1
    2716             : 
    2717             :       case ('SIZEFIX_NC')
    2718           0 :         stats_metadata%iSIZEFIX_NC= k
    2719             : 
    2720             :         call stat_assign( var_index=stats_metadata%iSIZEFIX_NC, var_name="SIZEFIX_NC", &
    2721             :              var_description="SIZEFIX_NC, Adjust cloud # conc. for large/small drops, +Ncm", &
    2722           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2723           0 :         k = k + 1
    2724             : 
    2725             :       case ('SIZEFIX_NI')
    2726           0 :         stats_metadata%iSIZEFIX_NI= k
    2727             : 
    2728             :         call stat_assign( var_index=stats_metadata%iSIZEFIX_NI, var_name="SIZEFIX_NI", &
    2729             :              var_description="SIZEFIX_NI, Adjust ice # conc. for large/small drops, +Nim", &
    2730           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2731           0 :         k = k + 1
    2732             : 
    2733             :       case ('SIZEFIX_NS')
    2734           0 :         stats_metadata%iSIZEFIX_NS= k
    2735             : 
    2736             :         call stat_assign( var_index=stats_metadata%iSIZEFIX_NS, var_name="SIZEFIX_NS", &
    2737             :              var_description="SIZEFIX_NS, Adjust snow # conc. for large/small drops, +Nsm", &
    2738           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2739           0 :         k = k + 1
    2740             : 
    2741             :       case ('SIZEFIX_NG')
    2742           0 :         stats_metadata%iSIZEFIX_NG= k
    2743             : 
    2744             :         call stat_assign( var_index=stats_metadata%iSIZEFIX_NG, var_name="SIZEFIX_NG", &
    2745             :              var_description="SIZEFIX_NG, Adjust graupel # conc. for large/small drops,+Ngm",&
    2746           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2747           0 :         k = k + 1
    2748             : 
    2749             :       case ('NEGFIX_NR')
    2750           0 :         stats_metadata%iNEGFIX_NR= k
    2751             : 
    2752             :         call stat_assign( var_index=stats_metadata%iNEGFIX_NR, var_name="NEGFIX_NR", &
    2753             :              var_description="NEGFIX_NR, Removal of negative rain drop number conc., -Nrm", &
    2754           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2755           0 :         k = k + 1
    2756             : 
    2757             :       case ('NEGFIX_NC')
    2758           0 :         stats_metadata%iNEGFIX_NC= k
    2759             : 
    2760             :         call stat_assign( var_index=stats_metadata%iNEGFIX_NC, var_name="NEGFIX_NC", &
    2761             :              var_description="NEGFIX_NC, Removal of negative cloud drop number conc., -Ncm", &
    2762           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2763           0 :         k = k + 1
    2764             : 
    2765             :       case ('NEGFIX_NI')
    2766           0 :         stats_metadata%iNEGFIX_NI= k
    2767             : 
    2768             :         call stat_assign( var_index=stats_metadata%iNEGFIX_NI, var_name="NEGFIX_NI", &
    2769             :              var_description="NEGFIX_NI, Removal of negative ice number conc., -Nim", &
    2770           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2771           0 :         k = k + 1
    2772             : 
    2773             :       case ('NEGFIX_NS')
    2774           0 :         stats_metadata%iNEGFIX_NS= k
    2775             : 
    2776             :         call stat_assign( var_index=stats_metadata%iNEGFIX_NS, var_name="NEGFIX_NS", &
    2777             :              var_description="NEGFIX_NS, Removal of negative snow number conc, -Nsm", &
    2778           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2779           0 :         k = k + 1
    2780             : 
    2781             :       case ('NEGFIX_NG')
    2782           0 :         stats_metadata%iNEGFIX_NG= k
    2783             : 
    2784             :         call stat_assign( var_index=stats_metadata%iNEGFIX_NG, var_name="NEGFIX_NG", &
    2785             :              var_description="NEGFIX_NG, Removal of negative graupel number conc., -Ngm", &
    2786           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2787           0 :         k = k + 1
    2788             : 
    2789             :       case ('NIM_MORR_CL')
    2790           0 :         stats_metadata%iNIM_MORR_CL= k
    2791             : 
    2792             :         call stat_assign( var_index=stats_metadata%iNIM_MORR_CL, var_name="NIM_MORR_CL", &
    2793             :              var_description="NIM_MORR_CL, Clipping of large ice concentrations, -Nim", &
    2794           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2795           0 :         k = k + 1
    2796             : 
    2797             :       case ('QC_INST')
    2798           0 :         stats_metadata%iQC_INST= k
    2799             : 
    2800             :         call stat_assign( var_index=stats_metadata%iQC_INST, var_name="QC_INST", &
    2801             :              var_description="QC_INST, Change in mixing ratio due to instantaneous " &
    2802             :              // "processes +rcm", &
    2803           0 :              var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2804           0 :         k = k + 1
    2805             : 
    2806             :       case ('QR_INST')
    2807           0 :         stats_metadata%iQR_INST= k
    2808             : 
    2809             :         call stat_assign( var_index=stats_metadata%iQR_INST, var_name="QR_INST", &
    2810             :              var_description="QR_INST, Change in mixing ratio from instantaneous processes, +rrm",&
    2811           0 :              var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2812           0 :         k = k + 1
    2813             : 
    2814             :       case ('QI_INST')
    2815           0 :         stats_metadata%iQI_INST= k
    2816             : 
    2817             :         call stat_assign( var_index=stats_metadata%iQI_INST, var_name="QI_INST", &
    2818             :              var_description="QI_INST, Change in mixing ratio from instantaneous processes +rim",&
    2819           0 :              var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2820           0 :         k = k + 1
    2821             : 
    2822             :       case ('QS_INST')
    2823           0 :         stats_metadata%iQS_INST= k
    2824             : 
    2825             :         call stat_assign( var_index=stats_metadata%iQS_INST, var_name="QS_INST", &
    2826             :              var_description="QS_INST, Change in mixing ratio from instantaneous processes +rsm",&
    2827           0 :              var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2828           0 :         k = k + 1
    2829             : 
    2830             :       case ('QG_INST')
    2831           0 :         stats_metadata%iQG_INST= k
    2832             : 
    2833             :         call stat_assign( var_index=stats_metadata%iQG_INST, var_name="QG_INST", &
    2834             :              var_description="QG_INST, Change in mixing ratio from instantaneous processes +rgm",&
    2835           0 :              var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2836           0 :         k = k + 1
    2837             : 
    2838             :       case ('NC_INST')
    2839           0 :         stats_metadata%iNC_INST= k
    2840             : 
    2841             :         call stat_assign( var_index=stats_metadata%iNC_INST, var_name="NC_INST", &
    2842             :              var_description="NC_INST, Change in # conc. from instantaneous processes +Ncm", &
    2843           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2844           0 :         k = k + 1
    2845             : 
    2846             :       case ('NR_INST')
    2847           0 :         stats_metadata%iNR_INST= k
    2848             : 
    2849             :         call stat_assign( var_index=stats_metadata%iNR_INST, var_name="NR_INST", &
    2850             :              var_description="NR_INST, Change in # conc. from instantaneous processes +Nrm", &
    2851           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2852           0 :         k = k + 1
    2853             : 
    2854             :       case ('NI_INST')
    2855           0 :         stats_metadata%iNI_INST= k
    2856             : 
    2857             :         call stat_assign( var_index=stats_metadata%iNI_INST, var_name="NI_INST", &
    2858             :              var_description="NI_INST, Change in # conc. from instantaneous processes +Nim", &
    2859           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2860           0 :         k = k + 1
    2861             : 
    2862             :       case ('NS_INST')
    2863           0 :         stats_metadata%iNS_INST= k
    2864             : 
    2865             :         call stat_assign( var_index=stats_metadata%iNS_INST, var_name="NS_INST", &
    2866             :              var_description="NS_INST, Change in # conc. from instantaneous processes +Nsm", &
    2867           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2868           0 :         k = k + 1
    2869             : 
    2870             :       case ('NG_INST')
    2871           0 :         stats_metadata%iNG_INST= k
    2872             : 
    2873             :         call stat_assign( var_index=stats_metadata%iNG_INST, var_name="NG_INST", &
    2874             :              var_description="NG_INST, Change in # conc. from instantaneous processes +Ngm", &
    2875           0 :              var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
    2876           0 :         k = k + 1
    2877             : 
    2878             : 
    2879             :       case ('T_in_K_mc')
    2880           0 :         stats_metadata%iT_in_K_mc= k
    2881             : 
    2882             :         call stat_assign( var_index=stats_metadata%iT_in_K_mc, var_name="T_in_K_mc", &
    2883             :              var_description="T_in_K_mc, Temperature tendency from Morrison microphysics", &
    2884           0 :              var_units="(K/s)", l_silhs=.true., grid_kind=stats_zt )
    2885           0 :         k = k + 1
    2886             : 
    2887             :       case ('w_KK_evap_covar_zt')
    2888           0 :         stats_metadata%iw_KK_evap_covar_zt = k
    2889             : 
    2890             :         call stat_assign( var_index=stats_metadata%iw_KK_evap_covar_zt, var_name="w_KK_evap_covar_zt", &
    2891             :              var_description="w_KK_evap_covar_zt, Covariance of w and KK evaporation rate", &
    2892           0 :              var_units="m*(kg/kg)/s^2", l_silhs=.false., grid_kind=stats_zt )
    2893           0 :        k = k + 1
    2894             : 
    2895             :       case ('rt_KK_evap_covar_zt')
    2896           0 :         stats_metadata%irt_KK_evap_covar_zt = k
    2897             : 
    2898             :         call stat_assign( var_index=stats_metadata%irt_KK_evap_covar_zt, var_name="rt_KK_evap_covar_zt", &
    2899             :              var_description="rt_KK_evap_covar_zt, Covariance of r_t and KK evaporation rate", &
    2900           0 :              var_units="(kg/kg)^2/s", l_silhs=.false., grid_kind=stats_zt )
    2901           0 :        k = k + 1
    2902             : 
    2903             :       case ('thl_KK_evap_covar_zt')
    2904           0 :         stats_metadata%ithl_KK_evap_covar_zt = k
    2905             : 
    2906             :         call stat_assign( var_index=stats_metadata%ithl_KK_evap_covar_zt, var_name="thl_KK_evap_covar_zt", &
    2907             :              var_description="thl_KK_evap_covar_zt, Covariance of theta_l and KK " &
    2908             :              // "evaporation rate", &
    2909           0 :              var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2910           0 :        k = k + 1
    2911             : 
    2912             :       case ('w_KK_auto_covar_zt')
    2913           0 :         stats_metadata%iw_KK_auto_covar_zt = k
    2914             : 
    2915             :         call stat_assign( var_index=stats_metadata%iw_KK_auto_covar_zt, var_name="w_KK_auto_covar_zt", &
    2916             :              var_description="w_KK_auto_covar_zt, Covariance of w and KK autoconversion rate", &
    2917           0 :              var_units="m*(kg/kg)/s^2", l_silhs=.false., grid_kind=stats_zt )
    2918           0 :        k = k + 1
    2919             : 
    2920             :       case ('rt_KK_auto_covar_zt')
    2921           0 :         stats_metadata%irt_KK_auto_covar_zt = k
    2922             : 
    2923             :         call stat_assign( var_index=stats_metadata%irt_KK_auto_covar_zt, var_name="rt_KK_auto_covar_zt", &
    2924             :              var_description="rt_KK_auto_covar_zt, Covariance of r_t and KK autoconversion rate",&
    2925           0 :              var_units="(kg/kg)^2/s", l_silhs=.false., grid_kind=stats_zt )
    2926           0 :        k = k + 1
    2927             : 
    2928             :       case ('thl_KK_auto_covar_zt')
    2929           0 :         stats_metadata%ithl_KK_auto_covar_zt = k
    2930             : 
    2931             :         call stat_assign( var_index=stats_metadata%ithl_KK_auto_covar_zt, var_name="thl_KK_auto_covar_zt", &
    2932             :              var_description="thl_KK_auto_covar_zt, Covariance of theta_l and " &
    2933             :              // "KK autoconversion rate", &
    2934           0 :              var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2935           0 :        k = k + 1
    2936             : 
    2937             :       case ('w_KK_accr_covar_zt')
    2938           0 :         stats_metadata%iw_KK_accr_covar_zt = k
    2939             : 
    2940             :         call stat_assign( var_index=stats_metadata%iw_KK_accr_covar_zt, var_name="w_KK_accr_covar_zt", &
    2941             :              var_description="w_KK_accr_covar_zt, Covariance of w and KK accretion rate", &
    2942             :              var_units="m*(kg/kg)/s^2", &
    2943           0 :              l_silhs=.false., grid_kind=stats_zt )
    2944           0 :        k = k + 1
    2945             : 
    2946             :       case ('rt_KK_accr_covar_zt')
    2947           0 :         stats_metadata%irt_KK_accr_covar_zt = k
    2948             : 
    2949             :         call stat_assign( var_index=stats_metadata%irt_KK_accr_covar_zt, var_name="rt_KK_accr_covar_zt", &
    2950             :              var_description="rt_KK_accr_covar_zt, Covariance of r_t and KK accretion rate", &
    2951             :              var_units="(kg/kg)^2/s", &
    2952           0 :              l_silhs=.false., grid_kind=stats_zt )
    2953           0 :        k = k + 1
    2954             : 
    2955             :       case ('thl_KK_accr_covar_zt')
    2956           0 :         stats_metadata%ithl_KK_accr_covar_zt = k
    2957             : 
    2958             :         call stat_assign( var_index=stats_metadata%ithl_KK_accr_covar_zt, var_name="thl_KK_accr_covar_zt", &
    2959             :              var_description="thl_KK_accr_covar_zt, Covariance of theta_l and KK accretion rate",&
    2960           0 :              var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
    2961           0 :        k = k + 1
    2962             : 
    2963             :       case ('rr_KK_mvr_covar_zt')
    2964           0 :         stats_metadata%irr_KK_mvr_covar_zt = k
    2965             : 
    2966             :         call stat_assign( var_index=stats_metadata%irr_KK_mvr_covar_zt, var_name="rr_KK_mvr_covar_zt", &
    2967             :              var_description="rr_KK_mvr_covar_zt, Covariance of r_r and KK rain drop mean " &
    2968             :              // "volume radius", &
    2969           0 :              var_units="(kg/kg)m", l_silhs=.false., grid_kind=stats_zt )
    2970           0 :        k = k + 1
    2971             : 
    2972             :       case ('Nr_KK_mvr_covar_zt')
    2973           0 :         stats_metadata%iNr_KK_mvr_covar_zt = k
    2974             : 
    2975             :         call stat_assign( var_index=stats_metadata%iNr_KK_mvr_covar_zt, var_name="Nr_KK_mvr_covar_zt", &
    2976             :              var_description="Nr_KK_mvr_covar_zt, Covariance of N_r and KK rain drop mean " &
    2977             :              // "volume radius", &
    2978           0 :              var_units="(num/kg)m", l_silhs=.false., grid_kind=stats_zt )
    2979           0 :        k = k + 1
    2980             : 
    2981             :       case ('KK_mvr_variance_zt')
    2982           0 :         stats_metadata%iKK_mvr_variance_zt = k
    2983             : 
    2984             :         call stat_assign( var_index=stats_metadata%iKK_mvr_variance_zt, var_name="KK_mvr_variance_zt", &
    2985             :              var_description="KK_mvr_variance_zt, Variance of KK rain drop mean volume radius", &
    2986           0 :              var_units="m^2", l_silhs=.false., grid_kind=stats_zt )
    2987           0 :        k = k + 1
    2988             : 
    2989             :       case ('vm_bt')
    2990           0 :         stats_metadata%ivm_bt = k
    2991             : 
