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PyART Basics#

Overview#

Within this notebook, we will cover:

  1. General overview of PyART and its functionality

  2. Reading data using PyART

  3. An overview of the pyart.Radar object

  4. Create a Plot of our Radar Data

Prerequisites#

Concepts

Importance

Notes

Intro to Cartopy

Helpful

Basic features

Matplotlib Basics

Helpful

Basic plotting

NumPy Basics

Helpful

Basic arrays

  • Time to learn: 45 minutes

Imports#

import os
import warnings

import cartopy.crs as ccrs
import matplotlib.pyplot as plt


import pyart

warnings.filterwarnings('ignore')

An Overview of PyART#

History of the PyART#

  • Development began to address the needs of ARM with the acquisition of a number of new scanning cloud and precipitation radar as part of the American Recovery Act.

  • The project has since expanded to work with a variety of weather radars and a wider user base including radar researchers and climate modelers.

  • The software has been released on GitHub as open source software under a BSD license. Runs on Linux, OS X. It also runs on Windows with more limited functionality.

What can PyART Do?#

Py-ART can be used for a variety of tasks from basic plotting to more complex processing pipelines. Specific uses for Py-ART include:

  • Reading radar data in a variety of file formats.

  • Creating plots and visualization of radar data.

  • Correcting radar moments while in antenna coordinates, such as:

    • Doppler unfolding/de-aliasing.

    • Attenuation correction.

    • Phase processing using a Linear Programming method.

  • Mapping data from one or multiple radars onto a Cartesian grid.

  • Performing retrievals.

  • Writing radial and Cartesian data to NetCDF files.

Reading in Data Using PyART#

Reading data in using pyart.io.read#

When reading in a radar file, we use the pyart.io.read module.

pyart.io.read can read a variety of different radar formats, such as Cf/Radial, LASSEN, and more. The documentation on what formats can be read by Py-ART can be found here:

For most file formats listed on the page, using pyart.io.read should suffice since Py-ART has the ability to automatically detect the file format.

Let’s check out what arguments arguments pyart.io.read() takes in!

pyart.io.read?

Let’s use a sample data file from pyart - which is cfradial format.

When we read this in, we get a pyart.Radar object!

file = 'data/swx_20120520_0641.nc'
radar = pyart.io.read(file)
radar

Investigate the pyart.Radar object#

Within this pyart.Radar object object are the actual data fields.

This is where data such as reflectivity and velocity are stored.

To see what fields are present we can add the fields and keys additions to the variable where the radar object is stored.

radar.fields.keys()

Extract a sample data field#

The fields are stored in a dictionary, each containing coordinates, units and more. All can be accessed by just adding the fields addition to the radar object variable.

For an individual field, we add a string in brackets after the fields addition to see the contents of that field.

Let’s take a look at 'corrected_reflectivity_horizontal', which is a common field to investigate.

print(radar.fields['corrected_reflectivity_horizontal'])

We can go even further in the dictionary and access the actual reflectivity data.

We use add 'data' at the end, which will extract the data array (which is a masked numpy array) from the dictionary.

reflectivity = radar.fields['corrected_reflectivity_horizontal']['data']
print(type(reflectivity), reflectivity)

Lets’ check the size of this array…

reflectivity.shape

This reflectivity data array, numpy array, is a two-dimensional array with dimensions:

  • Gates (number of samples away from the radar)

  • Rays (direction around the radar)

print(radar.nrays, radar.ngates)

If we wanted to look the 300th ray, at the second gate, we would use something like the following:

print(reflectivity[300, 2])

Plotting our Radar Data#

An Overview of PyART Plotting Utilities#

Now that we have loaded the data and inspected it, the next logical thing to do is to visualize the data! Py-ART’s visualization functionality is done through the objects in the pyart.graph module.

In Py-ART there are 4 primary visualization classes in pyart.graph:

Plotting grid data

Use the RadarMapDisplay with our data#

For the this example, we will be using RadarMapDisplay, using Cartopy to deal with geographic coordinates.

We start by creating a figure first.

fig = plt.figure(figsize=[10, 10])

Once we have a figure, let’s add our RadarMapDisplay

fig = plt.figure(figsize=[10, 10])
display = pyart.graph.RadarMapDisplay(radar)

Adding our map display without specifying a field to plot won’t do anything we need to specifically add a field to field using .plot_ppi_map()

display.plot_ppi_map('corrected_reflectivity_horizontal')

By default, it will plot the elevation scan, the the default colormap from Matplotlib… let’s customize!

We add the following arguements:

  • sweep=3 - The fourth elevation scan (since we are using Python indexing)

  • vmin=-20 - Minimum value for our plotted field/colorbar

  • vmax=60 - Maximum value for our plotted field/colorbar

  • projection=ccrs.PlateCarree() - Cartopy latitude/longitude coordinate system

  • cmap='pyart_HomeyerRainbow' - Colormap to use, selecting one provided by PyART

fig = plt.figure(figsize=[12, 12])
display = pyart.graph.RadarMapDisplay(radar)
display.plot_ppi_map('corrected_reflectivity_horizontal',
                     sweep=3,
                     vmin=-20,
                     vmax=60,
                     projection=ccrs.PlateCarree(),
                     cmap='pyart_HomeyerRainbow')
plt.show()

You can change many parameters in the graph by changing the arguments to plot_ppi_map. As you can recall from earlier. simply view these arguments in a Jupyter notebook by typing:

display.plot_ppi_map?

For example, let’s change the colormap to something different

fig = plt.figure(figsize=[12, 12])
display = pyart.graph.RadarMapDisplay(radar)
display.plot_ppi_map('corrected_reflectivity_horizontal',
                     sweep=3,
                     vmin=-20,
                     vmax=60,
                     projection=ccrs.PlateCarree(),
                     cmap='pyart_Carbone42')
plt.show()

Or, let’s view a different elevation scan! To do this, change the sweep parameter in the plot_ppi_map function.

fig = plt.figure(figsize=[12, 12])
display = pyart.graph.RadarMapDisplay(radar)
display.plot_ppi_map('corrected_reflectivity_horizontal',
                     sweep=0,
                     vmin=-20,
                     vmax=60,
                     projection=ccrs.PlateCarree(),
                     cmap='pyart_Carbone42')
plt.show()

Let’s take a look at a different field - for example, correlation coefficient (corr_coeff)

fig = plt.figure(figsize=[12, 12])
display = pyart.graph.RadarMapDisplay(radar)
display.plot_ppi_map('copol_coeff',
                     sweep=0,
                     vmin=0.8,
                     vmax=1.,
                     projection=ccrs.PlateCarree(),
                     cmap='pyart_Carbone42')
plt.show()

Summary#

Within this notebook, we covered the basics of working with radar data using pyart, including:

  • Reading in a file using pyart.io

  • Investigating the Radar object

  • Visualizing radar data using the RadarMapDisplay

What’s Next#

In the next few notebooks, we walk through gridding radar data, applying data cleaning methods, and advanced visualization methods!

Resources and References#

Py-ART essentials links:

  • Landing page

  • Examples

  • Source Code

  • Mailing list

  • Issue Tracker