diff --git a/plotting/compressible_euler/plot_dcmip_3_1_gravity_wave.py b/plotting/compressible_euler/plot_dcmip_3_1_gravity_wave.py
new file mode 100644
index 00000000..1575bfab
--- /dev/null
+++ b/plotting/compressible_euler/plot_dcmip_3_1_gravity_wave.py
@@ -0,0 +1,277 @@
+"""
+Plots the DCMIP 3-1 gravity wave test case.
+
+This plots the initial conditions @ t = 0 s, with
+(a) zonal wind, (b) theta (c) theta perturbation: all on a lon-lat slice,
+(d) zonal wind, (e) theta (f) theta perturbation: on a lat-z slice,
+
+and the final state @ t = 3600 s, with
+(a) theta perturbation on a lon-lat slice,
+(b) theta perturbation on a lon-z slice.
+"""
+from os.path import abspath, dirname
+import matplotlib.pyplot as plt
+import numpy as np
+from netCDF4 import Dataset
+from tomplot import (
+    set_tomplot_style, tomplot_cmap, plot_contoured_field,
+    add_colorbar_ax, tomplot_field_title, extract_gusto_coords,
+    extract_gusto_field, reshape_gusto_data, extract_gusto_vertical_slice,
+    regrid_vertical_slice
+)
+
+test = 'dcmip_3_1_gravity_wave'
+
+# ---------------------------------------------------------------------------- #
+# Directory for results and plots
+# ---------------------------------------------------------------------------- #
+# When copying this example these paths need editing, which will usually involve
+# removing the abspath part to set directory paths relative to this file
+results_file_name = f'{abspath(dirname(__file__))}/../../results/{test}/field_output.nc'
+plot_stem = f'{abspath(dirname(__file__))}/../../figures/compressible_euler/{test}'
+
+# ---------------------------------------------------------------------------- #
+# Initial plot details
+# ---------------------------------------------------------------------------- #
+init_field_names = ['u_zonal', 'theta', 'theta_perturbation',
+                    'u_zonal', 'theta', 'theta_perturbation']
+init_colour_schemes = ['YlOrBr', 'PuRd', 'OrRd',
+                       'YlOrBr', 'PuRd', 'OrRd',]
+init_field_labels = [r'$u$ (m s$^{-1}$)', r'$\theta$ (K)', r'$\Delta\theta$ (K)',
+                     r'$u$ (m s$^{-1}$)', r'$\theta$ (K)', r'$\Delta\theta$ (K)']
+init_contours = [np.linspace(0, 25, 11),
+                 np.linspace(300, 335, 13),
+                 np.linspace(0.0, 1.0, 11),
+                 np.linspace(0, 25, 11),
+                 np.linspace(300, 335, 13),
+                 np.linspace(0.0, 1.0, 11)]
+init_contours_to_remove = [None, None, None, None, None, None]
+init_slice_along = ['z', 'z', 'z', 'lon', 'lon', 'lon']
+
+# ---------------------------------------------------------------------------- #
+# Final plot details
+# ---------------------------------------------------------------------------- #
+final_field_names = ['theta_perturbation', 'theta_perturbation']
+final_colour_schemes = ['RdBu_r', 'RdBu_r']
+final_field_labels = [r'$\Delta\theta$ (K)', r'$\Delta\theta$ (K)']
+final_contours = [np.linspace(-0.1, 0.1, 21),
+                  np.linspace(-0.1, 0.1, 21)]
+final_contours_to_remove = [0.0, 0.0]
+final_slice_along = ['z', 'lat']
+
+# ---------------------------------------------------------------------------- #
+# General options
+# ---------------------------------------------------------------------------- #
+contour_method = 'tricontour'
+lon_lims = [-180, 180]
+lat_lims = [-90, 90]
+z_lims = [0, 10]
+level = 5
+slice_at_lon = 120.0
+slice_at_lat = 0.0
+
+# 1D grids for vertical regridding
+coords_lon_1d = np.linspace(-180, 180, 50)
+coords_lat_1d = np.linspace(-90, 90, 50)
+# Dictionary to hold plotting grids -- keys are "slice_along" values
+plotting_grid = {'lat': coords_lon_1d, 'lon': coords_lat_1d}
+
+cbar_format = {'u_zonal': '1.0f',
+               'theta': '1.0f',
+               'theta_perturbation': '1.1f'}
+
+# Things that are likely the same for all plots --------------------------------
+set_tomplot_style()
+data_file = Dataset(results_file_name, 'r')
+
+# ---------------------------------------------------------------------------- #
+# INITIAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig, axarray = plt.subplots(2, 3, figsize=(18, 12), sharex='row', sharey='row')
+time_idx = 0
+
+for i, (ax, field_name, field_label, colour_scheme, contours,
+        to_remove, slice_along) in \
+        enumerate(zip(axarray.flatten(), init_field_names, init_field_labels,
+                      init_colour_schemes, init_contours,
+                      init_contours_to_remove, init_slice_along)):
+
+    # Data extraction ----------------------------------------------------------
+    time = data_file['time'][time_idx]
+
+    if slice_along == 'z':
+        field_full = extract_gusto_field(data_file, field_name, time_idx)
+        coords_X_full, coords_Y_full, coords_Z_full = \
+            extract_gusto_coords(data_file, field_name)
+
+        # Reshape
+        field_full, coords_X_full, coords_Y_full, _ = \
+            reshape_gusto_data(field_full, coords_X_full,
+                               coords_Y_full, coords_Z_full)
+
+        # Take level for a horizontal slice
+        field_data = field_full[:, level]
+        # Abuse of names for coord variables but simplifies code below
+        coords_X = coords_X_full[:, level]
+        coords_Y = coords_Y_full[:, level]
+
+    else:
+        orig_field_data, orig_coords_X, orig_coords_Y, orig_coords_Z = \
+            extract_gusto_vertical_slice(
+                data_file, field_name, time_idx,
+                slice_along=slice_along, slice_at=slice_at_lon
+            )
+
+        # Slices need regridding as points don't cleanly live along lon or lat = 0.0
+        field_data, coords_X, coords_Y = \
+            regrid_vertical_slice(
+                plotting_grid[slice_along], slice_along, slice_at_lon,
+                orig_coords_X, orig_coords_Y, orig_coords_Z, orig_field_data
+            )
+        # Scale coordinates
+        coords_Y /= 1000.
