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gfs_hrly_ptype_soundings.py
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import cartopy.crs as ccrs
import cartopy.feature as cfeature
import numpy as np
import matplotlib.pyplot as plt
import netCDF4
import xarray as xr
import metpy
from datetime import datetime
import datetime as dt
from metpy.units import units
import scipy.ndimage as ndimage
from metpy.plots import USCOUNTIES
import cartopy
from scipy.ndimage.filters import generic_filter as gf
from metpy.plots import USCOUNTIES
from metpy.plots import SkewT
import matplotlib.patches as mpatches
import matplotlib.lines as lines
import metpy.calc as mpcalc
# make unique directory to store output
def mkdir_p(mypath):
'''Creates a directory. equivalent to using mkdir -p on the command line'''
from errno import EEXIST
from os import makedirs,path
try:
makedirs(mypath)
except OSError as exc: # Python >2.5
if exc.errno == EEXIST and path.isdir(mypath):
pass
else: raise
def wet_bulb(temp,dewpoint):
tdd = temp-dewpoint
wet_bulb = temp-((1/3)*tdd)
return wet_bulb
def fram(ice,wet_bulb,velocity):
ilr_p = ice
ilr_t = (-0.0071*(wet_bulb**3))-(0.039*(wet_bulb**2))-(0.3904*wet_bulb)+0.5545
ilr_v = (0.0014*(velocity**2))+(0.0027*velocity)+0.7574
cond_1 = np.ma.masked_where(wet_bulb>-0.35,ice)
cond_2 = np.ma.masked_where((wet_bulb<-0.35) & (velocity>12.),ice)
cond_3 = np.ma.masked_where((wet_bulb<-0.35) & (velocity<=12.),ice)
cond_1 = cond_1.filled(0)
cond_2 = cond_2.filled(0)
cond_3 = cond_3.filled(0)
ilr_1 = (0.7*ilr_p)+(0.29*ilr_t)+(0.01*ilr_v)
ilr_2 = (0.73*ilr_p)+(0.01*ilr_t)+(0.26*ilr_v)
ilr_3 = (0.79*ilr_p)+(0.2*ilr_t)+(0.01*ilr_v)
accretion_1 = cond_1*ilr_1
accretion_2 = cond_2*ilr_2
accretion_3 = cond_3*ilr_3
total_accretion=accretion_1+accretion_2+accretion_3
return total_accretion
#grabbing data from NOMADS
startTime=datetime.now()
m_date='20200903'
m_hour='12'
year = startTime.year
if startTime.month <10:
month = '0'+str(startTime.month)
else:
month = str(startTime.month)
if startTime.day <10:
day = '0'+str(startTime.day)
else:
day = str(startTime.day)
if startTime.hour <10:
hour = '0'+str(startTime.hour)
else:
hour = str(startTime.hour)
mdate = str(year)+str(month)+str(day)
def get_init_hr(hour):
if int(hour) <6:
init_hour = '00'
elif int(hour) <12:
init_hour = '06'
elif int(hour) <17:
init_hour = '12'
elif int(hour) <22:
init_hour = '18'
else:
init_hour = '00'
return(init_hour)
url = 'http://nomads.ncep.noaa.gov:80/dods/gfs_0p25_1hr/gfs'+mdate+'/gfs_0p25_1hr_'+get_init_hr(hour)+'z'
#url = 'http://nomads.ncep.noaa.gov:80/dods/gfs_0p25_1hr/gfs'+mdate+'/gfs_0p25_1hr_06z'
init_hour = get_init_hr(hour)
# Create new directory
output_dir = str(year)+str(month)+str(day)+'_'+str(init_hour)+'00'
mkdir_p(output_dir)
mkdir_p(output_dir+'/GFS')
#Parse data using MetPy
ds = xr.open_dataset(url)
init_hr = dt.datetime(year,int(month),int(day),int(init_hour))
