@ -40,9 +40,9 @@ Plotting a Two-dimensional Field
import cartopy.crs as crs import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature from cartopy.feature import NaturalEarthFeature
from wrf import (to_np, getvar, smooth2d, get_cartopy, latlon_coords, from wrf import to_np, getvar, smooth2d, get_cartopy, cartopy_xlim, cartopy_ylim
cartopy_xlim, cartopy_ylim)
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00") ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the sea level pressure # Get the sea level pressure
@ -51,16 +51,15 @@ Plotting a Two-dimensional Field
# Smooth the sea level pressure since it tends to be noisy near the mountains # Smooth the sea level pressure since it tends to be noisy near the mountains
smooth_slp = smooth2d(slp, 3) smooth_slp = smooth2d(slp, 3)
# Get the latitude and longitude coordinate arrays # Get the latitude and longitude points
lats, lons = latlon_coords(smooth_slp) lats, lons = latlon_coords(slp)
# The WrfProj.cartopy() method returns a cartopy.crs projection object
cart_proj = get_cartopy(smooth_slp)
# Create a figure that's 10x10 # Get the cartopy mapping object
fig = plt.figure(figsize=(10,10) ) cart_proj = get_cartopy(slp)
# Get the GeoAxes set to the projection used by WRF # Create a figure
fig = plt.figure(figsize=(12,9))
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj) ax = plt.axes(projection=cart_proj)
# Download and add the states and coastlines # Download and add the states and coastlines
@ -70,22 +69,21 @@ Plotting a Two-dimensional Field
ax.coastlines('50m', linewidth=0.8) ax.coastlines('50m', linewidth=0.8)
# Make the contour outlines and filled contours for the smoothed sea level pressure. # Make the contour outlines and filled contours for the smoothed sea level pressure.
plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors="black", plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors="black", transform=crs.PlateCarree())
transform=crs.PlateCarree()) plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10, transform=crs.PlateCarree(), cmap=get_cmap("jet"))
plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10, transform=crs.PlateCarree())
# Add a color bar # Add a color bar
plt.colorbar(ax=ax, shrink=.47 ) plt.colorbar(ax=ax, shrink=.62 )
# Set the map limits # Set the map limits. Not really necessary, but used for demonstration.
ax.set_xlim(cartopy_xlim(smooth_slp)) ax.set_xlim(cartopy_xlim(smooth_slp))
ax.set_ylim(cartopy_ylim(smooth_slp)) ax.set_ylim(cartopy_ylim(smooth_slp))
# Add the gridlines # Add the gridlines
ax.gridlines() ax.gridlines(color="black", linestyle="dotted" )
plt.title("Sea Level Pressure (hPa)") plt.title("Sea Level Pressure (hPa)")
plt.show() plt.show()
Horizontal Interpolation to a Pressure Level Horizontal Interpolation to a Pressure Level
@ -104,9 +102,9 @@ Horizontal Interpolation to a Pressure Level
import cartopy.crs as crs import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature from cartopy.feature import NaturalEarthFeature
from wrf import (getvar, interplevel, to_np, get_cartopy, from wrf import getvar, interplevel, to_np, latlon_coords, get_cartopy, cartopy_xlim, cartopy_ylim
latlon_coords, cartopy_xlim, cartopy_ylim)
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00") ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Extract the pressure, geopotential height, and wind variables # Extract the pressure, geopotential height, and wind variables
@ -125,12 +123,12 @@ Horizontal Interpolation to a Pressure Level
# Get the lat/lon coordinates # Get the lat/lon coordinates
lats, lons = latlon_coords(ht_500) lats, lons = latlon_coords(ht_500)
# Get the cartopy map projection information # Get the map projection information
cart_proj = get_cartopy(ht_500) cart_proj = get_cartopy(ht_500)
# Create the figure # Create the figure
fig = plt.figure(figsize=(10,10 )) fig = plt.figure(figsize=(12,9 ))
ax = plt.axes([0.1,0.1,0.8,0.8], projection=cart_proj) ax = plt.axes(projection=cart_proj)
# Download and add the states and coastlines # Download and add the states and coastlines
states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none', states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
@ -138,13 +136,13 @@ Horizontal Interpolation to a Pressure Level
ax.add_feature(states, linewidth=0.5) ax.add_feature(states, linewidth=0.5)
ax.coastlines('50m', linewidth=0.8) ax.coastlines('50m', linewidth=0.8)
# Create the 500 hPa geopotential height contours # Add the 500 hPa geopotential height contours
levels = np.arange(520., 580., 6.) levels = np.arange(520., 580., 6.)
