Plotting Data from a Field Campaign (TRACER)¶

Overview¶

Within this notebook, we will cover:

How to access data from the Atmospheric Radiation Measurment (ARM) user facility
How to setup a workflow to plot both cross sections (RHIs) and horizontal scans (PPIs)

Prerequisites¶

Concepts	Importance	Notes
Matplotlib Basics	Required	Basic plotting
Py-ART Basics	Required	IO/Visualization

Imports¶

import glob
import os
from pathlib import Path

import act
import imageio.v2 as imageio
import matplotlib
import matplotlib.pyplot as plt
import pyart

/home/runner/micromamba/envs/radar-cookbook-dev/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm


## You are using the Python ARM Radar Toolkit (Py-ART), an open source
## library for working with weather radar data. Py-ART is partly supported
## by the U.S. Department of Energy Office of Science as part of
## the Atmospheric Radiation Measurement (ARM) User Facility.
##
## If you use this software to prepare a publication, please cite:
##
##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119

Grab Data from ARM¶

One of the better cases was from June 2, 2022, where several cold pools and single-cell storms traversed through the domain.

The Tracking Aerosol Convection Interactions ExpeRiment (TRACER) Field Campaign¶

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

Use the ARM Live API to Download the Data, using ACT¶

The Atmospheric Data Community Toolkit (ACT) has a helpful module to interface with the data server:

Download Data API

act.discovery.download_arm_data?

Setup our Download Query¶

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:

ARM Live Signup

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))

0 0

june2_csapr_files = act.discovery.download_arm_data(arm_username,
                                                arm_password,
                                                datastream,
                                                start_date,
                                                end_date,
                                               )

---------------------------------------------------------------------------
HTTPError                                 Traceback (most recent call last)
Cell In[4], line 1
----> 1 june2_csapr_files = act.discovery.download_arm_data(arm_username,
      2                                                 arm_password,
      3                                                 datastream,
      4                                                 start_date,

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/site-packages/act/discovery/arm.py:122, in download_arm_data(username, token, datastream, startdate, enddate, time, output)
    119 req = Request(query_url, None, headers)
    120 # get url response, read the body of the message,
    121 # and decode from bytes type to utf-8 string
--> 122 response_body = urlopen(req).read().decode('utf-8')
    123 # if the response is an html doc, then there was an error with the user
    124 if response_body[1:14] == '!DOCTYPE html':

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:215, in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    213 else:
    214     opener = _opener
--> 215 return opener.open(url, data, timeout)

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:521, in OpenerDirector.open(self, fullurl, data, timeout)
    519 for processor in self.process_response.get(protocol, []):
    520     meth = getattr(processor, meth_name)
--> 521     response = meth(req, response)
    523 return response

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:630, in HTTPErrorProcessor.http_response(self, request, response)
    627 # According to RFC 2616, "2xx" code indicates that the client's
    628 # request was successfully received, understood, and accepted.
    629 if not (200 <= code < 300):
--> 630     response = self.parent.error(
    631         'http', request, response, code, msg, hdrs)
    633 return response

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:553, in OpenerDirector.error(self, proto, *args)
    551     http_err = 0
    552 args = (dict, proto, meth_name) + args
--> 553 result = self._call_chain(*args)
    554 if result:
    555     return result

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:492, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
    490 for handler in handlers:
    491     func = getattr(handler, meth_name)
--> 492     result = func(*args)
    493     if result is not None:
    494         return result

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:745, in HTTPRedirectHandler.http_error_302(self, req, fp, code, msg, headers)
    742 fp.read()
    743 fp.close()
--> 745 return self.parent.open(new, timeout=req.timeout)

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:521, in OpenerDirector.open(self, fullurl, data, timeout)
    519 for processor in self.process_response.get(protocol, []):
    520     meth = getattr(processor, meth_name)
--> 521     response = meth(req, response)
    523 return response