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=to_remove)
+    cf, lines = plot_contoured_field(
+        ax, coords_X, coords_Y, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines
+    )
+
+    add_colorbar_ax(
+        fig, cf, field_label, location='bottom', cbar_labelpad=-10,
+        cbar_format=cbar_format[field_name]
+    )
+    if slice_along == 'z':
+        tomplot_field_title(
+            ax, '$z = $ 5 km', minmax=True, field_data=field_data
+        )
+    elif slice_along == 'lon':
+        tomplot_field_title(
+            ax, r'$\lambda = $ 120 deg', minmax=True, field_data=field_data
+        )
+
+    # Labels -------------------------------------------------------------------
+    if i == 0:
+        ax.set_ylabel(r'$\vartheta$ (deg)', labelpad=-20)
+        ax.set_ylim(lat_lims)
+        ax.set_yticks(lat_lims)
+        ax.set_yticklabels(lat_lims)
+    elif i == 3:
+        ax.set_ylabel(r'$z$ (km)', labelpad=-20)
+        ax.set_ylim(z_lims)
+        ax.set_yticks(z_lims)
+        ax.set_yticklabels(z_lims)
+
+    if i < 3:
+        ax.set_xlabel(r'$\lambda$ (deg)', labelpad=-10)
+        ax.set_xlim(lon_lims)
+        ax.set_xticks(lon_lims)
+        ax.set_xticklabels(lon_lims)
+    else:
+        ax.set_xlabel(r'$\vartheta$ (deg)', labelpad=-10)
+        ax.set_xlim(lat_lims)
+        ax.set_xticks(lat_lims)
+        ax.set_xticklabels(lat_lims)
+
+# Save figure ------------------------------------------------------------------
+plt.suptitle(f't = {time:.1f} s', y=0.95)
+fig.subplots_adjust(wspace=0.25, hspace=0.1)
+plot_name = f'{plot_stem}_initial.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
+
+# ---------------------------------------------------------------------------- #
+# FINAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig, axarray = plt.subplots(1, 2, figsize=(12, 6))
+time_idx = -1
+time = data_file['time'][time_idx]
+
+for i, (ax, field_name, field_label, colour_scheme, contours,
+        to_remove, slice_along) in \
+        enumerate(zip(axarray.flatten(), final_field_names, final_field_labels,
+                      final_colour_schemes, final_contours,
+                      final_contours_to_remove, final_slice_along)):
+
+    # Data extraction ----------------------------------------------------------
+    if slice_along == 'z':
+        field_full = extract_gusto_field(data_file, field_name, time_idx)
+        coords_X_full, coords_Y_full, coords_Z_full = \
+            extract_gusto_coords(data_file, field_name)
+
+        # Reshape
+        field_full, coords_X_full, coords_Y_full, _ = \
+            reshape_gusto_data(field_full, coords_X_full,
+                               coords_Y_full, coords_Z_full)
+
+        # Take level for a horizontal slice
+        field_data = field_full[:, level]
+        # Abuse of names for coord variables but simplifies code below
+        coords_X = coords_X_full[:, level]
+        coords_Y = coords_Y_full[:, level]
+
+    else:
+        orig_field_data, orig_coords_X, orig_coords_Y, orig_coords_Z = \
+            extract_gusto_vertical_slice(
+                data_file, field_name, time_idx,
+                slice_along=slice_along, slice_at=slice_at_lat
+            )
+
+        # Slices need regridding as points don't cleanly live along lon or lat = 0.0
+        field_data, coords_X, coords_Y = \
+            regrid_vertical_slice(
+                plotting_grid[slice_along], slice_along, slice_at_lat,
+                orig_coords_X, orig_coords_Y, orig_coords_Z, orig_field_data
+            )
+        # Scale coordinates
+        coords_Y /= 1000.
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=to_remove)
+    cf, lines = plot_contoured_field(
+        ax, coords_X, coords_Y, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines
+    )
+
+    add_colorbar_ax(
+        fig, cf, field_label, location='bottom', cbar_labelpad=-10,
+        cbar_format=cbar_format[field_name]
+    )
+    if slice_along == 'z':
+        tomplot_field_title(
+            ax, r'$z = $ 5 km', minmax=True, field_data=field_data
+        )
+    elif slice_along == 'lat':
+        tomplot_field_title(
+            ax, r'$\vartheta = $ 0 deg', minmax=True, field_data=field_data
+        )
+
+    # Labels -------------------------------------------------------------------
+    if i == 0:
+        ax.set_xlabel(r'$\lambda$ (deg)', labelpad=-10)
+        ax.set_xlim(lon_lims)
+        ax.set_xticks(lon_lims)
+        ax.set_xticklabels(lon_lims)
+        ax.set_ylabel(r'$\vartheta$ (deg)', labelpad=-20)
+        ax.set_ylim(lat_lims)
+        ax.set_yticks(lat_lims)
+        ax.set_yticklabels(lat_lims)
+    else:
+        ax.set_xlabel(r'$\lambda$ (deg)', labelpad=-10)
+        ax.set_xlim(lon_lims)
+        ax.set_xticks(lon_lims)
+        ax.set_xticklabels(lon_lims)
+        ax.set_ylabel(r'$z$ (km)', labelpad=-20)
+        ax.set_ylim(z_lims)
+        ax.set_yticks(z_lims)
+        ax.set_yticklabels(z_lims)
+
+# Save figure ------------------------------------------------------------------
+plt.suptitle(f't = {time:.0f} s')
+fig.subplots_adjust(wspace=0.18)
+plot_name = f'{plot_stem}_final.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
diff --git a/plotting/shallow_water/plot_linear_williamson_2.py b/plotting/shallow_water/plot_linear_williamson_2.py
new file mode 100644
index 00000000..4851e018
--- /dev/null
+++ b/plotting/shallow_water/plot_linear_williamson_2.py
@@ -0,0 +1,172 @@
+"""
+Plots the linear Williamson 2 test case.
+
+The initial conditions are plotted:
+(a) the velocity field, (b) the depth field.
+
+And after 5 days, this plots:
+(a) the relative vorticity field, (b) the error in the depth field.
