In this example, we'll focus on interacting with the Radar Query Service to retrieve radar data.
But first! Bookmark these resources for when you want to use Siphon later!
First, we point at the top level of the Radar Query Service (the "Radar Server") to see what radar collections are available:
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from siphon.catalog import TDSCatalog
cat = TDSCatalog('http://thredds-aws.unidata.ucar.edu/thredds/radarServer/catalog.xml')
list(cat.catalog_refs)
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Next we create an instance of the RadarServer object to point at one of these collections. This downloads some top level metadata and sets things up so we can easily query the server.
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from siphon.radarserver import RadarServer
rs = RadarServer(cat.catalog_refs['S3 NEXRAD Level II'].href)
We can use rs.variables to see a list of radar products available to view from this access URL.
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print(sorted(rs.variables))
We can also see a list of the stations. Each station has associated location information.
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print(sorted(rs.stations))
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rs.stations['KTLX']
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Next, we'll create a new query object to help request the data. Using the chaining methods, let's ask for the volume from radar TLX (Oklahoma City) for the current time. We see that when the query is represented as a string, it shows the encoded URL.
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from datetime import datetime
query = rs.query()
query.stations('KTLX').time(datetime.utcnow())
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The query also supports time range queries, queries for closest to a lon/lat point, or getting all radars within a lon/lat box.
We can use the RadarServer instance to check our query, to make sure we have required parameters and that we have chosen valid station(s) and variable(s).
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rs.validate_query(query)
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Make the request, which returns an instance of TDSCatalog. This handles parsing the catalog
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catalog = rs.get_catalog(query)
We can look at the datasets on the catalog to see what data we found by the query. We find one volume file in the return
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catalog.datasets
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We'll work through doing some more queries on the radar server. Some useful links:
See if you can write Python code for the following queries:
Get the volume for 3 days ago from the radar nearest to Hays, KS (lon -99.324403, lat 38.874929). No map necessary!
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Get the volumes the last two hours from all of the radars in Wyoming (call it the bounding box with lower left corner 41.008717, -111.056360 and upper right corner 44.981008, -104.042719)
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We can pull that dataset out of the dictionary and look at the available access URLs. We see URLs for OPeNDAP, CDMRemote, and HTTPServer (direct download).
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ds = list(catalog.datasets.values())[0]
ds.access_urls
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We need the OPENDAP link to feed to pyart:
We pull out the variables we need for azimuth and range, as well as the data itself.
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import matplotlib.pyplot as plt
import pyart
%matplotlib inline
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radar = pyart.io.read_nexrad_cdm(ds.access_urls['OPENDAP'])
display = pyart.graph.RadarDisplay(radar)
fig = plt.figure(figsize=(6, 5))
# plot super resolution reflectivity
ax = fig.add_subplot(111)
display.plot('reflectivity', 0, title='NEXRAD Reflectivity',
vmin=-32, vmax=64, colorbar_label='', ax=ax)
display.plot_range_ring(radar.range['data'][-1]/1000., ax=ax)
display.set_limits(xlim=(-500, 500), ylim=(-500, 500), ax=ax)
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