times = ds['tmp2m'].metpy.time
init_time = ds['time'][0]
lats = np.arange(20,55,0.25)
lons = np.arange(240,310,0.25)
ds = ds.sel(lat = lats, lon = lons)
#Initialize ptype arrays by grabbing first hour of categorical precip
catrain = ds['crainsfc'].squeeze().isel(time=1).squeeze()
catsnow = ds['csnowsfc'].squeeze().isel(time=1).squeeze()
catsleet = ds['cicepsfc'].squeeze().isel(time=1).squeeze()
catice = ds['cfrzrsfc'].squeeze().isel(time=1).squeeze()
total_precip=ds['apcpsfc'].isel(time=1).squeeze()*.0393700787402
acc_rain = np.ma.masked_where(catrain==0,total_precip)
acc_sleet = np.ma.masked_where(catsleet==0,total_precip)
acc_ice = np.ma.masked_where(catice==0,total_precip)
acc_snow = np.ma.masked_where(catsnow==0,total_precip)
acc_rain = acc_rain.filled(0)
acc_sleet = acc_sleet.filled(0)
acc_ice = acc_ice.filled(0)
acc_snow = acc_snow.filled(0)
t2mi = ds['tmp2m'].isel(time=1).squeeze()-273.15
td2mi = ds['tmp2m'].isel(time=1).squeeze()-273.15
u10 = ds['ugrd10m'].isel(time=1).squeeze()*1.94384449
v10 = ds['vgrd10m'].isel(time=1).squeeze()*1.94384449
ws10 = ((u10**2)+(v10**2))**.5
acc_fram = fram(acc_ice,wet_bulb(t2mi,td2mi),ws10)
print("INITIALIZATION SUCCESSFUL")
#Now loop through the rest to come up with a ptype map each hour and
#ideally a four-panel accumulation map
for i in range(2,120):
fc_hr = init_hr+dt.timedelta(hours=1*i)
forecast_hour = times[0].values
data = ds.metpy.parse_cf()
data = data.isel(time=i)
#Rename variables to useful things
data = data.rename({
'cfrzrsfc':'catice',
'cicepsfc':'catsleet',
'crainsfc':'catrain',
'csnowsfc':'catsnow',
'tcdcclm':'tcc',
'tmpprs': 'temperature',
'ugrd10m': 'u',
'vgrd10m': 'v',
'hgtprs': 'height',
'prmslmsl':'mslp',
'tmp2m':'sfc_temp',
'dpt2m':'sfc_td',
'refcclm':'radar',
'apcpsfc':'qpf',
'rhprs':'rh'
})
catrain = data['catrain'].squeeze()
catsnow = data['catsnow'].squeeze()
catsleet = data['catsleet'].squeeze()
catice = data['catice'].squeeze()
#This extends each ptype one gridpoint outwards to prevent a gap between
#different ptypes
radius = 1
kernel = np.zeros((2*radius+1,2*radius+1))
y1,x1 = np.ogrid[-radius:radius+1,-radius:radius+1]
mask=x1**2+y1**2 <=radius**2
kernel[mask]=1
snowc= gf(catsnow,np.max,footprint=kernel)
icec = gf(catice,np.max,footprint=kernel)
sleetc = gf(catsleet,np.max,footprint=kernel)
rainc = gf(catrain,np.max,footprint=kernel)
#Coordinate stuff
vertical, = data['temperature'].metpy.coordinates('vertical')
time = data['temperature'].metpy.time
x, y = data['height'].metpy.coordinates('x', 'y')
lat, lon = xr.broadcast(y, x)
zH5_crs = data['temperature'].metpy.cartopy_crs
#data['temperature'].metpy.convert_units('degC')
t2m = data['sfc_temp'].squeeze()
t2m = ((t2m - 273.15)*(9./5.))+32.
td2m = data['sfc_td'].squeeze()
td2m = ((td2m - 273.15)*(9./5.))+32.
td2ms = ndimage.gaussian_filter(td2m,sigma=5,order=0)
wb2m = wet_bulb(t2m,td2m)
wb2mc = (wb2m-32.)*(5./9.)