+"""
+from os.path import abspath, dirname
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import numpy as np
+from netCDF4 import Dataset
+from tomplot import (
+    set_tomplot_style, tomplot_contours, tomplot_cmap, plot_contoured_field,
+    add_colorbar_ax, plot_field_quivers, tomplot_field_title,
+    extract_gusto_coords, extract_gusto_field, regrid_horizontal_slice
+)
+
+test_name = 'linear_williamson_2'
+
+# ---------------------------------------------------------------------------- #
+# Directory for results and plots
+# ---------------------------------------------------------------------------- #
+# When copying this example these paths need editing, which will usually involve
+# removing the abspath part to set directory paths relative to this file
+results_file_name = f'{abspath(dirname(__file__))}/../../results/{test_name}/field_output.nc'
+plot_stem = f'{abspath(dirname(__file__))}/../../figures/{test_name}'
+
+# ---------------------------------------------------------------------------- #
+# Initial plot details
+# ---------------------------------------------------------------------------- #
+init_field_names = ['u', 'D']
+init_colour_schemes = ['Oranges', 'YlGnBu']
+init_field_labels = [r'$|u|$ (m s$^{-1}$)', r'$D$ (m)']
+init_contours_to_remove = [None, None, None]
+init_contours = [np.linspace(0, 40, 9),
+                 np.linspace(-2000, 0, 11)]
+
+# ---------------------------------------------------------------------------- #
+# Final plot details
+# ---------------------------------------------------------------------------- #
+final_field_names = ['RelativeVorticity', 'D_error']
+final_colour_schemes = ['RdBu_r', 'PiYG']
+final_field_labels = [r'$\zeta \ / $ s$^{-1}$', r'$\Delta D$ (m)']
+final_contours_to_remove = [0.0, 0.0]
+
+# ---------------------------------------------------------------------------- #
+# General options
+# ---------------------------------------------------------------------------- #
+projection = ccrs.Robinson()
+contour_method = 'contour'
+xlims = [-180, 180]
+ylims = [-90, 90]
+
+# We need to regrid onto lon-lat grid -- specify that here
+lon_1d = np.linspace(-180.0, 180.0, 120)
+lat_1d = np.linspace(-90, 90, 120)
+lon_2d, lat_2d = np.meshgrid(lon_1d, lat_1d, indexing='ij')
+
+# Things that are likely the same for all plots --------------------------------
+set_tomplot_style()
+data_file = Dataset(results_file_name, 'r')
+
+# ---------------------------------------------------------------------------- #
+# INITIAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(15, 5))
+time_idx = 0
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove, contours) in \
+    enumerate(zip(
+        init_field_names, init_colour_schemes,
+        init_field_labels, init_contours_to_remove, init_contours)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 2, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    if field_name == 'u':
+        zonal_data = extract_gusto_field(data_file, 'u_zonal', time_idx=time_idx)
+        meridional_data = extract_gusto_field(data_file, 'u_meridional', time_idx=time_idx)
+        field_data = np.sqrt(zonal_data**2 + meridional_data**2)
+        coords_X, coords_Y = extract_gusto_coords(data_file, 'u_zonal')
+
+    else:
+        field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+        coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(ax, cf, field_label, location='bottom', cbar_labelpad=-10)
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+    # Add quivers --------------------------------------------------------------
+    if field_name == 'u':
+        # Need to re-grid to lat-lon grid to get sensible looking quivers
+        regrid_zonal_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, zonal_data,
+            periodic_fix='sphere'
+        )
+        regrid_meridional_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, meridional_data,
+            periodic_fix='sphere'
+        )
+        plot_field_quivers(
+            ax, lon_2d, lat_2d, regrid_zonal_data, regrid_meridional_data,
+            spatial_filter_step=6, magnitude_filter=1.0,
+            projection=ccrs.PlateCarree()
+        )
+
+# Save figure ------------------------------------------------------------------
+fig.subplots_adjust(wspace=0.25)
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_initial.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
+
+# ---------------------------------------------------------------------------- #
+# FINAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(15, 5))
+time_idx = -1
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove) in \
+    enumerate(zip(
+        final_field_names, final_colour_schemes,
+        final_field_labels, final_contours_to_remove)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 2, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+    coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    contours = tomplot_contours(field_data)
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(ax, cf, field_label, location='bottom', cbar_labelpad=-10)
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+# Save figure ------------------------------------------------------------------
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_final.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
diff --git a/plotting/shallow_water/plot_moist_convective_williamson_2.py b/plotting/shallow_water/plot_moist_convective_williamson_2.py
new file mode 100644
index 00000000..3c0a4254
--- /dev/null
+++ b/plotting/shallow_water/plot_moist_convective_williamson_2.py
@@ -0,0 +1,186 @@
+"""
+Plots the moist convective Williamson 2 test case.
+
+The initial conditions are plotted:
+(a) the velocity field, (b) the depth field, (c) water vapour field.
+
+And after 5 days, this plots:
+(a) relative vorticity, (b) depth error (c) cloud.