contours = plt.contour(to_np(lons), to_np(lats), to_np(ht_500), levels=levels, colors="black", contours = plt.contour(to_np(lons), to_np(lats), to_np(ht_500), levels=levels, colors="black",
transform=crs.PlateCarree()) transform=crs.PlateCarree())
plt.clabel(contours, inline=1, fontsize=10, fmt="%i") plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
# Wind S peed contours # Add the wind s peed contours
levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120] levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]
wspd_contours = plt.contourf(to_np(lons), to_np(lats), to_np(wspd_500), levels=levels, wspd_contours = plt.contourf(to_np(lons), to_np(lats), to_np(wspd_500), levels=levels,
cmap=get_cmap("rainbow"), cmap=get_cmap("rainbow"),
@ -186,19 +184,18 @@ plot, see :ref:`cross_example`.
import cartopy.feature as cfeature import cartopy.feature as cfeature
from netCDF4 import Dataset from netCDF4 import Dataset
from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair, GeoBounds, from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair, GeoBounds, get_cartopy,
latlon_coords, get_cartopy , cartopy_xlim, cartopy_ylim) latlon_coords, cartopy_xlim, cartopy_ylim)
# Open the output NetCDF file # Open the NetCDF file
filename = "wrfout_d01_2016-10-07_00_00_00" ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
ncfile = DataSet(filename)
# Get the WRF variables # Get the WRF variables
slp = getvar(ncfile, "slp") slp = getvar(ncfile, "slp")
smooth_slp = smooth2d(slp, 3) smooth_slp = smooth2d(slp, 3)
ctt = getvar(ncfile, "ctt") ctt = getvar(ncfile, "ctt")
height = getvar(ncfile, "z" ) z = getvar(ncfile, "z", timeidx=0 )
dbz = getvar(ncfile, "dbz") dbz = getvar(ncfile, "dbz", timeidx=0 )
Z = 10**(dbz/10.) Z = 10**(dbz/10.)
wspd = getvar(ncfile, "wspd_wdir", units="kt")[0,:] wspd = getvar(ncfile, "wspd_wdir", units="kt")[0,:]
@ -206,53 +203,47 @@ plot, see :ref:`cross_example`.
start_point = CoordPair(lat=26.76, lon=-80.0) start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8) end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the # Compute the vertical cross-section interpolation. Also, include the lat/lon points along the cross-section