cloudcover = data['tcc'].squeeze()
reflectivity = data['radar'].squeeze()
hrly_precip = data['qpf'].squeeze()*0.0393700787402
new_precip = hrly_precip-total_precip
total_precip = hrly_precip
'''
rain = np.ma.masked_where(rainc==0,new_precip)
sleet = np.ma.masked_where(catsleet==0,new_precip)
ice = np.ma.masked_where(catice==0,new_precip)
snow = np.ma.masked_where(snowc==0,new_precip)
'''
rain = np.ma.masked_where(rainc==0,reflectivity)
sleet = np.ma.masked_where(sleetc==0,reflectivity)
ice = np.ma.masked_where(icec==0,reflectivity)
snow = np.ma.masked_where(snowc==0,reflectivity)
#Smooth rain
#rain = ndimage.gaussian_filter(rain,sigma=1,order=0)
mslp = data['mslp']/100.
mslpc = mslp.squeeze()
mslpc=ndimage.gaussian_filter(mslpc,sigma=1,order=0)
ws2 = slice(5,-5,5)
wind_slice = slice(5,-5,5)
u_10m = data['u'].squeeze()
v_10m = data['v'].squeeze()
u_10m = u_10m*1.94384449
v_10m = v_10m*1.94384449
ws10m = ((u_10m**2)+(v_10m**2))**.5
###COMPUTE ICE ACCRETION###
fram_accretion=fram(ice,wb2mc,ws10m)
fram_accretion=fram_accretion.filled(0)
acc_fram = acc_fram+fram_accretion
dtfs = str(time.dt.strftime('%Y-%m-%d_%H%MZ').item())
########## SET UP FIGURE ##################################################
fig = plt.figure(figsize=(15,15))
ax1 = fig.add_subplot(111, projection = zH5_crs)
ax1.coastlines(resolution='10m')
ax1.add_feature(cfeature.BORDERS.with_scale('10m'))
ax1.add_feature(cfeature.STATES.with_scale('10m'))
########## PLOTTING #######################################################
#tmp_2m = ax1.contourf(x,y,t2m,cmap='RdYlBu_r', alpha = 0.8, levels = range(-20,100,5),transform=zH5_crs)
tmp_2m32 = ax1.contour(x,y,t2m,colors='b', alpha = 0.8, levels = [32])
#cbr = fig.colorbar(tmp_2m, orientation = 'horizontal', aspect = 80, ax = ax1, pad = 0.01,
# extendrect=False, ticks = range(-20,100,5))
#cbr.set_label('2m Temperature (F)', fontsize = 14)
h_contour = ax1.contour(x, y, mslpc, colors='dimgray', levels=range(940,1080,4),linewidths=1,alpha=0.7)
h_contour.clabel(fontsize=14, colors='dimgray', inline=1, inline_spacing=4, fmt='%i mb', rightside_up=True, use_clabeltext=True)
ref_levs = [1,5,10,15,20, 25, 30, 35, 40, 45, 50, 55, 60, 65]
#q_levs = [0.01,0.05,0.1,0.25,0.5,0.75,1,1.25]
q_levs = [0.01,0.02,0.03,0.04,0.05,0.1,0.2,0.5]
qarlevs = [0.01,0.05,0.1,0.25,0.5,0.75,1,1.25,1.5,1.75,2,2.5,3,3.5,4,4.5,5,6,7,8,9,10]
qaslevs = [0.01,0.05,0.1,0.25,0.5,0.75,1,1.25,1.5,1.75,2,2.5,3,3.5,4,4.5]
qazlevs = [0.01,0.05,0.1,0.25,0.5,0.75,1,1.25,1.5,1.75,2,2.5,3]
qr_cols = ['#cfffbf','#a7ff8a','#85ff5c','#60ff2b','#40ff00','#ffff00','#e6e600','#cccc00','#e4cc00']
qs_cols = ['#b8ffff','#82ffff','#00ffff','#00cccc','#00adad','#007575','#0087f5','#0039f5','#1d00f5']
qi_cols = ['#eeccff','#dd99ff','#cc66ff','#bb33ff','#aa00ff','#8800cc','#660099','#440066','#6600cc']
qz_cols = ['#ff0066','#ff0080','#ff33cc','#ff00bf','#cc0099','#990073','#66004d','#b30000','#ff3333']