+"""
+from os.path import abspath, dirname
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import numpy as np
+from netCDF4 import Dataset
+from tomplot import (
+    set_tomplot_style, tomplot_contours, tomplot_cmap, plot_contoured_field,
+    add_colorbar_ax, plot_field_quivers, tomplot_field_title,
+    extract_gusto_coords, extract_gusto_field, regrid_horizontal_slice
+)
+
+test_name = 'moist_convective_williamson_2'
+
+# ---------------------------------------------------------------------------- #
+# Directory for results and plots
+# ---------------------------------------------------------------------------- #
+# When copying this example these paths need editing, which will usually involve
+# removing the abspath part to set directory paths relative to this file
+results_file_name = f'{abspath(dirname(__file__))}/../../results/{test_name}/field_output.nc'
+plot_stem = f'{abspath(dirname(__file__))}/../../figures/shallow_water/{test_name}'
+
+# ---------------------------------------------------------------------------- #
+# Initial plot details
+# ---------------------------------------------------------------------------- #
+init_field_names = ['u', 'D', 'water_vapour']
+init_colour_schemes = ['Oranges', 'YlGnBu', 'Purples']
+init_field_labels = [r'$|u|$ (m s$^{-1}$)', r'$D$ (m)', r'$m_v$ (kg kg$^{-1}$)']
+init_contours_to_remove = [None, None, None]
+init_contours = [np.linspace(0, 20, 9),
+                 np.linspace(1900, 3100, 13),
+                 np.linspace(0, 0.012, 13)]
+init_data_formats = ['1.0f', '1.0f', '1.2e']
+
+# ---------------------------------------------------------------------------- #
+# Final plot details
+# ---------------------------------------------------------------------------- #
+final_field_names = ['RelativeVorticity', 'D_error', 'cloud_water']
+final_colour_schemes = ['RdBu_r', 'PiYG', 'Blues']
+final_field_labels = [r'$\zeta \ / $ s$^{-1}$', r'$\Delta D$ (m)', r'$m_{cl}$ (kg kg$^{-1}$)']
+final_contours_to_remove = [0.0, 0.0, None]
+final_data_formats = ['1.1e', '1.1f', '1.2e']
+
+# ---------------------------------------------------------------------------- #
+# General options
+# ---------------------------------------------------------------------------- #
+projection = ccrs.Robinson()
+contour_method = 'contour'
+xlims = [-180, 180]
+ylims = [-90, 90]
+
+# We need to regrid onto lon-lat grid -- specify that here
+lon_1d = np.linspace(-180.0, 180.0, 120)
+lat_1d = np.linspace(-90, 90, 120)
+lon_2d, lat_2d = np.meshgrid(lon_1d, lat_1d, indexing='ij')
+
+# Things that are likely the same for all plots --------------------------------
+set_tomplot_style()
+data_file = Dataset(results_file_name, 'r')
+
+# ---------------------------------------------------------------------------- #
+# INITIAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(21, 5))
+time_idx = 0
+
+for i, (field_name, colour_scheme, field_label,
+        contour_to_remove, contours, data_format) in \
+    enumerate(zip(
+        init_field_names, init_colour_schemes, init_field_labels,
+        init_contours_to_remove, init_contours, init_data_formats)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 3, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    if field_name == 'u':
+        zonal_data = extract_gusto_field(data_file, 'u_zonal', time_idx=time_idx)
+        meridional_data = extract_gusto_field(data_file, 'u_meridional', time_idx=time_idx)
+        field_data = np.sqrt(zonal_data**2 + meridional_data**2)
+        coords_X, coords_Y = extract_gusto_coords(data_file, 'u_zonal')
+
+    else:
+        field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+        coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(
+        ax, cf, field_label, location='bottom', cbar_labelpad=-10,
+        cbar_format=data_format
+    )
+    tomplot_field_title(
+        ax, None, minmax=True, field_data=field_data, minmax_format=data_format
+    )
+
+    # Add quivers --------------------------------------------------------------
+    if field_name == 'u':
+        # Need to re-grid to lat-lon grid to get sensible looking quivers
+        regrid_zonal_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, zonal_data,
+            periodic_fix='sphere'
+        )
+        regrid_meridional_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, meridional_data,
+            periodic_fix='sphere'
+        )
+        plot_field_quivers(
+            ax, lon_2d, lat_2d, regrid_zonal_data, regrid_meridional_data,
+            spatial_filter_step=6, magnitude_filter=1.0,
+            projection=ccrs.PlateCarree()
+        )
+
+# Save figure ------------------------------------------------------------------
+fig.subplots_adjust(wspace=0.25)
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_initial.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
+
+# ---------------------------------------------------------------------------- #
+# FINAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(21, 5))
+time_idx = -1
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove, data_format) in \
+    enumerate(zip(
+        final_field_names, final_colour_schemes,
+        final_field_labels, final_contours_to_remove, final_data_formats)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 3, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+    coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    contours = tomplot_contours(field_data)
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(
+        ax, cf, field_label, location='bottom', cbar_labelpad=-10,
+        cbar_format=data_format
+    )
+    tomplot_field_title(
+        ax, None, minmax=True, field_data=field_data, minmax_format=data_format
+    )
+
+# Save figure ------------------------------------------------------------------
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_final.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
diff --git a/plotting/shallow_water/plot_moist_thermal_williamson_5.py b/plotting/shallow_water/plot_moist_thermal_williamson_5.py
new file mode 100644
index 00000000..9c5bf8ec
--- /dev/null
+++ b/plotting/shallow_water/plot_moist_thermal_williamson_5.py
@@ -0,0 +1,195 @@
+"""
+Plots the moist thermal Williamson 5 test case.
+
+The initial conditions are plotted:
+(a) velocity, (b) depth field,
+(c) buoyancy, (d) water vapour.
+
+And after 50 days, this plots:
+(a) relative vorticity, (b) free-surface height,
+(c) buoyancy, (d) cloud.
