Reading NEXRAD Data from Google Cloud

Within this example, we show how you can remotely access Next Generation Weather Radar (NEXRAD) Data from Google Cloud Storage and plot quick looks of the datasets.

print(__doc__)

# Author: Zach Sherman
# License: BSD 3 clause

import tarfile

# This example requires installation of the gcsfs library
import gcsfs
import matplotlib.pyplot as plt

import pyart

Read NEXRAD Level 2 Data

Let’s start first with NEXRAD Level 2 data, which is ground-based radar data collected by the National Oceanic and Atmospheric Administration (NOAA), as a part of the National Weather Service ### Configure our Filepath for NEXRAD Level 2 Data We will access data from Google cloud storage, with the data organized as:

gcp-public-data-nexrad-l2/year/month/day/radarsite/NWS_NEXRAD_NXL2DPBL_{radarsite}_{year}{month}{date}{hour}0000_{year}{month}{date}{hour}{minute}.tar

Where in our case, we are using a sample data file from Miami, Florida (KAMX) on October 7, 2016, at 0401:25 UTC. This means our path would look like:

gcs_path = "gcp-public-data-nexrad-l2/2016/10/07/KAMX/NWS_NEXRAD_NXL2DPBL_KAMX_20161007040000_20161007045959.tar"

First we can create a file system and retrieve the tar file to our current directory.

fs = gcsfs.GCSFileSystem()
fs.get(gcs_path, ".")

[None]

The we can use the tarfile module to extract the files within.

file = tarfile.open("NWS_NEXRAD_NXL2DPBL_KAMX_20161007040000_20161007045959.tar")
members = file.getmembers()

# This case we are getting only the first file, use extract_all if wanting all files.
file.extract(members[0].name, ".")
file.close()

We can use the pyart.io.read_nexrad_archive module to access our data, passing in the member filepath.

radar = pyart.io.read_nexrad_archive(members[0].name)

Let’s take a look at a summary of what fields are available.

print(list(radar.fields))

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

Let’s plot the reflectivity field as a first step to investigating our dataset.

fig = plt.figure(figsize=(12, 4))
display = pyart.graph.RadarMapDisplay(radar)

display.plot_ppi_map("reflectivity")
plt.show()