qra_cols = ['#cfffbf','#a7ff8a','#85ff5c','#60ff2b','#40ff00','#40ff00','#ffff00','#e6e600','#cccc00','#e4cc00','#ffcc00','#ff9500','#ff4800','#ff2900','#ff1200','#ff0000','#cc0000','#990000','#990033','#b3003b','#ff3333','#ff6666','#ffffff']
qrs_cols = ['#b8ffff','#82ffff','#00ffff','#00cccc','#00adad','#007575','#0087f5','#0039f5','#1d00f5','#4f01f6','#7a00f5','#9e00f5','#b833ff','#d280ff','#cc00f1','#ad00cc','#820099','#4700b3']
qzr_cols = ['#ff0066','#ff33cc','#ff00bf','#cc0099','#990073','#66004d','#b30000','#ff3333','#ff6666','#ff9999','#ffcccc','#ffffff']
qip_cols = ['#eeccff','#dd99ff','#cc66ff','#bb33ff','#aa00ff','#8800cc','#660099','#440066','#6600cc','#9933ff','#bf80ff','#e6ccff','#ffffff']
q_levs_r = [0.01,0.05,0.1,0.25,0.5,0.75,1,1.25,1.5,1.75,2,2.5,3]
try:
ra = ax1.contourf(x,y,rain,colors=qr_cols,levels=ref_levs,alpha=0.4,extend='max')
except:
print('no rain')
try:
sn = ax1.contourf(x,y,snow,colors=qs_cols,levels=ref_levs,alpha=0.4,extend='max')
except:
print('no snow')
try:
ip = ax1.contourf(x,y,sleet,colors=qi_cols,levels=ref_levs,alpha=0.4,extend='max')
except:
print('no sleet')
try:
zr = ax1.contourf(x,y,ice, colors=qz_cols,levels=ref_levs,alpha=0.4,extend='max')
except:
print('no ice')
#ax1.barbs(x[wind_slice],y[wind_slice],u_10m[wind_slice,wind_slice],v_10m[wind_slice,wind_slice], length=7)
ax1.set_title('Precipitation Type and Selected Soundings',fontsize=16)
ax1.set_title('\n Valid: '+time.dt.strftime('%Y-%m-%d %H:%MZ').item(),fontsize=11,loc='right')
ax1.set_title('\n GFS Init: '+init_time.dt.strftime('%Y-%m-%d %H:%MZ').item(),fontsize=11,loc='left')
ax1.set_extent((255, 285, 25, 50))#, crs = zH5_crs) # Set a title and show the plot
soundlat = 35
soundlon = 360-99
sound_pres = data.lev
ptop=300
startlat=78
londelt=5.85
for i in range(1,5):
soundlat = 27.15
soundlon = 360-(startlat+(londelt*i))
sound_temps = data['temperature'].interp(lat=soundlat,lon=soundlon)-273.15
sound_rh = data['rh'].interp(lat=soundlat,lon=soundlon)
sound_dp = mpcalc.dewpoint_from_relative_humidity(sound_temps.data*units.degC,sound_rh.data*units.percent)
skew = SkewT(fig=fig,rect=(0.75-(0.15*i),0.2,.15,.1))
skew.plot(sound_pres,sound_dp,'g',linewidth=3)
skew.plot(sound_pres,sound_temps,'r',linewidth=3)
skew.ax.axvline(0, color='purple', linestyle='--', linewidth=3)
skew.ax.set_ylim((1000,ptop))
skew.ax.axis('off')
#skew.ax.text(0.1,0.1,str(soundlat)+' '+str(soundlon))
for i in range(0,5):
soundlat = 31
soundlon = 360-(startlat+(londelt*i))
sound_temps = data['temperature'].interp(lat=soundlat,lon=soundlon)-273.15
sound_rh = data['rh'].interp(lat=soundlat,lon=soundlon)
sound_dp = mpcalc.dewpoint_from_relative_humidity(sound_temps.data*units.degC,sound_rh.data*units.percent)
skew = SkewT(fig=fig,rect=(0.75-(0.15*i),0.3,.15,.1))
skew.plot(sound_pres,sound_dp,'g',linewidth=3)