+"""
+from os.path import abspath, dirname
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import numpy as np
+from netCDF4 import Dataset
+from tomplot import (
+    set_tomplot_style, tomplot_cmap, plot_contoured_field,
+    add_colorbar_ax, plot_field_quivers, tomplot_field_title,
+    extract_gusto_coords, extract_gusto_field, regrid_horizontal_slice
+)
+
+test_name = 'moist_thermal_williamson_5'
+
+# ---------------------------------------------------------------------------- #
+# Directory for results and plots
+# ---------------------------------------------------------------------------- #
+# When copying this example these paths need editing, which will usually involve
+# removing the abspath part to set directory paths relative to this file
+results_file_name = f'{abspath(dirname(__file__))}/../../results/{test_name}/field_output.nc'
+plot_stem = f'{abspath(dirname(__file__))}/../../figures/shallow_water/{test_name}'
+
+# ---------------------------------------------------------------------------- #
+# Initial plot details
+# ---------------------------------------------------------------------------- #
+init_field_names = ['u', 'D', 'b', 'water_vapour']
+init_colour_schemes = ['Oranges', 'YlGnBu', 'PuRd_r', 'Purples']
+init_field_labels = [r'$|u|$ (m s$^{-1}$)', r'$D$ (m)',
+                     r'$b$ (m s$^{-2}$)', r'$m_v$ (kg kg$^{-1}$)']
+init_contours_to_remove = [None, None, None, None]
+init_contours = [np.linspace(0, 20, 9),
+                 np.linspace(3800, 6000, 13),
+                 np.linspace(8.8, 11.2, 13),
+                 np.linspace(0.0, 0.02, 11)]
+
+# ---------------------------------------------------------------------------- #
+# Final plot details
+# ---------------------------------------------------------------------------- #
+final_field_names = ['RelativeVorticity', 'D_plus_topography', 'b', 'cloud_water']
+final_colour_schemes = ['RdBu_r', 'YlGnBu', 'PuRd_r', 'Blues']
+final_field_labels = [r'$\zeta \ / $ s$^{-1}$', r'$D+B$ (m)',
+                      r'$b$ (m s$^{-2}$)', r'$m_{cl}$ (kg kg$^{-1}$)']
+final_contours_to_remove = [0.0, None, None, 0.0]
+final_contours = [np.linspace(-5e-5, 5e-5, 11),
+                  np.linspace(4800, 6000, 13),
+                  np.linspace(8.8, 11.2, 13),
+                  np.linspace(-5e-5, 5e-4, 12)]
+
+# ---------------------------------------------------------------------------- #
+# General options
+# ---------------------------------------------------------------------------- #
+projection = ccrs.Robinson()
+contour_method = 'contour'
+xlims = [-180, 180]
+ylims = [-90, 90]
+
+# We need to regrid onto lon-lat grid -- specify that here
+lon_1d = np.linspace(-180.0, 180.0, 120)
+lat_1d = np.linspace(-90, 90, 120)
+lon_2d, lat_2d = np.meshgrid(lon_1d, lat_1d, indexing='ij')
+
+cbar_format = {'RelativeVorticity': '1.1e',
+               'u': '1.0f',
+               'D': '1.0f',
+               'D_plus_topography': '1.0f',
+               'b': '1.1f',
+               'water_vapour': '1.2f',
+               'cloud_water': '1.1e'}
+
+# Things that are likely the same for all plots --------------------------------
+set_tomplot_style()
+data_file = Dataset(results_file_name, 'r')
+
+# ---------------------------------------------------------------------------- #
+# INITIAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(15, 10))
+time_idx = 0
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove, contours) in \
+    enumerate(zip(
+        init_field_names, init_colour_schemes,
+        init_field_labels, init_contours_to_remove, init_contours)):
+
+    # Make axes
+    ax = fig.add_subplot(2, 2, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    if field_name == 'u':
+        zonal_data = extract_gusto_field(data_file, 'u_zonal', time_idx=time_idx)
+        meridional_data = extract_gusto_field(data_file, 'u_meridional', time_idx=time_idx)
+        field_data = np.sqrt(zonal_data**2 + meridional_data**2)
+        coords_X, coords_Y = extract_gusto_coords(data_file, 'u_zonal')
+
+    else:
+        field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+        coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(
+        ax, cf, field_label, location='bottom', cbar_labelpad=-10,
+        cbar_format=cbar_format[field_name]
+    )
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+    # Add quivers --------------------------------------------------------------
+    if field_name == 'u':
+        # Need to re-grid to lat-lon grid to get sensible looking quivers
+        regrid_zonal_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, zonal_data,
+            periodic_fix='sphere'
+        )
+        regrid_meridional_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, meridional_data,
+            periodic_fix='sphere'
+        )
+        plot_field_quivers(
+            ax, lon_2d, lat_2d, regrid_zonal_data, regrid_meridional_data,
+            spatial_filter_step=6, magnitude_filter=1.0,
+            projection=ccrs.PlateCarree()
+        )
+
+# Save figure ------------------------------------------------------------------
+fig.subplots_adjust(wspace=0.25)
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_initial.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
+
+# ---------------------------------------------------------------------------- #
+# FINAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(15, 10))
+time_idx = -1
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove, contours) in \
+    enumerate(zip(
+        final_field_names, final_colour_schemes,
+        final_field_labels, final_contours_to_remove, final_contours)):
+
+    # Make axes
+    ax = fig.add_subplot(2, 2, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+    coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(
+        ax, cf, field_label, location='bottom', cbar_labelpad=-10,
+        cbar_format=cbar_format[field_name]
+    )
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+# Save figure ------------------------------------------------------------------
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_final.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
diff --git a/plotting/shallow_water/plot_shallow_water_1d_wave.py b/plotting/shallow_water/plot_shallow_water_1d_wave.py
new file mode 100644
index 00000000..256ac1c1
--- /dev/null
+++ b/plotting/shallow_water/plot_shallow_water_1d_wave.py
@@ -0,0 +1,102 @@
+"""
+Plots the fields from the 1D shallow water wave.
+
+This plots:
+(a) u @ t = 0 s, (b) v @ t = 0 s, (c) D @ t = 0 s
+(d) u @ t = 1 s, (e) v @ t = 1 s, (f) D @ t = 1 s
+"""
+from os.path import abspath, dirname
+import matplotlib.pyplot as plt
+import numpy as np
+from netCDF4 import Dataset
+import pandas as pd
+from tomplot import (
+    set_tomplot_style, tomplot_field_title, extract_gusto_coords,
+    extract_gusto_field
+)
+
+test = 'shallow_water_1d_wave'
+
+# ---------------------------------------------------------------------------- #
+# Directory for results and plots
+# ---------------------------------------------------------------------------- #
+# When copying this example these paths need editing, which will usually involve
+# removing the abspath part to set directory paths relative to this file
+results_file_name = f'{abspath(dirname(__file__))}/../../results/{test}/field_output.nc'
+plot_stem = f'{abspath(dirname(__file__))}/../../figures/shallow_water/{test}'
+
+# ---------------------------------------------------------------------------- #
+# Plot details
+# ---------------------------------------------------------------------------- #
+field_names = ['u', 'v', 'D', 'u', 'v', 'D']
+time_idxs = [0, 0, 0, -1, -1, -1]
+
+# ---------------------------------------------------------------------------- #
+# General options
+# ---------------------------------------------------------------------------- #
+xlims = [0, 2*np.pi]
+xlims_labels = [0, r'$2\pi$']
+
+ylims = {
+    'u': [-0.5, 0.5],
+    'v': [-0.5, 0.5],
+    'D': [8, 12]
+}
+field_labels = {
+    'u': r'$u$ (m s$^{-1}$)',
+    'v': r'$v$ (m s$^{-1}$)',
+    'D': r'$D$ (m)'
+}
+
+# Things that are likely the same for all plots --------------------------------
+set_tomplot_style()
+data_file = Dataset(results_file_name, 'r')
+
+# ---------------------------------------------------------------------------- #
+# PLOTTING
+# ---------------------------------------------------------------------------- #
+fig, axarray = plt.subplots(2, 3, figsize=(16, 6), sharex='all', sharey='col')
+
+for i, (ax, time_idx, field_name) in \
+        enumerate(zip(axarray.flatten(), time_idxs, field_names)):
+
+    # Data extraction ----------------------------------------------------------
+    field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+    coords_X = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx]
+
+    # Get coordinates in order
+    data_frame = pd.DataFrame({'coords': coords_X, 'field': field_data})
+    data_frame = data_frame.sort_values(by=['coords'])
+    coords_X = data_frame['coords']
+    field_data = data_frame['field']
+
+    # Convert coordinates to m
+    coords_X *= 1000.
