import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib.pyplot as plt
import xarray as xr
import numpy as np
import metpy
import metpy.calc as mpcalc
from metpy.plots import ctables
from metpy.cbook import get_test_data
from metpy.units import units
import os
import scipy.integrate as integrate
import datetime as dt
import glob
import json
from datetime import datetime
from datetime import timedelta
from metpy.plots import ctables
from matplotlib.colors import Normalize
from matplotlib.colors import ListedColormap, LinearSegmentedColormap, BoundaryNorm
#import wrf
import scipy
#import xcape
import xarray
file_dir = '/data/icond2/'
os.chdir(file_dir)
#data_det_rr = xr.open_dataset('icond2_precip.nc').sel(longitude=slice(3,9,1),latitude=slice(49,51,1))
data_det_snow = xr.open_dataset('icond2_snow.nc').sel(longitude=slice(3,9,1),latitude=slice(49,51,1))
#data_det_uhmax = xr.open_dataset('icond2_uh_max.nc').sel(longitude=slice(3,9,1),latitude=slice(49,51,1))
file_dir = '/data/icond2eps/'
# Changing the directory
os.chdir(file_dir)
data = xarray.open_dataset('icond2eps_snow_latlon.grib2', engine='cfgrib')
data.to_netcdf('icond2eps_snow.nc')
data_snow = xr.open_dataset('icond2eps_snow.nc').sel(longitude=slice(3,9,1),latitude=slice(49,51,1))
print(data_snow)
Ignoring index file 'icond2eps_snow_latlon.grib2.923a8.idx' older than GRIB file
<xarray.Dataset>
Dimensions: (number: 20, step: 49, latitude: 100, longitude: 301)
Coordinates:
* number (number) int64 1 2 3 4 5 6 7 8 9 ... 12 13 14 15 16 17 18 19 20
time datetime64[ns] ...
* step (step) timedelta64[ns] 00:00:00 01:00:00 ... 2 days 00:00:00
surface float64 ...
* latitude (latitude) float64 49.0 49.02 49.04 49.06 ... 50.94 50.96 50.98
* longitude (longitude) float64 3.0 3.02 3.04 3.06 ... 8.94 8.96 8.98 9.0
valid_time (step) datetime64[ns] ...
Data variables:
sde (number, step, latitude, longitude) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: edzw
GRIB_centreDescription: Offenbach
GRIB_subCentre: 255
Conventions: CF-1.7
institution: Offenbach
history: 2023-07-13T05:48 GRIB to CDM+CF via cfgrib-0.9.9...
# To parse the full dataset, we can call parse_cf without an argument, and assign the returned Dataset.
data_snow = data_snow.metpy.parse_cf()
data_det_snow = data_det_snow.metpy.parse_cf()
x, y = data_snow['sde'].metpy.coordinates('x', 'y')
time = data_snow['sde'].step
member = data_snow['sde'].number
#time2 = data_det_rr['tp'].metpy.time
timeinit = data_snow.time
timeinit = datetime.utcfromtimestamp(timeinit.item()/1e9)
print(timeinit)
#uh = data_uh['UH_MAX']
snow = data_snow['sde']
#rr = data_rr['tp']
#det_rr = data_det_rr['tp']
det_snow = data_det_snow['sde']
#det_uh = data_det_uhmax['UH_MAX']
snow_median = np.empty((49,100,301))
snow_95 = np.empty((49,100,301))
for i in range(0,49):
for j in range (0,100):
for k in range(0,301):
snow_median[i,j,k] = np.percentile(snow.data[:,i,j,k],50)
print(np.shape(snow_median))
for i in range(0,49):
for j in range (0,100):
for k in range(0,301):
snow_95[i,j,k] = np.percentile(snow.data[:,i,j,k],95)
/home/lmathias/anaconda3/envs/metpy/lib/python3.9/site-packages/metpy/xarray.py:349: UserWarning: More than one time coordinate present for variable "sde".