skew.plot(sound_pres,sound_temps,'r',linewidth=3)
skew.ax.axvline(0, color='purple', linestyle='--', linewidth=3)
skew.ax.set_ylim((1000,ptop))
skew.ax.axis('off')
#skew.ax.text(0.1,0.1,str(soundlat)+' '+str(soundlon))
for i in range(0,5):
soundlat = 34.75
soundlon = 360-(startlat+(londelt*i))
sound_temps = data['temperature'].interp(lat=soundlat,lon=soundlon)-273.15
sound_rh = data['rh'].interp(lat=soundlat,lon=soundlon)
sound_dp = mpcalc.dewpoint_from_relative_humidity(sound_temps.data*units.degC,sound_rh.data*units.percent)
skew = SkewT(fig=fig,rect=(0.75-(0.15*i),0.4,.15,.1))
skew.plot(sound_pres,sound_dp,'g',linewidth=3)
skew.plot(sound_pres,sound_temps,'r',linewidth=3)
skew.ax.axvline(0, color='purple', linestyle='--', linewidth=3)
skew.ax.set_ylim((1000,ptop))
skew.ax.axis('off')
#skew.ax.text(0.1,0.1,str(soundlat)+' '+str(soundlon))
for i in range(0,5):
soundlat = 38.75
soundlon = 360-(startlat+(londelt*i))
sound_temps = data['temperature'].interp(lat=soundlat,lon=soundlon)-273.15
sound_rh = data['rh'].interp(lat=soundlat,lon=soundlon)
sound_dp = mpcalc.dewpoint_from_relative_humidity(sound_temps.data*units.degC,sound_rh.data*units.percent)
skew = SkewT(fig=fig,rect=(0.75-(0.15*i),0.5,.15,.1))
skew.plot(sound_pres,sound_dp,'g',linewidth=3)
skew.plot(sound_pres,sound_temps,'r',linewidth=3)
skew.ax.axvline(0, color='purple', linestyle='--', linewidth=3)
skew.ax.set_ylim((1000,ptop))
skew.ax.axis('off')
#skew.ax.text(0.1,0.1,str(soundlat)+' '+str(soundlon))
for i in range(0,5):
soundlat = 42.65
soundlon = 360-(startlat+(londelt*i))
sound_temps = data['temperature'].interp(lat=soundlat,lon=soundlon)-273.15
sound_rh = data['rh'].interp(lat=soundlat,lon=soundlon)
sound_dp = mpcalc.dewpoint_from_relative_humidity(sound_temps.data*units.degC,sound_rh.data*units.percent)
skew = SkewT(fig=fig,rect=(0.75-(0.15*i),0.6,.15,.1))
skew.plot(sound_pres,sound_dp,'g',linewidth=3)
skew.plot(sound_pres,sound_temps,'r',linewidth=3)
skew.ax.axvline(0, color='purple', linestyle='--', linewidth=3)
skew.ax.set_ylim((1000,ptop))
skew.ax.axis('off')
#skew.ax.text(0.1,0.1,str(soundlat)+' '+str(soundlon))
for i in range(0,5):
soundlat = 46.35
soundlon = 360-(startlat+(londelt*i))
sound_temps = data['temperature'].interp(lat=soundlat,lon=soundlon)-273.15
sound_rh = data['rh'].interp(lat=soundlat,lon=soundlon)
sound_dp = mpcalc.dewpoint_from_relative_humidity(sound_temps.data*units.degC,sound_rh.data*units.percent)
skew = SkewT(fig=fig,rect=(0.75-(0.15*i),0.7,.15,.1))
skew.plot(sound_pres,sound_dp,'g',linewidth=3)
skew.plot(sound_pres,sound_temps,'r',linewidth=3)
skew.ax.axvline(0, color='purple', linestyle='--', linewidth=3)
skew.ax.axis('off')
skew.ax.set_ylim((1000,ptop))
#skew.ax.text(0.1,0.1,str(soundlat)+' '+str(soundlon))
#purple = mpatches.Patch(color='purple',label='Frontogenesis')