    2992             :         call stat_assign( var_index=stats_metadata%ivm_bt, var_name="vm_bt", &
    2993             :              var_description="vm_bt, vm budget: vm time tendency", var_units="m s^{-2}", &
    2994           0 :              l_silhs=.false., grid_kind=stats_zt )
    2995           0 :         k = k + 1
    2996             : 
    2997             :       case ('vm_ma')
    2998           0 :         stats_metadata%ivm_ma = k
    2999             :         call stat_assign( var_index=stats_metadata%ivm_ma, var_name="vm_ma", &
    3000             :              var_description="vm_ma, vm budget: vm vertical mean advection", &
    3001           0 :              var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
    3002           0 :         k = k + 1
    3003             : 
    3004             :       case ('vm_gf')
    3005           0 :         stats_metadata%ivm_gf = k
    3006             : 
    3007             :         call stat_assign( var_index=stats_metadata%ivm_gf, var_name="vm_gf", &
    3008             :              var_description="vm_gf, vm budget: vm geostrophic forcing", &
    3009           0 :              var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
    3010           0 :         k = k + 1
    3011             : 
    3012             :       case ('vm_cf')
    3013           0 :         stats_metadata%ivm_cf = k
    3014             : 
    3015             :         call stat_assign( var_index=stats_metadata%ivm_cf, var_name="vm_cf", &
    3016             :              var_description="vm_cf, vm budget: vm coriolis forcing", var_units="m s^{-2}", &
    3017           0 :              l_silhs=.false., grid_kind=stats_zt )
    3018           0 :         k = k + 1
    3019             : 
    3020             :       case ('vm_ta')
    3021           0 :         stats_metadata%ivm_ta = k
    3022             : 
    3023             :         call stat_assign( var_index=stats_metadata%ivm_ta, var_name="vm_ta", &
    3024             :              var_description="vm_ta, vm budget: vm turbulent transport", &
    3025           0 :              var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
    3026           0 :         k = k + 1
    3027             : 
    3028             :       case ('vm_f')
    3029           0 :         stats_metadata%ivm_f = k
    3030             :         call stat_assign( var_index=stats_metadata%ivm_f, var_name="vm_f", &
    3031             :              var_description="vm_f, vm budget: vm forcing", var_units="m s^{-2}", &
    3032           0 :              l_silhs=.false., grid_kind=stats_zt )
    3033           0 :         k = k + 1
    3034             : 
    3035             :       case ('vm_sdmp')
    3036           0 :         stats_metadata%ivm_sdmp = k
    3037             :         call stat_assign( var_index=stats_metadata%ivm_sdmp, var_name="vm_sdmp", &
    3038             :              var_description="vm_sdmp, vm budget: vm sponge damping", var_units="m s^{-2}", &
    3039           0 :              l_silhs=.false., grid_kind=stats_zt )
    3040           0 :         k = k + 1
    3041             : 
    3042             :       case ('vm_ndg')
    3043           0 :         stats_metadata%ivm_ndg = k
    3044             :         call stat_assign( var_index=stats_metadata%ivm_ndg, var_name="vm_ndg", &
    3045             :              var_description="vm_ndg, vm budget: vm nudging", var_units="m s^{-2}", &
    3046           0 :              l_silhs=.false., grid_kind=stats_zt )
    3047           0 :         k = k + 1
    3048             : 
    3049             :       case ('vm_mfl')
    3050           0 :         stats_metadata%ivm_mfl = k
    3051             :         call stat_assign( var_index=stats_metadata%ivm_mfl, var_name="vm_mfl", &
    3052             :              var_description="vm_mfl, vm budget: vm monotonic flux limiter", &
    3053             :              var_units="m s^{-2}", &
    3054           0 :              l_silhs=.false., grid_kind=stats_zt )
    3055           0 :         k = k + 1
    3056             : 
    3057             :       case ('um_bt')
    3058           0 :         stats_metadata%ium_bt = k
    3059             : 
    3060             :         call stat_assign( var_index=stats_metadata%ium_bt, var_name="um_bt", &
    3061             :              var_description="um_bt, um budget: um time tendency", var_units="m s^{-2}", &
    3062           0 :              l_silhs=.false., grid_kind=stats_zt )
    3063           0 :         k = k + 1
    3064             : 
    3065             :       case ('um_ma')
    3066           0 :         stats_metadata%ium_ma = k
    3067             : 
    3068             :         call stat_assign( var_index=stats_metadata%ium_ma, var_name="um_ma", &
    3069             :              var_description="um_ma, um budget: um vertical mean advection", &
    3070           0 :              var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
    3071           0 :         k = k + 1
    3072             : 
    3073             :       case ('um_gf')
    3074           0 :         stats_metadata%ium_gf = k
    3075             :         call stat_assign( var_index=stats_metadata%ium_gf, var_name="um_gf", &
    3076             :              var_description="um_gf, um budget: um geostrophic forcing", &
    3077           0 :              var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
    3078           0 :         k = k + 1
    3079             : 
    3080             :       case ('um_cf')
    3081           0 :         stats_metadata%ium_cf = k
    3082             :         call stat_assign( var_index=stats_metadata%ium_cf, var_name="um_cf", &
    3083             :              var_description="um_cf, um budget: um coriolis forcing", var_units="m s^{-2}", &
    3084           0 :              l_silhs=.false., grid_kind=stats_zt )
    3085           0 :         k = k + 1
    3086             : 
    3087             :       case ('um_ta')
    3088           0 :         stats_metadata%ium_ta = k
    3089             :         call stat_assign( var_index=stats_metadata%ium_ta, var_name="um_ta", &
    3090             :              var_description="um_ta, um budget: um turbulent advection", &
    3091           0 :              var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
    3092           0 :         k = k + 1
    3093             : 
    3094             :       case ('um_f')
    3095           0 :         stats_metadata%ium_f = k
    3096             :         call stat_assign( var_index=stats_metadata%ium_f, var_name="um_f", &
    3097             :              var_description="um_f, um budget: um forcing", var_units="m s^{-2}", &
    3098           0 :              l_silhs=.false., grid_kind=stats_zt )
    3099           0 :         k = k + 1
    3100             : 
    3101             :       case ('um_sdmp')
    3102           0 :         stats_metadata%ium_sdmp = k
    3103             :         call stat_assign( var_index=stats_metadata%ium_sdmp, var_name="um_sdmp", &
    3104             :              var_description="um_sdmp, um budget: um sponge damping", var_units="m s^{-2}", &
    3105           0 :              l_silhs=.false., grid_kind=stats_zt )
    3106           0 :         k = k + 1
    3107             : 
    3108             :       case ('um_ndg')
    3109           0 :         stats_metadata%ium_ndg = k
    3110             :         call stat_assign( var_index=stats_metadata%ium_ndg, var_name="um_ndg", &
    3111             :              var_description="um_ndg, um budget: um nudging", var_units="m s^{-2}", &
    3112           0 :              l_silhs=.false., grid_kind=stats_zt )
    3113           0 :         k = k + 1
    3114             : 
    3115             :       case ('um_mfl')
    3116           0 :         stats_metadata%ium_mfl = k
    3117             :         call stat_assign( var_index=stats_metadata%ium_mfl, var_name="um_mfl", &
    3118             :              var_description="um_mfl, um budget: um monotonic flux limiter", &
    3119             :              var_units="m s^{-2}",&
    3120           0 :              l_silhs=.false., grid_kind=stats_zt )
    3121           0 :         k = k + 1
    3122             : 
    3123             :       case ('mixt_frac')
    3124           0 :         stats_metadata%imixt_frac = k
    3125             :         call stat_assign( var_index=stats_metadata%imixt_frac, var_name="mixt_frac", &
    3126             :              var_description="mixt_frac, pdf parameter: mixture fraction", var_units="count", &
    3127           0 :              l_silhs=.false., grid_kind=stats_zt )
    3128           0 :         k = k + 1
    3129             : 
    3130             :       case ('w_1')
    3131           0 :         stats_metadata%iw_1 = k
    3132             :         call stat_assign( var_index=stats_metadata%iw_1, var_name="w_1", &
    3133             :              var_description="w_1, pdf parameter: mean w of component 1", var_units="m/s", &
    3134           0 :              l_silhs=.false., grid_kind=stats_zt )
    3135             : 
    3136           0 :         k = k + 1
    3137             : 
    3138             :       case ('w_2')
    3139           0 :         stats_metadata%iw_2 = k
    3140             : 
    3141             :         call stat_assign( var_index=stats_metadata%iw_2, var_name="w_2", &
    3142             :              var_description="w_2, pdf paramete: mean w of component 2", var_units="m/s", &
    3143           0 :              l_silhs=.false., grid_kind=stats_zt )
    3144           0 :         k = k + 1
    3145             : 
    3146             :       case ('varnce_w_1')
    3147           0 :         stats_metadata%ivarnce_w_1 = k
    3148             :         call stat_assign( var_index=stats_metadata%ivarnce_w_1, var_name="varnce_w_1", &
    3149             :              var_description="varnce_w_1, pdf parameter: w variance of component 1", &
    3150           0 :              var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
    3151             : 
    3152           0 :         k = k + 1
    3153             : 
    3154             :       case ('varnce_w_2')
    3155           0 :         stats_metadata%ivarnce_w_2 = k
    3156             : 
    3157             :         call stat_assign( var_index=stats_metadata%ivarnce_w_2, var_name="varnce_w_2", &
    3158             :              var_description="varnce_w_2, pdf parameter: w variance of component 2", &
    3159           0 :              var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
    3160           0 :         k = k + 1
    3161             : 
    3162             :       case ('thl_1')
    3163           0 :         stats_metadata%ithl_1 = k
    3164             : 
    3165             :         call stat_assign( var_index=stats_metadata%ithl_1, var_name="thl_1", &
    3166             :              var_description="thl_1, pdf parameter: mean thl of component 1", var_units="K", &
    3167           0 :              l_silhs=.false., grid_kind=stats_zt )
    3168             : 
    3169           0 :         k = k + 1
    3170             : 
    3171             :       case ('thl_2')
    3172           0 :         stats_metadata%ithl_2 = k
    3173             : 
    3174             :         call stat_assign( var_index=stats_metadata%ithl_2, var_name="thl_2", &
    3175             :              var_description="thl_2, pdf parameter: mean thl of component 2", var_units="K", &
    3176           0 :              l_silhs=.false., grid_kind=stats_zt )
    3177           0 :         k = k + 1
    3178             : 
    3179             :       case ('varnce_thl_1')
    3180           0 :         stats_metadata%ivarnce_thl_1 = k
    3181             : 
    3182             :         call stat_assign( var_index=stats_metadata%ivarnce_thl_1, var_name="varnce_thl_1", &
    3183             :              var_description="varnce_thl_1, pdf parameter: thl variance of component 1", &
    3184             :              var_units="K^2", &
    3185           0 :              l_silhs=.false., grid_kind=stats_zt )
    3186             : 
    3187           0 :         k = k + 1
    3188             : 
    3189             :       case ('varnce_thl_2')
    3190           0 :         stats_metadata%ivarnce_thl_2 = k
    3191             :         call stat_assign( var_index=stats_metadata%ivarnce_thl_2, var_name="varnce_thl_2", &
    3192             :              var_description="varnce_thl_2, pdf parameter: thl variance of component 2", &
    3193             :              var_units="K^2", &
    3194           0 :              l_silhs=.false., grid_kind=stats_zt )
    3195             : 
    3196           0 :         k = k + 1
    3197             : 
    3198             :       case ('rt_1')
    3199           0 :         stats_metadata%irt_1 = k
    3200             :         call stat_assign( var_index=stats_metadata%irt_1, var_name="rt_1", &
    3201             :              var_description="rt_1, pdf parameter: mean rt of component 1", var_units="kg/kg", &
    3202           0 :              l_silhs=.false., grid_kind=stats_zt )
    3203             : 
    3204           0 :         k = k + 1
    3205             : 
    3206             :       case ('rt_2')
    3207           0 :         stats_metadata%irt_2 = k
    3208             : 
    3209             :         call stat_assign( var_index=stats_metadata%irt_2, var_name="rt_2", &
    3210             :              var_description="rt_2, pdf parameter: mean rt of component 2", var_units="kg/kg", &
    3211           0 :              l_silhs=.false., grid_kind=stats_zt )
    3212           0 :         k = k + 1
    3213             : 
    3214             :       case ('varnce_rt_1')
    3215           0 :         stats_metadata%ivarnce_rt_1 = k
    3216             :         call stat_assign( var_index=stats_metadata%ivarnce_rt_1, var_name="varnce_rt_1", &
    3217             :              var_description="varnce_rt_1, pdf parameter: rt variance of component 1", &
    3218           0 :              var_units="(kg^2)/(kg^2)", l_silhs=.false., grid_kind=stats_zt )
    3219           0 :         k = k + 1
    3220             : 
    3221             :       case ('varnce_rt_2')
    3222           0 :         stats_metadata%ivarnce_rt_2 = k
    3223             : 
    3224             :         call stat_assign( var_index=stats_metadata%ivarnce_rt_2, var_name="varnce_rt_2", &
    3225             :              var_description="varnce_rt_2, pdf parameter: rt variance of component 2", &
    3226           0 :              var_units="(kg^2)/(kg^2)", l_silhs=.false., grid_kind=stats_zt )
    3227           0 :         k = k + 1
    3228             : 
    3229             :       case ('rc_1')
    3230           0 :         stats_metadata%irc_1 = k
    3231             : 
    3232             :         call stat_assign( var_index=stats_metadata%irc_1, var_name="rc_1", &
    3233             :              var_description="rc_1, pdf parameter: mean rc of component 1", var_units="kg/kg", &
    3234           0 :              l_silhs=.false., grid_kind=stats_zt )
    3235           0 :         k = k + 1
    3236             : 
    3237             :       case ('rc_2')
    3238           0 :         stats_metadata%irc_2 = k
    3239             : 
    3240             :         call stat_assign( var_index=stats_metadata%irc_2, var_name="rc_2", &
    3241             :              var_description="rc_2, pdf parameter: mean rc of component 2", var_units="kg/kg", &
    3242           0 :              l_silhs=.false., grid_kind=stats_zt )
    3243           0 :         k = k + 1
    3244             : 
    3245             :       case ('rsatl_1')
    3246           0 :         stats_metadata%irsatl_1 = k
    3247             : 
    3248             :         call stat_assign( var_index=stats_metadata%irsatl_1, var_name="rsatl_1", &
    3249             :              var_description="rsatl_1, pdf parameter: sat mix rat based on tl1", &
    3250           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
    3251           0 :         k = k + 1
    3252             : 
    3253             :       case ('rsatl_2')
    3254           0 :         stats_metadata%irsatl_2 = k
    3255             : 
    3256             :         call stat_assign( var_index=stats_metadata%irsatl_2, var_name="rsatl_2", &
    3257             :              var_description="rsatl_2, pdf parameter: sat mix rat based on tl2", &
    3258           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
    3259           0 :         k = k + 1
    3260             : 
    3261             :       case ('cloud_frac_1')
    3262           0 :         stats_metadata%icloud_frac_1 = k
    3263             :         call stat_assign( var_index=stats_metadata%icloud_frac_1, var_name="cloud_frac_1", &
    3264             :              var_description="cloud_frac_1, pdf parameter cloud_frac_1", var_units="-", &
    3265           0 :              l_silhs=.false., grid_kind=stats_zt )
    3266           0 :         k = k + 1
    3267             : 
    3268             :       case ('cloud_frac_2')
    3269           0 :         stats_metadata%icloud_frac_2 = k
    3270             : 
    3271             :         call stat_assign( var_index=stats_metadata%icloud_frac_2, var_name="cloud_frac_2", &
    3272             :              var_description="cloud_frac_2, pdf parameter cloud_frac_2", var_units="-", &