warnings.warn('More than one ' + axis + ' coordinate present for variable'
Found valid latitude/longitude coordinates, assuming latitude_longitude for projection grid_mapping variable
Found valid latitude/longitude coordinates, assuming latitude_longitude for projection grid_mapping variable
2023-07-13 03:00:00 (49, 100, 301)
snow_prob_25 = np.empty((49,100,301))
snow_prob_10 = np.empty((49,100,301))
snow_prob_5 = np.empty((49,100,301))
snow_prob_1 = np.empty((49,100,301))
z = 25
v = 10
w = 5
n = 1
for i in range(0,49):
for j in range (0,100):
for k in range(0,301):
snow_prob_25[i,j,k] = (sum(h > z for h in snow.data[:,i,j,k]*100)/20)*100
for i in range(0,49):
for j in range (0,100):
for k in range(0,301):
snow_prob_10[i,j,k] = (sum(h > v for h in snow.data[:,i,j,k]*100)/20)*100
for i in range(0,49):
for j in range (0,100):
for k in range(0,301):
snow_prob_5[i,j,k] = (sum(h > w for h in snow.data[:,i,j,k]*100)/20)*100
for i in range(0,49):
for j in range (0,100):
for k in range(0,301):
snow_prob_1[i,j,k] = (sum(h > n for h in snow.data[:,i,j,k]*100)/20)*100
def plot_background(ax):
ax.set_extent([5, 7, 49.1, 50.5])
ax.add_feature(cfeature.COASTLINE.with_scale('10m'), LineWidth=2)
ax.add_feature(cfeature.BORDERS.with_scale('10m'),LineWidth=2)
#gl = ax.gridlines(draw_labels=True,linewidth=0.5, color='gray', alpha=0.5, linestyle='--')
#gl.xlabels_top = False
#gl.ylabels_right = False
#gl.xlabel_style = {'size': 12, 'color': 'black'}
#gl.ylabel_style = {'size': 12, 'color': 'black'}
#gl.xformatter = LONGITUDE_FORMATTER
#gl.yformatter = LATITUDE_FORMATTER
return ax
#import matplotlib
#cmap = matplotlib.cm.get_cmap('cubehelix_r')
#for i in range(20):
#rgba = cmap(i)
# rgb2hex accepts rgb or rgba
#print(rgba)
cmap = ctables.colortables.get_colortable('NWSStormClearReflectivity')
newcmap = ListedColormap(cmap(np.linspace(0.25, 0.92, 28)))
cmap2 = ctables.colortables.get_colortable('NWSReflectivity')
newcmap2 = ListedColormap(cmap2(np.linspace(0.2, 0.96, 28)))
cmap4 = ctables.colortables.get_colortable('precipitation')
newcmap4 = ListedColormap(cmap4(np.linspace(0, 0.75, 15)))
colors=[(1,1,1),(0.0, 0.9254901960784314, 0.9254901960784314),
(0.00392156862745098, 0.6274509803921569, 0.9647058823529412),
(0.0, 0.0, 0.9647058823529412),
(0.0, 1.0, 0.0),
(0.0, 0.7843137254901961, 0.0),
(0.0, 0.5647058823529412, 0.0),
(1.0, 1.0, 0.0),
(0.9058823529411765, 0.7529411764705882, 0.0),
(1.0, 0.5647058823529412, 0.0),
(1.0, 0.16078431372, 0.16078431372),
(0.7529411764705882, 0.0, 0.0),
(0.59765625, 0.0, 0.0),
(1.0, 0.0, 1.0),
(0.6, 0.3333333333333333, 0.788235294117647),
(0.27,0,0.4)]
colors2 = [(1,1,1),