+
+    # Plot data ----------------------------------------------------------------
+    ax.plot(coords_X, field_data, color='black', linestyle='-', marker='')
+
+    tomplot_field_title(
+        ax, f't = {time:.1f} s', minmax=True, field_data=field_data,
+        minmax_format='1.2f'
+    )
+
+    # Labels -------------------------------------------------------------------
+    ax.set_ylabel(field_labels[field_name], labelpad=-15)
+    ax.set_ylim(ylims[field_name])
+    ax.set_yticks(ylims[field_name])
+    ax.set_yticklabels(ylims[field_name])
+
+    if i > 2:
+        ax.set_xlabel(r'$x$ (m)', labelpad=-10)
+        ax.set_xlim(xlims)
+        ax.set_xticks(xlims)
+        ax.set_xticklabels(xlims_labels)
+
+# Save figure ------------------------------------------------------------------
+fig.subplots_adjust(wspace=0.2)
+plot_name = f'{plot_stem}.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
diff --git a/plotting/shallow_water/plot_thermal_williamson_2.py b/plotting/shallow_water/plot_thermal_williamson_2.py
new file mode 100644
index 00000000..32ab1483
--- /dev/null
+++ b/plotting/shallow_water/plot_thermal_williamson_2.py
@@ -0,0 +1,173 @@
+"""
+Plots the thermal Williamson 2 test case.
+
+The initial conditions are plotted:
+(a) the velocity field, (b) the depth field, (c) buoyancy field.
+
+And after 5 days, this plots:
+(a) relative vorticity, (b) depth error (c) buoyancy error.
+"""
+from os.path import abspath, dirname
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import numpy as np
+from netCDF4 import Dataset
+from tomplot import (
+    set_tomplot_style, tomplot_contours, tomplot_cmap, plot_contoured_field,
+    add_colorbar_ax, plot_field_quivers, tomplot_field_title,
+    extract_gusto_coords, extract_gusto_field, regrid_horizontal_slice
+)
+
+test_name = 'thermal_williamson_2'
+
+# ---------------------------------------------------------------------------- #
+# Directory for results and plots
+# ---------------------------------------------------------------------------- #
+# When copying this example these paths need editing, which will usually involve
+# removing the abspath part to set directory paths relative to this file
+results_file_name = f'{abspath(dirname(__file__))}/../../results/{test_name}/field_output.nc'
+plot_stem = f'{abspath(dirname(__file__))}/../../figures/{test_name}'
+
+# ---------------------------------------------------------------------------- #
+# Initial plot details
+# ---------------------------------------------------------------------------- #
+init_field_names = ['u', 'D', 'b']
+init_colour_schemes = ['Oranges', 'YlGnBu', 'PuRd_r']
+init_field_labels = [r'$|u|$ (m s$^{-1}$)', r'$D$ (m)', r'$b$ (m s$^{-2}$)']
+init_contours_to_remove = [None, None, None]
+init_contours = [np.linspace(0, 20, 9),
+                 np.linspace(1900, 3100, 13),
+                 np.linspace(9, 10, 11)]
+
+# ---------------------------------------------------------------------------- #
+# Final plot details
+# ---------------------------------------------------------------------------- #
+final_field_names = ['RelativeVorticity', 'D_error', 'b_error']
+final_colour_schemes = ['RdBu_r', 'PiYG', 'PuOr']
+final_field_labels = [r'$\zeta \ / $ s$^{-1}$', r'$\Delta D$ (m)', r'$\Delta b$ (m s$^{-2}$)']
+final_contours_to_remove = [0.0, 0.0, 0.0]
+
+# ---------------------------------------------------------------------------- #
+# General options
+# ---------------------------------------------------------------------------- #
+projection = ccrs.Robinson()
+contour_method = 'contour'
+xlims = [-180, 180]
+ylims = [-90, 90]
+
+# We need to regrid onto lon-lat grid -- specify that here
+lon_1d = np.linspace(-180.0, 180.0, 120)
+lat_1d = np.linspace(-90, 90, 120)
+lon_2d, lat_2d = np.meshgrid(lon_1d, lat_1d, indexing='ij')
+
+# Things that are likely the same for all plots --------------------------------
+set_tomplot_style()
+data_file = Dataset(results_file_name, 'r')
+
+# ---------------------------------------------------------------------------- #
+# INITIAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(21, 5))
+time_idx = 0
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove, contours) in \
+    enumerate(zip(
+        init_field_names, init_colour_schemes,
+        init_field_labels, init_contours_to_remove, init_contours)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 3, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    if field_name == 'u':
+        zonal_data = extract_gusto_field(data_file, 'u_zonal', time_idx=time_idx)
+        meridional_data = extract_gusto_field(data_file, 'u_meridional', time_idx=time_idx)
+        field_data = np.sqrt(zonal_data**2 + meridional_data**2)
+        coords_X, coords_Y = extract_gusto_coords(data_file, 'u_zonal')
+
+    else:
+        field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+        coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(ax, cf, field_label, location='bottom', cbar_labelpad=-10)
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+    # Add quivers --------------------------------------------------------------
+    if field_name == 'u':
+        # Need to re-grid to lat-lon grid to get sensible looking quivers
+        regrid_zonal_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, zonal_data,
+            periodic_fix='sphere'
+        )
+        regrid_meridional_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, meridional_data,
+            periodic_fix='sphere'
+        )
+        plot_field_quivers(
+            ax, lon_2d, lat_2d, regrid_zonal_data, regrid_meridional_data,
+            spatial_filter_step=6, magnitude_filter=1.0,
+            projection=ccrs.PlateCarree()
+        )
+
+# Save figure ------------------------------------------------------------------
+fig.subplots_adjust(wspace=0.25)
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_initial.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
+
+# ---------------------------------------------------------------------------- #
+# FINAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(21, 5))
+time_idx = -1
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove) in \
+    enumerate(zip(
+        final_field_names, final_colour_schemes,
+        final_field_labels, final_contours_to_remove)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 3, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+    coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    contours = tomplot_contours(field_data)
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(ax, cf, field_label, location='bottom', cbar_labelpad=-10)
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+# Save figure ------------------------------------------------------------------
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_final.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
diff --git a/plotting/shallow_water/plot_williamson_2.py b/plotting/shallow_water/plot_williamson_2.py
new file mode 100644
index 00000000..1f8eaef5
--- /dev/null
+++ b/plotting/shallow_water/plot_williamson_2.py
@@ -0,0 +1,172 @@
+"""
+Plots the Williamson 2 test case.
