def orog_dif_plot():
'''Plot spatial differences between MetUM model output using default and updated orography and coastline files during
foehn and non-foehn conditions. Thesis Figure 4.8. '''
# Load necessary files
old_orog = iris.load_cube('/data/clivarm/wip/ellgil82/new_ancils/km1p5/orog/orog_original.nc', 'OROGRAPHY (/STRAT LOWER BC)')[0,0,:,:]
new_orog = iris.load_cube('/data/clivarm/wip/ellgil82/new_ancils/km1p5/orog/new_orog_smoothed.nc', 'Height')[0, 0,:, :]
old_lsm = iris.load_cube('/data/clivarm/wip/ellgil82/new_ancils/km1p5/lsm/lsm_original.nc', 'LAND MASK (No halo) (LAND=TRUE)')[0, 0, :, :]
new_lsm = iris.load_cube('/data/clivarm/wip/ellgil82/new_ancils/km1p5/lsm/new_mask.nc', 'LAND MASK (No halo) (LAND=TRUE)')[0,0,:,:]
# Set up figure
fig, ax = plt.subplots(1, 1, figsize=(10,11.5))
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.tick_params(axis='both', which='both', length=0, labelbottom='off', labelleft='off')
# Plot Cabinet Inlet AWS
cube = iris.load_cube('/data/clivarm/wip/ellgil82/May_2016/Re-runs/CS2/20160522T1200Z_Peninsula_km1p5_ctrl_pa000.pp','surface_altitude')
real_lon, real_lat = rotate_data(cube, 0, 1)
ax.plot(-63.37105, -66.48272, markersize=15, marker='o', color='#f68080', zorder=10)
# Calculate differences
orog_dif = new_orog.data - old_orog.data
lsm_dif = new_lsm.data - old_lsm.data
# Mask data where no difference is seen
orog_dif = ma.masked_where((lsm_dif == 0) & (new_lsm.data == 0), orog_dif)
lsm_dif = ma.masked_where(lsm_dif == 0, lsm_dif)
# Truncate colormap to minimise visual impact of one or two extreme values
squished_bwr = shiftedColorMap(cmap=matplotlib.cm.bwr, min_val=-800, max_val=800, name='squished_bwr', var=orog_dif, start = .15, stop = .85)
# Plot differences between old and new orography and LSM
c = ax.pcolormesh(real_lon, real_lat, orog_dif, cmap='squished_bwr', vmin=-800, vmax=800, zorder = 1)
lsm = ax.contourf(real_lon, real_lat, lsm_dif, cmap = 'bwr', vmax = 1, vmin = -1, zorder = 2)
# Add new LSM and 25 m orography contour
ax.contour(real_lon, real_lat, new_lsm.data, lw=3, colors='dimgrey', zorder = 3)
ax.contour(real_lon, real_lat, new_orog.data, lw = 2, levels = [100], colors = 'dimgrey', zorder = 4)
# Set up colour bar
cbaxes = fig.add_axes([0.22, 0.12, 0.56, 0.03])
cbticks = np.linspace(-800, 800, 4)
cbticklabs = [-800, 0, 800]
cb = plt.colorbar(c, cax=cbaxes, orientation='horizontal', ticks=cbticks)
cb.set_ticks(cbticks, cbticklabs)
cb.ax.set_xlabel('Surface elevation difference (m)', fontsize=30, labelpad=20, color='dimgrey')
cb.ax.text(-0.3, 2.2, 'Area removed \nfrom new LSM', fontsize = 30, color = 'dimgrey')
cb.ax.text(0.78, 2.2, 'Area added \nto new LSM', fontsize = 30, color = 'dimgrey')
cb.outline.set_edgecolor('dimgrey')
cb.outline.set_linewidth(2)
cb.solids.set_edgecolor('face')
cb.ax.tick_params(labelsize=30, tick1On=False, tick2On=False, labelcolor='dimgrey', pad=10)
[l.set_visible(False) for (w, l) in enumerate(cb.ax.xaxis.get_ticklabels()) if w % 3 != 0]
plt.subplots_adjust(bottom=0.27, left=0.11, right=0.89, top=0.95, hspace=0.05)
plt.savefig('/users/ellgil82/figures/new_ancils/orog_difs_km1p5.png', transparent = True)
plt.savefig('/users/ellgil82/figures/new_ancils/orog_difs_km1p5.eps', transparent = True)
plt.savefig('/users/ellgil82/figures/new_ancils/orog_difs_km1p5.pdf', transparent=True)
plt.show()
def mask_difs():
difs = glm_SIC.data-ERA_SIC.data
difs[glm_SIC==0] = 0
difs_m = np.ma.masked_equal(difs, 0)
difs_m = np.ma.masked_greater_equal(difs, -0.001)
fig, ax = plt.subplots(1,1, figsize = (12,9))
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.tick_params(which = 'both', tick1On = 'False', tick2On = 'False')
ax.axis('off')
plt.setp(ax.get_yticklabels(), visible=False)
plt.setp(ax.get_xticklabels(), visible = False)
bwr_zero = shiftedColorMap(cmap=matplotlib.cm.bwr, min_val=-2.5, max_val=3.5, name='bwr_zero', var='s', start=0., stop=1.)
GLM = ax.contourf(glm_SST.data, cmap=bwr_zero, vmin=-2.5, vmax=3.5)
difs = ax.contourf(difs_m, cmap = 'viridis')
lsm1 = ax.contour(glm_LSM.data, colors='dimgrey', linewidths=3, levels=[0])
thresh1 = ax.contour(glm_SIC.data, levels =[0.1], linewidths=5, colors = 'r', zorder = 9, )
thresh2 = ax.contour(ERA_SIC.data, levels= [0] , linewidths=5,colors = 'green')
CBarXTicks = [-2, 0, 2]
SST_axes = fig.add_axes([0.1, 0.1, 0.03, 0.7]) #left, bottom, width, height
dif_axes = fig.add_axes([0.82, 0.1, 0.03, 0.7])
CBar1 = plt.colorbar(GLM, cax=SST_axes, orientation='vertical', ticks=CBarXTicks) #
ax.text(-0.25, 1.1, s = 'SST ($^{\circ}$C)', transform = ax.transAxes, rotation = 0, color = 'dimgrey', fontsize = '34')
CBar2 = plt.colorbar(difs, cax = dif_axes, orientation='vertical', ticks = [0, -0.05, -0.1])
ax.text(0.98, 1.1, s = 'glm SIC - \nERA SIC', transform = ax.transAxes, rotation = 0, color = 'dimgrey', fontsize = '34')
for CBar in [CBar1, CBar2]:
CBar.solids.set_edgecolor("face")
CBar.outline.set_edgecolor('dimgrey')
CBar.ax.tick_params(which='both', axis='both', labelsize=34, labelcolor='dimgrey', pad=10, size=0, tick1On=False, tick2On=False)
CBar.outline.set_linewidth(2)
lns = [Line2D([0], [0], color='red', linewidth=5),
Line2D([0], [0], color='green', linewidth=5)]
labs = ['glm SIC = 0.1','ERA SIC edge']# ' ',' '
lgd = plt.legend(lns, labs, ncol=1, bbox_to_anchor=(-6,1.2), borderaxespad=0., prop={'size': 24})
for ln in lgd.get_texts():
plt.setp(ln, color='dimgrey')
lgd.get_frame().set_linewidth(0.0)
plt.subplots_adjust(left = 0.2, bottom = 0.1, right = 0.78, top = 0.8)
plt.show()
def plot_SMB(SMBvar, comp_dict, region, mean_or_SD, calc):
range_dict = {
'pr': (0, 1000),
'sbl': (0, 300),
'snm': (0, 10),
'mrro': (0, 10),
'SMB': (-200, 1000),
'evap': (0, 300)
}
fig = plt.figure(figsize=[10, 6])
ax = plt.subplot(1, 1, 1, projection=ccrs.SouthPolarStereo())
fig.subplots_adjust(bottom=0.05,
top=0.9,
left=0.04,
right=0.85,
wspace=0.02)