    3273           0 :              l_silhs=.false., grid_kind=stats_zt )
    3274           0 :         k = k + 1
    3275             : 
    3276             :       case ('chi_1')
    3277           0 :         stats_metadata%ichi_1 = k
    3278             : 
    3279             :         call stat_assign( var_index=stats_metadata%ichi_1, var_name="chi_1", &
    3280             :              var_description="chi_1, pdf parameter: Mellor's s (extended liq) for component 1", &
    3281           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
    3282           0 :         k = k + 1
    3283             : 
    3284             :       case ('chi_2')
    3285           0 :         stats_metadata%ichi_2 = k
    3286             : 
    3287             :         call stat_assign( var_index=stats_metadata%ichi_2, var_name="chi_2", &
    3288             :              var_description="chi_2, pdf parameter: Mellor's s (extended liq) for component 2", &
    3289           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
    3290           0 :         k = k + 1
    3291             : 
    3292             :       case ('stdev_chi_1')
    3293           0 :         stats_metadata%istdev_chi_1 = k
    3294             : 
    3295             :         call stat_assign( var_index=stats_metadata%istdev_chi_1, var_name="stdev_chi_1", &
    3296             :              var_description="stdev_chi_1, pdf parameter: Std dev of chi_1", var_units="kg/kg", &
    3297           0 :              l_silhs=.false., grid_kind=stats_zt )
    3298           0 :         k = k + 1
    3299             : 
    3300             :       case ('stdev_chi_2')
    3301           0 :         stats_metadata%istdev_chi_2 = k
    3302             : 
    3303             :         call stat_assign( var_index=stats_metadata%istdev_chi_2, var_name="stdev_chi_2", &
    3304             :              var_description="stdev_chi_2, pdf parameter: Std dev of chi_2", var_units="kg/kg", &
    3305           0 :              l_silhs=.false., grid_kind=stats_zt )
    3306           0 :         k = k + 1
    3307             : 
    3308             :       case ('chip2')
    3309           0 :         stats_metadata%ichip2 = k
    3310             :         call stat_assign( var_index=stats_metadata%ichip2, var_name="chip2", &
    3311             :              var_description="chip2, Variance of chi(s) (overall)", var_units="(kg/kg)^2", &
    3312           0 :              l_silhs=.false., grid_kind=stats_zt )
    3313           0 :         k = k + 1
    3314             : 
    3315             :       case ('stdev_eta_1')
    3316           0 :         stats_metadata%istdev_eta_1 = k
    3317             : 
    3318             :         call stat_assign( var_index=stats_metadata%istdev_eta_1, var_name="stdev_eta_1", &
    3319             :              var_description="stdev_eta_1, Standard dev. of eta(t) (1st PDF component)", &
    3320           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
    3321           0 :         k = k + 1
    3322             : 
    3323             :       case ('stdev_eta_2')
    3324           0 :         stats_metadata%istdev_eta_2 = k
    3325             : 
    3326             :         call stat_assign( var_index=stats_metadata%istdev_eta_2, var_name="stdev_eta_2", &
    3327             :              var_description="stdev_eta_2, Standard dev. of eta(t) (2nd PDF component)", &
    3328           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
    3329           0 :         k = k + 1
    3330             : 
    3331             :       case ('covar_chi_eta_1')
    3332           0 :         stats_metadata%icovar_chi_eta_1 = k
    3333             : 
    3334             :         call stat_assign( var_index=stats_metadata%icovar_chi_eta_1, var_name="covar_chi_eta_1", &
    3335             :              var_description="covar_chi_eta_1, Covariance of chi(s) and eta(t) " &
    3336             :              // "(1st PDF component)", &
    3337           0 :              var_units="kg^2/kg^2", l_silhs=.false., grid_kind=stats_zt )
    3338           0 :         k = k + 1
    3339             : 
    3340             :       case ('covar_chi_eta_2')
    3341           0 :         stats_metadata%icovar_chi_eta_2 = k
    3342             : 
    3343             :         call stat_assign( var_index=stats_metadata%icovar_chi_eta_2, var_name="covar_chi_eta_2", &
    3344             :              var_description="covar_chi_eta_2, Covariance of chi(s) and eta(t) " &
    3345             :              // "(2nd PDF component)", &
    3346           0 :              var_units="kg^2/kg^2", l_silhs=.false., grid_kind=stats_zt )
    3347           0 :         k = k + 1
    3348             : 
    3349             :       case ('corr_w_chi_1')
    3350           0 :         stats_metadata%icorr_w_chi_1 = k
    3351             : 
    3352             :         call stat_assign( var_index=stats_metadata%icorr_w_chi_1, var_name="corr_w_chi_1", &
    3353             :                           var_description="corr_w_chi_1, Correlation of w and chi (s)" &
    3354             :                           // " (1st PDF component)", var_units="-", &
    3355           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3356           0 :         k = k + 1
    3357             : 
    3358             :       case ('corr_w_chi_2')
    3359           0 :         stats_metadata%icorr_w_chi_2 = k
    3360             : 
    3361             :         call stat_assign( var_index=stats_metadata%icorr_w_chi_2, var_name="corr_w_chi_2", &
    3362             :                           var_description="corr_w_chi_2, Correlation of w and chi (s)" &
    3363             :                           // " (2nd PDF component)", var_units="-", &
    3364           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3365           0 :         k = k + 1
    3366             : 
    3367             :       case ('corr_w_eta_1')
    3368           0 :         stats_metadata%icorr_w_eta_1 = k
    3369             : 
    3370             :         call stat_assign( var_index=stats_metadata%icorr_w_eta_1, var_name="corr_w_eta_1", &
    3371             :                           var_description="corr_w_eta_1, Correlation of w and eta (t)" &
    3372             :                           // " (1st PDF component)", var_units="-", &
    3373           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3374           0 :         k = k + 1
    3375             : 
    3376             :       case ('corr_w_eta_2')
    3377           0 :         stats_metadata%icorr_w_eta_2 = k
    3378             : 
    3379             :         call stat_assign( var_index=stats_metadata%icorr_w_eta_2, var_name="corr_w_eta_2", &
    3380             :                           var_description="corr_w_eta_2, Correlation of w and eta (t)" &
    3381             :                           // " (2nd PDF component)", var_units="-", &
    3382           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3383           0 :         k = k + 1
    3384             : 
    3385             :       case ('corr_chi_eta_1')
    3386           0 :         stats_metadata%icorr_chi_eta_1 = k
    3387             : 
    3388             :         call stat_assign( var_index=stats_metadata%icorr_chi_eta_1, &
    3389             :                           var_name="corr_chi_eta_1", &
    3390             :                           var_description="corr_chi_eta_1, Correlation of chi (s) and" &
    3391             :                           // " eta (t) (1st PDF component)", &
    3392             :                           var_units="-", &
    3393           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3394           0 :         k = k + 1
    3395             : 
    3396             :       case ('corr_chi_eta_2')
    3397           0 :         stats_metadata%icorr_chi_eta_2 = k
    3398             : 
    3399             :         call stat_assign( var_index=stats_metadata%icorr_chi_eta_2, &
    3400             :                           var_name="corr_chi_eta_2", &
    3401             :                           var_description="corr_chi_eta_2, Correlation of chi (s) and" &
    3402             :                           // " eta (t) (2nd PDF component)", &
    3403             :                           var_units="-", &
    3404           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3405           0 :         k = k + 1
    3406             : 
    3407             :       case ('corr_w_rt_1')
    3408           0 :         stats_metadata%icorr_w_rt_1 = k
    3409             : 
    3410             :         call stat_assign( var_index=stats_metadata%icorr_w_rt_1, var_name="corr_w_rt_1", &
    3411             :                           var_description="corr_w_rt_1, Correlation of w and rt" &
    3412             :                           // " (1st PDF component)", var_units="-", &
    3413           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3414           0 :         k = k + 1
    3415             : 
    3416             :       case ('corr_w_rt_2')
    3417           0 :         stats_metadata%icorr_w_rt_2 = k
    3418             : 
    3419             :         call stat_assign( var_index=stats_metadata%icorr_w_rt_2, var_name="corr_w_rt_2", &
    3420             :                           var_description="corr_w_rt_2, Correlation of w and rt" &
    3421             :                           // " (2nd PDF component)", var_units="-", &
    3422           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3423           0 :         k = k + 1
    3424             : 
    3425             :       case ('corr_w_thl_1')
    3426           0 :         stats_metadata%icorr_w_thl_1 = k
    3427             : 
    3428             :         call stat_assign( var_index=stats_metadata%icorr_w_thl_1, var_name="corr_w_thl_1", &
    3429             :                           var_description="corr_w_thl_1, Correlation of w and thl" &
    3430             :                           // " (1st PDF component)", var_units="-", &
    3431           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3432           0 :         k = k + 1
    3433             : 
    3434             :       case ('corr_w_thl_2')
    3435           0 :         stats_metadata%icorr_w_thl_2 = k
    3436             : 
    3437             :         call stat_assign( var_index=stats_metadata%icorr_w_thl_2, var_name="corr_w_thl_2", &
    3438             :                           var_description="corr_w_thl_2, Correlation of w and thl" &
    3439             :                           // " (2nd PDF component)", var_units="-", &
    3440           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3441           0 :         k = k + 1
    3442             : 
    3443             :       case ('corr_rt_thl_1')
    3444           0 :         stats_metadata%icorr_rt_thl_1 = k
    3445             : 
    3446             :         call stat_assign( var_index=stats_metadata%icorr_rt_thl_1, var_name="corr_rt_thl_1", &
    3447             :                           var_description="corr_rt_thl_1, Correlation of rt and thl" &
    3448             :                           // " (1st PDF component)", var_units="-", &
    3449           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3450           0 :         k = k + 1
    3451             : 
    3452             :       case ('corr_rt_thl_2')
    3453           0 :         stats_metadata%icorr_rt_thl_2 = k
    3454             : 
    3455             :         call stat_assign( var_index=stats_metadata%icorr_rt_thl_2, var_name="corr_rt_thl_2", &
    3456             :                           var_description="corr_rt_thl_2, Correlation of rt and thl" &
    3457             :                           // " (2nd PDF component)", var_units="-", &
    3458           0 :                           l_silhs=.false., grid_kind=stats_zt )
    3459           0 :         k = k + 1
    3460             : 
    3461             :       case ('crt_1')
    3462           0 :         stats_metadata%icrt_1 = k
    3463             : 
    3464             :         call stat_assign( var_index=stats_metadata%icrt_1, var_name="crt_1", &
    3465             :                           var_description="crt_1, Coefficient on rt in chi/eta" &
    3466             :                           // " equations (1st PDF comp.)", &
    3467           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3468           0 :         k = k + 1
    3469             : 
    3470             :       case ('crt_2')
    3471           0 :         stats_metadata%icrt_2 = k
    3472             : 
    3473             :         call stat_assign( var_index=stats_metadata%icrt_2, var_name="crt_2", &
    3474             :                           var_description="crt_2, Coefficient on rt in chi/eta" &
    3475             :                           // " equations (2nd PDF comp.)", &
    3476           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3477           0 :         k = k + 1
    3478             : 
    3479             :       case ('cthl_1')
    3480           0 :         stats_metadata%icthl_1 = k
    3481             : 
    3482             :         call stat_assign( var_index=stats_metadata%icthl_1, var_name="cthl_1", &
    3483             :                           var_description="cthl_1, Coefficient on theta-l in chi/eta" &
    3484             :                           // " equations (1st PDF comp.)", &
    3485           0 :                           var_units="kg/kg/K", l_silhs=.false., grid_kind=stats_zt )
    3486           0 :         k = k + 1
    3487             : 
    3488             :       case ('cthl_2')
    3489           0 :         stats_metadata%icthl_2 = k
    3490             : 
    3491             :         call stat_assign( var_index=stats_metadata%icthl_2, var_name="cthl_2", &
    3492             :                           var_description="cthl_2, Coefficient on theta-l in chi/eta" &
    3493             :                           // " equations (2nd PDF comp.)", &
    3494           0 :                           var_units="kg/kg/K", l_silhs=.false., grid_kind=stats_zt )
    3495           0 :         k = k + 1
    3496             : 
    3497             :       case('F_w')
    3498           0 :         stats_metadata%iF_w = k
    3499             : 
    3500             :         call stat_assign( var_index=stats_metadata%iF_w, var_name="F_w", &
    3501             :                           var_description="F_w, Parameter for the spread of the" &
    3502             :                           // " PDF component means of w (new PDF)", &
    3503           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3504           0 :         k = k + 1
    3505             : 
    3506             :       case('F_rt')
    3507           0 :         stats_metadata%iF_rt = k
    3508             : 
    3509             :         call stat_assign( var_index=stats_metadata%iF_rt, var_name="F_rt", &
    3510             :                           var_description="F_rt, Parameter for the spread of the" &
    3511             :                           // " PDF component means of rt (new PDF)", &
    3512           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3513           0 :         k = k + 1
    3514             : 
    3515             :       case('F_thl')
    3516           0 :         stats_metadata%iF_thl = k
    3517             : 
    3518             :         call stat_assign( var_index=stats_metadata%iF_thl, var_name="F_thl", &
    3519             :                           var_description="F_thl, Parameter for the spread of the" &
    3520             :                           // " PDF component means of thl (new PDF)", &
    3521           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3522           0 :         k = k + 1
    3523             : 
    3524             :       case('min_F_w')
    3525           0 :         stats_metadata%imin_F_w = k
    3526             : 
    3527             :         call stat_assign( var_index=stats_metadata%imin_F_w, var_name="min_F_w", &
    3528             :                           var_description="min_F_w, Minimum allowable value of the" &
    3529             :                           // " parameter F_w (new PDF)", &
    3530           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3531           0 :         k = k + 1
    3532             : 
    3533             :       case('max_F_w')
    3534           0 :         stats_metadata%imax_F_w = k
    3535             : 
    3536             :         call stat_assign( var_index=stats_metadata%imax_F_w, var_name="max_F_w", &
    3537             :                           var_description="max_F_w, Maximum allowable value of the" &
    3538             :                           // " parameter F_w (new PDF)", &
    3539           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3540           0 :         k = k + 1
    3541             : 
    3542             :       case('min_F_rt')
    3543           0 :         stats_metadata%imin_F_rt = k
    3544             : 
    3545             :         call stat_assign( var_index=stats_metadata%imin_F_rt, var_name="min_F_rt", &
    3546             :                           var_description="min_F_rt, Minimum allowable value of the" &
    3547             :                           // " parameter F_rt (new PDF)", &
    3548           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3549           0 :         k = k + 1