+
+The initial conditions are plotted:
+(a) the velocity field, (b) the depth field.
+
+And after 5 days, this plots:
+(a) the relative vorticity field, (b) the error in the depth field.
+"""
+from os.path import abspath, dirname
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import numpy as np
+from netCDF4 import Dataset
+from tomplot import (
+    set_tomplot_style, tomplot_contours, tomplot_cmap, plot_contoured_field,
+    add_colorbar_ax, plot_field_quivers, tomplot_field_title,
+    extract_gusto_coords, extract_gusto_field, regrid_horizontal_slice
+)
+
+test_name = 'williamson_2'
+
+# ---------------------------------------------------------------------------- #
+# Directory for results and plots
+# ---------------------------------------------------------------------------- #
+# When copying this example these paths need editing, which will usually involve
+# removing the abspath part to set directory paths relative to this file
+results_file_name = f'{abspath(dirname(__file__))}/../../results/{test_name}/field_output.nc'
+plot_stem = f'{abspath(dirname(__file__))}/../../figures/{test_name}'
+
+# ---------------------------------------------------------------------------- #
+# Initial plot details
+# ---------------------------------------------------------------------------- #
+init_field_names = ['u', 'D']
+init_colour_schemes = ['Oranges', 'YlGnBu']
+init_field_labels = [r'$|u|$ (m s$^{-1}$)', r'$D$ (m)']
+init_contours_to_remove = [None, None, None]
+init_contours = [np.linspace(0, 40, 9),
+                 np.linspace(4000, 6000, 9)]
+
+# ---------------------------------------------------------------------------- #
+# Final plot details
+# ---------------------------------------------------------------------------- #
+final_field_names = ['RelativeVorticity', 'D_error']
+final_colour_schemes = ['RdBu_r', 'PiYG']
+final_field_labels = [r'$\zeta \ / $ s$^{-1}$', r'$\Delta D$ (m)']
+final_contours_to_remove = [0.0, 0.0]
+
+# ---------------------------------------------------------------------------- #
+# General options
+# ---------------------------------------------------------------------------- #
+projection = ccrs.Robinson()
+contour_method = 'contour'
+xlims = [-180, 180]
+ylims = [-90, 90]
+
+# We need to regrid onto lon-lat grid -- specify that here
+lon_1d = np.linspace(-180.0, 180.0, 120)
+lat_1d = np.linspace(-90, 90, 120)
+lon_2d, lat_2d = np.meshgrid(lon_1d, lat_1d, indexing='ij')
+
+# Things that are likely the same for all plots --------------------------------
+set_tomplot_style()
+data_file = Dataset(results_file_name, 'r')
+
+# ---------------------------------------------------------------------------- #
+# INITIAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(15, 5))
+time_idx = 0
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove, contours) in \
+    enumerate(zip(
+        init_field_names, init_colour_schemes,
+        init_field_labels, init_contours_to_remove, init_contours)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 2, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    if field_name == 'u':
+        zonal_data = extract_gusto_field(data_file, 'u_zonal', time_idx=time_idx)
+        meridional_data = extract_gusto_field(data_file, 'u_meridional', time_idx=time_idx)
+        field_data = np.sqrt(zonal_data**2 + meridional_data**2)
+        coords_X, coords_Y = extract_gusto_coords(data_file, 'u_zonal')
+
+    else:
+        field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+        coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(ax, cf, field_label, location='bottom', cbar_labelpad=-10)
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+    # Add quivers --------------------------------------------------------------
+    if field_name == 'u':
+        # Need to re-grid to lat-lon grid to get sensible looking quivers
+        regrid_zonal_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, zonal_data,
+            periodic_fix='sphere'
+        )
+        regrid_meridional_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, meridional_data,
+            periodic_fix='sphere'
+        )
+        plot_field_quivers(
+            ax, lon_2d, lat_2d, regrid_zonal_data, regrid_meridional_data,
+            spatial_filter_step=6, magnitude_filter=1.0,
+            projection=ccrs.PlateCarree()
+        )
+
+# Save figure ------------------------------------------------------------------
+fig.subplots_adjust(wspace=0.25)
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_initial.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
+
+# ---------------------------------------------------------------------------- #
+# FINAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(15, 5))
+time_idx = -1
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove) in \
+    enumerate(zip(
+        final_field_names, final_colour_schemes,
+        final_field_labels, final_contours_to_remove)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 2, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+    coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    contours = tomplot_contours(field_data)
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(ax, cf, field_label, location='bottom', cbar_labelpad=-10)
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+# Save figure ------------------------------------------------------------------
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_final.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
diff --git a/plotting/shallow_water/plot_williamson_5.py b/plotting/shallow_water/plot_williamson_5.py
new file mode 100644
index 00000000..51ce0e80
--- /dev/null
+++ b/plotting/shallow_water/plot_williamson_5.py
@@ -0,0 +1,184 @@
+"""
+Plots the Williamson 5 test case.
+
+The initial conditions are plotted:
+(a) the velocity field, (b) the depth field.
+
+And after 50 days, this plots:
+(a) the relative vorticity field, (b) free-surface height.