# Limit the map to -60 degrees latitude and below.
ax.set_extent([-180, 180, -90, -59.5], ccrs.PlateCarree())
lsm_full = iris.load_cube(filepath + 'sftlf.nc')
if region == 'AIS':
lsm = lsm_full
else:
lsm = iris.load_cube(filepath + 'sftlf.nc', region)
gridlons = lsm.coord('longitude').contiguous_bounds()
gridlats = lsm.coord('latitude').contiguous_bounds()
ax.spines['geo'].set_visible(False)
if calc == 'yes':
if mean_or_SD == 'mean':
mean_data = comp_dict[SMBvar].collapsed('time',
iris.analysis.MEAN).data
elif mean_or_SD == 'SD':
mean_data = comp_dict[SMBvar].collapsed('time',
iris.analysis.STD_DEV).data
else:
mean_data = comp_dict[SMBvar]
squished_cmap = shiftedColorMap(cmap=matplotlib.cm.coolwarm_r,
min_val=range_dict[SMBvar][0],
max_val=range_dict[SMBvar][1],
name='squished_cmap',
var=mean_data,
start=0.3,
stop=0.7)
f = ax.pcolormesh(gridlons,
gridlats,
np.ma.masked_where(lsm.data == 0, mean_data),
transform=ccrs.PlateCarree(),
cmap=squished_cmap,
vmin=range_dict[SMBvar][0],
vmax=range_dict[SMBvar][1])
ax.contour(lsm_full.coord('longitude').points,
lsm_full.coord('latitude').points,
lsm_full.data > 0,
levels=[0],
linewidths=2,
colors='k',
transform=ccrs.PlateCarree())
CbAx = fig.add_axes([0.85, 0.15, 0.02, 0.6])
cb = plt.colorbar(f,
orientation='vertical',
cax=CbAx,
extend='both',
ticks=[
range_dict[SMBvar][0], 0, range_dict[SMBvar][1] / 2,
range_dict[SMBvar][1]
]) #shrink = 0.8,
cb.solids.set_edgecolor("face")
cb.outline.set_edgecolor('dimgrey')
cb.ax.tick_params(which='both',
axis='both',
labelsize=20,
labelcolor='dimgrey',
pad=10,
size=0,
tick1On=False,
tick2On=False)
cb.outline.set_linewidth(2)
cb.ax.set_title('Annual mean\n' + SMBvar + ' (kg m$^{-2}$ yr$^{-1}$)',
fontname='Helvetica',
color='dimgrey',
fontsize=20,
pad=20)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
y_inline=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle=':')
gl.xlabel_style = {
'color': 'dimgrey',
'size': 20,
}
gl.ylabel_style = {
'color': 'dimgrey',
'size': 20,
}
plt.savefig(filepath + 'ERA-Interim_historical_' + mean_or_SD + SMBvar +
string_dict[region] + '.png')
plt.show()
def plot_SMB_components(region, mean_or_SD, components):
orog_full = iris.load_cube(filepath + 'orog.nc')
lsm_full = iris.load_cube(filepath + 'sftlf.nc')
if region == 'AIS':
lsm = lsm_full
else:
lsm = iris.load_cube(filepath + 'sftlf.nc', region)
lsm.coord('latitude').guess_bounds()
lsm.coord('longitude').guess_bounds()
# turn the iris Cube data structure into numpy arrays
gridlons = lsm.coord('longitude').contiguous_bounds()
gridlats = lsm.coord('latitude').contiguous_bounds()
projection = ccrs.SouthPolarStereo()
axes_class = (GeoAxes, dict(map_projection=projection))
fig = plt.figure(figsize=(20, 14))
axgr = AxesGrid(
fig,
111,
axes_class=axes_class,
nrows_ncols=(2, 3),
axes_pad=2,
#cbar_location='right',
#cbar_mode='single',
#cbar_pad=0.4,
#cbar_size='2.5%',
label_mode='')
titles = ['pr', 'evapsbl', 'sbl', 'mrro', 'snm', 'SMB',
'SD of SMB'] # Will miss SD SMB out if sbl inc.