    3550             : 
    3551             :       case('max_F_rt')
    3552           0 :         stats_metadata%imax_F_rt = k
    3553             : 
    3554             :         call stat_assign( var_index=stats_metadata%imax_F_rt, var_name="max_F_rt", &
    3555             :                           var_description="max_F_rt, Maximum allowable value of the" &
    3556             :                           // " parameter F_rt (new PDF)", &
    3557           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3558           0 :         k = k + 1
    3559             : 
    3560             :       case('min_F_thl')
    3561           0 :         stats_metadata%imin_F_thl = k
    3562             : 
    3563             :         call stat_assign( var_index=stats_metadata%imin_F_thl, var_name="min_F_thl", &
    3564             :                           var_description="min_F_thl, Minimum allowable value of the" &
    3565             :                           // " parameter F_thl (new PDF)", &
    3566           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3567           0 :         k = k + 1
    3568             : 
    3569             :       case('max_F_thl')
    3570           0 :         stats_metadata%imax_F_thl = k
    3571             : 
    3572             :         call stat_assign( var_index=stats_metadata%imax_F_thl, var_name="max_F_thl", &
    3573             :                           var_description="max_F_thl, Maximum allowable value of the" &
    3574             :                           // " parameter F_thl (new PDF)", &
    3575           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3576           0 :         k = k + 1
    3577             : 
    3578             :       case ( 'coef_wprtp2_implicit' )
    3579           0 :         stats_metadata%icoef_wprtp2_implicit = k
    3580             :         call stat_assign( var_index=stats_metadata%icoef_wprtp2_implicit, &
    3581             :                           var_name="coef_wprtp2_implicit", &
    3582             :                           var_description="coef_wprtp2_implicit, wprtp2" &
    3583             :                                           // " = coef_wprtp2_implicit" &
    3584             :                                           // " * rtp2" &
    3585             :                                           // " + term_wprtp2_explicit", &
    3586           0 :                           var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
    3587           0 :         k = k + 1
    3588             : 
    3589             :       case ( 'term_wprtp2_explicit' )
    3590           0 :         stats_metadata%iterm_wprtp2_explicit = k
    3591             :         call stat_assign( var_index=stats_metadata%iterm_wprtp2_explicit, &
    3592             :                           var_name="term_wprtp2_explicit", &
    3593             :                           var_description="term_wprtp2_explicit, wprtp2" &
    3594             :                                           // " = coef_wprtp2_implicit" &
    3595             :                                           // " * rtp2" &
    3596             :                                           // " + term_wprtp2_explicit", &
    3597             :                           var_units="m/s kg^2/kg^2", l_silhs=.false., &
    3598           0 :                           grid_kind=stats_zt )
    3599           0 :         k = k + 1
    3600             : 
    3601             :       case ( 'coef_wpthlp2_implicit' )
    3602           0 :         stats_metadata%icoef_wpthlp2_implicit = k
    3603             :         call stat_assign( var_index=stats_metadata%icoef_wpthlp2_implicit, &
    3604             :                           var_name="coef_wpthlp2_implicit", &
    3605             :                           var_description="coef_wpthlp2_implicit, wpthlp2" &
    3606             :                                           // " = coef_wpthlp2_implicit" &
    3607             :                                           // " * thlp2" &
    3608             :                                           // " + term_wpthlp2_explicit", &
    3609           0 :                           var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
    3610           0 :         k = k + 1
    3611             : 
    3612             :       case ( 'term_wpthlp2_explicit' )
    3613           0 :         stats_metadata%iterm_wpthlp2_explicit = k
    3614             :         call stat_assign( var_index=stats_metadata%iterm_wpthlp2_explicit, &
    3615             :                           var_name="term_wpthlp2_explicit", &
    3616             :                           var_description="term_wpthlp2_explicit, wpthlp2" &
    3617             :                                           // " = coef_wpthlp2_implicit" &
    3618             :                                           // " * thlp2" &
    3619             :                                           // " + term_wpthlp2_explicit", &
    3620             :                           var_units="m/s K^2", l_silhs=.false., &
    3621           0 :                           grid_kind=stats_zt )
    3622           0 :         k = k + 1
    3623             : 
    3624             :       case ( 'coef_wprtpthlp_implicit' )
    3625           0 :         stats_metadata%icoef_wprtpthlp_implicit = k
    3626             :         call stat_assign( var_index=stats_metadata%icoef_wprtpthlp_implicit, &
    3627             :                           var_name="coef_wprtpthlp_implicit", &
    3628             :                           var_description="coef_wprtpthlp_implicit, wprtpthlp" &
    3629             :                                           // " = coef_wprtpthlp_implicit" &
    3630             :                                           // " * rtpthlp" &
    3631             :                                           // " + term_wprtpthlp_explicit", &
    3632           0 :                           var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
    3633           0 :         k = k + 1
    3634             : 
    3635             :       case ( 'term_wprtpthlp_explicit' )
    3636           0 :         stats_metadata%iterm_wprtpthlp_explicit = k
    3637             :         call stat_assign( var_index=stats_metadata%iterm_wprtpthlp_explicit, &
    3638             :                           var_name="term_wprtpthlp_explicit", &
    3639             :                           var_description="term_wprtpthlp_explicit, wprtpthlp" &
    3640             :                                           // " = coef_wprtpthlp_implicit" &
    3641             :                                           // " * rtpthlp" &
    3642             :                                           // " + term_wprtpthlp_explicit]", &
    3643             :                           var_units="m/s (kg/kg) K", l_silhs=.false., &
    3644           0 :                           grid_kind=stats_zt )
    3645           0 :         k = k + 1
    3646             : 
    3647             :       case ( 'coef_wp2rtp_implicit' )
    3648           0 :         stats_metadata%icoef_wp2rtp_implicit = k
    3649             :         call stat_assign( var_index=stats_metadata%icoef_wp2rtp_implicit, &
    3650             :                           var_name="coef_wp2rtp_implicit", &
    3651             :                           var_description="coef_wp2rtp_implicit, wp2rtp" &
    3652             :                                           // " = coef_wp2rtp_implicit" &
    3653             :                                           // " * wprtp" &
    3654             :                                           // " + term_wp2rtp_explicit", &
    3655           0 :                           var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
    3656           0 :         k = k + 1
    3657             : 
    3658             :       case ( 'term_wp2rtp_explicit' )
    3659           0 :         stats_metadata%iterm_wp2rtp_explicit = k
    3660             :         call stat_assign( var_index=stats_metadata%iterm_wp2rtp_explicit, &
    3661             :                           var_name="term_wp2rtp_explicit", &
    3662             :                           var_description="term_wp2rtp_explicit, wp2rtp" &
    3663             :                                           // " = coef_wp2rtp_implicit" &
    3664             :                                           // " * wprtp" &
    3665             :                                           // " + term_wp2rtp_explicit", &
    3666             :                           var_units="m^2/s^2 kg/kg", l_silhs=.false., &
    3667           0 :                           grid_kind=stats_zt )
    3668           0 :         k = k + 1
    3669             : 
    3670             :       case ( 'coef_wp2thlp_implicit' )
    3671           0 :         stats_metadata%icoef_wp2thlp_implicit = k
    3672             :         call stat_assign( var_index=stats_metadata%icoef_wp2thlp_implicit, &
    3673             :                           var_name="coef_wp2thlp_implicit", &
    3674             :                           var_description="coef_wp2thlp_implicit, wp2thlp" &
    3675             :                                           // " = coef_wp2thlp_implicit" &
    3676             :                                           // " * wpthlp" &
    3677             :                                           // " + term_wp2thlp_explicit", &
    3678           0 :                           var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
    3679           0 :         k = k + 1
    3680             : 
    3681             :       case ( 'term_wp2thlp_explicit' )
    3682           0 :         stats_metadata%iterm_wp2thlp_explicit = k
    3683             :         call stat_assign( var_index=stats_metadata%iterm_wp2thlp_explicit, &
    3684             :                           var_name="term_wp2thlp_explicit", &
    3685             :                           var_description="term_wp2thlp_explicit, wp2thlp" &
    3686             :                                           // " = coef_wp2thlp_implicit" &
    3687             :                                           // " * wpthlp" &
    3688             :                                           // " + term_wp2thlp_explicit", &
    3689             :                           var_units="m^2/s^2 K", l_silhs=.false., &
    3690           0 :                           grid_kind=stats_zt )
    3691           0 :         k = k + 1
    3692             : 
    3693             :       case('wp2_zt')
    3694           0 :         stats_metadata%iwp2_zt = k
    3695             : 
    3696             :         call stat_assign( var_index=stats_metadata%iwp2_zt, var_name="wp2_zt", &
    3697             :              var_description="wp2_zt, w'^2 interpolated to thermodynamic levels", &
    3698           0 :              var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
    3699           0 :         k = k + 1
    3700             : 
    3701             :       case('thlp2_zt')
    3702           0 :         stats_metadata%ithlp2_zt = k
    3703             : 
    3704             :         call stat_assign( var_index=stats_metadata%ithlp2_zt, var_name="thlp2_zt", &
    3705             :              var_description="thlp2_zt, thl'^2 interpolated to thermodynamic levels", &
    3706           0 :              var_units="K^2", l_silhs=.false., grid_kind=stats_zt )
    3707           0 :         k = k + 1
    3708             : 
    3709             :       case('wpthlp_zt')
    3710           0 :         stats_metadata%iwpthlp_zt = k
    3711             : 
    3712             :         call stat_assign( var_index=stats_metadata%iwpthlp_zt, var_name="wpthlp_zt", &
    3713             :              var_description="wpthlp_zt, w'thl' interpolated to thermodynamic levels", &
    3714           0 :              var_units="(m K)/s", l_silhs=.false., grid_kind=stats_zt )
    3715           0 :         k = k + 1
    3716             : 
    3717             :       case('wprtp_zt')
    3718           0 :         stats_metadata%iwprtp_zt = k
    3719             : 
    3720             :         call stat_assign( var_index=stats_metadata%iwprtp_zt, var_name="wprtp_zt", &
    3721             :              var_description="wprtp_zt, w'rt' interpolated to thermodynamic levels", &
    3722           0 :              var_units="(m kg)/(s kg)", l_silhs=.false., grid_kind=stats_zt )
    3723           0 :         k = k + 1
    3724             : 
    3725             :       case('rtp2_zt')
    3726           0 :         stats_metadata%irtp2_zt = k
    3727             : 
    3728             :         call stat_assign( var_index=stats_metadata%irtp2_zt, var_name="rtp2_zt", &
    3729             :              var_description="rtp2_zt, rt'^2 interpolated to thermodynamic levels", &
    3730           0 :              var_units="(kg/kg)^2", l_silhs=.false., grid_kind=stats_zt )
    3731           0 :         k = k + 1
    3732             : 
    3733             :       case('rtpthlp_zt')
    3734           0 :         stats_metadata%irtpthlp_zt = k
    3735             : 
    3736             :         call stat_assign( var_index=stats_metadata%irtpthlp_zt, var_name="rtpthlp_zt", &
    3737             :              var_description="rtpthlp_zt, rt'thl' interpolated to thermodynamic levels", &
    3738           0 :              var_units="(kg K)/kg", l_silhs=.false., grid_kind=stats_zt )
    3739           0 :         k = k + 1
    3740             : 
    3741             :       case ('up2_zt')
    3742           0 :         stats_metadata%iup2_zt = k
    3743             :         call stat_assign( var_index=stats_metadata%iup2_zt, var_name="up2_zt", &
    3744             :              var_description="up2_zt, u'^2 interpolated to thermodynamic levels", &
    3745           0 :              var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
    3746           0 :         k = k + 1
    3747             : 
    3748             :       case ('vp2_zt')
    3749           0 :         stats_metadata%ivp2_zt = k
    3750             :         call stat_assign( var_index=stats_metadata%ivp2_zt, var_name="vp2_zt", &
    3751             :              var_description="vp2_zt, v'^2 interpolated to thermodynamic levels", &
    3752           0 :              var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
    3753           0 :         k = k + 1
    3754             : 
    3755             :       case ('upwp_zt')
    3756           0 :         stats_metadata%iupwp_zt = k
    3757             :         call stat_assign( var_index=stats_metadata%iupwp_zt, var_name="upwp_zt", &
    3758             :              var_description="upwp_zt, u'w' interpolated to thermodynamic levels", &
    3759           0 :              var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
    3760           0 :         k = k + 1
    3761             : 
    3762             :       case ('vpwp_zt')
    3763           0 :         stats_metadata%ivpwp_zt = k
    3764             :         call stat_assign( var_index=stats_metadata%ivpwp_zt, var_name="vpwp_zt", &
    3765             :              var_description="vpwp_zt, v'w' interpolated to thermodynamic levels", &
    3766           0 :              var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
    3767           0 :         k = k + 1
    3768             : 
    3769             :       case ('Skw_zt')
    3770           0 :         stats_metadata%iSkw_zt = k
    3771             :         call stat_assign( var_index=stats_metadata%iSkw_zt, var_name="Skw_zt", &
    3772             :              var_description="Skw_zt, Skewness of w on thermodynamic levels", &
    3773           0 :              var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3774           0 :         k = k + 1
    3775             : 
    3776             :       case ('Skthl_zt')
    3777           0 :         stats_metadata%iSkthl_zt = k
    3778             :         call stat_assign( var_index=stats_metadata%iSkthl_zt, var_name="Skthl_zt", &
    3779             :              var_description="Skthl_zt, Skewness of thl on thermodynamic levels", &
    3780           0 :              var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3781           0 :         k = k + 1
    3782             : 
    3783             :       case ('Skrt_zt')
    3784           0 :         stats_metadata%iSkrt_zt = k
    3785             :         call stat_assign( var_index=stats_metadata%iSkrt_zt, var_name="Skrt_zt", &
    3786             :              var_description="Skrt_zt, Skewness of rt on thermodynamic levels", &
    3787           0 :              var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3788           0 :         k = k + 1
    3789             : 
    3790             :       case ('rcm_supersat_adj')
    3791           0 :         stats_metadata%ircm_supersat_adj = k
    3792             :         call stat_assign( var_index=stats_metadata%ircm_supersat_adj, var_name="rcm_supersat_adj", &
    3793             :              var_description="rcm_supersat_adj, rcm adjustment due to spurious supersaturation", &
    3794           0 :              var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
    3795           0 :         k = k + 1
    3796             : 
    3797             :       ! Hydrometeor overall variances for each hydrometeor type.
