+ +
+

Note

+

Go to the end +to download the full example code

+
+
+

Sounding Plot with Complex Layout#

+

This example combines simple MetPy plotting functionality, metpy.calc +computation functionality, and a few basic tricks to create an +advanced sounding plotter with a clean layout & high readability.

+
import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+import metpy.calc as mpcalc
+from metpy.cbook import get_test_data
+from metpy.plots import add_metpy_logo, Hodograph, SkewT
+from metpy.units import units
+
+
+

Upper air data can easily be obtained using the siphon package, +but for this example we will use some of MetPy’s sample data.

+
col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']
+
+df = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),
+                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)
+
+# Drop any rows with all NaN values for T, Td, winds
+df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'),
+               how='all').reset_index(drop=True)
+
+
+

We will pull the data out of the example dataset into +individual variables and assign units.

+
p = df['pressure'].values * units.hPa
+z = df['height'].values * units.m
+T = df['temperature'].values * units.degC
+Td = df['dewpoint'].values * units.degC
+wind_speed = df['speed'].values * units.knots
+wind_dir = df['direction'].values * units.degrees
+u, v = mpcalc.wind_components(wind_speed, wind_dir)
+
+
+

Now lets make a Skew-T Log-P diagram using some simply +MetPy functionality

+
# Create a new figure. The dimensions here give a good aspect ratio
+fig = plt.figure(figsize=(9, 9))
+add_metpy_logo(fig, 430, 30, size='large')
+
+skew = SkewT(fig, rotation=45, rect=(0.1, 0.1, 0.55, 0.85))
+
+# Plot the data using normal plotting functions, in this case using
+# log scaling in Y, as dictated by the typical meteorological plot
+skew.plot(p, T, 'r')
+skew.plot(p, Td, 'g')
+skew.plot_barbs(p, u, v)
+
+# Change to adjust data limits and give it a semblance of what we want
+skew.ax.set_adjustable('datalim')
+skew.ax.set_ylim(1000, 100)
+skew.ax.set_xlim(-20, 30)
+
+# Add the relevant special lines
+skew.plot_dry_adiabats()
+skew.plot_moist_adiabats()
+skew.plot_mixing_lines()
+
+# Create a hodograph
+ax = plt.axes((0.7, 0.75, 0.2, 0.2))
+h = Hodograph(ax, component_range=60.)
+h.add_grid(increment=20)
+h.plot(u, v)
+
+
+Complex Sounding Plot
[<matplotlib.lines.Line2D object at 0x7f3705659050>]
+
+
+

This layout isn’t bad, especially for how little code it required, +but we could add a few simple tricks to greatly increase the +readability and complexity of our Skew-T/Hodograph layout. Lets +try another Skew-T with a few more advanced features:

+

STEP 1: CREATE THE SKEW-T OBJECT AND MODIFY IT TO CREATE A +NICE, CLEAN PLOT

+
# Create a new figure. The dimensions here give a good aspect ratio
+fig = plt.figure(figsize=(18, 12))
+skew = SkewT(fig, rotation=45, rect=(0, 0, 0.50, 0.90))
+# add the metpy logo
+add_metpy_logo(fig, 100, 80, size='small')
+
+# Change to adjust data limits and give it a semblance of what we want
+skew.ax.set_adjustable('datalim')
+skew.ax.set_ylim(1000, 100)
+skew.ax.set_xlim(-20, 30)
+
+# Set some better labels than the default to increase readability
+skew.ax.set_xlabel(str.upper(f'Temperature ({T.units:~P})'), weight='bold')
+skew.ax.set_ylabel(str.upper(f'Pressure ({p.units:~P})'), weight='bold')
+
+# Set the facecolor of the Skew Object and the Figure to white
+fig.set_facecolor('#ffffff')
+skew.ax.set_facecolor('#ffffff')
+
+# Here we can use some basic math and Python functionality to make a cool
+# shaded isotherm pattern.
+x1 = np.linspace(-100, 40, 8)
+x2 = np.linspace(-90, 50, 8)
+y = [1100, 50]
+for i in range(0, 8):
+    skew.shade_area(y=y, x1=x1[i], x2=x2[i], color='gray', alpha=0.02, zorder=1)
+
+
+Complex Sounding Plot