maxvals = [3000, 100, 100, 100, 10, 3000, 300]
minvals = [-100, -100, -100, -100, -10, -100, -10]
for i, ax in enumerate(axgr):
ax.set_extent([-180, 180, -90, -59.5], ccrs.PlateCarree())
squished_cmap = shiftedColorMap(cmap=matplotlib.cm.coolwarm_r,
min_val=-200.,
max_val=2000.,
name='squished_cmap',
var=components[i],
start=0.3,
stop=0.7)
ax.contour(lsm_full.coord('longitude').points,
lsm_full.coord('latitude').points,
lsm_full.data > 0,
levels=[0],
linewidths=2,
colors='k',
transform=ccrs.PlateCarree())
f = ax.pcolormesh(gridlons,
gridlats,
np.ma.masked_where(lsm.data == 0, components[i]),
norm=colors.SymLogNorm(linthresh=0.1,
linscale=0.1,
vmin=minvals[i],
vmax=maxvals[i]),
cmap='coolwarm_r',
transform=ccrs.PlateCarree())
ax.spines['geo'].set_visible(False)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
y_inline=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle=':')
gl.xlabel_style = {
'color': 'dimgrey',
'size': 20,
}
gl.ylabel_style = {
'color': 'dimgrey',
'size': 20,
}
ax.set_title(titles[i],
fontsize=24,
fontweight='bold',
color='dimgrey',
pad=20)
cb = plt.colorbar(f, extend='both', orientation='vertical', ax=ax)
cb.solids.set_edgecolor("face")
cb.outline.set_edgecolor('dimgrey')
cb.ax.tick_params(which='both',
axis='both',
labelsize=20,
labelcolor='dimgrey',
pad=10,
size=0,
tick1On=False,
tick2On=False)
cb.outline.set_linewidth(2)
#cb = axgr.cbar_axes[0].colorbar(f, extend='both')#, shrink = 0.8), orientation='vertical',
#CbAx = fig.add_axes([0.9, 0.15, 0.015, 0.6])
#cb = fig.colorbar(f, extend = 'both', orientation = 'vertical', cax = CbAx)
#cb.solids.set_edgecolor("face")
#cb.outline.set_edgecolor('dimgrey')
#cb.ax.tick_params(which='both', axis='both', labelsize=20, labelcolor='dimgrey', pad=10, size=0, tick1On=False,
# tick2On=False)
#cb.outline.set_linewidth(2)
#cb.ax.set_title('Annual mean\n (kg m$^{-2}$ yr$^{-1}$)', fontname='Helvetica', color='dimgrey', fontsize=20,
# pad=20)
fig.subplots_adjust(bottom=0.05,
top=0.9,
left=0.05,
right=0.85,
wspace=0.18,
hspace=0.18)
plt.savefig(filepath + 'ERA-Interim_historical_SMB_components_' +
mean_or_SD + '_' + string_dict[region] + '.png')
plt.show()
def plot_synop_composite(cf_var, c_var, u_var, v_var):
fig = plt.figure(frameon=False, figsize=(
9,
10)) # !!change figure dimensions when you have a larger model domain
fig.patch.set_visible(False)
ax = fig.add_subplot(111) #, projection=ccrs.PlateCarree())
plt.axis = 'off'
PlotLonMin = np.min(var_dict['lon'][120:170])
PlotLonMax = np.max(var_dict['lon'][120:170])
PlotLatMin = np.min(var_dict['lat'][130:175])
PlotLatMax = np.max(var_dict['lat'][130:175])
XTicks = np.linspace(PlotLonMin, PlotLonMax, 3)
XTickLabels = [None] * len(XTicks)
for i, XTick in enumerate(XTicks):
if XTick < 0:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick), '$^{\circ}$W')
else:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick),
'$^{\circ}$E') #
plt.xticks(XTicks, XTickLabels)
ax.set_xlim(PlotLonMin, PlotLonMax)
ax.tick_params(which='both', pad=10, labelsize=34, color='dimgrey')
YTicks = np.linspace(PlotLatMin, PlotLatMax, 3)
YTickLabels = [None] * len(YTicks)
for i, YTick in enumerate(YTicks):
if YTick < 0:
YTickLabels[i] = '{:.0f}{:s}'.format(np.abs(YTick), '$^{\circ}$S')
else:
YTickLabels[i] = '{:.0f}{:s}'.format(np.abs(YTick), '$^{\circ}$N')
plt.yticks(YTicks, YTickLabels)
ax.set_ylim(PlotLatMin, PlotLatMax)
ax.tick_params(which='both',
axis='both',
labelsize=34,
labelcolor='dimgrey',
pad=10,
size=0,
tick1On=False,
tick2On=False)
bwr_zero = shiftedColorMap(cmap=matplotlib.cm.bwr,
min_val=-15.,
max_val=2.,
name='bwr_zero',
var=cf_var.data,
start=0.15,
stop=0.85) #-6, 3
xlon, ylat = np.meshgrid(var_dict['lon'][120:170],
var_dict['lat'][130:175])
c = ax.pcolormesh(xlon,
ylat,
cf_var,
cmap=bwr_zero,
vmin=-15.,
vmax=2.,
zorder=1) # latlon=True, transform=ccrs.PlateCarree(),
cs = ax.contour(xlon,
ylat,
np.ma.masked_where(
var_dict['lsm'].data[130:175, 120:170] == 1, c_var),
latlon=True,
colors='k',
zorder=4)
ax.clabel(cs, inline=True, fontsize=24, inline_spacing=30, fmt='%1.0f')
coast = ax.contour(xlon,
ylat,
var_dict['lsm'].data[130:175, 120:170],
levels=[0],
colors='#222222',
lw=2,
latlon=True,
zorder=2)
topog = ax.contour(xlon,
ylat,
var_dict['orog'].data[130:175, 120:170],
levels=[50],
colors='#222222',
linewidth=1.5,
latlon=True,
zorder=3)
CBarXTicks = [
-15, 0, 2
] # CLevs[np.arange(0,len(CLevs),int(np.ceil(len(CLevs)/5.)))]
CBAxes = fig.add_axes([0.22, 0.2, 0.6, 0.025])
CBar = plt.colorbar(c,
cax=CBAxes,
orientation='horizontal',
ticks=CBarXTicks,
extend='both') #
CBar.set_label('Mean daily maximum 1.5 m \nair temperature ($^{\circ}$C)',
fontsize=34,
labelpad=10,
color='dimgrey')
CBar.solids.set_edgecolor("face")
CBar.outline.set_edgecolor('dimgrey')
CBar.ax.tick_params(which='both',
axis='both',
labelsize=34,
labelcolor='dimgrey',
pad=10,
size=0,
tick1On=False,
tick2On=False)
CBar.outline.set_linewidth(2)
x, y = np.meshgrid(var_dict['lon'], var_dict['lat'])
q = ax.quiver(x[::8, ::8],
y[::8, ::8],
u_var[::8, ::8],
v_var[::8, ::8],
pivot='middle',
scale=50,
zorder=5,
width=0.006)
plt.quiverkey(
q,
0.25,
0.9,
5,
r'$5$ $m$ $s^{-1}$',
labelpos='N',
color='dimgrey',
labelcolor='dimgrey',
fontproperties={
'size': '32',
'weight': 'bold'
},
coordinates='figure',
)
plt.subplots_adjust(left=0.2, bottom=0.33, right=0.85)
plt.savefig(
'/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/synoptic_cond_Tair_max_LarsenB_melt_period_zoomed.png'
)
plt.savefig(
'/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/synoptic_cond_Tair_max_LarsenB_melt_period_zoomed.eps'
)
plt.show()
def subplots_simple(var, clim, savefig):
fig, ax = plt.subplots(1,3, figsize = (22,8))
ax.flatten()
if clim == 'True' or clim == 'yes':
case_list = ['Climatology', 'GLM', 'LAM']
else:
case_list = ['ERA-Interim', 'GLM', 'LAM']
lab_dict = {0: 'a', 1: 'b', 2: 'c'}
for a in [0, 1, 2]:
ax[a].spines['right'].set_visible(False)
ax[a].spines['left'].set_visible(False)
ax[a].spines['top'].set_visible(False)
ax[a].spines['bottom'].set_visible(False)
ax[a].tick_params(which = 'both', tick1On = 'False', tick2On = 'False')
ax[a].axis('off')
plt.setp(ax[a].get_yticklabels(), visible=False)
plt.setp(ax[a].get_xticklabels(), visible = False)
lab = ax[a].text(0.1, 0.85, transform = ax[a].transAxes, zorder=100, s=lab_dict[a], fontsize=32, fontweight='bold', color='dimgrey')
ax[a].set_title(case_list[a], fontsize=40, color='dimgrey')
if var == 'SST':
bwr_zero = shiftedColorMap(cmap=matplotlib.cm.bwr, min_val=-2.5, max_val=3.5, name='bwr_zero', var='s', start=0., stop=1.)