    3798             :       case('hmp2_zt')
    3799             : 
    3800           0 :          do hm_idx = 1, hydromet_dim, 1
    3801             : 
    3802           0 :             hm_type = hydromet_list(hm_idx)
    3803             : 
    3804             :             ! The overall variance of the hydrometeor.
    3805           0 :             stats_metadata%ihmp2_zt(hm_idx) = k
    3806             : 
    3807           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    3808             : 
    3809           0 :                call stat_assign( var_index=stats_metadata%ihmp2_zt(hm_idx), &
    3810             :                                  var_name=trim( hm_type(1:2) )//"p2_zt", &
    3811             :                                  var_description="<" &
    3812             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    3813             :                                  // "'^2> on thermodynamic levels (from " &
    3814             :                                  // "integration over PDF) [(kg/kg)^2]", &
    3815             :                                  var_units="(kg/kg)^2", &
    3816           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    3817             : 
    3818             :             else ! Concentration
    3819             : 
    3820           0 :                call stat_assign( var_index=stats_metadata%ihmp2_zt(hm_idx), &
    3821             :                                  var_name=trim( hm_type(1:2) )//"p2_zt", &
    3822             :                                  var_description="<" &
    3823             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    3824             :                                  // "'^2> on thermodynamic levels (from " &
    3825             :                                  // "integration over PDF) [(num/kg)^2]", &
    3826             :                                  var_units="(num/kg)^2", &
    3827           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    3828             : 
    3829             :             endif ! l_mix_rat_hm(hm_idx)
    3830             : 
    3831           0 :             k = k + 1
    3832             : 
    3833             :          enddo ! hm_idx = 1, hydromet_dim, 1
    3834             : 
    3835             :       case ('C11_Skw_fnc')
    3836           0 :         stats_metadata%iC11_Skw_fnc = k
    3837             : 
    3838             :         call stat_assign( var_index=stats_metadata%iC11_Skw_fnc, var_name="C11_Skw_fnc", &
    3839             :              var_description="C11_Skw_fnc, C_11 parameter with Sk_w applied", var_units="count", &
    3840           0 :              l_silhs=.false., grid_kind=stats_zt )
    3841           0 :         k = k + 1
    3842             : 
    3843             :       case ('chi')
    3844           0 :         stats_metadata%ichi = k
    3845             : 
    3846             :         call stat_assign( var_index=stats_metadata%ichi, var_name="chi", &
    3847             :              var_description="chi, Mellor's s (extended liq)", var_units="kg/kg", &
    3848           0 :              l_silhs=.false., grid_kind=stats_zt )
    3849           0 :         k = k + 1
    3850             : 
    3851             :       case ( 'a3_coef_zt' )
    3852           0 :         stats_metadata%ia3_coef_zt = k
    3853             :         call stat_assign( var_index=stats_metadata%ia3_coef_zt, var_name="a3_coef_zt", &
    3854             :              var_description="a3_coef_zt, The a3 coefficient interpolated the the zt grid", &
    3855           0 :              var_units="count", l_silhs=.false., grid_kind=stats_zt )
    3856           0 :         k = k + 1
    3857             : 
    3858             :       case ( 'wp3_on_wp2_zt' )
    3859           0 :         stats_metadata%iwp3_on_wp2_zt = k
    3860             :         call stat_assign( var_index=stats_metadata%iwp3_on_wp2_zt, var_name="wp3_on_wp2_zt", &
    3861             :              var_description="wp3_on_wp2_zt, Smoothed version of wp3 / wp2", var_units="m/s", &
    3862           0 :              l_silhs=.false., grid_kind=stats_zt )
    3863           0 :         k = k + 1
    3864             : 
    3865             :       ! Hydrometeor component mean values for each PDF component and hydrometeor
    3866             :       ! type.
    3867             :       case ( "hm_i" )
    3868             : 
    3869           0 :          do hm_idx = 1, hydromet_dim, 1
    3870             : 
    3871           0 :             hm_type = hydromet_list(hm_idx)
    3872             : 
    3873             :             ! The mean of the hydrometeor in the 1st PDF component.
    3874           0 :             stats_metadata%ihm_1(hm_idx) = k
    3875             : 
    3876           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    3877             : 
    3878           0 :                call stat_assign( var_index=stats_metadata%ihm_1(hm_idx), &
    3879             :                                  var_name=trim( hm_type(1:2) )//"_1", &
    3880             :                                  var_description="Mean of " &
    3881             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    3882             :                                  // " (1st PDF component) [kg/kg]", &
    3883             :                                  var_units="kg/kg", &
    3884           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    3885             : 
    3886             :             else ! Concentration
    3887             : 
    3888           0 :                call stat_assign( var_index=stats_metadata%ihm_1(hm_idx), &
    3889             :                                  var_name=trim( hm_type(1:2) )//"_1", &
    3890             :                                  var_description="Mean of " &
    3891             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    3892             :                                  // " (1st PDF component) [num/kg]", &
    3893             :                                  var_units="num/kg", &
    3894           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    3895             : 
    3896             :             endif ! l_mix_rat_hm(hm_idx)
    3897             : 
    3898           0 :             k = k + 1
    3899             : 
    3900             :             ! The mean of the hydrometeor in the 2nd PDF component.
    3901           0 :             stats_metadata%ihm_2(hm_idx) = k
    3902             : 
    3903           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    3904             : 
    3905           0 :                call stat_assign( var_index=stats_metadata%ihm_2(hm_idx), &
    3906             :                                  var_name=trim( hm_type(1:2) )//"_2", &
    3907             :                                  var_description="Mean of " &
    3908             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    3909             :                                  // " (2nd PDF component) [kg/kg]", &
    3910             :                                  var_units="kg/kg", &
    3911           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    3912             : 
    3913             :             else ! Concentration
    3914             : 
    3915           0 :                call stat_assign( var_index=stats_metadata%ihm_2(hm_idx), &
    3916             :                                  var_name=trim( hm_type(1:2) )//"_2", &
    3917             :                                  var_description="Mean of " &
    3918             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    3919             :                                  // " (2nd PDF component) [num/kg]", &
    3920             :                                  var_units="num/kg", &
    3921           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    3922             : 
    3923             :             endif ! l_mix_rat_hm(hm_idx)
    3924             : 
    3925           0 :             k = k + 1
    3926             : 
    3927             :          enddo ! hm_idx = 1, hydromet_dim, 1
    3928             : 
    3929             :       case ( 'precip_frac' )
    3930           0 :         stats_metadata%iprecip_frac = k
    3931             :         call stat_assign( var_index=stats_metadata%iprecip_frac, var_name="precip_frac", &
    3932             :              var_description="precip_frac, Precipitation Fraction", var_units="-", &
    3933           0 :              l_silhs=.false., grid_kind=stats_zt )
    3934           0 :         k = k + 1
    3935             : 
    3936             :       case ( 'precip_frac_1' )
    3937           0 :         stats_metadata%iprecip_frac_1 = k
    3938             :         call stat_assign( var_index=stats_metadata%iprecip_frac_1, var_name="precip_frac_1", &
    3939             :              var_description="precip_frac_1, Precipitation Fraction (1st PDF component)", &
    3940           0 :              var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3941           0 :         k = k + 1
    3942             : 
    3943             :       case ( 'precip_frac_2' )
    3944           0 :         stats_metadata%iprecip_frac_2 = k
    3945             :         call stat_assign( var_index=stats_metadata%iprecip_frac_2, var_name="precip_frac_2", &
    3946             :              var_description="precip_frac_2, Precipitation Fraction (2nd PDF component)", &
    3947           0 :              var_units="-", l_silhs=.false., grid_kind=stats_zt )
    3948           0 :         k = k + 1
    3949             : 
    3950             :       case ( 'Ncnm' )
    3951           0 :         stats_metadata%iNcnm = k
    3952             :         call stat_assign( var_index=stats_metadata%iNcnm, var_name="Ncnm", &
    3953             :              var_description="Ncnm, Cloud nuclei concentration (PDF)", &
    3954           0 :              var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
    3955           0 :         k = k + 1
    3956             : 
    3957             :       ! Hydrometeor component mean values (in-precip) for each PDF component and
    3958             :       ! hydrometeor type.
    3959             :       case ( 'mu_hm_i' )
    3960             : 
    3961           0 :          do hm_idx = 1, hydromet_dim, 1
    3962             : 
    3963           0 :             hm_type = hydromet_list(hm_idx)
    3964             : 
    3965             :             ! The in-precip mean of the hydrometeor in the 1st PDF component.
    3966           0 :             stats_metadata%imu_hm_1(hm_idx) = k
    3967             : 
    3968           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    3969             : 
    3970           0 :                call stat_assign( var_index=stats_metadata%imu_hm_1(hm_idx), &
    3971             :                                  var_name="mu_"//trim( hm_type(1:2) )//"_1", &
    3972             :                                  var_description="Mean (in-precip) of " &
    3973             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    3974             :                                  // " (1st PDF component) [kg/kg]", &
    3975             :                                  var_units="kg/kg", &
    3976           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    3977             : 
    3978             :             else ! Concentration
    3979             : 
    3980           0 :                call stat_assign( var_index=stats_metadata%imu_hm_1(hm_idx), &
    3981             :                                  var_name="mu_"//trim( hm_type(1:2) )//"_1", &
    3982             :                                  var_description="Mean (in-precip) of " &
    3983             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    3984             :                                  // " (1st PDF component) [num/kg]", &
    3985             :                                  var_units="num/kg", &
    3986           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    3987             : 
    3988             :             endif ! l_mix_rat_hm(hm_idx)
    3989             : 
    3990           0 :             k = k + 1
    3991             : 
    3992             :             ! The in-precip mean of the hydrometeor in the 2nd PDF component.
    3993           0 :             stats_metadata%imu_hm_2(hm_idx) = k
    3994             : 
    3995           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    3996             : 
    3997           0 :                call stat_assign( var_index=stats_metadata%imu_hm_2(hm_idx), &
    3998             :                                  var_name="mu_"//trim( hm_type(1:2) )//"_2", &
    3999             :                                  var_description="Mean (in-precip) of " &
    4000             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4001             :                                  // " (2nd PDF component) [kg/kg]", &
    4002             :                                  var_units="kg/kg", &
    4003           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4004             : 
    4005             :             else ! Concentration
    4006             : 
    4007           0 :                call stat_assign( var_index=stats_metadata%imu_hm_2(hm_idx), &
    4008             :                                  var_name="mu_"//trim( hm_type(1:2) )//"_2", &
    4009             :                                  var_description="Mean (in-precip) of " &
    4010             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4011             :                                  // " (2nd PDF component) [num/kg]", &
    4012             :                                  var_units="num/kg", &
    4013           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4014             : 
    4015             :             endif ! l_mix_rat_hm(hm_idx)
    4016             : 
    4017           0 :             k = k + 1
    4018             : 
    4019             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4020             : 
    4021             :       case ( 'mu_Ncn_i' )
    4022           0 :         stats_metadata%imu_Ncn_1 = k
    4023             : 
    4024             :          call stat_assign( var_index=stats_metadata%imu_Ncn_1, &
    4025             :                            var_name="mu_Ncn_1", &
    4026             :                            var_description="mu_Ncn_1, Mean of N_cn (1st PDF component) " &
    4027             :                            // "[num/kg]", var_units="num/kg", &
    4028           0 :                            l_silhs=.false., grid_kind=stats_zt )
    4029             : 
    4030           0 :          k = k + 1
    4031             : 
    4032           0 :          stats_metadata%imu_Ncn_2 = k
    4033             : 
    4034             :          call stat_assign( var_index=stats_metadata%imu_Ncn_2, &
    4035             :                            var_name="mu_Ncn_2", &
    4036             :                            var_description="mu_Ncn_2, Mean of N_cn (2nd PDF component)", &
    4037             :                            var_units="num/kg", &
    4038           0 :                            l_silhs=.false., grid_kind=stats_zt )
    4039             : 
    4040           0 :          k = k + 1
    4041             : 
    4042             :       ! Hydrometeor component mean values (in-precip) for ln hm for each PDF
    4043             :       ! component and hydrometeor type.
    4044             :       case ( 'mu_hm_i_n' )
    4045             : 
    4046           0 :          do hm_idx = 1, hydromet_dim, 1
    4047             : 
    4048           0 :             hm_type = hydromet_list(hm_idx)
    4049             : 
    4050             :             ! The in-precip mean of ln hm in the 1st PDF component.
    4051           0 :             stats_metadata%imu_hm_1_n(hm_idx) = k
    4052             : 
    4053           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    4054             : 
    4055           0 :                call stat_assign( var_index=stats_metadata%imu_hm_1_n(hm_idx), &
    4056             :                                  var_name="mu_"//trim( hm_type(1:2) )//"_1_n", &
    4057             :                                  var_description="Mean (in-precip) of ln " &
    4058             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4059             :                                  // " (1st PDF component) [ln(kg/kg)]", &
    4060             :                                  var_units="ln(kg/kg)", &
    4061           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4062             : 
    4063             :             else ! Concentration
    4064             : 
    4065           0 :                call stat_assign( var_index=stats_metadata%imu_hm_1_n(hm_idx), &
    4066             :                                  var_name="mu_"//trim( hm_type(1:2) )//"_1_n", &
    4067             :                                  var_description="Mean (in-precip) of ln " &
    4068             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4069             :                                  // " (1st PDF component) [ln(num/kg)]", &
    4070             :                                  var_units="ln(num/kg)", &
    4071           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4072             : 
    4073             :             endif ! l_mix_rat_hm(hm_idx)
    4074             : 
    4075           0 :             k = k + 1
    4076             : 
    4077             :             ! The in-precip mean of ln hm in the 2nd PDF component.