(0.388235, 0.462745, 0.658824), (0.372549, 0.45098, 0.654902), (0.372549, 0.45098, 0.654902),
(0.356863, 0.439216, 0.65098), (0.341176, 0.427451, 0.643137), (0.32549, 0.415686, 0.639216),
(0.309804, 0.403922, 0.635294), (0.294118, 0.392157, 0.631373), (0.278431, 0.380392, 0.627451),
(0.262745, 0.368627, 0.623529), (0.262745, 0.380392, 0.635294), (0.270588, 0.407843, 0.65098),
(0.282353, 0.435294, 0.666667), (0.290196, 0.462745, 0.682353), (0.301961, 0.490196, 0.698039),
(0.309804, 0.517647, 0.713725), (0.317647, 0.545098, 0.733333), (0.329412, 0.572549, 0.74902),
(0.337255, 0.6, 0.764706), (0.34902, 0.623529, 0.780392), (0.356863, 0.65098, 0.796078),
(0.368627, 0.678431, 0.811765), (0.376471, 0.705882, 0.831373), (0.384314, 0.733333, 0.847059),
(0.396078, 0.760784, 0.862745), (0.403922, 0.788235, 0.878431), (0.415686, 0.815686, 0.894118),
(0.435294, 0.839216, 0.909804), (0.407843, 0.839216, 0.843137), (0.380392, 0.839216, 0.772549),
(0.34902, 0.839216, 0.701961), (0.321569, 0.839216, 0.635294), (0.294118, 0.839216, 0.564706),
(0.262745, 0.839216, 0.494118), (0.235294, 0.839216, 0.427451), (0.207843, 0.839216, 0.356863),
(0.066667, 0.835294, 0.094118), (0.066667, 0.819608, 0.090196), (0.062745, 0.803922, 0.090196),
(0.062745, 0.784314, 0.086275), (0.062745, 0.768627, 0.086275), (0.058824, 0.752941, 0.082353),
(0.058824, 0.737255, 0.082353), (0.058824, 0.717647, 0.078431), (0.054902, 0.701961, 0.078431),
(0.054902, 0.686275, 0.07451), (0.054902, 0.670588, 0.07451), (0.05098, 0.65098, 0.070588),
(0.05098, 0.635294, 0.070588), (0.05098, 0.619608, 0.066667), (0.047059, 0.6, 0.066667),
(0.047059, 0.584314, 0.062745), (0.047059, 0.568627, 0.062745), (0.043137, 0.552941, 0.058824),
(0.043137, 0.533333, 0.058824), (0.043137, 0.517647, 0.054902), (0.039216, 0.501961, 0.054902),
(0.039216, 0.486275, 0.05098), (0.039216, 0.466667, 0.05098), (0.035294, 0.45098, 0.047059),
(0.035294, 0.435294, 0.047059), (0.035294, 0.419608, 0.043137), (0.031373, 0.4, 0.043137),
(0.031373, 0.384314, 0.039216), (0.035294, 0.368627, 0.035294), (0.113725, 0.407843, 0.035294),
(0.196078, 0.45098, 0.031373), (0.27451, 0.490196, 0.031373), (0.356863, 0.533333, 0.027451),
(0.435294, 0.572549, 0.027451), (0.517647, 0.615686, 0.023529), (0.596078, 0.658824, 0.023529),
(0.678431, 0.698039, 0.019608), (0.756863, 0.741176, 0.019608), (0.839216, 0.780392, 0.015686),
(0.917647, 0.823529, 0.015686), (1.0, 0.886275, 0.0), (1.0, 0.847059, 0.0), (1.0, 0.827451, 0.0),
(1.0, 0.788235, 0.0), (1.0, 0.768627, 0.0), (1.0, 0.733333, 0.0), (1.0, 0.713725, 0.0),
(1.0, 0.693725, 0.0), (1.0, 0.67451, 0.0), (1.0, 0.654902, 0.0), (1.0, 0.619608, 0.0),