if clim == 'True' or clim == 'yes':
if date == '20080101' or date == '20080130':
clim = ax[0].pcolormesh(Jan_SST.data, cmap=bwr_zero, vmin= -2.5, vmax = 3.5)
elif date == '20080207':
clim = ax[0].pcolormesh(Feb_SST.data, cmap=bwr_zero, vmin= -2.5, vmax = 3.5)
else:
ERA = ax[0].pcolormesh(ERA_SST.data, cmap=bwr_zero,vmin= -2.5, vmax = 3.5)
lsm0 = ax[0].contour(glm_LSM.data, colors='dimgrey', linewidths=3, levels=[0])
GLM = ax[1].pcolormesh(glm_SST.data, cmap = bwr_zero, vmin= -2.5, vmax = 3.5)
lsm1 = ax[1].contour(glm_LSM.data, colors='dimgrey', linewidths = 3, levels = [0])
LAM = ax[2].pcolormesh(LAM_SST.data, cmap =bwr_zero, vmin= -2.5, vmax = 3.5)
lsm2 = ax[2].contour(LAM_LSM.data, colors='dimgrey', linewidths = 3, levels = [0])
CBarXTicks = [-2, 0, 2]
elif var == 'SIC':
if clim == 'True' or clim == 'yes':
if date == '20080101' or date == '20080130':
clim = ax[0].pcolormesh(Jan_SIC.data, cmap='Blues_r', vmin= 0., vmax = 1.)
elif date == '20080207':
clim = ax[0].pcolormesh(Feb_SIC.data, cmap='Blues_r', vmin= 0., vmax = 1.)
else:
ERA = ax[0].pcolormesh(ERA_SIC.data, cmap = 'Blues_r', vmin = 0., vmax = 1.)
lsm0 = ax[0].contour(glm_LSM.data, colors='dimgrey', linewidths=3, levels=[0])
GLM = ax[1].pcolormesh(glm_SIC.data, cmap = 'Blues_r', vmin = -0., vmax = 1.)
lsm1 = ax[1].contour(glm_LSM.data, colors = 'dimgrey', linewidths = 3, levels = [0])
LAM = ax[2].pcolormesh(LAM_SIC.data, cmap = 'Blues_r', vmin = 0., vmax = 1.)
lsm2 = ax[2].contour(LAM_LSM.data, colors = 'dimgrey', linewidths = 3, levels = [0])
CBarXTicks = [ 0., 0.5, 1.] # CLevs[np.arange(0,len(CLevs),int(np.ceil(len(CLevs)/5.)))]
ax[2].yaxis.tick_right()
CBAxes = fig.add_axes([0.4, 0.15, 0.2, 0.03])
CBar = plt.colorbar(LAM, cax=CBAxes, orientation='horizontal', ticks=CBarXTicks) #
CBar.solids.set_edgecolor("face")
CBar.outline.set_edgecolor('dimgrey')
CBar.ax.tick_params(which='both', axis='both', labelsize=34, labelcolor='dimgrey', pad=10, size=0, tick1On=False, tick2On=False)
CBar.outline.set_linewidth(2)
plt.subplots_adjust(left = 0.08, right = 0.92, top = 0.9, bottom = 0.28, wspace = 0.23, hspace = 0.18)
if savefig == 'yes' or savefig == True:
if var == 'SIC':
CBar.set_label('Sea ice fraction', fontsize=34, labelpad=10, color='dimgrey')
if clim == 'yes':
plt.savefig(filepath + date + '_clim_SIC_inheritance_glm_ERA_native.png')
plt.savefig(filepath + date + '_clim_SIC_inheritance_glm_ERA_native.eps')
else:
plt.savefig(filepath + date + '_SIC_inheritance_glm_ERA_native.png')
plt.savefig(filepath + date + '_SIC_inheritance_glm_ERA_native.eps')
elif var == 'SST':
CBar.set_label('SST ($^{\circ}$C)', fontsize=34, labelpad=10, color='dimgrey')
if clim == 'yes':
plt.savefig(filepath+date+'_clim_SST_inheritance_glm_ERA_native.png')
plt.savefig(filepath+date+'_clim_SST_inheritance_glm_ERA_native.eps')
else:
plt.savefig(filepath + date + '_SST_inheritance_glm_ERA_native.png')
plt.savefig(filepath + date + '_SST_inheritance_glm_ERA_native.eps')
plt.show()
def plot_ERA():
'Plot ERA-5 reanalysis data at the onset of foehn conditions during both cases. Thesis Figure 4.1.'
fig, axs = plt.subplots(1,2, figsize=(20, 12), frameon=False)
lab_dict = {0: 'a', 1: 'b'}
#ax = fig.add_axes([0.18, 0.25, 0.75, 0.63], frameon=False) # , projection=ccrs.PlateCarree())#
case_list = ['CS1', 'CS2']
for a in [0,1]:
axs[a].spines['right'].set_visible(False)
axs[a].spines['left'].set_visible(False)
axs[a].spines['top'].set_visible(False)
axs[a].spines['bottom'].set_visible(False)
ERA_dict = load_ERA(case=case_list[a])
MetUM_dict = load_files(case=case_list[a], period='onset',res='km4p0')
# Regrid ERA data onto MetUM 4 km domain grid
regridded_P = P_ERA.regrid(MetUM_dict['P'], iris.analysis.Linear())
regridded_P = np.ma.masked_where(lsm.data == 1, regridded_P.data)
regridded_Tair = T_ERA.regrid(MetUM_dict['T_air'], iris.analysis.Linear())
regridded_u = u_ERA.regrid(MetUM_dict['u_wind'], iris.analysis.Linear())
regridded_v = v_ERA.regrid(MetUM_dict['v_wind'], iris.analysis.Linear())
axs[a].contour(MetUM_dict['lsm'].coord('longitude').points, MetUM_dict['lsm'].coord('latitude').points, MetUM_dict['lsm'].data, colors='#535454', linewidths=2.5,
zorder=2) # , transform=RH.coord('grid_longitude').coord_system.as_cartopy_projection())
axs[a].contour(MetUM_dict['orog'].coord('longitude').points, MetUM_dict['orog'].coord('latitude').points, MetUM_dict['orog'].data, colors='#535454', levels=[15],
linewidths=2.5, zorder=3)
P_lev = axs[a].contour(MetUM_dict['lsm'].coord('longitude').points, MetUM_dict['lsm'].coord('latitude').points, regridded_P, colors='#222222', linewidths=3,
levels=range(960, 1040, 8), zorder=4)
axs[a].clabel(P_lev, v=[960, 968, 976, 982, 990], inline=True, inline_spacing=3, fontsize=28, fmt='%1.0f')
bwr_zero = shiftedColorMap(cmap=matplotlib.cm.bwr, min_val=-20., max_val=10., name='bwr_zero', var=regridded_Tair.data, start = 0., stop = 1.)