dashed_red_line = lines.Line2D([], [], linestyle='solid', color='r', label='Temperature')
dashed_purple_line = lines.Line2D([],[],linestyle='dashed',color='purple',label='0C Isotherm')
dashed_green_line = lines.Line2D([], [], linestyle='solid', color='g', label='Dew Point')
grey_line = lines.Line2D([], [], color='darkgray', label='MSLP (hPa)')
blue_line = lines.Line2D([], [], color='b',label='2m 0C Isotherm')
leg = ax1.legend(handles=[dashed_red_line,dashed_green_line,dashed_purple_line],title='Sounding Legend',loc=4,framealpha=1)
leg.set_zorder(100)
#leg2 = ax1.legend(handles=[grey_line,blue_line],title='Map Legend',loc=3,framealpha=1)
plt.savefig(output_dir+'/GFS/gfs_hrly_pytpesound_27_'+dtfs+'.png',bbox_inches='tight',pad_inches=0.1)
fcst_hr = str(0)
plt.close()
plt.clf()
'''
######################################################################
fig2 = plt.figure(figsize=(25,15))
ax2 = fig2.add_subplot(221,projection=zH5_crs)
ax3 = fig2.add_subplot(222,projection=zH5_crs)
ax4 = fig2.add_subplot(223,projection=zH5_crs)
ax5 = fig2.add_subplot(224,projection=zH5_crs)
ax2.coastlines(resolution='10m')
ax2.add_feature(cfeature.BORDERS.with_scale('10m'))
ax2.add_feature(cfeature.STATES.with_scale('10m'))
ax3.coastlines(resolution='10m')
ax3.add_feature(cfeature.BORDERS.with_scale('10m'))
ax3.add_feature(cfeature.STATES.with_scale('10m'))
ax4.coastlines(resolution='10m')
ax4.add_feature(cfeature.BORDERS.with_scale('10m'))
ax4.add_feature(cfeature.STATES.with_scale('10m'))
ax5.coastlines(resolution='10m')
ax5.add_feature(cfeature.BORDERS.with_scale('10m'))
ax5.add_feature(cfeature.STATES.with_scale('10m'))
fig2.suptitle('GFS Accumulated Precipitation By Precipitation Type \n Initialized: '+init_time.dt.strftime('%Y-%m-%d %H:%MZ').item()+' \n Valid: '+time.dt.strftime('%Y-%m-%d %H:%MZ').item()+' | '+time.dt.strftime('%a %b %d %H:%MZ').item(),fontsize=14)
try:
ra = ax5.contourf(x,y,acc_rain,colors=qra_cols,levels=qarlevs,alpha=0.7)
rac = fig2.colorbar(ra,orientation = 'vertical', aspect = 20, ax = ax5, pad = 0.01,
extendrect=False, ticks = [0.5,1,1.5,2,3,4,5,7,9],shrink=0.7)
except:
print('no rain')
try:
sn = ax2.contourf(x,y,acc_snow,colors=qrs_cols,levels=qaslevs,alpha=0.7)
snc = fig2.colorbar(sn,orientation = 'vertical', aspect = 20, ax = ax2, pad = 0.01,
extendrect=False, ticks = range(0,5,1),shrink=0.7)
except:
print('no snow')
try:
ip = ax3.contourf(x,y,acc_sleet,colors=qip_cols,levels=qazlevs,alpha=0.7)
ipc = fig2.colorbar(ip,orientation = 'vertical', aspect = 20, ax = ax3, pad = 0.01,
extendrect=False, ticks = [0.01,0.1,0.5,1,1.5,2,3],shrink=0.7)
except:
print('no sleet')
try:
zr = ax4.contourf(x,y,acc_ice, colors=qzr_cols,levels=qazlevs,alpha=0.7)
zrc = fig2.colorbar(zr,orientation = 'vertical', aspect = 20, ax = ax4, pad = 0.01,
extendrect=False, ticks = [0.01,0.1,0.5,1,1.5,2,3],shrink=0.7)
except:
print('no ice')
