NCL_lcmask_1.pyΒΆ

This script illustrates the following concepts:
  • Drawing filled contours over a Lambert Conformal map

  • Zooming in on a particular area on a Lambert Conformal map

  • Creating a custom plot boundary

  • Using a blue-white-red color map

  • Setting contour levels using a min/max contour level and a spacing

See following URLs to see the reproduced NCL plot & script:

Import packages:

import cartopy.crs as ccrs
import geocat.datafiles as gdf
import matplotlib.path as mpath
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from geocat.viz import cmaps as gvcmaps
from geocat.viz import util as gvutil

Read in data:

# Open a netCDF data file using xarray default engine and load the data into
# xarrays and disable time decoding due to missing necessary metadata
ds = xr.open_dataset(gdf.get("netcdf_files/atmos.nc"), decode_times=False)
# Extract a slice of the data
ds = ds.isel(time=0).drop_vars(names=["time"])
ds = ds.isel(lev=0).drop_vars(names=["lev"])
V = ds.V
# Ensure longitudes range from 0 to 360 degrees
V = gvutil.xr_add_cyclic_longitudes(V, "lon")

Plot unmasked data:

# Generate figure and projection using Cartopy
plt.figure(figsize=(7, 10))
proj = ccrs.LambertConformal(central_longitude=0, standard_parallels=(45, 89))
# Set axis projection
ax = plt.axes(projection=proj, frameon=False)
# Set extent to include all longitudes and the northern hemisphere
ax.set_extent((0, 359, 0, 89), crs=ccrs.PlateCarree())
ax.coastlines(linewidth=0.5)

# Plot data and create colorbar
newcmp = gvcmaps.BlWhRe

wind = V.plot.contourf(ax=ax,
                       cmap=newcmp,
                       transform=ccrs.PlateCarree(),
                       add_colorbar=False,
                       levels=24)
cbar = plt.colorbar(wind,
                    ax=ax,
                    orientation='horizontal',
                    drawedges=True,
                    ticks=np.arange(-48, 48, 8),
                    pad=0.1,
                    aspect=12)
cbar.ax.tick_params(length=0)  # remove tick marks but leave in labels

# Use geocat.viz.util convenience function to add left and right titles
gvutil.set_titles_and_labels(ax,
                             lefttitle=V.long_name,
                             lefttitlefontsize=16,
                             righttitle=V.units,
                             righttitlefontsize=16)

plt.show()
meridional wind component, m/s

Mask data

masked = V.where(V.lat > 20)
masked = masked.where(masked.lat < 80)
masked = masked.where(masked.lon > 90)
masked = masked.where(masked.lon < 220)

# Rotate data to match NCL example
masked['lon'] = masked['lon'] + 180

Plot masked data

# Generate figure and projection using Cartopy
plt.figure(figsize=(10, 7))
proj = ccrs.LambertConformal(central_longitude=-22.5,
                             standard_parallels=(45, 89))
# Set axis projection
ax = plt.axes(projection=proj)
ax.coastlines(linewidth=0.5)

# Make a custom boundary using convenience function
gvutil.set_map_boundary(ax, [-85, 40], [20, 80], south_pad=1)

# Plot data and create colorbar
wind = masked.plot.contourf(ax=ax,
                            cmap=newcmp,
                            transform=ccrs.PlateCarree(),
                            add_colorbar=False,
                            levels=24)
cbar = plt.colorbar(wind,
                    ax=ax,
                    orientation='horizontal',
                    drawedges=True,
                    ticks=np.arange(-40, 44, 4),
                    pad=0.1,
                    aspect=18)
cbar.ax.tick_params(length=0)  # remove tick marks but leave in labels

# Use geocat.viz.util convenience function to add left and right titles
gvutil.set_titles_and_labels(ax,
                             lefttitle=V.long_name,
                             lefttitlefontsize=16,
                             righttitle=V.units,
                             righttitlefontsize=16)
plt.show()
meridional wind component, m/s

Total running time of the script: ( 0 minutes 0.987 seconds)

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