LCOV - code coverage report
Current view: | top level - physics/pumas - tau_neural_net_quantile.F90 (source / functions) |  |  | Hit | Total | Coverage
Test: | coverage.info |  | Lines: | 0 | 34 | 0.0 %
Date: | 2024-12-17 17:57:11 |  | Functions: | 0 | 2 | 0.0 %

              Line data    Source code
           1             : module tau_neural_net_quantile
           2             :
           3             :     use shr_kind_mod,   only: r8=>shr_kind_r8
           4             :
           5             :     use module_neural_net, only : Dense, init_neural_net, load_quantile_scale_values
           6             :     use module_neural_net, only : quantile_transform, quantile_inv_transform, neural_net_predict
           7             :
           8             :     implicit none
           9             :     integer, parameter, public :: i8 = selected_int_kind(18)
          10             :     integer, parameter :: num_inputs = 7
          11             :     integer, parameter :: num_outputs = 3
          12             :     integer, parameter :: batch_size = 1
          13             :
          14             :     ! Neural networks and scale values saved within the scope of the module.
          15             :     ! Need to call initialize_tau_emulators to load weights and tables from disk.
          16             :     type(Dense), allocatable, save :: q_all(:)
          17             :     real(r8), dimension(:, :), allocatable, save :: input_scale_values
          18             :     real(r8), dimension(:, :), allocatable, save :: output_scale_values
          19             : contains
          20             :
          21             :
          22           0 :     subroutine initialize_tau_emulators( stochastic_emulated_filename_quantile, stochastic_emulated_filename_input_scale, &
          23           0 :                                          stochastic_emulated_filename_output_scale, iulog, errstring)
          24             :
          25             :     ! Load neural network netCDF files and scaling values. Values are placed in to emulators,
          26             :     ! input_scale_values, and output_scale_values.
          27             :     character(len=*), intent(in) ::  stochastic_emulated_filename_quantile, stochastic_emulated_filename_input_scale, &
          28             :                                      stochastic_emulated_filename_output_scale
          29             :     integer,          intent(in)  :: iulog
          30             :     character(128),   intent(out) :: errstring  ! output status (non-blank for error return)
          31             :
          32           0 :         errstring = ''
          33             :
          34           0 :         write(iulog,*) "Begin loading neural nets"
          35           0 :         call init_neural_net(trim(stochastic_emulated_filename_quantile), batch_size, q_all, iulog, errstring)
          36           0 :         if (trim(errstring) /= '') return
          37           0 :         write(iulog,*) "End loading neural nets"
          38             :         ! Load the scale values from a csv file.
          39           0 :         call load_quantile_scale_values(trim(stochastic_emulated_filename_input_scale), input_scale_values, iulog, errstring)
          40           0 :         call load_quantile_scale_values(trim(stochastic_emulated_filename_output_scale), output_scale_values, iulog, errstring)
          41           0 :         write(iulog,*) "Loaded neural nets scaling values"
          42             :
          43             :     end subroutine initialize_tau_emulators
          44             :
          45             :
          46           0 :     subroutine tau_emulated_cloud_rain_interactions(qc, nc, qr, nr, rho, lcldm, &
          47           0 :             precip_frac, mgncol, q_small, qc_tend, qr_tend, nc_tend, nr_tend)
          48             :         ! Calculates emulated tau microphysics tendencies from neural networks.
          49             :         !
          50             :         ! Input args:
          51             :         !   qc: cloud water mixing ratio in kg kg-1
          52             :         !   nc: cloud water number concentration in particles m-3
          53             :         !   qr: rain water mixing ratio in kg kg-1
          54             :         !   nr: rain water number concentration in particles m-3
          55             :         !   rho: density of air in kg m-3
          56             :         !   q_small: minimum cloud water mixing ratio value for running the microphysics
          57             :         !   mgncol: MG number of grid cells in vertical column
          58             :         ! Output args:
          59             :         !    qc_tend: qc tendency
          60             :         !    qr_tend: qr tendency
          61             :         !    nc_tend: nc tendency
          62             :         !    nr_tend: nr tendency
          63             :         !
          64             :         integer, intent(in) :: mgncol
          65             :         real(r8), dimension(mgncol), intent(in) :: qc, qr, nc, nr, rho, lcldm, precip_frac
          66             :         real(r8), intent(in) :: q_small
          67             :         real(r8), dimension(mgncol), intent(out) :: qc_tend, qr_tend, nc_tend, nr_tend
          68             :         integer(i8) :: i, j
          69             :         real(r8), dimension(batch_size, num_inputs) :: nn_inputs, nn_quantile_inputs
          70             :         real(r8), dimension(batch_size, num_outputs) :: nn_quantile_outputs, nn_outputs
          71             :         real(r8), parameter :: dt = 1800.0
          72           0 :         do i = 1, mgncol
          73           0 :             if (qc(i) >= q_small) then
          74           0 :                 nn_inputs(1, 1) = qc(i)
          75           0 :                 nn_inputs(1, 2) = qr(i)
          76           0 :                 nn_inputs(1, 3) = nc(i)
          77           0 :                 nn_inputs(1, 4) = nr(i)
          78           0 :                 nn_inputs(1, 5) = rho(i)
          79           0 :                 nn_inputs(1, 6) = precip_frac(i)
          80           0 :                 nn_inputs(1, 7) = lcldm(i)
          81           0 :                 call quantile_transform(nn_inputs, input_scale_values, nn_quantile_inputs)
          82           0 :                 call neural_net_predict(nn_quantile_inputs, q_all, nn_quantile_outputs)
          83           0 :                 call quantile_inv_transform(nn_quantile_outputs, output_scale_values, nn_outputs)
          84           0 :                 qr_tend(i) = (nn_outputs(1, 1) - qr(i)) / dt
          85           0 :                 qr_tend(i) = (nn_outputs(1, 1) - qr(i)) / dt
          86           0 :                 qc_tend(i) = -qr_tend(i)
          87           0 :                 nc_tend(i) = (nn_outputs(1, 2) - nc(i)) / dt
          88           0 :                 nr_tend(i) = (nn_outputs(1, 3) - nr(i)) / dt
          89             :             else
          90           0 :                 qc_tend(i) = 0._r8
          91           0 :                 qr_tend(i) = 0._r8
          92           0 :                 nc_tend(i) = 0._r8
          93           0 :                 nr_tend(i) = 0._r8
          94             :             end if
          95             :         end do
          96           0 :     end subroutine tau_emulated_cloud_rain_interactions
          97             : end module tau_neural_net_quantile

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