diff --git a/examples/xradar/plot_dealias_xradar.py b/examples/xradar/plot_dealias_xradar.py
new file mode 100644
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+++ b/examples/xradar/plot_dealias_xradar.py
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+"""
+=================================================
+Dealias Radial Velocities Using Xradar and Py-ART
+=================================================
+
+An example which uses xradar and Py-ART to dealias radial velocities.
+
+"""
+
+# Author: Max Grover (mgrover@anl.gov)
+# License: BSD 3 clause
+
+
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import xradar as xd
+
+import pyart
+from pyart.testing import get_test_data
+
+# Locate the test data and read in using xradar
+filename = get_test_data("swx_20120520_0641.nc")
+tree = xd.io.open_cfradial1_datatree(filename)
+
+# Give the tree Py-ART radar methods
+radar = pyart.xradar.Xradar(tree)
+
+# Determine the nyquist velocity using the maximum radial velocity from the first sweep
+nyq = radar["sweep_0"]["mean_doppler_velocity"].max().values
+
+# Set the nyquist to what we captured above
+# Calculate the velocity texture
+vel_texture = pyart.retrieve.calculate_velocity_texture(
+    radar, vel_field="mean_doppler_velocity", nyq=nyq
+)
+radar.add_field("velocity_texture", vel_texture, replace_existing=True)
+
+##########################################
+# **Visualize our velocity texture field**
+# Let's use the RadarMapDisplay to visualize the texture field
+
+fig = plt.figure(figsize=[8, 8])
+display = pyart.graph.RadarMapDisplay(radar)
+display.plot_ppi_map(
+    "velocity_texture",
+    sweep=2,
+    resolution="50m",
+    vmin=0,
+    vmax=10,
+    projection=ccrs.PlateCarree(),
+    cmap="pyart_balance",
+)
+plt.show()
+
+##################################################
+# **Determine a Velocity Texture Threshold Value**
+#
+# We can use the xradar/xarray plotting functionality here
+radar["sweep_0"]["velocity_texture"].plot.hist()
+plt.show()
+
+#############################
+# **Apply a Gatefilter Mask**
+#
+# We now apply this threshold, along with a reflectivity threshold,
+# and make use of the region-based dealiasing algorithm
+
+# Configure the gatefilter
+gatefilter = pyart.filters.GateFilter(radar)
+gatefilter.exclude_above("velocity_texture", 4)
+gatefilter.exclude_below("corrected_reflectivity_horizontal", 0)
+
+# At this point, we can simply used dealias_region_based to dealias the velocities
+# and then add the new field to the radar.
+velocity_dealiased = pyart.correct.dealias_region_based(
+    radar,
+    vel_field="mean_doppler_velocity",
+    nyquist_vel=nyq,
+    centered=True,
+    gatefilter=gatefilter,
+)
+radar.add_field("corrected_velocity", velocity_dealiased, replace_existing=True)
+
+#############################################
+# **Visualize the Cleaned Radial Velocities**
+#
+# We can visualize the uncorrected and corrected radial velocity fields
+fig = plt.figure(figsize=(14, 5))
+display = pyart.graph.RadarMapDisplay(radar)
+ax1 = plt.subplot(121)
+display.plot_ppi(
+    "mean_doppler_velocity",
+    cmap="twilight_shifted",
+    vmin=-40,
+    vmax=40,
+    colorbar_label="Uncorrected Radial Velocity (m/s)",
+    ax=ax1,
+)
+ax2 = plt.subplot(122)
+display.plot_ppi(
+    "corrected_velocity",
+    cmap="twilight_shifted",
+    vmin=-40,
+    vmax=40,
+    colorbar_label="Corrected Radial Velocity (m/s)",
+    ax=ax2,
+)
+plt.show()