.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/plotting/plot_nexrad_image_muted_reflectivity.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_plotting_plot_nexrad_image_muted_reflectivity.py: ======================================= Create an image-muted reflectivity plot ======================================= An example which creates an image-muted PPI plot from a NEXRAD file. Image muting reduces the visual prominence of the reflectivities within identified melting and mixed precipitation features in winter storms (i.e. regions with low correlation coefficient values). Reflectivities corresponding to melting and mixed precipitation features are deemphasized using a gray scale and the regions with just snow and just rain are depicted in a corresponding full-color scale. The ultimate utility of image muting radar reflectivity is to reduce the misinterpretation of regions of melting or mixed precipitation as opposed to heavy snow or heavy rain. .. GENERATED FROM PYTHON SOURCE LINES 16-68 .. image-sg:: /examples/plotting/images/sphx_glr_plot_nexrad_image_muted_reflectivity_001.png :alt: KBGM 0.5 Deg. 2020-02-07T13:26:42Z Equivalent reflectivity factor :srcset: /examples/plotting/images/sphx_glr_plot_nexrad_image_muted_reflectivity_001.png :class: sphx-glr-single-img .. code-block:: Python print(__doc__) # Author: Laura Tomkins (lmtomkin@ncsu.edu) # License: BSD 3 clause # citation: Tomkins, L. M., Yuter, S. E., Miller, M. A., and Allen, L. R., 2022: # Image muting of mixed precipitation to improve identification of regions # of heavy snow in radar data. Atmos. Meas. Tech., 15, 5515–5525, # https://doi.org/10.5194/amt-15-5515-2022 import matplotlib.colors as mcolors import matplotlib.pyplot as plt import numpy as np import pyart # Read in file nexrad_file = "s3://noaa-nexrad-level2/2020/02/07/KBGM/KBGM20200207_132642_V06" radar = pyart.io.read_nexrad_archive(nexrad_file) # Mute radar object # Regions where rhoHV < 0.97 and reflectivity > 20 will be muted radar = pyart.util.image_mute_radar( radar, field="reflectivity", mute_field="cross_correlation_ratio", mute_threshold=0.97, field_threshold=20, ) # adjust colormaps for visual separation # this example uses perceptually uniform colormaps magma_cmap = plt.get_cmap("magma_r") grays_cmap = plt.get_cmap("gray_r") nonmuted_cmap = mcolors.LinearSegmentedColormap.from_list( "nonmuted_cmap", magma_cmap(np.linspace(0, 0.9, magma_cmap.N)) ) muted_cmap = mcolors.LinearSegmentedColormap.from_list( "muted_cmap", grays_cmap(np.linspace(0, 0.7, grays_cmap.N)) ) # create plot using RadarDisplay display = pyart.graph.RadarDisplay(radar) fig = plt.figure() ax = plt.axes() display.plot("nonmuted_reflectivity", 0, vmin=5, vmax=45, cmap=nonmuted_cmap) display.plot("muted_reflectivity", 0, vmin=5, vmax=45, cmap=muted_cmap) display.set_limits((-300, 300), (-300, 300)) ax.set_aspect("equal") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.406 seconds) .. _sphx_glr_download_examples_plotting_plot_nexrad_image_muted_reflectivity.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nexrad_image_muted_reflectivity.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nexrad_image_muted_reflectivity.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_