STEP 2: PLOT DATA ON THE SKEW-T. TAKE A COUPLE EXTRA STEPS TO +INCREASE READABILITY

+
# Plot the data using normal plotting functions, in this case using
+# log scaling in Y, as dictated by the typical meteorological plot
+# set the linewidth to 4 for increased readability.
+# We will also add the 'label' kew word argument for our legend.
+skew.plot(p, T, 'r', lw=4, label='TEMPERATURE')
+skew.plot(p, Td, 'g', lw=4, label='DEWPOINT')
+
+# again we can use some simple python math functionality to 'resample'
+# the wind barbs for a cleaner output with increased readability.
+# Something like this would work.
+interval = np.logspace(2, 3, 40) * units.hPa
+idx = mpcalc.resample_nn_1d(p, interval)
+skew.plot_barbs(pressure=p[idx], u=u[idx], v=v[idx])
+
+# Add the relevant special lines native to the Skew-T Log-P diagram &
+# provide basic adjustments to linewidth and alpha to increase readability
+# first we add a matplotlib axvline to highlight the 0 degree isotherm
+skew.ax.axvline(0 * units.degC, linestyle='--', color='blue', alpha=0.3)
+skew.plot_dry_adiabats(lw=1, alpha=0.3)
+skew.plot_moist_adiabats(lw=1, alpha=0.3)
+skew.plot_mixing_lines(lw=1, alpha=0.3)
+
+# Calculate LCL height and plot as black dot. Because `p`'s first value is
+# ~1000 mb and its last value is ~250 mb, the `0` index is selected for
+# `p`, `T`, and `Td` to lift the parcel from the surface. If `p` was inverted,
+# i.e. start from low value, 250 mb, to a high value, 1000 mb, the `-1` index
+# should be selected.
+lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
+skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
+
+# Calculate full parcel profile and add to plot as black line
+prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
+skew.plot(p, prof, 'k', linewidth=2, label='SB PARCEL PATH')
+
+# Shade areas of CAPE and CIN
+skew.shade_cin(p, T, prof, Td, alpha=0.2, label='SBCIN')
+skew.shade_cape(p, T, prof, alpha=0.2, label='SBCAPE')
+
+
+
<matplotlib.collections.PolyCollection object at 0x7f37079407d0>
+
+
+

STEP 3: CREATE THE HODOGRAPH INSET. TAKE A FEW EXTRA STEPS TO +INCREASE READABILITY

+
# Create a hodograph object: first we need to add an axis
+# then we can create the metpy Hodograph
+hodo_ax = plt.axes((0.43, 0.40, 0.5, 0.5))
+h = Hodograph(hodo_ax, component_range=80.)
+# Add two separate grid increments for a cooler look. This also
+# helps to increase readability
+h.add_grid(increment=20, ls='-', lw=1.5, alpha=0.5)
+h.add_grid(increment=10, ls='--', lw=1, alpha=0.2)
+# The next few steps makes for a clean hodograph inset, removing the
+# tick marks, tick labels and axis labels
+h.ax.set_box_aspect(1)
+h.ax.set_yticklabels([])
+h.ax.set_xticklabels([])
+h.ax.set_xticks([])
+h.ax.set_yticks([])
+h.ax.set_xlabel(' ')
+h.ax.set_ylabel(' ')
+
+# Here we can add a simple python for loop that adds tick marks
+# to the inside of the hodograph plot to increase readability!
+plt.xticks(np.arange(0, 0, 1))
+plt.yticks(np.arange(0, 0, 1))
+for i in range(10, 120, 10):
+    h.ax.annotate(str(i), (i, 0), xytext=(0, 2), textcoords='offset pixels',
+                  clip_on=True, fontsize=10, weight='bold', alpha=0.3, zorder=0)
+for i in range(10, 120, 10):
+    h.ax.annotate(str(i), (0, i), xytext=(0, 2), textcoords='offset pixels',
+                  clip_on=True, fontsize=10, weight='bold', alpha=0.3, zorder=0)
+
+# plot the hodograph itself, using plot_colormapped, colored
+# by height
+h.plot_colormapped(u, v, c=z, linewidth=6, label='0-12km WIND')
+# compute Bunkers storm motion so we can plot it on the hodograph!
+RM, LM, MW = mpcalc.bunkers_storm_motion(p, u, v, z)
+h.ax.text((RM[0].m + 0.5), (RM[1].m - 0.5), 'RM', weight='bold', ha='left',
+          fontsize=13, alpha=0.6)
+h.ax.text((LM[0].m + 0.5), (LM[1].m - 0.5), 'LM', weight='bold', ha='left',
+          fontsize=13, alpha=0.6)
+h.ax.text((MW[0].m + 0.5), (MW[1].m - 0.5), 'MW', weight='bold', ha='left',
+          fontsize=13, alpha=0.6)
+h.ax.arrow(0, 0, RM[0].m - 0.3, RM[1].m - 0.3, linewidth=2, color='black',
+           alpha=0.2, label='Bunkers RM Vector',
+           length_includes_head=True, head_width=2)
+
+
+Complex Sounding Plot
<matplotlib.patches.FancyArrow object at 0x7f3705670250>
+
+
+