(1.0, 0.6, 0.0), (1.0, 0.580392, 0.0), (1.0, 0.541176, 0.0), (1.0, 0.521569, 0.0),
(1.0, 0.501569, 0.0), (0.945098, 0.0, 0.0),
(0.917647, 0.0, 0.0), (0.890196, 0.0, 0.0), (0.862745, 0.0, 0.0), (0.835294, 0.0, 0.0),
(0.803922, 0.0, 0.0), (0.776471, 0.0, 0.0), (0.74902, 0.0, 0.0), (0.721569, 0.0, 0.0),
(0.694118, 0.0, 0.0), (0.666667, 0.0, 0.0), (0.639216, 0.0, 0.0), (0.607843, 0.0, 0.0),
(0.580392, 0.0, 0.0), (0.552941, 0.0, 0.0), (0.52549, 0.0, 0.0), (0.498039, 0.0, 0.0),
(0.470588, 0.0, 0.0), (0.443137, 0.0, 0.0), (1.0, 0.960784, 1.0),
(1.0, 0.917647, 1.0), (1.0, 0.87451, 1.0), (1.0, 0.831373, 1.0), (1.0, 0.788235, 1.0),
(1.0, 0.745098, 1.0), (1.0, 0.701961, 1.0), (1.0, 0.658824, 1.0), (1.0, 0.615686, 1.0),
(1.0, 0.572549, 1.0), (1.0, 0.458824, 1.0), (0.988235, 0.419608, 0.992157),
(0.976471, 0.376471, 0.980392), (0.964706, 0.337255, 0.968627), (0.952941, 0.294118, 0.956863),
(0.941176, 0.25098, 0.945098), (0.929412, 0.211765, 0.937255), (0.917647, 0.168627, 0.92549),
(0.905882, 0.12549, 0.913725), (0.894118, 0.086275, 0.901961), (0.882353, 0.043137, 0.890196),
(0.698039, 0.0, 1.0), (0.67451, 0.0, 0.988235), (0.643137, 0.0, 0.968627), (0.607843, 0.0, 0.956863),
(0.576471, 0.0, 0.937255), (0.533333, 0.0, 0.917647), (0.513725, 0.0, 0.909804),
(0.47451, 0.0, 0.886275), (0.447059, 0.0, 0.866667), (0.411765, 0.0, 0.858824),
(0.388235, 0.0, 0.839216)]
colors3 = [(1,1,1), (0.961,0.949,0.847),
(0.886,0.933,0.776),
(0.776,0.910,0.737),
(0.643,0.878,0.718),
(0.494,0.835,0.722),
(0.341,0.784,0.737),
(0.204,0.722,0.753),
(0.180,0.647,0.761),
(0.278,0.557,0.757),
(0.392,0.455,0.725),
(0.478,0.333,0.671),
(0.518,0.208,0.565),
(0.502,0.078,0.431)]
cmap1 = ListedColormap(colors)
newcmap1 = ListedColormap(cmap1(np.linspace(0, 1, 17)))
cmap3 = ListedColormap(colors3)
newcmap3 = ListedColormap(cmap3(np.linspace(0, 1, 14)))
bounds = [0,0.1,0.5,1,3,5,10,15,20,25,30,35,40,45,50,55]
norm = BoundaryNorm(bounds, newcmap4.N)
bounds6 = [0,0.1,5,10,15,20,25,30,40,50,60,70,80,90,100,110]
norm6 = BoundaryNorm(bounds6, newcmap4.N)
bounds24 = [0,0.1,5,10,20,30,40,50,60,70,80,90,100,110,120,130]
norm24 = BoundaryNorm(bounds6, newcmap4.N)
cmap5 = plt.cm.get_cmap('ocean_r')
newcmap5 = ListedColormap(cmap5(np.linspace(0, 1, 13)))
#cmap2 = ListedColormap(colors2)
#newcmap2 = ListedColormap(cmap2(np.linspace(0, 0.9, 29)))
# Create the figure and plot background on different axes
crs = ccrs.Mercator()
for i in range(1,49):
fig, axarr = plt.subplots(nrows=1, ncols=3, figsize=(25, 10), constrained_layout=False,
subplot_kw={'projection': crs})
# Set height padding for plots
fig.set_constrained_layout_pads(w_pad=0., h_pad=10, hspace=0., wspace=0.)
