[WIP] Improvement of testing routines #68
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AdelekeBankole
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Thanks @AdelekeBankole This looks great, thank you!
We have just run these locally and everything is passing.
Just left a couple of comments for things to check or suggestions to make things a little clearer.
| integer, parameter :: input_ver_dim = 30 | ||
| !! The number of cells in a SAM atmospheric column on which the neural net was trained | ||
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| integer, parameter :: num_sam_cells = 30 | ||
| !! number of SAM cells |
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These fulfill the same purpose - number of cells in a SAM column for which the net was trained, so we should look at combining them into one.
| !! Define a CAM grid from 1111.0 to 10.0 | ||
| !! Check interpolation to SAM grid by defining an idential CAM grid and | ||
| !! interpolating a variable equal to the pressure | ||
| !! Define a CAM grid consiting of 4 atmospheric columns |
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| !! Define a CAM grid consiting of 4 atmospheric columns | |
| !! Define a CAM grid consisting of 4 atmospheric columns |
| real(dp), dimension(num_cols, nrf) :: p_cam, p_int_cam | ||
| real(dp), dimension(num_cols, nrf) :: var_cam | ||
| real(dp), dimension(num_cols) :: var_cam_surface | ||
| real(dp), dimension(num_cols) :: ps_cam | ||
| real(dp), dimension(num_cols, nrf) :: var_sam, var_sam_exp |
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These used to be length 48, but now use nrf which is length 30.
Should they be size sam_sounding instead of nrf?
| p_cam = reshape((/ 1000.0, 1000.0, 1000.0, 1000.0, 500.0, 500.0, 500.0, 500.0, 10.0, 10.0, 10.0, 10.0 /), (/ 4, 3 /)) | ||
| real(dp), dimension(num_cols, num_cells) :: p_cam | ||
| real(dp), dimension(num_cols, num_cells) :: var_cam | ||
| real(dp), dimension(num_cols) :: var_cam_surface | ||
| real(dp), dimension(num_cols) :: ps_cam | ||
| real(dp), dimension(num_cols, num_sam_cells) :: var_sam, var_sam_exp | ||
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| ! Set up a coarse CAM grid of 4 columns of pressures [1000, 500, 10] hPa with surface pressure 1111 hPa | ||
| do i = 1, num_cols | ||
| p_cam(i, 1) = 1000.0 | ||
| p_cam(i, 2) = 500.0 | ||
| p_cam(i, 3) = 10.0 | ||
| end do |
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This is much much better than the hardcoded and reshaped array!
| !! Check interpolation to SAM grid by interpolating pressure to pressure | ||
| !! Set top of CAM to 1.0d-4 | ||
| !! => should match pres from SAM | ||
| !! Use a coarse CAM grid of 3 cells and 4 columns | ||
| !! => expected variable on SAM grid should be pressure at that point |
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Perhaps we should provide a bit more of an explanation as to what this test is checking.
e.g.
We are setting the variable equal to the coordinates (pressure) on the CAM grid, i.e. y=x.
We expect the interpolated values on the SAM grid to also satisfy y=x on that grid
| rho_cam(:, i) = var_cam(:, i) / (p_int_cam(:, i)-p_int_cam(:, i+1)) | ||
| subroutine test_interp_to_cam_coarse(test_name) | ||
| !! Check conservative regridding to CAM coarse grid | ||
| !! => should conserve density |
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| !! => should conserve density | |
| !! => should conserve mass |
Mass I think - as discussed.
| ! Fetch SAM grid data | ||
| call fetch_sam_data(pres_sam, presi_sam, gamaz_sam, rho_sam, z_sam) | ||
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| ! Define CAM grid coarser than SAM grid |
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| ! Define CAM grid coarser than SAM grid | |
| ! Define CAM grid coarser than SAM grid - one CAM cell to every 3 SAM cells. |
| do j = 1, num_cols | ||
| ! Set SAM variable (density) equal to 1.0 | ||
| var_sam(j, :) = 1.0 | ||
| end do |
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| do j = 1, num_cols | |
| ! Set SAM variable (density) equal to 1.0 | |
| var_sam(j, :) = 1.0 | |
| end do | |
| ! Set SAM variable (density) equal to 1.0 | |
| var_sam(:, :) = 1.0 |
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| subroutine test_interp_to_cam_coarse_variable_density(test_name) | ||
| !! Check conservative regridding to CAM coarse grid with variable density | ||
| !! => With integrated sum, this test should conserve density |
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| !! => With integrated sum, this test should conserve density | |
| !! => With integrated sum, this test should conserve mass |
| do j = 1, num_cols | ||
| do i = 1, num_sam_cells | ||
| ! Set SAM variable density not equal to 1.0 | ||
| var_sam(j, i) = tan(real(i * j)) | ||
| end do | ||
| end do |
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This is a good function to use, and also because it varies across columns, so will check that our scheme is robust in that sense as well! Great job.
This PR will close #64