Within this notebook, we will cover:
Concepts | Importance | Notes |
---|---|---|
Matplotlib Basics | Required | Basic plotting |
Py-ART Basics | Required | IO/Visualization |
import glob
import os
from pathlib import Path
import act
import imageio.v2 as imageio
import matplotlib
import matplotlib.pyplot as plt
import pyart
One of the better cases was from June 2, 2022, where several cold pools and single-cell storms traversed through the domain.
Data is available from the Atmospheric Radiation Measurment user facility, which helped to lead the TRACER field campaign in Houston, Texas.
The data are available from the ARM data portal (https://adc.arm.gov/).
We are interested in the C-band radar, which is utilizing a cell-tracking algorithm, with the datastream
houcsapr2cfrS2.a1
The Atmospheric Data Community Toolkit (ACT) has a helpful module to interface with the data server:
act.discovery.download_arm_data?
Before downloading our data, we need to make sure we have an ARM Data Account, and ARM Live token. Both of these can be found using this link:
Once you sign up, you will see your token. Copy and replace that where we have arm_username
and arm_password
below.
arm_username = os.getenv("ARM_USERNAME")
arm_password = os.getenv("ARM_PASSWORD")
datastream = "houcsapr2cfrS2.a1"
start_date = "2022-06-02T11:30:00"
end_date = "2022-06-02T11:40:00"
print(len(arm_username), len(arm_password))
june2_csapr_files = act.discovery.download_arm_data(arm_username,
arm_password,
datastream,
start_date,
end_date,
)
Before following running the next cells, make sure you have created the following directories:
quicklooks/ppi
quicklooks/rhi
quicklooks/vpt
!mkdir quicklooks
!mkdir quicklooks/rhi
!mkdir quicklooks/ppi
!mkdir quicklooks/vpt
We read in the data, check the scan type, and plot a basic RadarDisplay
which will automatically detect whether the plot is a vertical cross section (RHI or VPT), or a horizontal scan (PPI).
This offers a solid "quick look", or initial visualization of the data.
for file in june2_csapr_files:
radar = pyart.io.read(file)
print(radar.scan_type)
display = pyart.graph.RadarDisplay(radar)
display.plot("reflectivity", 0)
plt.savefig(f"quicklooks/{radar.scan_type}/{Path(file).stem}.png", dpi=200)
plt.show()
plt.close()
Let's focus on the vertical scans of the data, or the RHIs.
You'll notice that we had some cells around 60 km from the radar, with the vertical axis less than 6 km.
Let's reflect that in the plots!
Before we plot, we can change the size of our font, and style using the following parameters:
font = {'family' : 'serif',
'weight' : 'bold',
'size' : 16}
matplotlib.rc('font', **font)
We:
for file in june2_csapr_files:
radar = pyart.io.read(file)
if radar.scan_type == 'rhi':
fig = plt.figure(figsize=(20,8))
display = pyart.graph.RadarDisplay(radar)
ax = plt.subplot(121)
display.plot("reflectivity",
0,
ax=ax,
vmin=-20,
vmax=70)
plt.xlim(62,68)
plt.ylim(0, 6)
ax2 = plt.subplot(122)
display.plot("mean_doppler_velocity",
0,
ax=ax2,
cmap='pyart_balance',
vmin=-15,
vmax=15)
plt.xlim(62,68)
plt.ylim(0, 6)
plt.savefig(f"quicklooks/{radar.scan_type}/{Path(file).stem}.png", dpi=200)
plt.show()
plt.close()
rhi_images = sorted(glob.glob("quicklooks/rhi/*"))
with imageio.get_writer('storm-animation.gif', mode='I') as writer:
for filename in rhi_images:
image = imageio.imread(filename)
writer.append_data(image)
Within this example, we walked through how to access ARM data from a field campaign in Texas, plot a quick look of the data, and refine our plots to investigate a storm!
We will showcase other data workflow examples, including field campaigns in other regions and data access methods from other data centers.