axlist = axarr.flatten()
for ax in axlist:
plot_background(ax)
timestep=timeinit+timedelta(hours=i)
time2 = data_det_snow['sde'].metpy.time
clevs_snow = np.arange(0,40,2)
# cmap = plt.get_cmap('gist_ncar')
# newcmap = ListedColormap(cmap(np.linspace(0.15, 0.9, 30)))
# Upper left plot
cf1 = axlist[0].contourf(data_snow.longitude, data_snow.latitude, snow_median[i,:,:]*100,
[0,0.1,0.5,1,3,5,10,15,20,25,30,35,40], cmap=newcmap5, norm=norm, extend='max', transform=ccrs.PlateCarree())
ccf1= axlist[0].contour(data_snow.longitude, data_snow.latitude, snow_median[i,:,:]*100,
[1,5,10,20,30,40], colors='darkviolet', linestyles="dashed", transform=ccrs.PlateCarree())
axlist[0].clabel(ccf1, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)
axlist[0].set_title('50th Percentile (Median)', fontsize=16)
#cb1= fig.colorbar(cf1, ax=axlist[0], orientation='vertical',
#ticks=(0,0.1,0.5,1,3,5,10,15,20,25,30,35,40),
#shrink=0.73, fraction=0.1, pad=0)
#cb1.set_label('cm', size='x-large')
cf2 = axlist[1].contourf(data_snow.longitude, data_snow.latitude, snow_95[i,:,:]*100,
[0,0.1,0.5,1,3,5,10,15,20,25,30,35,40], cmap=newcmap5, norm=norm, extend='max', transform=ccrs.PlateCarree())
ccf2= axlist[1].contour(data_snow.longitude, data_snow.latitude, snow_95[i,:,:]*100,
[1,5,10,20,30,40], colors='darkviolet', linestyles="dashed", transform=ccrs.PlateCarree())
axlist[1].clabel(ccf2, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)
axlist[1].set_title('95th Percentile', fontsize=16)
#cb2= fig.colorbar(cf2, ax=axlist[1], orientation='vertical',
# ticks=(0,0.1,0.5,1,3,5,10,15,20,25,30,35,40),
#shrink=0.73, fraction=0.1, pad=0)
#cb2.set_label('cm', size='x-large')
cf3 = axlist[2].contourf(data_det_snow.longitude, data_det_snow.latitude, det_snow.metpy.loc[{'time': time2[i-1]}]*100,
[0,0.1,0.5,1,3,5,10,15,20,25,30,35,40], cmap=newcmap5, norm=norm, extend='max',transform=ccrs.PlateCarree())
ccf3= axlist[2].contour(data_det_snow.longitude, data_det_snow.latitude, det_snow.metpy.loc[{'time': time2[i-1]}]*100,
[1,5,10,20,30,40], colors='darkviolet', linestyles="dashed", transform=ccrs.PlateCarree())
axlist[2].clabel(ccf3, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)
axlist[2].set_title('Deterministic', fontsize=16)
cb = fig.colorbar(cf1, ax=axarr.ravel().tolist(), orientation='vertical',
ticks=(0,0.1,0.5,1,3,5,10,15,20,25,30,35,40), fraction=0.01, aspect=30, pad=0.02)
cb.set_label('cm', size='x-large')
# Set figure title
plt.gcf().text(0.130, 0.90, 'Model: ICON-D2-EPS 0.02° | ' + timeinit.strftime('Init: %d.%m.%Y %H:%M UTC | ')+timestep.strftime('Valid: %d.%m.%Y %H:%M UTC'), fontsize=20)
plt.gcf().text(0.130, 0.86, 'Parameter: Snow Depth', fontsize=20)
# Display the plot
time2 = str(i*1)
base_filename='icond2eps_snow_perc_'
suffix='.jpeg'
my_file = base_filename+time2+suffix
print(my_file)
plt.savefig(my_file, format="jpeg", bbox_inches='tight', dpi=85)
plt.close(fig)