# lat/lon points along the cross-section in the metadata by setting # in the metadata by setting latlon to True.
# latlon to True. z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
z_cross = vertcross(Z, height, wrfin=ncfile, start_point=start_point, wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
end_point=end_point, latlon=True, meta=True)
wspd_cross = vertcross(wspd, height, wrfin=ncfile, start_point=start_point,
end_point=end_point, latlon=True, meta=True)
dbz_cross = 10.0 * np.log10(z_cross) dbz_cross = 10.0 * np.log10(z_cross)
# Get the latitude and longitude coordinate array s # Get the lat/lon points
lats, lons = latlon_coords(slp) lats, lons = latlon_coords(slp)
# Get the cartopy projection object # Get the cartopy projection object
cart_proj = get_cartopy(slp) cart_proj = get_cartopy(slp)
# Create the figure which will have 3 subplots, one on the left, and # Create a figure that will have 3 subplots
# two vertically stacked on the right. fig = plt.figure(figsize=(10,7))
fig = plt.figure(figsize=(7,5))
ax_ctt = fig.add_subplot(1,2,1,projection=cart_proj) ax_ctt = fig.add_subplot(1,2,1,projection=cart_proj)
ax_wspd = fig.add_subplot(2,2,2) ax_wspd = fig.add_subplot(2,2,2)
ax_dbz = fig.add_subplot(2,2,4) ax_dbz = fig.add_subplot(2,2,4)
# Download and create the states, land, and oceans using cartopy features # Download and create the states, land, and oceans using cartopy features
states = cfeature.NaturalEarthFeature(category='cultural', scale='50m', facecolor='none', states = cfeature.NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
name='admin_1_states_provinces_shp') name='admin_1_states_provinces_shp')
land = cfeature.NaturalEarthFeature(category='physical', name='land', scale='50m', land = cfeature.NaturalEarthFeature(category='physical', name='land', scale='50m',
facecolor=cfeature.COLORS['land']) facecolor=cfeature.COLORS['land'])
ocean = cfeature.NaturalEarthFeature(category='physical', name='ocean', scale='50m', ocean = cfeature.NaturalEarthFeature(category='physical', name='ocean', scale='50m',
facecolor=cfeature.COLORS['water']) facecolor=cfeature.COLORS['water'])
# Make the ctt contours. # Make the pressure contours
contour_levels = [960, 965, 970, 975, 980, 990] contour_levels = [960, 965, 970, 975, 980, 990]
c1 = ax_ctt.contour(to_np( lons) , to_np( lats) , to_np(smooth_slp), levels=contour_levels, c1 = ax_ctt.contour(lons, lats, to_np(smooth_slp), levels=contour_levels, colors="white" ,
colors="white", transform=crs.PlateCarree(), zorder=3, linewidths=1.0) transform=crs.PlateCarree(), zorder=3, linewidths=1.0)
# Create the filled cloud top temperature contours # Create the filled cloud top temperature contours
contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10] contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10]
ctt_contours = ax_ctt.contourf(lons, lats, to_np(ctt), contour_levels, ctt_contours = ax_ctt.contourf(to_np(lons), to_np(lats), to_np(ctt), contour_levels, cmap=get_cmap("Greys"),
cmap=get_cmap("Greys"), transform=crs.PlateCarree(), transform=crs.PlateCarree(), zorder=2)
zorder=2)
# Draw the yellow cross section line ax_ctt.plot([start_point.lon, end_point.lon], [start_point.lat, end_point.lat], color="yellow",
ax_ctt.plot([start_point.lon, end_point.lon], [start_point.lat, end_point.lat], marker="o", transform=crs.PlateCarree(), zorder=3)
color="yellow", marker="o", transform=crs.PlateCarree(), zorder=3)
# Create the label bar for cloud top temperature # Create the color bar for cloud top temperature
cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.5 ) cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60 )
cb_ctt.ax.tick_params(labelsize=5) cb_ctt.ax.tick_params(labelsize=5)
# Draw the oceans, land, and states # Draw the oceans, land, and states
@ -265,21 +256,21 @@ plot, see :ref:`cross_example`.