    4078           0 :             stats_metadata%imu_hm_2_n(hm_idx) = k
    4079             : 
    4080           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    4081             : 
    4082           0 :                call stat_assign( var_index=stats_metadata%imu_hm_2_n(hm_idx), &
    4083             :                                  var_name="mu_"//trim( hm_type(1:2) )//"_2_n", &
    4084             :                                  var_description="Mean (in-precip) of ln " &
    4085             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4086             :                                  // " (2nd PDF component) [ln(kg/kg)]", &
    4087             :                                  var_units="ln(kg/kg)", &
    4088           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4089             : 
    4090             :             else ! Concentration
    4091             : 
    4092           0 :                call stat_assign( var_index=stats_metadata%imu_hm_2_n(hm_idx), &
    4093             :                                  var_name="mu_"//trim( hm_type(1:2) )//"_2_n", &
    4094             :                                  var_description="Mean (in-precip) of ln " &
    4095             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4096             :                                  // " (2nd PDF component) [ln(num/kg)]", &
    4097             :                                  var_units="ln(num/kg)", &
    4098           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4099             : 
    4100             :             endif ! l_mix_rat_hm(hm_idx)
    4101             : 
    4102           0 :             k = k + 1
    4103             : 
    4104             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4105             : 
    4106             :       case ( 'mu_Ncn_i_n' )
    4107             : 
    4108           0 :          stats_metadata%imu_Ncn_1_n = k
    4109             : 
    4110             :          call stat_assign( var_index=stats_metadata%imu_Ncn_1_n, &
    4111             :                            var_name="mu_Ncn_1_n", &
    4112             :                            var_description="mu_Ncn_1_n, Mean of ln N_cn " &
    4113             :                            // "(1st PDF component)", &
    4114             :                            var_units="ln(num/kg)", &
    4115           0 :                            l_silhs=.false., grid_kind=stats_zt )
    4116             : 
    4117           0 :          k = k + 1
    4118             : 
    4119           0 :          stats_metadata%imu_Ncn_2_n = k
    4120             : 
    4121             :          call stat_assign( var_index=stats_metadata%imu_Ncn_2_n, &
    4122             :                            var_name="mu_Ncn_2_n", &
    4123             :                            var_description="mu_Ncn_2_n, Mean of ln N_cn " &
    4124             :                            // "(2nd PDF component)", &
    4125             :                            var_units="ln(num/kg)", &
    4126           0 :                            l_silhs=.false., grid_kind=stats_zt )
    4127             : 
    4128           0 :          k = k + 1
    4129             : 
    4130             :       ! Hydrometeor component standard deviations (in-precip) for each PDF
    4131             :       ! component and hydrometeor type.
    4132             :       case ( 'sigma_hm_i' )
    4133             : 
    4134           0 :          do hm_idx = 1, hydromet_dim, 1
    4135             : 
    4136           0 :             hm_type = hydromet_list(hm_idx)
    4137             : 
    4138             :             ! The in-precip standard deviation of the hydrometeor in the 1st PDF
    4139             :             ! component.
    4140           0 :             stats_metadata%isigma_hm_1(hm_idx) = k
    4141             : 
    4142           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    4143             : 
    4144           0 :                call stat_assign( var_index=stats_metadata%isigma_hm_1(hm_idx), &
    4145             :                                  var_name="sigma_" &
    4146             :                                  // trim( hm_type(1:2) )//"_1", &
    4147             :                                  var_description="Standard deviation " &
    4148             :                                  // "(in-precip) of " &
    4149             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4150             :                                  // " (1st PDF component) [kg/kg]", &
    4151             :                                  var_units="kg/kg", &
    4152           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4153             : 
    4154             :             else ! Concentration
    4155             : 
    4156           0 :                call stat_assign( var_index=stats_metadata%isigma_hm_1(hm_idx), &
    4157             :                                  var_name="sigma_" &
    4158             :                                  // trim( hm_type(1:2) )//"_1", &
    4159             :                                  var_description="Standard deviation " &
    4160             :                                  // "(in-precip) of " &
    4161             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4162             :                                  // " (1st PDF component) [num/kg]", &
    4163             :                                  var_units="num/kg", &
    4164           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4165             : 
    4166             :             endif ! l_mix_rat_hm(hm_idx)
    4167             : 
    4168           0 :             k = k + 1
    4169             : 
    4170             :             ! The in-precip standard deviation of the hydrometeor in the 2nd PDF
    4171             :             ! component.
    4172           0 :             stats_metadata%isigma_hm_2(hm_idx) = k
    4173             : 
    4174           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    4175             : 
    4176           0 :                call stat_assign( var_index=stats_metadata%isigma_hm_2(hm_idx), &
    4177             :                                  var_name="sigma_" &
    4178             :                                  // trim( hm_type(1:2) )//"_2", &
    4179             :                                  var_description="Standard deviation " &
    4180             :                                  // "(in-precip) of " &
    4181             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4182             :                                  // " (2nd PDF component) [kg/kg]", &
    4183             :                                  var_units="kg/kg", &
    4184           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4185             : 
    4186             :             else ! Concentration
    4187             : 
    4188           0 :                call stat_assign( var_index=stats_metadata%isigma_hm_2(hm_idx), &
    4189             :                                  var_name="sigma_" &
    4190             :                                  // trim( hm_type(1:2) )//"_2", &
    4191             :                                  var_description="Standard deviation " &
    4192             :                                  // "(in-precip) of " &
    4193             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4194             :                                  // " (2nd PDF component) [num/kg]", &
    4195             :                                  var_units="num/kg", &
    4196           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4197             : 
    4198             :             endif ! l_mix_rat_hm(hm_idx)
    4199             : 
    4200           0 :             k = k + 1
    4201             : 
    4202             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4203             : 
    4204             :       case ( 'sigma_Ncn_i' )
    4205             : 
    4206           0 :          stats_metadata%isigma_Ncn_1 = k
    4207             : 
    4208             :          call stat_assign( var_index=stats_metadata%isigma_Ncn_1, &
    4209             :                            var_name="sigma_Ncn_1", &
    4210             :                            var_description="sigma_Ncn_1, Standard deviation of N_cn " &
    4211             :                            // "(1st PDF component)", &
    4212           0 :                            var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
    4213             : 
    4214           0 :          k = k + 1
    4215             : 
    4216           0 :          stats_metadata%isigma_Ncn_2 = k
    4217             : 
    4218             :          call stat_assign( var_index=stats_metadata%isigma_Ncn_2, &
    4219             :                            var_name="sigma_Ncn_2", &
    4220             :                            var_description="sigma_Ncn_2, Standard deviation of N_cn " &
    4221             :                            // "(2nd PDF component)", &
    4222           0 :                            var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
    4223             : 
    4224           0 :          k = k + 1
    4225             : 
    4226             :       ! Hydrometeor component standard deviations (in-precip) for ln hm for each
    4227             :       ! PDF component and hydrometeor type.
    4228             :       case ( 'sigma_hm_i_n' )
    4229             : 
    4230           0 :          do hm_idx = 1, hydromet_dim, 1
    4231             : 
    4232           0 :             hm_type = hydromet_list(hm_idx)
    4233             : 
    4234             :             ! The in-precip standard deviation of ln hm in the 1st PDF
    4235             :             ! component.
    4236           0 :             stats_metadata%isigma_hm_1_n(hm_idx) = k
    4237             : 
    4238           0 :             call stat_assign( var_index=stats_metadata%isigma_hm_1_n(hm_idx), &
    4239             :                               var_name="sigma_" &
    4240             :                               // trim( hm_type(1:2) )//"_1_n", &
    4241             :                               var_description="Standard deviation " &
    4242             :                               // "(in-precip) of ln " &
    4243             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4244             :                               // " (1st PDF component) [-]", &
    4245             :                               var_units="-", &
    4246           0 :                               l_silhs=.false., grid_kind=stats_zt )
    4247             : 
    4248           0 :             k = k + 1
    4249             : 
    4250             :             ! The in-precip standard deviation of ln hm in the 2nd PDF
    4251             :             ! component.
    4252           0 :             stats_metadata%isigma_hm_2_n(hm_idx) = k
    4253             : 
    4254           0 :             call stat_assign( var_index=stats_metadata%isigma_hm_2_n(hm_idx), &
    4255             :                               var_name="sigma_" &
    4256             :                               // trim( hm_type(1:2) )//"_2_n", &
    4257             :                               var_description="Standard deviation " &
    4258             :                               // "(in-precip) of ln " &
    4259             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4260             :                               // " (2nd PDF component) [-]", &
    4261             :                               var_units="-", &
    4262           0 :                               l_silhs=.false., grid_kind=stats_zt )
    4263             : 
    4264           0 :             k = k + 1
    4265             : 
    4266             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4267             : 
    4268             :       case ( 'sigma_Ncn_i_n' )
    4269           0 :         stats_metadata%isigma_Ncn_1_n = k
    4270             : 
    4271             :          call stat_assign( var_index=stats_metadata%isigma_Ncn_1_n, &
    4272             :                            var_name="sigma_Ncn_1_n", &
    4273             :                            var_description="sigma_Ncn_1_n, Standard deviation of ln N_cn " &
    4274             :                            // "(1st PDF component)", &
    4275           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4276             : 
    4277           0 :          k = k + 1
    4278             : 
    4279           0 :          stats_metadata%isigma_Ncn_2_n = k
    4280             : 
    4281             :          call stat_assign( var_index=stats_metadata%isigma_Ncn_2_n, &
    4282             :                            var_name="sigma_Ncn_2_n", &
    4283             :                            var_description="sigma_Ncn_2_n, Standard deviation of ln N_cn " &
    4284             :                            // "(2nd PDF component)", &
    4285           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4286             : 
    4287           0 :          k = k + 1
    4288             : 
    4289             :       case ('corr_w_chi_1_ca')
    4290           0 :         stats_metadata%icorr_w_chi_1_ca = k
    4291             : 
    4292             :         call stat_assign( var_index=stats_metadata%icorr_w_chi_1_ca, &
    4293             :                           var_name="corr_w_chi_1_ca", &
    4294             :                           var_description="corr_w_chi_1_ca, Correlation of w and chi" &
    4295             :                           // " (1st PDF component) found in the correlation" &
    4296             :                           // " array", var_units="-", &
    4297           0 :                           l_silhs=.false., grid_kind=stats_zt )
    4298           0 :         k = k + 1
    4299             : 
    4300             :       case ('corr_w_chi_2_ca')
    4301           0 :         stats_metadata%icorr_w_chi_2_ca = k
    4302             : 
    4303             :         call stat_assign( var_index=stats_metadata%icorr_w_chi_2_ca, &
    4304             :                           var_name="corr_w_chi_2_ca", &
    4305             :                           var_description="corr_w_chi_2_ca, Correlation of w and chi" &
    4306             :                           // " (2nd PDF component) found in the correlation" &
    4307             :                           // " array", var_units="-", &
    4308           0 :                           l_silhs=.false., grid_kind=stats_zt )
    4309           0 :         k = k + 1
    4310             : 
    4311             :       case ('corr_w_eta_1_ca')
    4312           0 :         stats_metadata%icorr_w_eta_1_ca = k
    4313             : 
    4314             :         call stat_assign( var_index=stats_metadata%icorr_w_eta_1_ca, &
    4315             :                           var_name="corr_w_eta_1_ca", &
    4316             :                           var_description="corr_w_eta_1_ca, Correlation of w and eta" &
    4317             :                           // " (1st PDF component) found in the correlation" &
    4318             :                           // " array", var_units="-", &
    4319           0 :                           l_silhs=.false., grid_kind=stats_zt )
    4320           0 :         k = k + 1
    4321             : 
    4322             :       case ('corr_w_eta_2_ca')
    4323           0 :         stats_metadata%icorr_w_eta_2_ca = k
    4324             : 
    4325             :         call stat_assign( var_index=stats_metadata%icorr_w_eta_2_ca, &
    4326             :                           var_name="corr_w_eta_2_ca", &
    4327             :                           var_description="corr_w_eta_2_ca, Correlation of w and eta" &
    4328             :                           // " (2nd PDF component) found in the correlation" &
    4329             :                           // " array", var_units="-", &
    4330           0 :                           l_silhs=.false., grid_kind=stats_zt )
    4331           0 :         k = k + 1
    4332             : 
    4333             :       ! Correlation of w and a hydrometeor (in-precip) for each PDF
    4334             :       ! component and hydrometeor type.
    4335             :       case ( 'corr_w_hm_i' )
    4336             : 
    4337           0 :          do hm_idx = 1, hydromet_dim, 1
    4338             : 
    4339           0 :             hm_type = hydromet_list(hm_idx)
    4340             : 
    4341             :             ! The in-precip correlation of w and the hydrometeor in the
    4342             :             ! 1st PDF component.
    4343           0 :             stats_metadata%icorr_w_hm_1(hm_idx) = k
    4344             : 
    4345           0 :             call stat_assign( var_index=stats_metadata%icorr_w_hm_1(hm_idx), &
    4346             :                               var_name="corr_w_"//trim( hm_type(1:2) )//"_1", &
    4347             :                               var_description="Correlation (in-precip) " &
    4348             :                               // "of w and " &
    4349             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4350             :                               // " (1st PDF component) [-]", &
    4351           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4352             : 
    4353           0 :             k = k + 1
    4354             : 
    4355             :             ! The in-precip correlation of w and the hydrometeor in the
    4356             :             ! 2nd PDF component.
    4357           0 :             stats_metadata%icorr_w_hm_2(hm_idx) = k
    4358             : 
    4359           0 :             call stat_assign( var_index=stats_metadata%icorr_w_hm_2(hm_idx), &
    4360             :                               var_name="corr_w_"//trim( hm_type(1:2) )//"_2", &
    4361             :                               var_description="Correlation (in-precip) " &
    4362             :                               // "of w and " &
    4363             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4364             :                               // " (2nd PDF component) [-]", &
    4365           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4366             : 
    4367           0 :             k = k + 1
    4368             : 
    4369             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4370             : 
    4371             :       case ( 'corr_w_Ncn_i' )
    4372           0 :         stats_metadata%icorr_w_Ncn_1 = k
    4373             : 
    4374             :          call stat_assign( var_index=stats_metadata%icorr_w_Ncn_1, &
    4375             :                            var_name="corr_w_Ncn_1", &
    4376             :                            var_description="corr_w_Ncn_1, Correlation of w and N_cn " &
    4377             :                            // "(1st PDF component)", &
    4378           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4379             : 
    4380           0 :          k = k + 1
    4381             : 
    4382           0 :          stats_metadata%icorr_w_Ncn_2 = k
    4383             : 
    4384             :          call stat_assign( var_index=stats_metadata%icorr_w_Ncn_2, &
    4385             :                            var_name="corr_w_Ncn_2", &
    4386             :                            var_description="corr_w_Ncn_2, Correlation of w and N_cn " &
    4387             :                            // "(2nd PDF component)", &
    4388           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4389             : 
    4390           0 :          k = k + 1
    4391             : 
    4392             :       case ('corr_chi_eta_1_ca')
    4393           0 :         stats_metadata%icorr_chi_eta_1_ca = k
    4394             : 
    4395             :         call stat_assign( var_index=stats_metadata%icorr_chi_eta_1_ca, &
    4396             :                           var_name="corr_chi_eta_1_ca", &
    4397             :                           var_description="corr_chi_eta_1_ca, Correlation of chi (s) and" &
    4398             :                           // " eta (t) (1st PDF component) found in the" &
    4399             :                           // " correlation array", var_units="-", &
    4400           0 :                           l_silhs=.false., grid_kind=stats_zt )
    4401           0 :         k = k + 1
    4402             : 
    4403             :       case ('corr_chi_eta_2_ca')
    4404           0 :         stats_metadata%icorr_chi_eta_2_ca = k
    4405             : 
    4406             :         call stat_assign( var_index=stats_metadata%icorr_chi_eta_2_ca, &
    4407             :                           var_name="corr_chi_eta_2_ca", &
    4408             :                           var_description="corr_chi_eta_2_ca, Correlation of chi (s) and" &
    4409             :                           // " eta (t) (2nd PDF component) found in the" &
    4410             :                           // " correlation array", var_units="-", &
    4411           0 :                           l_silhs=.false., grid_kind=stats_zt )
    4412           0 :         k = k + 1
    4413             : 
    4414             :       ! Correlation of chi(s) and a hydrometeor (in-precip) for each PDF
    4415             :       ! component and hydrometeor type.