/home/lmathias/anaconda3/envs/metpy/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1548: UserWarning: No contour levels were found within the data range. result = matplotlib.axes.Axes.contour(self, *args, **kwargs)
icond2eps_snow_perc_1.jpeg
/home/lmathias/anaconda3/envs/metpy/lib/python3.9/site-packages/cartopy/mpl/feature_artist.py:211: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later c = matplotlib.collections.PathCollection(paths,
icond2eps_snow_perc_2.jpeg icond2eps_snow_perc_3.jpeg icond2eps_snow_perc_4.jpeg icond2eps_snow_perc_5.jpeg icond2eps_snow_perc_6.jpeg icond2eps_snow_perc_7.jpeg icond2eps_snow_perc_8.jpeg icond2eps_snow_perc_9.jpeg icond2eps_snow_perc_10.jpeg icond2eps_snow_perc_11.jpeg icond2eps_snow_perc_12.jpeg icond2eps_snow_perc_13.jpeg icond2eps_snow_perc_14.jpeg icond2eps_snow_perc_15.jpeg icond2eps_snow_perc_16.jpeg icond2eps_snow_perc_17.jpeg icond2eps_snow_perc_18.jpeg icond2eps_snow_perc_19.jpeg icond2eps_snow_perc_20.jpeg icond2eps_snow_perc_21.jpeg icond2eps_snow_perc_22.jpeg icond2eps_snow_perc_23.jpeg icond2eps_snow_perc_24.jpeg icond2eps_snow_perc_25.jpeg icond2eps_snow_perc_26.jpeg icond2eps_snow_perc_27.jpeg icond2eps_snow_perc_28.jpeg icond2eps_snow_perc_29.jpeg icond2eps_snow_perc_30.jpeg icond2eps_snow_perc_31.jpeg icond2eps_snow_perc_32.jpeg icond2eps_snow_perc_33.jpeg icond2eps_snow_perc_34.jpeg icond2eps_snow_perc_35.jpeg icond2eps_snow_perc_36.jpeg icond2eps_snow_perc_37.jpeg icond2eps_snow_perc_38.jpeg icond2eps_snow_perc_39.jpeg icond2eps_snow_perc_40.jpeg icond2eps_snow_perc_41.jpeg icond2eps_snow_perc_42.jpeg icond2eps_snow_perc_43.jpeg icond2eps_snow_perc_44.jpeg icond2eps_snow_perc_45.jpeg icond2eps_snow_perc_46.jpeg icond2eps_snow_perc_47.jpeg icond2eps_snow_perc_48.jpeg
crs = ccrs.Mercator()
snow_prob_25_smooth = scipy.ndimage.zoom(snow_prob_25, 1.2)
snow_prob_10_smooth = scipy.ndimage.zoom(snow_prob_10, 1.2)
snow_prob_5_smooth = scipy.ndimage.zoom(snow_prob_5, 1.2)
snow_prob_1_smooth = scipy.ndimage.zoom(snow_prob_1, 1.2)
x_smooth = scipy.ndimage.zoom(x, 1.2)
y_smooth = scipy.ndimage.zoom(y, 1.2)
snow_prob_25_smooth = np.where(snow_prob_25_smooth < 100, snow_prob_25_smooth, 100)
snow_prob_10_smooth = np.where(snow_prob_10_smooth < 100, snow_prob_10_smooth, 100)
snow_prob_5_smooth = np.where(snow_prob_5_smooth < 100, snow_prob_5_smooth, 100)
snow_prob_1_smooth = np.where(snow_prob_1_smooth < 100, snow_prob_1_smooth, 100)
snow_prob_25_smooth = np.where(snow_prob_25_smooth > 0, snow_prob_25_smooth, 0)
snow_prob_10_smooth = np.where(snow_prob_10_smooth > 0, snow_prob_10_smooth, 0)
snow_prob_5_smooth = np.where(snow_prob_5_smooth > 0, snow_prob_5_smooth, 0)
snow_prob_1_smooth = np.where(snow_prob_1_smooth > 0, snow_prob_1_smooth, 0)
#print(np.max(ceil_prob_15_smooth))
for i in range(1,49):
fig, axarr = plt.subplots(nrows=1, ncols=4, figsize=(25, 10), constrained_layout=False,
subplot_kw={'projection': crs})
# Set height padding for plots
fig.set_constrained_layout_pads(w_pad=0., h_pad=10, hspace=0., wspace=0.)