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:630, in HTTPErrorProcessor.http_response(self, request, response)
    627 # According to RFC 2616, "2xx" code indicates that the client's
    628 # request was successfully received, understood, and accepted.
    629 if not (200 <= code < 300):
--> 630     response = self.parent.error(
    631         'http', request, response, code, msg, hdrs)
    633 return response

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:559, in OpenerDirector.error(self, proto, *args)
    557 if http_err:
    558     args = (dict, 'default', 'http_error_default') + orig_args
--> 559     return self._call_chain(*args)

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:492, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
    490 for handler in handlers:
    491     func = getattr(handler, meth_name)
--> 492     result = func(*args)
    493     if result is not None:
    494         return result

File ~/micromamba/envs/radar-cookbook-dev/lib/python3.12/urllib/request.py:639, in HTTPDefaultErrorHandler.http_error_default(self, req, fp, code, msg, hdrs)
    638 def http_error_default(self, req, fp, code, msg, hdrs):
--> 639     raise HTTPError(req.full_url, code, msg, hdrs, fp)

HTTPError: HTTP Error 404: Not Found

Read in and Plot the Data¶

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

Loop through and plot¶

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"{radar.scan_type}_{Path(file).stem}.png", dpi=200)
    plt.show()
    plt.close()

Refine our Plot, Plot Velocity¶

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!

Customize our plot look¶

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)

Apply our Plotting Loop¶

We:

Check to see if the scan is an RHI
Plot the reflectivity on one subplot
Plot velocity on the other
Save our plots

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='balance',
                     vmin=-15,
                     vmax=15)
        plt.xlim(62,68)
        plt.ylim(0, 6)
        plt.savefig(f"{radar.scan_type}_{Path(file).stem}.png", dpi=200)
        plt.show()
        plt.close()

Create a GIF of the RHI images¶

rhi_images = sorted(glob.glob(f"{radar.scan_type}*"))

with imageio.get_writer('storm-animation.gif', mode='I') as writer:
    for filename in rhi_images:
        image = imageio.imread(filename)
        writer.append_data(image)

Summary¶

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!

What’s Next?¶

We will showcase other data workflow examples, including field campaigns in other regions and data access methods from other data centers.

Resources and References¶

ARM Data Discovery
TRACER Field Campaign
CSAPR Radar Data:
- Bharadwaj, N., Collis, S., Hardin, J., Isom, B., Lindenmaier, I., Matthews, A., & Nelson, D. C-Band Scanning ARM Precipitation Radar (CSAPR2CFR). Atmospheric Radiation Measurement (ARM) User Facility. Bharadwaj et al. (2021)
Py-ART:
- Helmus, J.J. & Collis, S.M., (2016). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software. 4(1), p.e25. DOI: Helmus & Collis (2016)
ACT:
- Adam Theisen, Ken Kehoe, Zach Sherman, Bobby Jackson, Alyssa Sockol, Corey Godine, Max Grover, Jason Hemedinger, Jenni Kyrouac, Maxwell Levin, Michael Giansiracusa (2022). The Atmospheric Data Community Toolkit (ACT). Zenodo. DOI: Theisen et al. (2022)

References¶

Bharadwaj, N., Collis, S., Hardin, J., Isom, B., Lindenmaier, I., Matthews, A., Nelson, D., Feng, Y.-C., Rocque, M., Wendler, T., & Castro, V. (2021). C-Band Scanning ARM Precipitation Radar, 2nd Generation. Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); ARM Data Center, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). 10.5439/1467901
Helmus, J. J., & Collis, S. M. (2016). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software, 4(1), 25. 10.5334/jors.119
Theisen, A., Kehoe, K., Sherman, Z., Jackson, B., Sockol, A., Godine, C., Grover, M., Hemedinger, J., Kyrouac, J., Levin, M., & Giansiracusa, M. (2022). ARM-DOE/ACT: ACT Release v1.1.9. Zenodo. 10.5281/ZENODO.6712343