    4416             :       case ( 'corr_chi_hm_i' )
    4417             : 
    4418           0 :          do hm_idx = 1, hydromet_dim, 1
    4419             : 
    4420           0 :             hm_type = hydromet_list(hm_idx)
    4421             : 
    4422             :             ! The in-precip correlation of chi and the hydrometeor in the
    4423             :             ! 1st PDF component.
    4424           0 :             stats_metadata%icorr_chi_hm_1(hm_idx) = k
    4425             : 
    4426           0 :             call stat_assign( var_index=stats_metadata%icorr_chi_hm_1(hm_idx), &
    4427             :                               var_name="corr_chi_"//trim(hm_type(1:2))//"_1", &
    4428             :                               var_description="Correlation (in-precip) " &
    4429             :                               // "of chi (s) and " &
    4430             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4431             :                               // " (1st PDF component) [-]", &
    4432           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4433             : 
    4434           0 :             k = k + 1
    4435             : 
    4436             :             ! The in-precip correlation of chi and the hydrometeor in the
    4437             :             ! 2nd PDF component.
    4438           0 :             stats_metadata%icorr_chi_hm_2(hm_idx) = k
    4439             : 
    4440           0 :             call stat_assign( var_index=stats_metadata%icorr_chi_hm_2(hm_idx), &
    4441             :                               var_name="corr_chi_"//trim(hm_type(1:2))//"_2", &
    4442             :                               var_description="Correlation (in-precip) " &
    4443             :                               // "of chi (s) and " &
    4444             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4445             :                               // " (2nd PDF component) [-]", &
    4446           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4447             : 
    4448           0 :             k = k + 1
    4449             : 
    4450             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4451             : 
    4452             :       case ( 'corr_chi_Ncn_i' )
    4453           0 :         stats_metadata%icorr_chi_Ncn_1 = k
    4454             : 
    4455             :          call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_1, &
    4456             :                            var_name="corr_chi_Ncn_1", &
    4457             :                            var_description="corr_chi_Ncn_1, Correlation of chi and N_cn " &
    4458             :                            // "(1st PDF component)", &
    4459           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4460             : 
    4461           0 :          k = k + 1
    4462             : 
    4463           0 :          stats_metadata%icorr_chi_Ncn_2 = k
    4464             : 
    4465             :          call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_2, &
    4466             :                            var_name="corr_chi_Ncn_2", &
    4467             :                            var_description="corr_chi_Ncn_2, Correlation of chi and N_cn " &
    4468             :                            // "(2nd PDF component)", &
    4469           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4470             : 
    4471           0 :          k = k + 1
    4472             : 
    4473             :       ! Correlation of eta(t) and a hydrometeor (in-precip) for each PDF
    4474             :       ! component and hydrometeor type.
    4475             :       case ( 'corr_eta_hm_i' )
    4476             : 
    4477           0 :          do hm_idx = 1, hydromet_dim, 1
    4478             : 
    4479           0 :             hm_type = hydromet_list(hm_idx)
    4480             : 
    4481             :             ! The in-precip correlation of eta and the hydrometeor in the
    4482             :             ! 1st PDF component.
    4483           0 :             stats_metadata%icorr_eta_hm_1(hm_idx) = k
    4484             : 
    4485           0 :             call stat_assign( var_index=stats_metadata%icorr_eta_hm_1(hm_idx), &
    4486             :                               var_name="corr_eta_"//trim(hm_type(1:2))//"_1", &
    4487             :                               var_description="Correlation (in-precip) " &
    4488             :                               // "of eta (t) and " &
    4489             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4490             :                               // " (1st PDF component) [-]", &
    4491           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4492             : 
    4493           0 :             k = k + 1
    4494             : 
    4495             :             ! The in-precip correlation of eta and the hydrometeor in the
    4496             :             ! 2nd PDF component.
    4497           0 :             stats_metadata%icorr_eta_hm_2(hm_idx) = k
    4498             : 
    4499           0 :             call stat_assign( var_index=stats_metadata%icorr_eta_hm_2(hm_idx), &
    4500             :                               var_name="corr_eta_"//trim(hm_type(1:2))//"_2", &
    4501             :                               var_description="Correlation (in-precip) " &
    4502             :                               // "of eta (t) and " &
    4503             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4504             :                               // " (2nd PDF component) [-]", &
    4505           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4506             : 
    4507           0 :             k = k + 1
    4508             : 
    4509             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4510             : 
    4511             :       case ( 'corr_eta_Ncn_i' )
    4512             : 
    4513           0 :          stats_metadata%icorr_eta_Ncn_1 = k
    4514             : 
    4515             :          call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_1, &
    4516             :                            var_name="corr_eta_Ncn_1", &
    4517             :                            var_description="corr_eta_Ncn_1, Correlation of eta and N_cn " &
    4518             :                            // "(1st PDF component)", &
    4519           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4520             : 
    4521           0 :          k = k + 1
    4522             : 
    4523           0 :          stats_metadata%icorr_eta_Ncn_2 = k
    4524             : 
    4525             :          call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_2, &
    4526             :                            var_name="corr_eta_Ncn_2", &
    4527             :                            var_description="corr_eta_Ncn_2, Correlation of eta and N_cn " &
    4528             :                            // "(2nd PDF component)", &
    4529           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4530             : 
    4531           0 :          k = k + 1
    4532             : 
    4533             :       ! Correlation of Ncn and a hydrometeor (in-precip) for each PDF
    4534             :       ! component and hydrometeor type.
    4535             :       case ( 'corr_Ncn_hm_i' )
    4536             : 
    4537           0 :          do hm_idx = 1, hydromet_dim, 1
    4538             : 
    4539           0 :             hm_type = hydromet_list(hm_idx)
    4540             : 
    4541             :             ! The in-precip correlation of Ncn and the hydrometeor in the
    4542             :             ! 1st PDF component.
    4543           0 :             stats_metadata%icorr_Ncn_hm_1(hm_idx) = k
    4544             : 
    4545           0 :             call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_1(hm_idx), &
    4546             :                               var_name="corr_Ncn_"//trim(hm_type(1:2))//"_1", &
    4547             :                               var_description="Correlation (in-precip) " &
    4548             :                               // "of N_cn and " &
    4549             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4550             :                               // " (1st PDF component) [-]", &
    4551           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4552             : 
    4553           0 :             k = k + 1
    4554             : 
    4555             :             ! The in-precip correlation of Ncn and the hydrometeor in the
    4556             :             ! 2nd PDF component.
    4557           0 :             stats_metadata%icorr_Ncn_hm_2(hm_idx) = k
    4558             : 
    4559           0 :             call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_2(hm_idx), &
    4560             :                               var_name="corr_Ncn_"//trim(hm_type(1:2))//"_2", &
    4561             :                               var_description="Correlation (in-precip) " &
    4562             :                               // "of N_cn and " &
    4563             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4564             :                               // " (2nd PDF component) [-]", &
    4565           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4566             : 
    4567           0 :             k = k + 1
    4568             : 
    4569             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4570             : 
    4571             :       ! Correlation (in-precip) of two different hydrometeors (hmx and hmy)
    4572             :       ! for each PDF component and hydrometeor type.
    4573             :       case ( 'corr_hmx_hmy_i' )
    4574             : 
    4575           0 :          do hmx_idx = 1, hydromet_dim, 1
    4576             : 
    4577           0 :             hmx_type = hydromet_list(hmx_idx)
    4578             : 
    4579           0 :             do hmy_idx = hmx_idx+1, hydromet_dim, 1
    4580             : 
    4581           0 :                hmy_type = hydromet_list(hmy_idx)
    4582             : 
    4583             :                ! The in-precip correlation of hmx and hmy in the 1st PDF
    4584             :                ! component.
    4585           0 :                stats_metadata%icorr_hmx_hmy_1(hmy_idx,hmx_idx) = k
    4586             : 
    4587           0 :                call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_1(hmy_idx,hmx_idx), &
    4588             :                                  var_name="corr_"//trim( hmx_type(1:2) )//"_" &
    4589             :                                  // trim( hmy_type(1:2) )//"_1", &
    4590             :                                  var_description="Correlation (in-precip) " &
    4591             :                                  // "of " &
    4592             :                                  // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
    4593             :                                  // " and " &
    4594             :                                  // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
    4595             :                                  // " (1st PDF component) [-]", &
    4596           0 :                                  var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4597             : 
    4598           0 :                k = k + 1
    4599             : 
    4600             :                ! The in-precip correlation of hmx and hmy in the 2nd PDF
    4601             :                ! component.
    4602           0 :                stats_metadata%icorr_hmx_hmy_2(hmy_idx,hmx_idx) = k
    4603             : 
    4604           0 :                call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_2(hmy_idx,hmx_idx), &
    4605             :                                  var_name="corr_"//trim( hmx_type(1:2) )//"_" &
    4606             :                                  // trim( hmy_type(1:2) )//"_2", &
    4607             :                                  var_description="Correlation (in-precip) " &
    4608             :                                  // "of " &
    4609             :                                  // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
    4610             :                                  // " and " &
    4611             :                                  // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
    4612             :                                  // " (2nd PDF component) [-]", &
    4613           0 :                                  var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4614             : 
    4615           0 :                k = k + 1
    4616             : 
    4617             :             enddo ! hmy_idx = hmx_idx+1, hydromet_dim, 1
    4618             : 
    4619             :          enddo ! hmx_idx = 1, hydromet_dim, 1
    4620             : 
    4621             :       ! Correlation (in-precip) of w and ln hm for each PDF component and
    4622             :       ! hydrometeor type.
    4623             :       case ( 'corr_w_hm_i_n' )
    4624             : 
    4625           0 :          do hm_idx = 1, hydromet_dim, 1
    4626             : 
    4627           0 :             hm_type = hydromet_list(hm_idx)
    4628             : 
    4629             :             ! The in-precip correlation of w and ln hm in the 1st PDF
    4630             :             ! component.
    4631           0 :             stats_metadata%icorr_w_hm_1_n(hm_idx) = k
    4632             : 
    4633           0 :             call stat_assign( var_index=stats_metadata%icorr_w_hm_1_n(hm_idx), &
    4634             :                               var_name="corr_w_"//trim(hm_type(1:2))//"_1_n", &
    4635             :                               var_description="Correlation (in-precip) " &
    4636             :                               // "of w and ln " &
    4637             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4638             :                               // " (1st PDF component) [-]", &
    4639           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4640             : 
    4641           0 :             k = k + 1
    4642             : 
    4643             :             ! The in-precip correlation of w and ln hm in the 2nd PDF
    4644             :             ! component.
    4645           0 :             stats_metadata%icorr_w_hm_2_n(hm_idx) = k
    4646             : 
    4647           0 :             call stat_assign( var_index=stats_metadata%icorr_w_hm_2_n(hm_idx), &
    4648             :                               var_name="corr_w_"//trim(hm_type(1:2))//"_2_n", &
    4649             :                               var_description="Correlation (in-precip) " &
    4650             :                               // "of w and ln " &
    4651             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4652             :                               // " (2nd PDF component) [-]", &
    4653           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4654             : 
    4655           0 :             k = k + 1
    4656             : 
    4657             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4658             : 
    4659             :       case ( 'corr_w_Ncn_i_n' )
    4660             : 
    4661           0 :          stats_metadata%icorr_w_Ncn_1_n = k
    4662             : 
    4663             :          call stat_assign( var_index=stats_metadata%icorr_w_Ncn_1_n, &
    4664             :                            var_name="corr_w_Ncn_1_n", &
    4665             :                            var_description="corr_w_Ncn_1_n, Correlation of w and " &
    4666             :                            // "ln N_cn (1st PDF component)", &
    4667           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4668             : 
    4669           0 :          k = k + 1
    4670             : 
    4671           0 :          stats_metadata%icorr_w_Ncn_2_n = k
    4672             : 
    4673             :          call stat_assign( var_index=stats_metadata%icorr_w_Ncn_2_n, &
    4674             :                            var_name="corr_w_Ncn_2_n", &
    4675             :                            var_description="corr_w_Ncn_2_n, Correlation of w and " &
    4676             :                            // "ln N_cn (2nd PDF component)", &
    4677           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4678             : 
    4679           0 :          k = k + 1
    4680             : 
    4681             :       ! Correlation (in-precip) of chi and ln hm for each PDF component and
    4682             :       ! hydrometeor type.
    4683             :       case ( 'corr_chi_hm_i_n' )
    4684             : 
    4685           0 :          do hm_idx = 1, hydromet_dim, 1
    4686             : 
    4687           0 :             hm_type = hydromet_list(hm_idx)
    4688             : 
    4689             :             ! The in-precip correlation of chi and ln hm in the 1st PDF
    4690             :             ! component.
    4691           0 :             stats_metadata%icorr_chi_hm_1_n(hm_idx) = k
    4692             : 
    4693           0 :             call stat_assign( var_index=stats_metadata%icorr_chi_hm_1_n(hm_idx), &
    4694             :                               var_name="corr_chi_"//trim(hm_type(1:2)) &
    4695             :                               // "_1_n", &
    4696             :                               var_description="Correlation (in-precip) " &
    4697             :                               // "of chi (s) and ln " &
    4698             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4699             :                               // " (1st PDF component) [-]", &
    4700           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4701             : 
    4702           0 :             k = k + 1
    4703             : 
    4704             :             ! The in-precip correlation of chi(s) and ln hm in the 2nd PDF
    4705             :             ! component.
    4706           0 :             stats_metadata%icorr_chi_hm_2_n(hm_idx) = k
    4707             : 
    4708           0 :             call stat_assign( var_index=stats_metadata%icorr_chi_hm_2_n(hm_idx), &
    4709             :                               var_name="corr_chi_"//trim(hm_type(1:2)) &
    4710             :                               // "_2_n", &
    4711             :                               var_description="Correlation (in-precip) " &
    4712             :                               // "of chi (s) and ln " &
    4713             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4714             :                               // " (2nd PDF component) [-]", &
    4715           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4716             : 
    4717           0 :             k = k + 1
    4718             : 
    4719             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4720             : 
    4721             :       case ( 'corr_chi_Ncn_i_n' )
    4722             : 
    4723           0 :          stats_metadata%icorr_chi_Ncn_1_n = k
    4724             : 
    4725             :         call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_1_n, &
    4726             :                            var_name="corr_chi_Ncn_1_n", &
    4727             :                            var_description="corr_chi_Ncn_1_n, Correlation of chi (s) and " &
    4728             :                            // "ln N_cn (1st PDF component)", &
    4729           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4730             : 
    4731           0 :          k = k + 1
    4732             : 
    4733           0 :          stats_metadata%icorr_chi_Ncn_2_n = k
    4734             : 
    4735             :          call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_2_n, &
    4736             :                            var_name="corr_chi_Ncn_2_n", &
    4737             :                            var_description="corr_chi_Ncn_2_n, Correlation of chi (s) and " &
    4738             :                            // "ln N_cn (2nd PDF component)", &
    4739           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4740             : 
    4741           0 :          k = k + 1
    4742             : 
    4743             :       ! Correlation (in-precip) of eta and ln hm for each PDF component and
    4744             :       ! hydrometeor type.