STEP 4: ADD A FEW EXTRA ELEMENTS TO REALLY MAKE A NEAT PLOT

+
# First we want to actually add values of data to the plot for easy viewing
+# to do this, lets first add a simple rectangle using matplotlib's 'patches'
+# functionality to add some simple layout for plotting calculated parameters
+#                                  xloc   yloc   xsize  ysize
+fig.patches.extend([plt.Rectangle((0.513, 0.00), 0.334, 0.37,
+                                  edgecolor='black', facecolor='white',
+                                  linewidth=1, alpha=1, transform=fig.transFigure,
+                                  figure=fig)])
+
+# now lets take a moment to calculate some simple severe-weather parameters using
+# metpy's calculations
+# here are some classic severe parameters!
+kindex = mpcalc.k_index(p, T, Td)
+total_totals = mpcalc.total_totals_index(p, T, Td)
+
+# mixed layer parcel properties!
+ml_t, ml_td = mpcalc.mixed_layer(p, T, Td, depth=50 * units.hPa)
+ml_p, _, _ = mpcalc.mixed_parcel(p, T, Td, depth=50 * units.hPa)
+mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, prof, depth=50 * units.hPa)
+
+# most unstable parcel properties!
+mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(p, T, Td, depth=50 * units.hPa)
+mucape, mucin = mpcalc.most_unstable_cape_cin(p, T, Td, depth=50 * units.hPa)
+
+# Estimate height of LCL in meters from hydrostatic thickness (for sig_tor)
+new_p = np.append(p[p > lcl_pressure], lcl_pressure)
+new_t = np.append(T[p > lcl_pressure], lcl_temperature)
+lcl_height = mpcalc.thickness_hydrostatic(new_p, new_t)
+
+# Compute Surface-based CAPE
+sbcape, sbcin = mpcalc.surface_based_cape_cin(p, T, Td)
+
+# Compute SRH
+(u_storm, v_storm), *_ = mpcalc.bunkers_storm_motion(p, u, v, z)
+*_, total_helicity1 = mpcalc.storm_relative_helicity(z, u, v, depth=1 * units.km,
+                                                     storm_u=u_storm, storm_v=v_storm)
+*_, total_helicity3 = mpcalc.storm_relative_helicity(z, u, v, depth=3 * units.km,
+                                                     storm_u=u_storm, storm_v=v_storm)
+*_, total_helicity6 = mpcalc.storm_relative_helicity(z, u, v, depth=6 * units.km,
+                                                     storm_u=u_storm, storm_v=v_storm)
+
+# Copmute Bulk Shear components and then magnitude
+ubshr1, vbshr1 = mpcalc.bulk_shear(p, u, v, height=z, depth=1 * units.km)
+bshear1 = mpcalc.wind_speed(ubshr1, vbshr1)
+ubshr3, vbshr3 = mpcalc.bulk_shear(p, u, v, height=z, depth=3 * units.km)
+bshear3 = mpcalc.wind_speed(ubshr3, vbshr3)
+ubshr6, vbshr6 = mpcalc.bulk_shear(p, u, v, height=z, depth=6 * units.km)
+bshear6 = mpcalc.wind_speed(ubshr6, vbshr6)
+
+# Use all computed pieces to calculate the Significant Tornado parameter
+sig_tor = mpcalc.significant_tornado(sbcape, lcl_height,
+                                     total_helicity3, bshear3).to_base_units()
+
+# Perform the calculation of supercell composite if an effective layer exists
+super_comp = mpcalc.supercell_composite(mucape, total_helicity3, bshear3)
+
+# there is a lot we can do with this data operationally, so lets plot some of
+# these values right on the plot, in the box we made
+# first lets plot some thermodynamic parameters
+plt.figtext(0.53, 0.32, 'SBCAPE: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.66, 0.32, f'{int(sbcape.m)} J/kg', weight='bold',
+            fontsize=15, color='orangered', ha='right')
+plt.figtext(0.53, 0.29, 'SBCIN: ', weight='bold',
+            fontsize=15, color='black', ha='left')
+plt.figtext(0.66, 0.29, f'{int(sbcin.m)} J/kg', weight='bold',
+            fontsize=15, color='lightblue', ha='right')
+
