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gfs_hrly_ar.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
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
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) <11:
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'
init_hour = get_init_hr(hour)
'''
for i in range(119):
fhr = i+1
'''
# 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(int(year),int(month),int(day),int(init_hour))
times = ds['tmp2m'].metpy.time
init_time = ds['time'][0]
lats = np.arange(25,55,0.25)
lons = np.arange(260,310,0.25)
for i in range(1,120):
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({
'absvprs':'avort',
'hgtprs':'gph',
'rhprs':'rh',
'tmpprs':'temp',
'ugrdprs':'u',
'vgrdprs': 'v',
'pwatclm':'pwat'
})
vertical, = data['temp'].metpy.coordinates('vertical')
time = data['temp'].metpy.time
zH5_crs = data['temp'].metpy.cartopy_crs
u8 = data['u'].sel(lev=850.0,lat=lats,lon=lons).squeeze()*1.94384449
v8 = data['v'].sel(lev=850.0,lat=lats,lon=lons).squeeze()*1.94384449
h8 = data['gph'].sel(lev=850.0,lat=lats,lon=lons).squeeze()
pwat = data['pwat'].sel(lat=lats,lon=lons).squeeze()*0.0393700787402
x, y = h8.metpy.coordinates('x', 'y')
lat, lon = xr.broadcast(y, x)
wind_slice = slice(5,-5,5)
#wspd = ((u2**2)+(v2**2))**.5
########## 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'))
#fig.suptitle("NAM Forecast valid at " + time[0].dt.strftime('%Y-%m-%d %H:%MZ').item(),fontsize=36)
pwlevs = []
for j in range(1,25):
lev = 0.1*j
pwlevs.append(lev)
########## PLOTTING #######################################################
h8c = ax1.contour(x,y,h8,colors='dimgray', levels = range(1100,1600,30),linewidths=1.5)
#t2c = ax1.contour(x,y,t2,colors='r', levels = range(-60,0,5),linestyles='dashed',linewidths=1)
pwc = ax1.contourf(x,y,pwat,cmap='Greens',levels=pwlevs,alpha=0.9)
a5cb = fig.colorbar(pwc, orientation = 'horizontal', aspect = 80, ax = ax1, pad = 0.01,
extendrect=False, ticks = [0.5,1,1.5,2,2.5])
a5cb.set_label('Precipitable Water (in)', fontsize = 12)
ax1.barbs(x[wind_slice],y[wind_slice],u8[wind_slice,wind_slice],v8[wind_slice,wind_slice], length=7)
#h_contour = ax1.contour(x, y, mslpc, colors='dimgray', levels=range(940,1040,4),linewidths=2)
#h_contour.clabel(fontsize=14, colors='dimgray', inline=1, inline_spacing=4, fmt='%i mb', rightside_up=True, use_clabeltext=True)
ax1.set_title('Precipitable Water (in), 850mb Heights (m), and Winds (kts)',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((265, 300, 25, 50))#, crs = zH5_crs) # Set a title and show the plot
plt.savefig(output_dir+'/GFS/gfs_hrly_pwat_v2_'+str(i)+'.png')
fcst_hr = str(0)
print('Hour '+str(i)+' completed!')
plt.close()
timeelapsed = datetime.now()-startTime
print(timeelapsed)
'''
url= 'http://nomads.ncep.noaa.gov:80/dods/gfs_0p25_1hr/gfs20200903/gfs_0p25_1hr_12z'
ds = xr.open_dataset(url)
t2m_ds = ds['tmp2m']
init_hr = t2m_ds['time'][0].values
#fc_hr = t2m.ds['time'][i].values
lats = np.arange(20,50,0.25)
lons = np.arange(240,300,0.25)
t2m = t2m_ds.sel(time = init_hr, lat = lats, lon = lons)
print(t2m)
fig = plt.figure(figsize = (12,12))
fig.clf()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_extent((240,300, 20, 50), crs = ccrs.PlateCarree())
t2m_c = ax.contourf(t2m, cmap='RdPu')
plt.savefig('testingnomads6.png')
'''