axlist = axarr.flatten()
for ax in axlist:
plot_background(ax)
timestep=timeinit+timedelta(hours=i)
time2 = data_det_snow['sde'].metpy.time
#time3 = data_hsurf['h'].metpy.time
clevs_prob= np.arange(0,110,10)
# cmap = plt.get_cmap('gist_ncar')
# newcmap = ListedColormap(cmap(np.linspace(0.15, 0.9, 30)))
# Upper left plot
cf1 = axlist[0].contourf(x_smooth, y_smooth, snow_prob_1_smooth[i,:,:],
clevs_prob, cmap='BuPu', transform=ccrs.PlateCarree())
#ccf1= axlist[0].contour(data_ceil.longitude, data_ceil.latitude, ceil_median[i,:,:],
#[100,200,500,1000,1500], colors='dimgrey', linestyles="dotted",transform=ccrs.PlateCarree())
#axlist[0].clabel(ccf1, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)
axlist[0].set_title('Probability h > 1 cm', fontsize=16)
#cb1= fig.colorbar(cf1, ax=axlist[0], orientation='vertical',
# ticks=(10,20,30,40,50,60,70,80,90,100,110,120,130),
# shrink=0.73, fraction=0.1, pad=0)
#cb1.set_label('km/h', size='x-large')
cf2 = axlist[1].contourf(x_smooth, y_smooth, snow_prob_5_smooth[i,:,:],
clevs_prob, cmap='BuPu', transform=ccrs.PlateCarree())
#ccf2= axlist[1].contour(data_ceil.longitude, data_ceil.latitude, ceil_95[i,:,:],
#[100,200,500,1000,1500], colors='dimgrey', linestyles="dotted",transform=ccrs.PlateCarree())
#axlist[1].clabel(ccf2, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)
axlist[1].set_title('Probability h > 5 cm', fontsize=16)
#cb2= fig.colorbar(cf2, ax=axlist[1], orientation='vertical',
#ticks=(10,20,30,40,50,60,70,80,90,100,110,120,130),
# shrink=0.73, fraction=0.1, pad=0)
# cb2.set_label('km/h', size='x-large')
cf3 = axlist[2].contourf(x_smooth, y_smooth, snow_prob_10_smooth[i,:,:],
clevs_prob, cmap='BuPu', transform=ccrs.PlateCarree())
#ccf3= axlist[2].contour(data_det_vmax.longitude, data_det_vmax.latitude, det_gust.metpy.loc[{'time': time2[i-1]}]*3.6,
#[30,50,70,90,110,130], colors='dimgrey', linestyles="dotted",transform=ccrs.PlateCarree())
#axlist[2].clabel(ccf3, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)
axlist[2].set_title('Probability h > 10 cm', fontsize=16)
# cb3= fig.colorbar(cf3, ax=axlist[2], orientation='vertical',
#ticks=(10,20,30,40,50,60,70,80,90,100,110,120,130),
#shrink=0.73, fraction=0.1, pad=0)
#cb3.set_label('km/h', size='x-large')
cf4 = axlist[3].contourf(x_smooth, y_smooth, snow_prob_25_smooth[i,:,:],
clevs_prob, cmap='BuPu',transform=ccrs.PlateCarree())