CoordPair(lat=30.0, lon=-72.0)) CoordPair(lat=30.0, lon=-72.0))
ax_ctt.set_xlim(cartopy_xlim(ctt, geobounds=hur_bounds)) ax_ctt.set_xlim(cartopy_xlim(ctt, geobounds=hur_bounds))
ax_ctt.set_ylim(cartopy_ylim(ctt, geobounds=hur_bounds)) ax_ctt.set_ylim(cartopy_ylim(ctt, geobounds=hur_bounds))
ax_ctt.gridlines() ax_ctt.gridlines(color="white", linestyle="dotted" )
# Make the contour plot for wspd # Make the contour plot for wind spee d
wspd_contours = ax_wspd.contourf(to_np(wspd_cross)) wspd_contours = ax_wspd.contourf(to_np(wspd_cross), cmap=get_cmap("jet") )
# Add the color bar # Add the color bar
cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd) cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd)
cb_wspd.ax.tick_params(labelsize=5) cb_wspd.ax.tick_params(labelsize=5)
# Make the contour plot for dbz # Make the contour plot for dbz
levels = [5 + 5*n for n in range(15)] levels = [5 + 5*n for n in range(15)]
dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels) dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap("jet") )
cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz) cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)
cb_dbz.ax.tick_params(labelsize=5) cb_dbz.ax.tick_params(labelsize=5)
# Set the x-ticks to use latitude and longitude labels. # Set the x-ticks to use latitude and longitude labels
coord_pairs = to_np(dbz_cross.coords["xy_loc"]) coord_pairs = to_np(dbz_cross.coords["xy_loc"])
x_ticks = np.arange(coord_pairs.shape[0]) x_ticks = np.arange(coord_pairs.shape[0])
x_labels = [pair.latlon_str() for pair in to_np(coord_pairs)] x_labels = [pair.latlon_str() for pair in to_np(coord_pairs)]
@ -288,7 +279,7 @@ plot, see :ref:`cross_example`.
ax_dbz.set_xticks(x_ticks[::20]) ax_dbz.set_xticks(x_ticks[::20])
ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=4) ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=4)
# Set the y-ticks to be height. # Set the y-ticks to be height
vert_vals = to_np(dbz_cross.coords["vertical"]) vert_vals = to_np(dbz_cross.coords["vertical"])
v_ticks = np.arange(vert_vals.shape[0]) v_ticks = np.arange(vert_vals.shape[0])
ax_wspd.set_yticks(v_ticks[::20]) ax_wspd.set_yticks(v_ticks[::20])
@ -301,7 +292,7 @@ plot, see :ref:`cross_example`.
ax_wspd.set_ylabel("Height (m)", fontsize=5) ax_wspd.set_ylabel("Height (m)", fontsize=5)
ax_dbz.set_ylabel("Height (m)", fontsize=5) ax_dbz.set_ylabel("Height (m)", fontsize=5)
# Add titles # Add a title
ax_ctt.set_title("Cloud Top Temperature (degC)", {"fontsize" : 7}) ax_ctt.set_title("Cloud Top Temperature (degC)", {"fontsize" : 7})
ax_wspd.set_title("Cross-Section of Wind Speed (kt)", {"fontsize" : 7}) ax_wspd.set_title("Cross-Section of Wind Speed (kt)", {"fontsize" : 7})
ax_dbz.set_title("Cross-Section of Reflectivity (dBZ)", {"fontsize" : 7}) ax_dbz.set_title("Cross-Section of Reflectivity (dBZ)", {"fontsize" : 7})
@ -333,25 +324,27 @@ Plotting a Two-Dimensional Field
from matplotlib.cm import get_cmap from matplotlib.cm import get_cmap
from mpl_toolkits.basemap import Basemap from mpl_toolkits.basemap import Basemap
from wrf import to_np, getvar, smooth2d, latlon_coords, get_basemap from wrf import to_np, getvar, smooth2d, get_basemap, latlon_coords
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00") ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the sea level pressure # Get the sea level pressure
slp = getvar(ncfile, "slp") slp = getvar(ncfile, "slp")
# Smooth the sea level pressure since it tends to be noise y near the mountains # Smooth the sea level pressure since it tends to be noisy near the mountains
smooth_slp = smooth2d(slp, 3) smooth_slp = smooth2d(slp, 3)
# Get the latitude and longitude coordinate s # Get the latitude and longitude point s
lats, lons = latlon_coords(slp) lats, lons = latlon_coords(slp)
# Get the basemap projection object # Get the basemap object
bm = get_basemap(slp) bm = get_basemap(slp)
# Create a figure that's 10x10 # Create a figure
fig = plt.figure(figsize=(10,10 )) fig = plt.figure(figsize=(12,9 ))
