A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model.
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from netCDF4 import Dataset
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from wrf import (to_np, getvar, smooth2d, get_cartopy, cartopy_xlim,
cartopy_ylim, latlon_coords)
# Open the NetCDF file
ncfile = Dataset("/Users/ladwig/Documents/wrf_files/"
"problem_files/cfrac_bug/wrfout_d02_1987-10-01_00:00:00")
# Get the sea level pressure
ctt = getvar(ncfile, "ctt")
# Get the latitude and longitude points
lats, lons = latlon_coords(ctt)
# Get the cartopy mapping object
cart_proj = get_cartopy(ctt)
# Create a figure
fig = plt.figure(figsize=(12, 9))
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Download and add the states and coastlines
states = NaturalEarthFeature(category='cultural', scale='50m',
facecolor='none',
name='admin_1_states_provinces_shp')
ax.add_feature(states, linewidth=.5)
ax.coastlines('50m', linewidth=0.8)
# Make the contour outlines and filled contours for the smoothed sea level
# pressure.
plt.contour(to_np(lons), to_np(lats), to_np(ctt), 10, colors="black",
transform=crs.PlateCarree())
plt.contourf(to_np(lons), to_np(lats), to_np(ctt), 10,
transform=crs.PlateCarree(),
cmap=get_cmap("jet"))
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
# Set the map limits. Not really necessary, but used for demonstration.
ax.set_xlim(cartopy_xlim(ctt))
ax.set_ylim(cartopy_ylim(ctt))
# Add the gridlines
ax.gridlines(color="black", linestyle="dotted")
plt.title("Cloud Top Temperature")
plt.show()