c = axs[a].pcolormesh(MetUM_dict['real_lon'], MetUM_dict['real_lat'], regridded_Tair.data, cmap=bwr_zero, vmin=-20., vmax=10., zorder=1) #
x, y = np.meshgrid(MetUM_dict['real_lon'], MetUM_dict['real_lat'])
q = axs[a].quiver(x[::40, ::40], y[::40, ::40], regridded_u.data[::40, ::40], regridded_v.data[::40, ::40], color='#414345',
pivot='middle', scale=150, zorder=5)
axs[a].tick_params(which='both', axis='both', labelsize=34, labelcolor='dimgrey', pad=10, size=0, tick1On=False, tick2On=False)
PlotLonMin = np.min(MetUM_dict['real_lon'])
PlotLonMax = np.max(MetUM_dict['real_lon'])
PlotLatMin = np.min(MetUM_dict['real_lat'])
PlotLatMax = np.max(MetUM_dict['real_lat'])
XTicks = np.linspace(PlotLonMin, PlotLonMax, 3)
XTickLabels = [None] * len(XTicks)
for i, XTick in enumerate(XTicks):
if XTick < 0:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick), '$^{\circ}$W')
else:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick), '$^{\circ}$E')
plt.sca(axs[a])
plt.xticks(XTicks, XTickLabels)
axs[a].set_xlim(PlotLonMin, PlotLonMax)
axs[a].tick_params(which='both', pad=10, labelsize=34, color='dimgrey')
YTicks = np.linspace(PlotLatMin, PlotLatMax, 4)
YTickLabels = [None] * len(YTicks)
for i, YTick in enumerate(YTicks):
if YTick < 0:
YTickLabels[i] = '{:.0f}{:s}'.format(np.abs(YTick), '$^{\circ}$S')
else:
YTickLabels[i] = '{:.0f}{:s}'.format(np.abs(YTick), '$^{\circ}$N')
plt.sca(axs[a])
plt.yticks(YTicks, YTickLabels)
axs[a].set_ylim(PlotLatMin, PlotLatMax)
lab = axs[a].text(-80, -61.5, zorder=100, s=lab_dict[a], fontsize=32, fontweight='bold', color='dimgrey')
axs[a].set_title(case_list[a], fontsize=34, color='dimgrey')
CBarXTicks = [-20, -10, 0, 10] # CLevs[np.arange(0,len(CLevs),int(np.ceil(len(CLevs)/5.)))]
CBAxes = fig.add_axes([0.35, 0.15, 0.3, 0.03])
CBar = plt.colorbar(c, cax=CBAxes, orientation='horizontal', ticks=CBarXTicks) #
CBar.set_label('2 m air temperature ($^{\circ}$C)', fontsize=34, labelpad=10, color='dimgrey')
CBar.solids.set_edgecolor("face")
CBar.outline.set_edgecolor('dimgrey')
CBar.ax.tick_params(which='both', axis='both', labelsize=34, labelcolor='dimgrey', pad=10, size=0, tick1On=False,
tick2On=False)
CBar.outline.set_linewidth(2)
plt.sca(axs[1])
plt.tick_params(axis = 'y', which= 'both', labelleft = 'off', labelright = 'on')
#yaxis.set_label_coords(1.27, 0.5)
plt.quiverkey(q, 0.51, 0.9, 20, r'$20$ $m$ $s^{-1}$', labelpos='N', color='#414345', labelcolor='#414345',
fontproperties={'size': '32', 'weight': 'bold'},
coordinates='figure', )
plt.draw()
plt.subplots_adjust(bottom = 0.25, top = 0.85)
plt.savefig('/users/ellgil82/figures/Wintertime melt/Re-runs/ERA_foehn_onset_both_cases.png', transparent=True)
plt.savefig('/users/ellgil82/figures/Wintertime melt/Re-runs/ERA_foehn_onset_both_cases.eps', transparent=True)
plt.show()
def plot_both(period, res):
''' Plot spatial maps of synoptic conditions during both cases from MetUM model output, either before or during the onset
of foehn conditions. Thesis Figure 4.4.
Inputs:
- period: string indicating either pre-foehn onset or during foehn onset, either 'pre' or 'onset'
- res: string indicating resolution of model output loaded, either 'km1p5' or 'km4p0'
Outputs:
- image files in .png and .eps format
'''
fig, axs = plt.subplots(1,2, figsize=(20, 12), frameon=False)
lab_dict = {0: 'c', 1: 'd'}
#ax = fig.add_axes([0.18, 0.25, 0.75, 0.63], frameon=False) # , projection=ccrs.PlateCarree())#
case_list = ['CS1', 'CS2']
for a in [0,1]:
axs[a].spines['right'].set_visible(False)
axs[a].spines['left'].set_visible(False)
axs[a].spines['top'].set_visible(False)
axs[a].spines['bottom'].set_visible(False)
MetUM_dict = load_files(case = case_list[a], period = period, res = res)
axs[a].contour(MetUM_dict['lsm'].coord('longitude').points, MetUM_dict['lsm'].coord('latitude').points, MetUM_dict['lsm'].data, colors='#535454', linewidths=2.5,
zorder=2) # , transform=RH.coord('grid_longitude').coord_system.as_cartopy_projection())
axs[a].contour(MetUM_dict['orog'].coord('longitude').points, MetUM_dict['orog'].coord('latitude').points, MetUM_dict['orog'].data, colors='#535454', levels=[50],
linewidths=2.5, zorder=3) # Plot orography at approx. height of grounding line
P_lev = axs[a].contour(MetUM_dict['lsm'].coord('longitude').points, MetUM_dict['lsm'].coord('latitude').points, MetUM_dict['P'], colors='#222222', linewidths=3,
levels=range(960, 1020, 4), zorder=4) # Plot MSLP contours
axs[a].clabel(P_lev, v=[960, 968, 976, 982, 990], inline=True, inline_spacing=3, fontsize=28, fmt='%1.0f')
bwr_zero = shiftedColorMap(cmap=matplotlib.cm.bwr, min_val=-30., max_val=10., name='bwr_zero', var=T_air.data, start = 0., stop = 1.)
c = axs[a].pcolormesh(MetUM_dict['real_lon'], MetUM_dict['real_lat'], MetUM_dict['T_air'].data, cmap=bwr_zero, vmin=-30., vmax=10., zorder=1) # Plot temperatures
x, y = np.meshgrid(MetUM_dict['real_lon'], MetUM_dict['real_lat'])
q = axs[a].quiver(x[::25, ::25], y[::25, ::25], MetUM_dict['u'].data[::25, ::25], MetUM_dict['v'].data[::25, ::25], color='#414345',
pivot='middle', scale=100, zorder=5) # Plot 10 m wind vectors
axs[a].tick_params(which='both', axis='both', labelsize=34, labelcolor='dimgrey', pad=10, size=0, tick1On=False, tick2On=False)
# Set up plot ticks and labels
PlotLonMin = np.min(real_lon)
PlotLonMax = np.max(real_lon)
PlotLatMin = np.min(real_lat)
PlotLatMax = np.max(real_lat)
XTicks = np.linspace(PlotLonMin, PlotLonMax, 3)
XTickLabels = [None] * len(XTicks)
for i, XTick in enumerate(XTicks):
if XTick < 0:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick), '$^{\circ}$W')
else:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick), '$^{\circ}$E')
plt.sca(axs[a])
plt.xticks(XTicks, XTickLabels)
axs[a].set_xlim(PlotLonMin, PlotLonMax)
axs[a].tick_params(which='both', pad=10, labelsize=34, color='dimgrey')
YTicks = np.linspace(PlotLatMin, PlotLatMax, 4)
YTickLabels = [None] * len(YTicks)
for i, YTick in enumerate(YTicks):
if YTick < 0:
YTickLabels[i] = '{:.0f}{:s}'.format(np.abs(YTick), '$^{\circ}$S')
else:
YTickLabels[i] = '{:.0f}{:s}'.format(np.abs(YTick), '$^{\circ}$N')
plt.sca(axs[a])
plt.yticks(YTicks, YTickLabels)
axs[a].set_ylim(PlotLatMin, PlotLatMax)
lab = axs[a].text(-80, -61.5, zorder=100, s=lab_dict[a], fontsize=32, fontweight='bold', color='dimgrey')
axs[a].set_title(case_list[a], fontsize=34, color='dimgrey')
CBarXTicks = [-30, -10, 10] # CLevs[np.arange(0,len(CLevs),int(np.ceil(len(CLevs)/5.)))]