# Add geographic outlines
bm.drawcoastlines(linewidth=0.25) bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25) bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25) bm.drawcountries(linewidth=0.25)
@ -362,12 +355,13 @@ Plotting a Two-Dimensional Field
# Draw the contours and filled contours # Draw the contours and filled contours
bm.contour(x, y, to_np(smooth_slp), 10, colors="black") bm.contour(x, y, to_np(smooth_slp), 10, colors="black")
bm.contourf(x, y, to_np(smooth_slp), 10) bm.contourf(x, y, to_np(smooth_slp), 10, cmap=get_cmap("jet") )
# Add a color bar # Add a color bar
plt.colorbar(shrink=.47 ) plt.colorbar(shrink=.62 )
plt.title("Sea Level Pressure (hPa)") plt.title("Sea Level Pressure (hPa)")
plt.show() plt.show()
@ -387,6 +381,7 @@ Horizontal Interpolation to a Pressure Level
from wrf import getvar, interplevel, to_np, get_basemap, latlon_coords from wrf import getvar, interplevel, to_np, get_basemap, latlon_coords
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00") ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Extract the pressure, geopotential height, and wind variables # Extract the pressure, geopotential height, and wind variables
@ -405,27 +400,28 @@ Horizontal Interpolation to a Pressure Level
# Get the lat/lon coordinates # Get the lat/lon coordinates
lats, lons = latlon_coords(ht_500) lats, lons = latlon_coords(ht_500)
# Get the basemap projection object # Get the basemap object
bm = get_basemap(ht_500) bm = get_basemap(ht_500)
# Create the figure # Create the figure
fig = plt.figure(figsize=(8,8 )) fig = plt.figure(figsize=(12,9 ))
ax = plt.axes([0.1,0.1,0.8,0.8] ) ax = plt.axes()
# Get the x and y coordinates # Convert the lat/lon coordinates to x/y coordinates in the projection space
x, y = bm(to_np(lons), to_np(lats)) x, y = bm(to_np(lons), to_np(lats))
# Create the 500 hPa geopotential height contours # Add the 500 hPa geopotential height contours
levels = np.arange(520., 580., 6.) levels = np.arange(520., 580., 6.)
contours = bm.contour(x, y, to_np(ht_500), levels=levels, colors="black") contours = bm.contour(x, y, to_np(ht_500), levels=levels, colors="black")
plt.clabel(contours, inline=1, fontsize=10, fmt="%i") plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
# Wind S peed contours # Add the wind s peed contours
levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120] levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]
wspd_contours = bm.contourf(x, y, to_np(wspd_500), levels=levels, wspd_contours = bm.contourf(x, y, to_np(wspd_500), levels=levels,
cmap=get_cmap("rainbow")) cmap=get_cmap("rainbow"))
plt.colorbar(wspd_contours, ax=ax, orientation="horizontal", pad=.05) plt.colorbar(wspd_contours, ax=ax, orientation="horizontal", pad=.05)
# Add the geographic boundaries
bm.drawcoastlines(linewidth=0.25) bm.drawcoastlines(linewidth=0.25)
bm.drawstates(linewidth=0.25) bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25) bm.drawcountries(linewidth=0.25)
@ -435,9 +431,10 @@ Horizontal Interpolation to a Pressure Level
to_np(v_500[::125, ::125]), length=6) to_np(v_500[::125, ::125]), length=6)
plt.title("500 MB Height (dm), Wind Speed (kt), Barbs (kt)") plt.title("500 MB Height (dm), Wind Speed (kt), Barbs (kt)")
plt.show() plt.show()
Panel Plots from the Front Page Panel Plots from the Front Page
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@ -458,9 +455,8 @@ plot, see :ref:`cross_example`.
from wrf import getvar, to_np, vertcross, smooth2d, CoordPair, get_basemap, latlon_coords from wrf import getvar, to_np, vertcross, smooth2d, CoordPair, get_basemap, latlon_coords
# Open the output NetCDF file # Open the NetCDF file
filename = "/Users/ladwig/Documents/wrf_files/wrfout_d01_2016-10-07_00_00_00" ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
ncfile = Dataset(filename)
# Get the WRF variables # Get the WRF variables
slp = getvar(ncfile, "slp") slp = getvar(ncfile, "slp")
@ -475,29 +471,28 @@ plot, see :ref:`cross_example`.