ax2.set_title('Accumulated Liquid Equivalent Snow')
ax3.set_title('Accumulated Liquid Equivalent Sleet')
ax4.set_title('Accumulated Liquid Equivalent Freezing Rain')
ax5.set_title('Accumulated Rain')
ax2.set_extent((260, 295, 25, 50))#, crs = zH5_crs) # Set a title and show the plot
ax3.set_extent((260, 295, 25, 50))#, crs = zH5_crs) # Set a title and show the plot
ax4.set_extent((260, 295, 25, 50))#, crs = zH5_crs) # Set a title and show the plot
ax5.set_extent((260, 295, 25, 50))#, crs = zH5_crs) # Set a title and show the plot
fig2.tight_layout()
plt.savefig(output_dir+'/GFS/ec_accum_ptype_'+dtfs+'.png')
ax2.set_extent((281,295,39,49))#, crs = zH5_crs) # Set a title and show the plot
ax3.set_extent((281,295,39,49))#, crs = zH5_crs) # Set a title and show the plot
ax4.set_extent((281,295,39,49))#, crs = zH5_crs) # Set a title and show the plot
ax5.set_extent((281,295,39,49))#, crs = zH5_crs) # Set a title and show the plot
ax2.add_feature(USCOUNTIES.with_scale('5m'), edgecolor='gray',linewidths=0.5)
ax3.add_feature(USCOUNTIES.with_scale('5m'), edgecolor='gray',linewidths=0.5)
ax4.add_feature(USCOUNTIES.with_scale('5m'), edgecolor='gray',linewidths=0.5)
ax5.add_feature(USCOUNTIES.with_scale('5m'), edgecolor='gray',linewidths=0.5)
plt.savefig(output_dir+'/GFS/ne_accum_ptype_'+dtfs+'.png')
plt.close()
plt.clf()
fig3 = plt.figure(figsize=(15,15))
ax6 = fig3.add_subplot(111,projection=zH5_crs)
try:
zr_acc = ax6.contourf(x,y,acc_fram,colors=qzr_cols,levels=qazlevs,alpha=0.8)
zrc = fig3.colorbar(zr_acc,orientation = 'horizontal', aspect = 80, ax = ax6, pad = 0.01,
extendrect=False, ticks = [0.01,0.1,0.5,1,1.5,2,3],shrink=0.9)
zrc.set_label('Inches of Horizontal Accretion')
except:
print('no ice')
ax6.coastlines(resolution='10m')
ax6.add_feature(cfeature.BORDERS.with_scale('10m'))
ax6.add_feature(cfeature.STATES.with_scale('10m'))
ax6.set_title('GFS Ice Accretion Forecast (FRAM)',fontsize=14)
ax6.set_title(' \n Valid: '+time.dt.strftime('%Y-%m-%d %H:%MZ').item(),fontsize=11,loc='right')
ax6.set_title(' \n Init: '+init_time.dt.strftime('%Y-%m-%d %H:%MZ').item(),fontsize=11,loc='left')
ax6.set_extent((265, 300, 25, 50))#, crs = zH5_crs) # Set a title and show the plot
plt.savefig(output_dir+'/GFS/ec_ice_accretion_fram_'+dtfs+'.png')
ax6.add_feature(USCOUNTIES.with_scale('5m'), edgecolor='gray',linewidths=0.5)
ax6.set_extent((281,295,39,49))
plt.savefig(output_dir+'/GFS/ne_ice_accretion_fram_'+dtfs+'.png')
ax6.set_extent((260,271,36,42))#, crs = zH5_crs) # Set a title and show the plot
#plt.savefig(output_dir+'/GFS/kc_outer_ice_accretion_fram_'+dtfs+'.png')
#ax6.set_extent((263,268,37.5,40.5))#, crs = zH5_crs) # Set a title and show the plot
#plt.savefig(output_dir+'/GFS/kc_ice_accretion_fram_'+dtfs+'.png')
plt.close()
plt.clf()
print('Hour '+str(i)+' completed!')
timeelapsed = datetime.now()-startTime
print(timeelapsed)
'''