#ccf3= axlist[2].contour(data_det_vmax.longitude, data_det_vmax.latitude, det_gust.metpy.loc[{'time': time2[i-1]}]*3.6,
#[30,50,70,90,110,130], colors='dimgrey', linestyles="dotted",transform=ccrs.PlateCarree())
#axlist[2].clabel(ccf3, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)
axlist[3].set_title('Probability h > 25 cm', fontsize=16)
cb = fig.colorbar(cf1, ax=axarr.ravel().tolist(), orientation='vertical',
ticks=(0,10,20,30,40,50,60,70,80,90,100), fraction=0.0075, aspect=30, pad=0.02)
cb.set_label('%', size='x-large')
# Set figure title
plt.gcf().text(0.130, 0.86, 'Model: ICON-D2-EPS 0.02° | ' + timeinit.strftime('Init: %d.%m.%Y %H:%M UTC | ')+timestep.strftime('Valid: %d.%m.%Y %H:%M UTC'), fontsize=20)
plt.gcf().text(0.130, 0.82, 'Parameter: Snow Depth', fontsize=20)
# Display the plot
time2 = str(i*1)
base_filename='icond2eps_snow_prob_'
suffix='.jpeg'
my_file = base_filename+time2+suffix
print(my_file)
plt.savefig(my_file, format="jpeg", bbox_inches='tight', dpi=85)
plt.close(fig)
icond2eps_snow_prob_1.jpeg icond2eps_snow_prob_2.jpeg icond2eps_snow_prob_3.jpeg icond2eps_snow_prob_4.jpeg icond2eps_snow_prob_5.jpeg icond2eps_snow_prob_6.jpeg icond2eps_snow_prob_7.jpeg icond2eps_snow_prob_8.jpeg icond2eps_snow_prob_9.jpeg icond2eps_snow_prob_10.jpeg icond2eps_snow_prob_11.jpeg icond2eps_snow_prob_12.jpeg icond2eps_snow_prob_13.jpeg icond2eps_snow_prob_14.jpeg icond2eps_snow_prob_15.jpeg icond2eps_snow_prob_16.jpeg icond2eps_snow_prob_17.jpeg icond2eps_snow_prob_18.jpeg icond2eps_snow_prob_19.jpeg icond2eps_snow_prob_20.jpeg icond2eps_snow_prob_21.jpeg icond2eps_snow_prob_22.jpeg icond2eps_snow_prob_23.jpeg icond2eps_snow_prob_24.jpeg icond2eps_snow_prob_25.jpeg icond2eps_snow_prob_26.jpeg icond2eps_snow_prob_27.jpeg icond2eps_snow_prob_28.jpeg icond2eps_snow_prob_29.jpeg icond2eps_snow_prob_30.jpeg icond2eps_snow_prob_31.jpeg icond2eps_snow_prob_32.jpeg icond2eps_snow_prob_33.jpeg icond2eps_snow_prob_34.jpeg icond2eps_snow_prob_35.jpeg icond2eps_snow_prob_36.jpeg icond2eps_snow_prob_37.jpeg icond2eps_snow_prob_38.jpeg icond2eps_snow_prob_39.jpeg icond2eps_snow_prob_40.jpeg icond2eps_snow_prob_41.jpeg icond2eps_snow_prob_42.jpeg icond2eps_snow_prob_43.jpeg icond2eps_snow_prob_44.jpeg icond2eps_snow_prob_45.jpeg icond2eps_snow_prob_46.jpeg icond2eps_snow_prob_47.jpeg icond2eps_snow_prob_48.jpeg