MetPy Monday #7ΒΆ

This week we learn about contouring a field on the map and some of the idiosyncrasies of cyclic points. In the end, we will have a plot of the globe with the Coriolis parameter contoured. You can use this functionality to create height maps and more!

import as ccrs
import cartopy.feature as cfeat
from cartopy.util import add_cyclic_point
import matplotlib
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from metpy.units import units
import numpy as np
lats = np.arange(-90, 91) * units.degrees
coriolis = mpcalc.coriolis_parameter(lats)
fig = plt.figure(figsize=(8, 5))
ax = plt.subplot(1, 1, 1)
ax.plot(lats, coriolis)
ax.set_xlabel('Latitude', fontsize=14)
ax.set_ylabel('Coriolis Parameter', fontsize=14)
lons = np.arange(0, 360)

coriolis = np.ones((181, 360)) * coriolis[:, np.newaxis]

cyclic_data, cyclic_lons = add_cyclic_point(coriolis, coord=lons)
# sphinx_gallery_thumbnail_number = 2

# Works with matplotlib's built-in transform support.
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())

# Sets the extent to cover the whole globe

# Add variety of features

# Set negative contours to be solid instead of dashed
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
CS = ax.contour(cyclic_lons, lats, cyclic_data, 20, colors='tab:brown',
ax.clabel(CS, inline=1, fontsize=10, fmt='%1.1f')

Total running time of the script: ( 0 minutes 0.208 seconds)

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