+plt.figtext(0.53, 0.24, 'MLCAPE: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.66, 0.24, f'{int(mlcape.m)} J/kg', weight='bold',
+            fontsize=15, color='orangered', ha='right')
+plt.figtext(0.53, 0.21, 'MLCIN: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.66, 0.21, f'{int(mlcin.m)} J/kg', weight='bold',
+            fontsize=15, color='lightblue', ha='right')
+
+plt.figtext(0.53, 0.16, 'MUCAPE: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.66, 0.16, f'{int(mucape.m)} J/kg', weight='bold',
+            fontsize=15, color='orangered', ha='right')
+plt.figtext(0.53, 0.13, 'MUCIN: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.66, 0.13, f'{int(mucin.m)} J/kg', weight='bold',
+            fontsize=15, color='lightblue', ha='right')
+
+plt.figtext(0.53, 0.08, 'TT-INDEX: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.66, 0.08, f'{int(total_totals.m)} Δ°C', weight='bold',
+            fontsize=15, color='orangered', ha='right')
+plt.figtext(0.53, 0.05, 'K-INDEX: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.66, 0.05, f'{int(kindex.m)} °C', weight='bold',
+            fontsize=15, color='orangered', ha='right')
+
+# now some kinematic parameters
+met_per_sec = (units.m * units.m) / (units.sec * units.sec)
+plt.figtext(0.68, 0.32, '0-1km SRH: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.83, 0.32, f'{int(total_helicity1.m)* met_per_sec:~P}',
+            weight='bold', fontsize=15, color='navy', ha='right')
+plt.figtext(0.68, 0.29, '0-1km SHEAR: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.83, 0.29, f'{int(bshear1.m)} kts', weight='bold',
+            fontsize=15, color='blue', ha='right')
+
+plt.figtext(0.68, 0.24, '0-3km SRH: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.83, 0.24, f'{int(total_helicity3.m)* met_per_sec:~P}',
+            weight='bold', fontsize=15, color='navy', ha='right')
+plt.figtext(0.68, 0.21, '0-3km SHEAR: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.83, 0.21, f'{int(bshear3.m)} kts', weight='bold',
+            fontsize=15, color='blue', ha='right')
+
+plt.figtext(0.68, 0.16, '0-6km SRH: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.83, 0.16, f'{int(total_helicity6.m)* met_per_sec:~P}',
+            weight='bold', fontsize=15, color='navy', ha='right')
+plt.figtext(0.68, 0.13, '0-6km SHEAR: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.83, 0.13, f'{int(bshear6.m)} kts', weight='bold',
+            fontsize=15, color='blue', ha='right')
+
+plt.figtext(0.68, 0.08, 'SIG TORNADO: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.83, 0.08, f'{int(sig_tor.m)}', weight='bold', fontsize=15,
+            color='orangered', ha='right')
+plt.figtext(0.68, 0.05, 'SUPERCELL COMP: ', weight='bold', fontsize=15,
+            color='black', ha='left')
+plt.figtext(0.83, 0.05, f'{int(super_comp.m)}', weight='bold', fontsize=15,
+            color='orangered', ha='right')
+
+# add legends to the skew and hodo
+skewleg = skew.ax.legend(loc='upper left')
+hodoleg = h.ax.legend(loc='upper left')
+
+# add a plot title
+plt.figtext(0.40, 0.92, 'OUN | MAY 4TH 1999 - 00Z VERTICAL PROFILE',
+            weight='bold', fontsize=20, ha='center')
+
+# Show the plot
+plt.show()
+
+
+Complex Sounding Plot
/home/runner/work/MetPy/MetPy/examples/Complex-Sounding-Plot.py:338: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  plt.figtext(0.83, 0.08, f'{int(sig_tor.m)}', weight='bold', fontsize=15,
+/home/runner/work/MetPy/MetPy/examples/Complex-Sounding-Plot.py:342: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  plt.figtext(0.83, 0.05, f'{int(super_comp.m)}', weight='bold', fontsize=15,
+
+
+

Total running time of the script: (0 minutes 0.818 seconds)

+ +

Gallery generated by Sphinx-Gallery

+
+ + +