CBAxes = fig.add_axes([0.35, 0.15, 0.3, 0.03])
CBar = plt.colorbar(c, cax=CBAxes, orientation='horizontal', ticks=CBarXTicks) #
CBar.set_label('1.5 m air temperature ($^{\circ}$C)', fontsize=34, labelpad=10, color='dimgrey')
CBar.solids.set_edgecolor("face")
CBar.outline.set_edgecolor('dimgrey')
CBar.ax.tick_params(which='both', axis='both', labelsize=34, labelcolor='dimgrey', pad=10, size=0, tick1On=False,
tick2On=False)
CBar.outline.set_linewidth(2)
plt.sca(axs[1])
plt.tick_params(axis = 'y', which= 'both', labelleft = 'off', labelright = 'on')
#yaxis.set_label_coords(1.27, 0.5)
plt.quiverkey(q, 0.51, 0.9, 10, r'$10$ $m$ $s^{-1}$', labelpos='N', color='#414345', labelcolor='#414345',
fontproperties={'size': '32', 'weight': 'bold'},
coordinates='figure', )
plt.draw()
plt.subplots_adjust(bottom = 0.25, top = 0.85)
plt.savefig('/users/ellgil82/figures/Wintertime melt/Re-runs/synop_cond_both_cases_'+ period + res + '.png')
plt.savefig('/users/ellgil82/figures/Wintertime melt/Re-runs/synop_cond_both_cases_'+ period + res + '.eps')
plt.show()
def plot_transect(res):
''''Plot transect through mountains during foehn conditions. Thesis Figure 4.7.
Inputs:
- res: string indicating resolution of MetUM model output.
Outputs:
- image files in .eps or .png format
'''
fig, ax = plt.subplots(1,1, figsize = (12,10))
ax.set_facecolor('#222222')
bwr_zero = shiftedColorMap(cmap=matplotlib.cm.bwr, min_val=-10., max_val=40., name='bwr_zero',
var=res['theta'].data, start=0., stop=1.)
if res == surf_4p0:
slice_idx = 163
res_str = '4km'
elif res == surf_1p5:
slice_idx = 235
res_str = '1p5km'
cf = iplt.contourf(res['theta'][12, :, slice_idx, :], cmap=bwr_zero, vmin=-10.1, vmax=40.,
levels=[-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 13, 16, 19, 21, 25, 30, 35,
40]) #np.linspace(-10., 40., 21))
c = iplt.contour(res['zonal'][:,slice_idx,:], levels = [0,8,16,24,32,40], colors = 'k')
if case_study == 'CS1':
plt.clabel(c, [8,16,24,32], rotation = 0, fontsize = 28, color = 'k', inline = True, fmt = '%.0f')
elif case_study == 'CS2':
plt.clabel(c, [8, 24, 32], rotation=0, fontsize=28, color='k', inline=True, fmt='%.0f')
plt.setp(ax.spines.values(), linewidth=2, color='dimgrey')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
PlotLonMin, PlotLonMax = -68, -60
ax.set_ylim(0,5000)
ax.set_yticks([0, 2500, 5000])
ax.tick_params(axis='both', which='both', labelsize=32, tick1On=False, tick2On=False, labelcolor='dimgrey', pad=10)
ax.set_ylabel('Altitude\n(m)', rotation=0, fontsize=32, labelpad=70, color='dimgrey')
[l.set_visible(False) for (w, l) in enumerate(ax.yaxis.get_ticklabels()) if w % 2 != 0]
XTicks = np.linspace(PlotLonMin, PlotLonMax, 3)
XTickLabels = [None] * len(XTicks)
for i, XTick in enumerate(XTicks):
if XTick < 0:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick), '$^{\circ}$W')
else:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick), '$^{\circ}$E')
plt.sca(ax)
plt.xticks(XTicks, XTickLabels)
ax.set_xlim(PlotLonMin, PlotLonMax)
CBarXTicks = [-10, 0, 10, 20, 30, 40] # CLevs[np.arange(0,len(CLevs),int(np.ceil(len(CLevs)/5.)))]
CBAxes = fig.add_axes([0.4, 0.15, 0.45, 0.03])
CBar = plt.colorbar(cf, cax=CBAxes, orientation='horizontal', ticks=CBarXTicks) #
CBar.set_label('Air potential temperature ($^{\circ}$C)', fontsize=34, labelpad=10, color='dimgrey')
CBar.solids.set_edgecolor("face")
CBar.outline.set_edgecolor('dimgrey')
CBar.ax.tick_params(which='both', axis='both', labelsize=32, labelcolor='dimgrey', pad=10, size=0, tick1On=False,
tick2On=False)
CBar.outline.set_linewidth(2)
plt.subplots_adjust(left = 0.3, right = 0.95, bottom = 0.27, top = 0.97)
plt.savefig('/users/ellgil82/figures/Wintertime melt/Re-runs/transects_theta_u_wind_'+case_study+'_'+res_str+'.png')
plt.savefig('/users/ellgil82/figures/Wintertime melt/Re-runs/transects_theta_u_wind_'+case_study+'_'+res_str+'.eps')
plt.show()
def correlation_maps(year_list, xvar, yvar):
if len(year_list) > 1:
fig, ax = plt.subplots(7, 3, figsize=(8, 18))
CbAx = fig.add_axes([0.25, 0.1, 0.5, 0.02])
ax = ax.flatten()
comp_r = np.zeros((220, 220))
plot = 0
for year in year_list:
vars_yr = load_vars(year)
ax[plot].axis('off')
r, r2, p, err, xmasked, y_masked, Larsen_mask = run_corr(vars_yr, xvar = xvar, yvar = yvar[:58444])
if np.mean(r) < 0:
squished_cmap = shiftedColorMap(cmap=matplotlib.cm.bone_r, min_val=-1., max_val=0., name='squished_cmap', var=r, start=0.25, stop=0.75)
c = ax[plot].pcolormesh(r, cmap=matplotlib.cm.Spectral, vmin=-1., vmax=0.)