start_point = CoordPair(lat=26.76, lon=-80.0) start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8) end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolations . Also, include the lat/lon points along the cross-section # Compute the vertical cross-section interpolation. Also, include the lat/lon points along the cross-section i n
# in the metadata by setting latlon to True. # the metadata by setting latlon to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True) z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True) wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
dbz_cross = 10.0 * np.log10(z_cross) dbz_cross = 10.0 * np.log10(z_cross)
# Extract the latitude and longitude coordinate array s # Get the latitude and longitude point s
lats, lons = latlon_coords(slp) lats, lons = latlon_coords(slp)
# Create a figure that will have 3 subplots. One on the left, # Create the figure that will have 3 subplots
# two vertically stacked on the right. fig = plt.figure(figsize=(10,7))
fig = plt.figure(figsize=(8,5))
ax_ctt = fig.add_subplot(1,2,1) ax_ctt = fig.add_subplot(1,2,1)
ax_wspd = fig.add_subplot(2,2,2) ax_wspd = fig.add_subplot(2,2,2)
ax_dbz = fig.add_subplot(2,2,4) ax_dbz = fig.add_subplot(2,2,4)
# Get the basemap pr ojection class # Get the basemap ob ject
bm = get_basemap(slp) bm = get_basemap(slp)
# Get the x, y values # Convert the lat/lon points in to x/y points in the projection space
x, y = bm(to_np(lons), to_np(lats)) x, y = bm(to_np(lons), to_np(lats))
# Make the pressure contours. # Make the pressure contours
contour_levels = [960, 965, 970, 975, 980, 990] contour_levels = [960, 965, 970, 975, 980, 990]
c1 = bm.contour(x, y, to_np(smooth_slp), levels=contour_levels, colors="white", c1 = bm.contour(x, y, to_np(smooth_slp), levels=contour_levels, colors="white",
zorder=3, linewidths=1.0, ax=ax_ctt) zorder=3, linewidths=1.0, ax=ax_ctt)
@ -511,12 +506,11 @@ plot, see :ref:`cross_example`.
bm.plot([point_x[0], point_x[1]], [point_y[0], point_y[1]], color="yellow", bm.plot([point_x[0], point_x[1]], [point_y[0], point_y[1]], color="yellow",
marker="o", zorder=3, ax=ax_ctt) marker="o", zorder=3, ax=ax_ctt)
# Create the label bar for cloud top temperature # Create the color bar for cloud top temperature
cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.68 ) cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60 )
cb_ctt.ax.tick_params(labelsize=5) cb_ctt.ax.tick_params(labelsize=5)
# Draw the oceans, land, and states. Use the same colors as the cartopy # Draw the oceans, land, and states
# example
bm.drawcoastlines(linewidth=0.25, ax=ax_ctt) bm.drawcoastlines(linewidth=0.25, ax=ax_ctt)
bm.drawstates(linewidth=0.25, ax=ax_ctt) bm.drawstates(linewidth=0.25, ax=ax_ctt)
bm.drawcountries(linewidth=0.25, ax=ax_ctt) bm.drawcountries(linewidth=0.25, ax=ax_ctt)
@ -526,13 +520,12 @@ plot, see :ref:`cross_example`.
# Draw Parallels # Draw Parallels
parallels = np.arange(np.amin(lats), 30., 2.5) parallels = np.arange(np.amin(lats), 30., 2.5)
bm.drawparallels(parallels, ax=ax_ctt) bm.drawparallels(parallels, ax=ax_ctt, color="white" )
merids = np.arange(-85.0, -72.0, 2.5) merids = np.arange(-85.0, -72.0, 2.5)
bm.drawmeridians(merids, ax=ax_ctt) bm.drawmeridians(merids, ax=ax_ctt, color="white" )
# Get the x and y coordinate ranges for the cropped region around the # Crop the image to the hurricane region
# hurricane
x_start, y_start = bm(-85.0, np.amin(lats)) x_start, y_start = bm(-85.0, np.amin(lats))
x_end, y_end = bm(-72.0, 30.0) x_end, y_end = bm(-72.0, 30.0)
@ -540,14 +533,14 @@ plot, see :ref:`cross_example`.