+"""
+from os.path import abspath, dirname
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import numpy as np
+from netCDF4 import Dataset
+from tomplot import (
+    set_tomplot_style, tomplot_cmap, plot_contoured_field,
+    add_colorbar_ax, plot_field_quivers, tomplot_field_title,
+    extract_gusto_coords, extract_gusto_field, regrid_horizontal_slice
+)
+
+test_name = 'williamson_5'
+
+# ---------------------------------------------------------------------------- #
+# Directory for results and plots
+# ---------------------------------------------------------------------------- #
+# When copying this example these paths need editing, which will usually involve
+# removing the abspath part to set directory paths relative to this file
+results_file_name = f'{abspath(dirname(__file__))}/../../results/{test_name}/field_output.nc'
+plot_stem = f'{abspath(dirname(__file__))}/../../figures/shallow_water/{test_name}'
+
+# ---------------------------------------------------------------------------- #
+# Initial plot details
+# ---------------------------------------------------------------------------- #
+init_field_names = ['u', 'D']
+init_colour_schemes = ['Oranges', 'YlGnBu']
+init_field_labels = [r'$|u|$ (m s$^{-1}$)', r'$D$ (m)']
+init_contours_to_remove = [None, None, None]
+init_contours = [np.linspace(0, 20, 9),
+                 np.linspace(3800, 6000, 12)]
+
+# ---------------------------------------------------------------------------- #
+# Final plot details
+# ---------------------------------------------------------------------------- #
+final_field_names = ['RelativeVorticity', 'D_plus_topography']
+final_colour_schemes = ['RdBu_r', 'PiYG']
+final_field_labels = [r'$\zeta \ / $ s$^{-1}$', r'$D+B$ (m)']
+final_contours = [np.linspace(-1e-4, 1e-4, 21),
+                  np.linspace(5000, 6000, 11)]
+final_contours_to_remove = [0.0, None]
+
+# ---------------------------------------------------------------------------- #
+# General options
+# ---------------------------------------------------------------------------- #
+projection = ccrs.Robinson()
+contour_method = 'contour'
+xlims = [-180, 180]
+ylims = [-90, 90]
+
+cbar_format = {'RelativeVorticity': '1.1e',
+               'u': '1.0f',
+               'D': '1.0f',
+               'D_plus_topography': '1.0f'}
+
+# We need to regrid onto lon-lat grid -- specify that here
+lon_1d = np.linspace(-180.0, 180.0, 120)
+lat_1d = np.linspace(-90, 90, 120)
+lon_2d, lat_2d = np.meshgrid(lon_1d, lat_1d, indexing='ij')
+
+# Things that are likely the same for all plots --------------------------------
+set_tomplot_style()
+data_file = Dataset(results_file_name, 'r')
+
+# ---------------------------------------------------------------------------- #
+# INITIAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(15, 5))
+time_idx = 0
+
+for i, (field_name, colour_scheme, field_label, contour_to_remove, contours) in \
+    enumerate(zip(
+        init_field_names, init_colour_schemes,
+        init_field_labels, init_contours_to_remove, init_contours)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 2, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    if field_name == 'u':
+        zonal_data = extract_gusto_field(data_file, 'u_zonal', time_idx=time_idx)
+        meridional_data = extract_gusto_field(data_file, 'u_meridional', time_idx=time_idx)
+        field_data = np.sqrt(zonal_data**2 + meridional_data**2)
+        coords_X, coords_Y = extract_gusto_coords(data_file, 'u_zonal')
+
+    else:
+        field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+        coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(
+        ax, cf, field_label, location='bottom', cbar_labelpad=-10,
+        cbar_format=cbar_format[field_name]
+    )
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+    # Add quivers --------------------------------------------------------------
+    if field_name == 'u':
+        # Need to re-grid to lat-lon grid to get sensible looking quivers
+        regrid_zonal_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, zonal_data,
+            periodic_fix='sphere'
+        )
+        regrid_meridional_data = regrid_horizontal_slice(
+            lon_2d, lat_2d, coords_X, coords_Y, meridional_data,
+            periodic_fix='sphere'
+        )
+        plot_field_quivers(
+            ax, lon_2d, lat_2d, regrid_zonal_data, regrid_meridional_data,
+            spatial_filter_step=6, magnitude_filter=1.0,
+            projection=ccrs.PlateCarree()
+        )
+
+# Save figure ------------------------------------------------------------------
+fig.subplots_adjust(wspace=0.25)
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_initial.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()
+
+# ---------------------------------------------------------------------------- #
+# FINAL PLOTTING
+# ---------------------------------------------------------------------------- #
+fig = plt.figure(figsize=(15, 5))
+time_idx = -1
+
+for i, (field_name, colour_scheme, field_label, contours, contour_to_remove) in \
+    enumerate(zip(
+        final_field_names, final_colour_schemes,
+        final_field_labels, final_contours, final_contours_to_remove)):
+
+    # Make axes
+    ax = fig.add_subplot(1, 2, 1+i, projection=projection)
+
+    # Data extraction ----------------------------------------------------------
+    field_data = extract_gusto_field(data_file, field_name, time_idx=time_idx)
+    coords_X, coords_Y = extract_gusto_coords(data_file, field_name)
+    time = data_file['time'][time_idx] / (24.*60.*60.)
+
+    # Regrid onto lon-lat grid
+    field_data = regrid_horizontal_slice(
+        lon_2d, lat_2d, coords_X, coords_Y, field_data, periodic_fix='sphere'
+    )
+
+    # Plot data ----------------------------------------------------------------
+    cmap, lines = tomplot_cmap(contours, colour_scheme, remove_contour=contour_to_remove)
+    cf, _ = plot_contoured_field(
+        ax, lon_2d, lat_2d, field_data, contour_method, contours,
+        cmap=cmap, line_contours=lines, projection=projection
+    )
+
+    add_colorbar_ax(
+        ax, cf, field_label, location='bottom', cbar_labelpad=-10,
+        cbar_format=cbar_format[field_name]
+    )
+    tomplot_field_title(ax, None, minmax=True, field_data=field_data)
+
+# Save figure ------------------------------------------------------------------
+plt.suptitle(f't = {time:.1f} days')
+plot_name = f'{plot_stem}_final.png'
+print(f'Saving figure to {plot_name}')
+fig.savefig(plot_name, bbox_inches='tight')
+plt.close()