diff --git a/examples/correct/plot_cloud_mask.py b/examples/correct/plot_cloud_mask.py
new file mode 100644
index 0000000000..88ccfb5dc4
--- /dev/null
+++ b/examples/correct/plot_cloud_mask.py
@@ -0,0 +1,66 @@
+"""
+=====================================
+Calculating and Plotting a Cloud Mask
+=====================================
+
+This example shows how to correct and plot reflectivity from an ARM
+KAZR using a noise floor cloud mask.
+
+"""
+print(__doc__)
+
+# Author: Adam Theisen and Zach Sherman
+# License: BSD 3 clause
+
+import matplotlib.pyplot as plt
+import numpy as np
+from open_radar_data import DATASETS
+
+import pyart
+
+############################
+# **Read and plot raw data**
+#
+# First let's read and plot our dataset without any mask.
+
+# Fetch and read in the ARM KAZR file.
+filename = DATASETS.fetch("sgpkazrgeC1.a1.20190529.000002.cdf")
+radar = pyart.aux_io.read_kazr(filename)
+
+# Let's now take a look at reflectivity data prior to any corrections.
+display = pyart.graph.RadarDisplay(radar)
+display.plot("reflectivity_copol")
+display.set_limits(xlim=(0, 55))
+plt.show()
+
+#################################################
+# **Calculate cloud mask and plot corrected data**
+#
+# Now lets apply a mask by using the calc_cloud_mask function
+# that will use a noise floor calculation from range and more
+# to calculate the mask.
+
+# First lets correct the data by calculating the mask.
+cloud_mask_radar = pyart.correct.calc_cloud_mask(radar, "reflectivity_copol", "range")
+
+# In this new radar object we should now have a new cloud mask field.
+print(cloud_mask_radar.fields["cloud_mask_2"])
+
+# Next we'll create a copy of the reflectivity field so we are not
+# overwriting the original data.
+cloud_mask_radar.add_field_like(
+    "reflectivity_copol",
+    "reflectivity_cloud_mask",
+    cloud_mask_radar.fields["reflectivity_copol"]["data"].copy(),
+)
+
+# Now let's apply the mask to the copied reflectivity data.
+cloud_mask_radar.fields["reflectivity_cloud_mask"]["data"][
+    cloud_mask_radar.fields["cloud_mask_2"]["data"] == 0
+] = np.nan
+
+# And now we can plot the masked reflectivity field.
+display = pyart.graph.RadarDisplay(cloud_mask_radar)
+display.plot("reflectivity_copol")
+display.set_limits(xlim=(0, 55))
+plt.show()