<jupyter_start><jupyter_text>==================Smoothing Contours==================Demonstrate how to smooth contour values from a higher resolutionmodel field.By: <NAME>Date: 13 April 2017 Do the needed imports<jupyter_code>from datetime import datetime

import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from metpy.units import units
from netCDF4 import num2date
import numpy as np
import scipy.ndimage as ndimage
from siphon.ncss import NCSS<jupyter_output><empty_output><jupyter_text>Set up netCDF Subset Service link<jupyter_code>dt = datetime(2016, 4, 16, 18)
base_url = 'https://www.ncei.noaa.gov/thredds/ncss/grid/namanl/'
ncss = NCSS('{}{dt:%Y%m}/{dt:%Y%m%d}/namanl_218_{dt:%Y%m%d}_'
            '{dt:%H}00_000.grb'.format(base_url, dt=dt))

# Data Query
hgt = ncss.query().time(dt)
hgt.variables('Geopotential_height_isobaric', 'u-component_of_wind_isobaric',
              'v-component_of_wind_isobaric').add_lonlat()

# Actually getting the data
data = ncss.get_data(hgt)<jupyter_output><empty_output><jupyter_text>Pull apart the data<jupyter_code># Get dimension names to pull appropriate variables
dtime = data.variables['Geopotential_height_isobaric'].dimensions[0]
dlev = data.variables['Geopotential_height_isobaric'].dimensions[1]
dlat = data.variables['Geopotential_height_isobaric'].dimensions[2]
dlon = data.variables['Geopotential_height_isobaric'].dimensions[3]

# Get lat and lon data, as well as time data and metadata
lats = data.variables['lat'][:]
lons = data.variables['lon'][:]
lons[lons > 180] = lons[lons > 180] - 360

# Need 2D lat/lons for plotting, do so if necessary
if lats.ndim < 2:
    lons, lats = np.meshgrid(lons, lats)

# Determine the level of 500 hPa
levs = data.variables[dlev][:]
lev_500 = np.where(levs == 500)[0][0]

# Create more useable times for output
times = data.variables[dtime]
vtimes = num2date(times[:], times.units)

# Pull out the 500 hPa Heights
hght = data.variables['Geopotential_height_isobaric'][:].squeeze() * units.meter
uwnd = units('m/s') * data.variables['u-component_of_wind_isobaric'][:].squeeze()
vwnd = units('m/s') * data.variables['v-component_of_wind_isobaric'][:].squeeze()

# Calculate the magnitude of the wind speed in kts
sped = mpcalc.wind_speed(uwnd, vwnd).to('knots')<jupyter_output><empty_output><jupyter_text>Set up the projection for LCC<jupyter_code>plotcrs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)
datacrs = ccrs.PlateCarree(central_longitude=0.)<jupyter_output><empty_output><jupyter_text>Subset and smooth<jupyter_code># Subset the data arrays to grab only 500 hPa
hght_500 = hght[lev_500]
uwnd_500 = uwnd[lev_500]
vwnd_500 = vwnd[lev_500]

# Smooth the 500-hPa geopotential height field
# Be sure to only smooth the 2D field
Z_500 = ndimage.gaussian_filter(hght_500, sigma=5, order=0)<jupyter_output><empty_output><jupyter_text>Plot the contours<jupyter_code># Start plot with new figure and axis
fig = plt.figure(figsize=(17., 11.))
ax = plt.subplot(1, 1, 1, projection=plotcrs)

# Add some titles to make the plot readable by someone else
plt.title('500-hPa Geo Heights (m; black), Smoothed 500-hPa Geo. Heights (m; red)',
          loc='left')
plt.title('VALID: {}'.format(vtimes[0]), loc='right')

# Set GAREA and add map features
ax.set_extent([-125., -67., 22., 52.], ccrs.PlateCarree())
ax.coastlines('50m', edgecolor='black', linewidth=0.75)
ax.add_feature(cfeature.STATES, linewidth=0.5)

# Set the CINT
clev500 = np.arange(5100, 6000, 60)

# Plot smoothed 500-hPa contours
cs2 = ax.contour(lons, lats, Z_500, clev500, colors='red',
                 linewidths=3, linestyles='solid', transform=datacrs)
c2 = plt.clabel(cs2, fontsize=12, colors='red', inline=1, inline_spacing=8,
                fmt='%i', rightside_up=True, use_clabeltext=True)

# Contour the 500 hPa heights with labels
cs = ax.contour(lons, lats, hght_500, clev500, colors='black',
                linewidths=2.5, linestyles='solid', alpha=0.6, transform=datacrs)
cl = plt.clabel(cs, fontsize=12, colors='k', inline=1, inline_spacing=8,
                fmt='%i', rightside_up=True, use_clabeltext=True)

plt.show()<jupyter_output><empty_output>
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