elif np.mean(r) > 0:
squished_cmap = shiftedColorMap(cmap = matplotlib.cm.gist_heat_r, min_val = 0, max_val = 1, name = 'squished_cmap', var = r, start = 0.25, stop = 0.75)
c = ax[plot].pcolormesh(r, cmap = matplotlib.cm.Spectral, vmin = 0., vmax = 1.)
ax[plot].contour(vars_yr['lsm'].data, colors='#222222', lw=2)
ax[plot].contour(vars_yr['orog'].data, colors='#222222', levels=[100])
comp_r = comp_r + r
ax[plot].text(0.4, 1.1, s=year_list[plot], fontsize=24, color='dimgrey', transform=ax[plot].transAxes)
unmasked_idx = np.where(y_masked.mask[0,:,:] == 0)
sig = np.ma.masked_array(p, mask = y_masked[0,:,:].mask)
sig = np.ma.masked_greater(sig, 0.01)
ax[plot].contourf(sig, hatches = '...')
plot = plot + 1
mean_r_composite = comp_r / len(year_list)
ax[-1].contour(vars_yr['lsm'].data, colors='#222222', lw=2)
if np.mean(mean_r_composite) < 0:
squished_cmap = shiftedColorMap(cmap=matplotlib.cm.bone_r, min_val=-1., max_val=0., name='squished_cmap',
var=mean_r_composite, start=0.25, stop=0.75)
c = ax[-1].pcolormesh(mean_r_composite, cmap=squished_cmap, vmin=-1., vmax=0.)
elif np.mean(r) > 0:
squished_cmap = shiftedColorMap(cmap=matplotlib.cm.gist_heat_r, min_val=0, max_val=1, name='squished_cmap',
var=mean_r_composite, start=0.25, stop=0.75)
c = ax[-1].pcolormesh(r, cmap=squished_cmap, vmin=0., vmax=1.)
ax[-1].contour(vars_yr['orog'].data, colors='#222222', levels=[100])
ax[-1].text(0., 1.1, s='Composite', fontsize=24, color='dimgrey', transform=ax[-1].transAxes)
cb = plt.colorbar(c, orientation='horizontal', cax=CbAx, ticks=[0, 0.5, 1])
cb.solids.set_edgecolor("face")
cb.outline.set_edgecolor('dimgrey')
cb.ax.tick_params(which='both', axis='both', labelsize=24, labelcolor='dimgrey', pad=10, size=0, tick1On=False, tick2On=False)
cb.outline.set_linewidth(2)
cb.ax.xaxis.set_ticks_position('bottom')
# cb.ax.set_xticks([0,4,8])
cb.set_label('Correlation coefficient', fontsize=24, color='dimgrey', labelpad=30)
plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.15, hspace=0.3, wspace=0.05)
if host == 'bsl':
plt.savefig('/users/ellgil82/figures/Hindcast/SMB/'+ xvar + '_v_' + yvar + '_all_years.png', transparent=True)
plt.savefig('/users/ellgil82/figures/Hindcast/SMB/'+ xvar + '_v_' + yvar + '_all_years.eps', transparent=True)
elif host == 'jasmin':
plt.savefig('/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/Drivers/'+ xvar + '_v_' + yvar + '_all_years.png', transparent=True)
plt.savefig('/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/Drivers/'+ xvar + '_v_' + yvar + '_all_years.eps', transparent=True)
# Save composite separately
elif len(year_list) == 1:
r, r2, p, err, xmasked, y_masked, Larsen_mask = run_corr(surf, xvar=xvar, yvar=yvar)
unmasked_idx = np.where(y_masked.mask[0, :, :] == 0)
sig = np.ma.masked_array(p, mask=y_masked[0, :, :].mask)
sig = np.ma.masked_greater(sig, 0.01)
mean_r_composite = r
fig, ax = plt.subplots(figsize=(8, 8))
ax.axis('off')
# Make masked areas white, instead of colour used for zero in colour map
ax.contourf(y_masked.mask[0, :, :], cmap='Greys_r')
# Plot coastline
ax.contour(surf['lsm'].data, colors='#222222', lw=2)
# Plot correlations
c = ax.pcolormesh(np.ma.masked_where((y_masked.mask[0, :, :] == 1.), mean_r_composite), cmap=matplotlib.cm.Spectral_r)#, vmin=-1, vmax=1)
# Plot 50 m orography contour on top
ax.contour(surf['orog'].data, colors='#222222', levels=[100])
# Overlay stippling to indicate signficance
ax.contourf(sig, hatches='...', alpha = 0.0)
# Set up colourbar
cb = plt.colorbar(c, orientation='horizontal')#, ticks=[-1, 0, 1])
cb.solids.set_edgecolor("face")
cb.outline.set_edgecolor('dimgrey')
cb.ax.tick_params(which='both', axis='both', labelsize=24, labelcolor='dimgrey', pad=10, size=0, tick1On=False,
tick2On=False)
cb.outline.set_linewidth(2)
cb.ax.xaxis.set_ticks_position('bottom')
cb.set_label('Correlation coefficient', fontsize=24, color='dimgrey', labelpad=30)
# Save figure
if host == 'bsl':
plt.savefig('/users/ellgil82/figures/Hindcast/SMB/' + xvar + '_v_' + yvar + '_composite.png', transparent=True)
plt.savefig('/users/ellgil82/figures/Hindcast/SMB/' + xvar + '_v_' + yvar + '_composite.eps', transparent=True)
elif host == 'jasmin':
plt.savefig('/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/Drivers/' + xvar + '_v_' + yvar + '_composite.png',transparent=True)
plt.savefig('/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/Drivers/' + xvar + '_v_' + yvar + '_composite.eps',transparent=True)
def plot_foehn_index(subplot):
Larsen_box = np.zeros((220, 220))
Larsen_box[40:135, 90:155] = 1.