    4745             :       case ( 'corr_eta_hm_i_n' )
    4746             : 
    4747           0 :          do hm_idx = 1, hydromet_dim, 1
    4748             : 
    4749           0 :             hm_type = hydromet_list(hm_idx)
    4750             : 
    4751             :             ! The in-precip correlation of eta and ln hm in the 1st PDF
    4752             :             ! component.
    4753           0 :             stats_metadata%icorr_eta_hm_1_n(hm_idx) = k
    4754             : 
    4755           0 :             call stat_assign( var_index=stats_metadata%icorr_eta_hm_1_n(hm_idx), &
    4756             :                               var_name="corr_eta_"//trim( hm_type(1:2) ) &
    4757             :                               // "_1_n", &
    4758             :                               var_description="Correlation (in-precip) " &
    4759             :                               // "of eta (t) and ln " &
    4760             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4761             :                               // " (1st PDF component) [-]", &
    4762           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4763             : 
    4764           0 :             k = k + 1
    4765             : 
    4766             :             ! The in-precip correlation of eta and ln hm in the 2nd PDF
    4767             :             ! component.
    4768           0 :             stats_metadata%icorr_eta_hm_2_n(hm_idx) = k
    4769             : 
    4770           0 :             call stat_assign( var_index=stats_metadata%icorr_eta_hm_2_n(hm_idx), &
    4771             :                               var_name="corr_eta_"//trim( hm_type(1:2) ) &
    4772             :                               // "_2_n", &
    4773             :                               var_description="Correlation (in-precip) " &
    4774             :                               // "of eta(t) and ln " &
    4775             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4776             :                               // " (2nd PDF component) [-]", &
    4777           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4778             : 
    4779           0 :             k = k + 1
    4780             : 
    4781             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4782             : 
    4783             :       case ( 'corr_eta_Ncn_i_n' )
    4784             : 
    4785           0 :          stats_metadata%icorr_eta_Ncn_1_n = k
    4786             : 
    4787             :          call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_1_n, &
    4788             :                            var_name="corr_eta_Ncn_1_n", &
    4789             :                            var_description="corr_eta_Ncn_1_n, Correlation of eta (t) and " &
    4790             :                            // "ln N_cn (1st PDF component)", &
    4791           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4792             : 
    4793           0 :          k = k + 1
    4794             : 
    4795           0 :          stats_metadata%icorr_eta_Ncn_2_n = k
    4796             : 
    4797             :          call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_2_n, &
    4798             :                            var_name="corr_eta_Ncn_2_n", &
    4799             :                            var_description="corr_eta_Ncn_2_n, Correlation of eta (t) and " &
    4800             :                            // "ln N_cn (2nd PDF component)", &
    4801           0 :                            var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4802             : 
    4803           0 :          k = k + 1
    4804             : 
    4805             :       ! Correlation (in-precip) of ln Ncn and ln hm for each PDF component
    4806             :       ! and hydrometeor type.
    4807             :       case ( 'corr_Ncn_hm_i_n' )
    4808             : 
    4809           0 :          do hm_idx = 1, hydromet_dim, 1
    4810             : 
    4811           0 :             hm_type = hydromet_list(hm_idx)
    4812             : 
    4813             :             ! The in-precip correlation of ln Ncn and ln hm in the 1st PDF
    4814             :             ! component.
    4815           0 :             stats_metadata%icorr_Ncn_hm_1_n(hm_idx) = k
    4816             : 
    4817           0 :             call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_1_n(hm_idx), &
    4818             :                               var_name="corr_Ncn_"//trim(hm_type(1:2)) &
    4819             :                               // "_1_n", &
    4820             :                               var_description="Correlation (in-precip) " &
    4821             :                               // "of ln N_cn and ln " &
    4822             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4823             :                               // " (1st PDF component) [-]", &
    4824           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4825             : 
    4826           0 :             k = k + 1
    4827             : 
    4828             :             ! The in-precip correlation of ln Ncn and ln hm in the 2nd PDF
    4829             :             ! component.
    4830           0 :             stats_metadata%icorr_Ncn_hm_2_n(hm_idx) = k
    4831             : 
    4832           0 :             call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_2_n(hm_idx), &
    4833             :                               var_name="corr_Ncn_"//trim(hm_type(1:2)) &
    4834             :                               // "_2_n", &
    4835             :                               var_description="Correlation (in-precip) " &
    4836             :                               // "of ln N_cn and ln " &
    4837             :                               // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4838             :                               // " (2nd PDF component) [-]", &
    4839           0 :                               var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4840             : 
    4841           0 :             k = k + 1
    4842             : 
    4843             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4844             : 
    4845             :       ! Correlation (in-precip) of ln hmx and ln hmy (hmx and hmy are two
    4846             :       ! different hydrometeors) for each PDF component and hydrometeor type.
    4847             :       case ( 'corr_hmx_hmy_i_n' )
    4848             : 
    4849           0 :          do hmx_idx = 1, hydromet_dim, 1
    4850             : 
    4851           0 :             hmx_type = hydromet_list(hmx_idx)
    4852             : 
    4853           0 :             do hmy_idx = hmx_idx+1, hydromet_dim, 1
    4854             : 
    4855           0 :                hmy_type = hydromet_list(hmy_idx)
    4856             : 
    4857             :                ! The in-precip correlation of ln hmx and ln hmy in the 1st
    4858             :                ! PDF component.
    4859           0 :                stats_metadata%icorr_hmx_hmy_1_n(hmy_idx,hmx_idx) = k
    4860             : 
    4861           0 :                call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_1_n(hmy_idx,hmx_idx), &
    4862             :                                  var_name="corr_"//trim( hmx_type(1:2) )//"_" &
    4863             :                                  // trim( hmy_type(1:2) )//"_1_n", &
    4864             :                                  var_description="Correlation (in-precip) " &
    4865             :                                  // "of ln " &
    4866             :                                  // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
    4867             :                                  // " and ln " &
    4868             :                                  // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
    4869             :                                  // " (1st PDF component) [-]", &
    4870           0 :                                  var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4871             : 
    4872           0 :                k = k + 1
    4873             : 
    4874             :                ! The in-precip correlation of ln hmx and ln hmy in the 2nd
    4875             :                ! PDF component.
    4876           0 :                stats_metadata%icorr_hmx_hmy_2_n(hmy_idx,hmx_idx) = k
    4877             : 
    4878           0 :                call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_2_n(hmy_idx,hmx_idx), &
    4879             :                                  var_name="corr_"//trim( hmx_type(1:2) )//"_" &
    4880             :                                  // trim( hmy_type(1:2) )//"_2_n", &
    4881             :                                  var_description="Correlation (in-precip) " &
    4882             :                                  // "of ln " &
    4883             :                                  // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
    4884             :                                  // " and ln " &
    4885             :                                  // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
    4886             :                                  // " (2nd PDF component) [-]", &
    4887           0 :                                  var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4888             : 
    4889           0 :                k = k + 1
    4890             : 
    4891             :             enddo ! hmy_idx = hmx_idx+1, hydromet_dim, 1
    4892             : 
    4893             :          enddo ! hmx_idx = 1, hydromet_dim, 1
    4894             : 
    4895             :       ! Third-order mixed moment < w'^2 hm' >, where hm is a hydrometeor.
    4896             :       case ('wp2hmp')
    4897             : 
    4898           0 :          do hm_idx = 1, hydromet_dim, 1
    4899             : 
    4900           0 :             hm_type = hydromet_list(hm_idx)
    4901             : 
    4902           0 :             stats_metadata%iwp2hmp(hm_idx) = k
    4903             : 
    4904           0 :             if ( l_mix_rat_hm(hm_idx) ) then
    4905             : 
    4906           0 :                call stat_assign( var_index=stats_metadata%iwp2hmp(hm_idx), &
    4907             :                                  var_name="wp2"//trim( hm_type(1:2) )//"p", &
    4908             :                                  var_description="Third-order moment < w'^2 " &
    4909             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4910             :                                  // "' > [(m/s)^2 kg/kg]", &
    4911             :                                  var_units="(m/s)^2 kg/kg", &
    4912           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4913             : 
    4914             :             else ! Concentration
    4915             : 
    4916           0 :                call stat_assign( var_index=stats_metadata%iwp2hmp(hm_idx), &
    4917             :                                  var_name="wp2"//trim( hm_type(1:2) )//"p", &
    4918             :                                  var_description="Third-order moment < w'^2 " &
    4919             :                                  // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
    4920             :                                  // "' > [(m/s)^2 num/kg]", &
    4921             :                                  var_units="(m/s)^2 num/kg", &
    4922           0 :                                  l_silhs=.false., grid_kind=stats_zt )
    4923             : 
    4924             :             endif ! l_mix_rat_hm(hm_idx)
    4925             : 
    4926           0 :             k = k + 1
    4927             : 
    4928             :          enddo ! hm_idx = 1, hydromet_dim, 1
    4929             : 
    4930             :       case ('cloud_frac_refined')
    4931           0 :         stats_metadata%icloud_frac_refined = k
    4932             :         call stat_assign( var_index=stats_metadata%icloud_frac_refined, var_name="cloud_frac_refined", &
    4933             :                           var_description="cloud_frac_refined, Cloud fraction computed on " &
    4934             :                           // "refined grid", &
    4935           0 :                           var_units="-", l_silhs=.false., grid_kind=stats_zt )
    4936           0 :         k = k + 1
    4937             : 
    4938             :       case ('rcm_refined')
    4939           0 :         stats_metadata%ircm_refined = k
    4940             :         call stat_assign( var_index=stats_metadata%ircm_refined, var_name="rcm_refined", &
    4941             :                           var_description="rcm_refined, Cloud water mixing ratio computed on " &
    4942             :                           // "refined grid &
    4943           0 :                           &[kg/kg]", var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt)
    4944           0 :         k = k + 1
    4945             : 
    4946             :       case ('hl_on_Cp_residual')
    4947           0 :         stats_metadata%ihl_on_Cp_residual = k
    4948             :         call stat_assign( var_index=stats_metadata%ihl_on_Cp_residual, var_name="hl_on_Cp_residual", &
    4949             :                           var_description="hl_on_Cp_residual, Residual change in HL/Cp from " &
    4950             :                           // "Morrison microphysics &
    4951             :                           &not due to sedimentation", &
    4952           0 :                           var_units="K", l_silhs=.true., grid_kind=stats_zt)
    4953           0 :         k = k + 1
    4954             : 
    4955             :       case ('qto_residual')
    4956           0 :         stats_metadata%iqto_residual = k
    4957             :         call stat_assign( var_index=stats_metadata%iqto_residual, var_name="qto_residual", &
    4958             :                           var_description="qto_residual, Residual change in total water " &
    4959             :                           // "from Morrison &
    4960             :                           &microphysics not due to sedimentation", &
    4961           0 :                           var_units="kg/kg", l_silhs=.true., grid_kind=stats_zt)
    4962           0 :         k = k + 1
    4963             : 
    4964             :       case ( 'sclrm' )
    4965           0 :         do j = 1, sclr_dim, 1
    4966           0 :           write(sclr_idx, * ) j
    4967           0 :           sclr_idx = adjustl(sclr_idx)
    4968           0 :         stats_metadata%isclrm(j) = k
    4969           0 :           call stat_assign( var_index=stats_metadata%isclrm(j), var_name="sclr"//trim(sclr_idx)//"m", &
    4970             :             var_description="passive scalar "//trim(sclr_idx), var_units="unknown", &
    4971           0 :             l_silhs=.false., grid_kind=stats_zt )
    4972           0 :           k = k + 1
    4973             :         end do
    4974             : 
    4975             :       case ( 'sclrm_f' )
    4976           0 :         do j = 1, sclr_dim, 1
    4977           0 :           write(sclr_idx, * ) j
    4978           0 :           sclr_idx = adjustl(sclr_idx)
    4979           0 :         stats_metadata%isclrm_f(j) = k
    4980           0 :           call stat_assign( var_index=stats_metadata%isclrm_f(j), var_name="sclr"//trim(sclr_idx)//"m_f", &
    4981             :             var_description="passive scalar forcing "//trim(sclr_idx), var_units="unknown", &
    4982           0 :             l_silhs=.false., grid_kind=stats_zt )
    4983           0 :           k = k + 1
    4984             :         end do
    4985             : 
    4986             :       case ( 'edsclrm' )
    4987           0 :         do j = 1, edsclr_dim, 1
    4988           0 :           write(sclr_idx, * ) j
    4989           0 :           sclr_idx = adjustl(sclr_idx)
    4990           0 :         stats_metadata%iedsclrm(j) = k
    4991           0 :           call stat_assign( var_index=stats_metadata%iedsclrm(j), var_name="edsclr"//trim(sclr_idx)//"m", &
    4992             :             var_description="passive scalar "//trim(sclr_idx), var_units="unknown", &
    4993           0 :             l_silhs=.false., grid_kind=stats_zt )
    4994           0 :           k = k + 1
    4995             :         end do
    4996             : 
    4997             :       case ( 'edsclrm_f' )
    4998           0 :         do j = 1, edsclr_dim, 1
    4999           0 :           write(sclr_idx, * ) j
    5000           0 :           sclr_idx = adjustl(sclr_idx)
    5001           0 :         stats_metadata%iedsclrm_f(j) = k
    5002           0 :           call stat_assign( var_index=stats_metadata%iedsclrm_f(j), var_name="edsclr"//trim(sclr_idx)//"m_f", &
    5003             :             var_description="passive scalar forcing "//trim(sclr_idx), var_units="unknown", &
    5004           0 :             l_silhs=.false., grid_kind=stats_zt )
    5005           0 :           k = k + 1
    5006             :         end do
    5007             : 
    5008             :       case default
    5009             : 
    5010           0 :         write(fstderr,*) 'Error:  unrecognized variable in vars_zt:  ', trim( vars_zt(i) )
    5011           0 :         l_error = .true.  ! This will stop the run.
    5012             : 
    5013             :       end select ! trim( vars_zt )
    5014             : 
    5015             : 
    5016             :     end do ! i=1,stats_zt%num_output_fields
    5017             : 
    5018             : 
    5019           0 :     return
    5020             : 
    5021             :   end subroutine stats_init_zt
    5022             : 
    5023             : !===============================================================================
    5024             : 
    5025             : end module stats_zt_module

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