ax_ctt.set_ylim([y_start, y_end]) ax_ctt.set_ylim([y_start, y_end])
# Make the contour plot for wspd # Make the contour plot for wspd
wspd_contours = ax_wspd.contourf(to_np(wspd_cross)) wspd_contours = ax_wspd.contourf(to_np(wspd_cross), cmap=get_cmap("jet") )
# Add the color bar # Add the color bar
cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd) cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd)
cb_wspd.ax.tick_params(labelsize=5) cb_wspd.ax.tick_params(labelsize=5)
# Make the contour plot for dbz # Make the contour plot for dbz
levels = [5 + 5*n for n in range(15)] levels = [5 + 5*n for n in range(15)]
dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels) dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap("jet") )
cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz) cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)
cb_dbz.ax.tick_params(labelsize=5) cb_dbz.ax.tick_params(labelsize=5)
@ -573,7 +566,7 @@ plot, see :ref:`cross_example`.
ax_wspd.set_ylabel("Height (m)", fontsize=5) ax_wspd.set_ylabel("Height (m)", fontsize=5)
ax_dbz.set_ylabel("Height (m)", fontsize=5) ax_dbz.set_ylabel("Height (m)", fontsize=5)
# Add a title # Add titles
ax_ctt.set_title("Cloud Top Temperature (degC)", {"fontsize" : 7}) ax_ctt.set_title("Cloud Top Temperature (degC)", {"fontsize" : 7})
ax_wspd.set_title("Cross-Section of Wind Speed (kt)", {"fontsize" : 7}) ax_wspd.set_title("Cross-Section of Wind Speed (kt)", {"fontsize" : 7})
ax_dbz.set_title("Cross-Section of Reflectivity (dBZ)", {"fontsize" : 7}) ax_dbz.set_title("Cross-Section of Reflectivity (dBZ)", {"fontsize" : 7})
@ -598,34 +591,34 @@ plotted using the standard matplotlib API.
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from netCDF4 import Dataset from netCDF4 import Dataset
from wrf import to_np, getvar, CoordPair, vertcross from wrf import to_np, getvar, CoordPair, vertcross
# Open the output NetCDF file with PyNIO # Open the NetCDF file
filename = "wrfout_d01_2016-10-07_00_00_00" filename = "wrfout_d01_2016-10-07_00_00_00"
ncfile = Dataset(filename) ncfile = Dataset(filename)
# Extract pressure and model height # Extract the model height and wind speed
z = getvar(ncfile, "z") z = getvar(ncfile, "z")
wspd = getvar(ncfile, "uvmet_wspd_wdir", units="kt")[0,:] wspd = getvar(ncfile, "uvmet_wspd_wdir", units="kt")[0,:]
# Define the start point and end point for the cross section using # Create the start point and end point for the cross section
# latitude and longitude coordinates.
start_point = CoordPair(lat=26.76, lon=-80.0) start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8) end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the lat/lon # Compute the vertical cross-section interpolation. Also, include the lat/lon points along the cross-section.
# points along the cross-section in the metadata by setting latlon to True. wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point,
end_point=end_point, latlon=True, meta=True)
# Create the figure # Create the figure
fig = plt.figure(figsize=(10,5 )) fig = plt.figure(figsize=(12,6 ))
ax = plt.axes([0.1,0.1,0.8,0.8] ) ax = plt.axes()
# Make the contour plot # Make the contour plot
wspd_contours = ax.contourf(to_np(wspd_cross)) wspd_contours = ax.contourf(to_np(wspd_cross), cmap=get_cmap("jet"))
# Add the color bar # Add the color bar
plt.colorbar(wspd_contours, ax=ax) plt.colorbar(wspd_contours, ax=ax)
Bill Ladwig
8 years ago