if subplot == False or subplot == 'no':
fig = plt.figure(frameon=False, figsize=(
8, 8
)) # !!change figure dimensions when you have a larger model domain
fig.patch.set_visible(False)
ax = fig.add_subplot(111) #, projection=ccrs.PlateCarree())
plt.axis = 'off'
#cf_var = np.nanmean(surf['foehn_idx'].data, axis = 0)
ax.text(0.,
1.1,
zorder=6,
transform=ax.transAxes,
s='e',
fontsize=36,
fontweight='bold',
color='dimgrey')
ax.text(0.4,
1.1,
transform=ax.transAxes,
s='ANN',
fontsize=30,
fontweight='bold',
color='dimgrey')
cf_var = mn_foehn
plt.setp(ax.spines.values(), linewidth=0, color='dimgrey')
PlotLonMin = np.min(real_lon)
PlotLonMax = np.max(real_lon)
PlotLatMin = np.min(real_lat)
PlotLatMax = np.max(real_lat)
XTicks = np.linspace(PlotLonMin, PlotLonMax, 3)
XTickLabels = [None] * len(XTicks)
for i, XTick in enumerate(XTicks):
if XTick < 0:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick),
'$^{\circ}$W')
else:
XTickLabels[i] = '{:.0f}{:s}'.format(np.abs(XTick),
'$^{\circ}$E') #
plt.xticks(XTicks, XTickLabels)
ax.set_xlim(PlotLonMin, PlotLonMax)
ax.tick_params(which='both', pad=10, labelsize=34, color='dimgrey')
YTicks = np.linspace(PlotLatMin, PlotLatMax, 3)
YTickLabels = [None] * len(YTicks)
for i, YTick in enumerate(YTicks):
if YTick < 0:
YTickLabels[i] = '{:.0f}{:s}'.format(np.abs(YTick),
'$^{\circ}$S')
else:
YTickLabels[i] = '{:.0f}{:s}'.format(np.abs(YTick),
'$^{\circ}$N')
plt.yticks(YTicks, YTickLabels)
ax.set_ylim(PlotLatMin, PlotLatMax)
ax.tick_params(which='both',
axis='both',
labelsize=34,
labelcolor='dimgrey',
pad=30,
size=0,
tick1On=False,
tick2On=False)
bwr_zero = shiftedColorMap(cmap=matplotlib.cm.bwr,
min_val=-0.25,
max_val=0.25,
name='bwr_zero',
var=cf_var.data,
start=0.15,
stop=0.85) #-6, 3
xlon, ylat = np.meshgrid(real_lon, real_lat)
c = ax.pcolormesh(xlon,
ylat,
np.ma.masked_where((Larsen_box == 0.), cf_var),
cmap='Spectral_r',
vmin=-.25,
vmax=.25)
#ax.contour(xlon, ylat, shaded, levels= [1.], colors='dimgrey', linewidths = 4, latlon=True)
#ax.text(0., 1.1, zorder=6, transform=ax.transAxes, s='b', fontsize=32, fontweight='bold', color='dimgrey')
coast = ax.contour(xlon,
ylat,
surf['lsm'].data,
levels=[0],
colors='#222222',
lw=2,
latlon=True,
zorder=2)
topog = ax.contour(xlon,
ylat,
surf['orog'].data,
levels=[50],
colors='#222222',
linewidth=1.5,
latlon=True,
zorder=3)
elif subplot == True or subplot == 'yes':
fig, axs = plt.subplots(2, 2, frameon=False, figsize=(
15, 17
)) # !!change figure dimensions when you have a larger model domain
fig.patch.set_visible(False)
axs = axs.flatten()
lab_dict = {
0: ('a', 'DJF'),
1: ('b', 'MAM'),
2: ('c', 'JJA'),
3: ('d', 'SON')
}
plot = 0
vars = [
np.nanmean(DJF_FI.data, axis=0),
np.nanmean(MAM_FI.data, axis=0),
np.nanmean(JJA_FI.data, axis=0),
np.nanmean(SON_FI.data, axis=0)
]
for ax in axs:
plt.sca(ax)
cf_var = vars[plot]
plt.axis = 'off'
plt.setp(ax.spines.values(), linewidth=0, color='dimgrey')
PlotLonMin = np.min(real_lon)
PlotLonMax = np.max(real_lon)
PlotLatMin = np.min(real_lat)
PlotLatMax = np.max(real_lat)
XTicks = np.linspace(PlotLonMin, PlotLonMax, 3)
XTickLabels = [None] * len(XTicks)
for i, XTick in enumerate(XTicks):
if XTick < 0:
XTickLabels[i] = '{:.0f}{:s}'.format(
np.abs(XTick), '$^{\circ}$W')
else:
XTickLabels[i] = '{:.0f}{:s}'.format(
np.abs(XTick), '$^{\circ}$E') #
plt.xticks(XTicks, XTickLabels)
ax.set_xlim(PlotLonMin, PlotLonMax)
ax.tick_params(which='both', pad=10, labelsize=34, color='dimgrey')
YTicks = np.linspace(PlotLatMin, PlotLatMax, 3)
YTickLabels = [None] * len(YTicks)
for i, YTick in enumerate(YTicks):
if YTick < 0:
YTickLabels[i] = '{:.0f}{:s}'.format(
np.abs(YTick), '$^{\circ}$S')
else:
YTickLabels[i] = '{:.0f}{:s}'.format(
np.abs(YTick), '$^{\circ}$N')
plt.yticks(YTicks, YTickLabels)
ax.set_ylim(PlotLatMin, PlotLatMax)
ax.tick_params(which='both',
axis='both',
labelsize=34,
labelcolor='dimgrey',
pad=30,
size=0,
tick1On=False,
tick2On=False)
bwr_zero = shiftedColorMap(cmap=matplotlib.cm.bwr,
min_val=-.25,
max_val=.25,
name='bwr_zero',
var=cf_var.data,
start=0.15,
stop=0.85) #-6, 3
xlon, ylat = np.meshgrid(real_lon, real_lat)
c = ax.pcolormesh(xlon,
ylat,
np.ma.masked_where((Larsen_box == 0.), cf_var),
cmap='Spectral_r',
vmin=-.25,
vmax=.25)
#ax.contour(xlon, ylat, shaded, levels= [1.], colors='dimgrey', linewidths = 4, latlon=True)
ax.text(0.,
1.1,
zorder=6,
transform=ax.transAxes,
s=lab_dict[plot][0],
fontsize=32,
fontweight='bold',
color='dimgrey')
ax.text(0.4,
1.1,
transform=ax.transAxes,
s=lab_dict[plot][1],
fontsize=28,
fontweight='bold',
color='dimgrey')
coast = ax.contour(xlon,
ylat,
surf['lsm'].data,
levels=[0],
colors='#222222',
lw=2,
latlon=True,
zorder=2)
topog = ax.contour(xlon,
ylat,
surf['orog'].data,
levels=[50],
colors='#222222',
linewidth=1.5,
latlon=True,
zorder=3)
plot = plot + 1
CBarXTicks = [
-.25, 0, .25
] # CLevs[np.arange(0,len(CLevs),int(np.ceil(len(CLevs)/5.)))]
CBAxes = fig.add_axes([0.35, 0.18, 0.4, 0.02])
CBar = plt.colorbar(c,
cax=CBAxes,
orientation='horizontal',
ticks=CBarXTicks,
extend='both') #
CBar.set_label('Mean foehn index \n(dimensionless)',
fontsize=34,
labelpad=10,
color='dimgrey')
CBar.solids.set_edgecolor("face")
CBar.outline.set_edgecolor('dimgrey')
CBar.ax.tick_params(which='both',
axis='both',
labelsize=34,
labelcolor='dimgrey',
pad=10,
size=0,
tick1On=False,
tick2On=False)
CBar.outline.set_linewidth(2)
plt.subplots_adjust(left=0.2,
bottom=0.3,
wspace=0.35,
hspace=0.35,
right=0.85)
if subplot == True or subplot == 'yes':
plt.savefig(
'/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/Mean_foehn_index_seas.png',
transparent=True)
plt.savefig(
'/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/Mean_foehn_index_seas.eps',
transparent=True)
else:
plt.savefig(
'/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/Mean_foehn_index.png',
transparent=True)
plt.savefig(
'/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/figures/Mean_foehn_index.eps',
transparent=True)
plt.show()