# Simple Py-ART Usage

Scott Collis1 and Jonathan Helmus1
1:Argonne National Laboratory

In this tutorial we are going to load some data from the Miami NEXRAD radar. Have a little look at the (non-standard) data by plotting it on a map. Do a simple modification to a field and take a closer look at the data!



In [1]:

#first we do some imports and check the version of Py-ART for consistency
import pyart
from matplotlib import pyplot as plt
import numpy as np
%matplotlib inline
print pyart.__version__






read the data into our data model



In [2]:

#you can grab the data here: http://engineering.arm.gov/~collis/KAMX_20140417_1056
filename = 'data/KAMX_20140417_1056'



Lets see what data we get with this radar



In [3]:




['differential_phase', 'cross_correlation_ratio', 'spectrum_width', 'reflectivity', 'differential_reflectivity', 'velocity']



To save some memory we are going to only store the first two sweeps from this radar volume.



In [4]:



Ok! We are going to use Py-ART's RadarMapDisplay class to visualize this PPI on a map of the coast line. This class is well documented and available here: Click!



In [5]:

#create an instance of the class using our radar
#create a Matplotlib figure
f = plt.figure(figsize = [17,4])
#now we are going to do a three panel plot, resolution is a basemap parameter and determines the resolution of
#the coastline.. here we set to intermediate or 'i' ('h' for high 'l' for low)
plt.subplot(1, 3, 1)
display.plot_ppi_map('differential_reflectivity', max_lat = 26.5, min_lat =25.4, min_lon = -81., max_lon = -79.5,
vmin = -7, vmax = 7, lat_lines = np.arange(20,28,.2), lon_lines = np.arange(-82, -79, .5),
resolution = 'i')
plt.subplot(1, 3, 2)
display.plot_ppi_map('reflectivity', max_lat = 26.5, min_lat =25.4, min_lon = -81., max_lon = -79.5,
vmin = -8, vmax = 64, lat_lines = np.arange(20,28,.2), lon_lines = np.arange(-82, -79, .5),
resolution = 'i')
plt.subplot(1, 3, 3)
display.plot_ppi_map('velocity', sweep = 1, max_lat = 26.5, min_lat =25.4, min_lon = -81., max_lon = -79.5,
vmin = -15, vmax = 15, lat_lines = np.arange(20,28,.2), lon_lines = np.arange(-82, -79, .5),
resolution = 'i')






Super cool! Hey, take a look at the interesting feature at 26.2$^\circ$N, 80.5$^\circ$W.. Clear radial velocity signature, intresting torus shape in $Z_e$ and a hint of something in differential reflectivity.. Lets do an 8-point smooth on the $Z_{dr}$ and see if things become more apparent.



In [ ]:

# First we create an independant copy of one of our radar fields.. we need to make sure changes made do not
# infer back onto the object it was copied from (common cause of issues in Python)

# Now for each of the radials in the volume we want to do an 8-point smooth by convolving the data with a Hanning window..
# rather than write out the code here we are going to use Py-ART's smooth and trim function which also makes sure
# the returned array has the same size as the original array..
# Check here: https://github.com/ARM-DOE/pyart/blob/master/pyart/correct/phase_proc.py#L242

for i in range(smooth_zdr.shape[0]):
smooth_zdr[i,:] = pyart.correct.phase_proc.smooth_and_trim(smooth_zdr[i,:], 8)

# Now that we have add this data as a new field, using the same metadata as the
# differential_reflectivity field.



ok! We have made a new field.. now for the cool part: Since the field conforms to our data model visualization routines simply work!



In [ ]:

f = plt.figure(figsize = [17,4])
plt.subplot(1, 3, 1)
display.plot_ppi_map('differential_reflectivity_smooth', max_lat = 26.5, min_lat =25.4, min_lon = -81., max_lon = -79.5,
vmin = -7, vmax = 7, lat_lines = np.arange(20,28,.2), lon_lines = np.arange(-82, -79, .5),
resolution = 'i')
plt.subplot(1, 3, 2)
display.plot_ppi_map('reflectivity', max_lat = 26.5, min_lat =25.4, min_lon = -81., max_lon = -79.5,
vmin = -8, vmax = 64, lat_lines = np.arange(20,28,.2), lon_lines = np.arange(-82, -79, .5),
resolution = 'i')
plt.subplot(1, 3, 3)
display.plot_ppi_map('velocity', sweep = 1, max_lat = 26.5, min_lat =25.4, min_lon = -81., max_lon = -79.5,
vmin = -15, vmax = 15, lat_lines = np.arange(20,28,.2), lon_lines = np.arange(-82, -79, .5),
resolution = 'i')



Hmm.. We are starting to see something in our $Z_{dr}$ here now.. lets zoom in for a closer look by changing the min/max_lat/lon



In [ ]:

f = plt.figure(figsize = [17,4])
plt.subplot(1, 3, 1)
display.plot_ppi_map('differential_reflectivity_smooth', max_lat = 26.4, min_lat =26, min_lon = -80.75, max_lon = -80.25,
vmin = -7, vmax = 7, lat_lines = np.arange(20,28,.1), lon_lines = np.arange(-82, -79, .2),
resolution = 'c')
plt.subplot(1, 3, 2)
display.plot_ppi_map('reflectivity', max_lat = 26.4, min_lat =26, min_lon = -80.75, max_lon = -80.25,
vmin = -8, vmax = 64, lat_lines = np.arange(20,28,.1), lon_lines = np.arange(-82, -79, .2),
resolution = 'c')
plt.subplot(1, 3, 3)
display.plot_ppi_map('velocity', sweep = 1, max_lat = 26.4, min_lat =26, min_lon = -80.75, max_lon = -80.25,
vmin = -15, vmax = 15, lat_lines = np.arange(20,28,.1), lon_lines = np.arange(-82, -79, .2),
resolution = 'c')



Nice Signature... Lets google this and see what we find! Here:

Take a read of the BAMS article by Zirnic and Ryzhkov: http://journals.ametsoc.org/doi/pdf/10.1175/1520-0477%281999%29080%3C0389%3APFWSR%3E2.0.CO%3B2

Should also be a $\delta_{dp}$ signal on top of $\phi_{dp}$.. Lets take a look



In [ ]:

f = plt.figure(figsize = [17,4])
plt.subplot(1, 3, 1)
display.plot_ppi_map('differential_reflectivity_smooth', max_lat = 26.4, min_lat =26, min_lon = -80.75, max_lon = -80.25,
vmin = -7, vmax = 7, lat_lines = np.arange(20,28,.1), lon_lines = np.arange(-82, -79, .2),
resolution = 'l')
plt.subplot(1, 3, 2)
display.plot_ppi_map('reflectivity', max_lat = 26.4, min_lat =26, min_lon = -80.75, max_lon = -80.25,
vmin = -8, vmax = 64, lat_lines = np.arange(20,28,.1), lon_lines = np.arange(-82, -79, .2),
resolution = 'l')
plt.subplot(1, 3, 3)
display.plot_ppi_map('differential_phase', sweep = 0, max_lat = 26.4, min_lat =26, min_lon = -80.75, max_lon = -80.25,
vmin = 0, vmax = 360, lat_lines = np.arange(20,28,.1), lon_lines = np.arange(-82, -79, .2),
resolution = 'l')



finally, now that we have gone to the trouble to do some value adding lets save the data out...



In [ ]:



And we can use ncdump to ensure it is a CF-Radial netCDF file



In [ ]:

!ncdump -h data/roostring.nc