Using Siphon to query the NetCDF Subset Service

First we construct a TDSCatalog instance pointing to our dataset of interest, in this case TDS' "Best" virtual dataset for the GFS global 0.5 degree collection of GRIB files. We see this catalog contains a single dataset.



In [1]:

    
from siphon.catalog import TDSCatalog
best_gfs = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p5deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p5deg/Best')
best_gfs.datasets









    Out[1]:





{'Best GFS Half Degree Forecast Time Series': <siphon.catalog.Dataset at 0x108cbde10>}

We pull out this dataset and look at the access urls.



In [2]:

    
best_ds = list(best_gfs.datasets.values())[0]
best_ds.access_urls









    Out[2]:





{'CdmRemote': 'http://thredds.ucar.edu/thredds/cdmremote/grib/NCEP/GFS/Global_0p5deg/Best',
 'ISO': 'http://thredds.ucar.edu/thredds/iso/grib/NCEP/GFS/Global_0p5deg/Best',
 'NCML': 'http://thredds.ucar.edu/thredds/ncml/grib/NCEP/GFS/Global_0p5deg/Best',
 'NetcdfSubset': 'http://thredds.ucar.edu/thredds/ncss/grib/NCEP/GFS/Global_0p5deg/Best',
 'OPENDAP': 'http://thredds.ucar.edu/thredds/dodsC/grib/NCEP/GFS/Global_0p5deg/Best',
 'UDDC': 'http://thredds.ucar.edu/thredds/uddc/grib/NCEP/GFS/Global_0p5deg/Best',
 'WCS': 'http://thredds.ucar.edu/thredds/wcs/grib/NCEP/GFS/Global_0p5deg/Best',
 'WMS': 'http://thredds.ucar.edu/thredds/wms/grib/NCEP/GFS/Global_0p5deg/Best'}

Note the NetcdfSubset entry, which we will use with our NCSS class.



In [3]:

    
from siphon.ncss import NCSS
ncss = NCSS(best_ds.access_urls['NetcdfSubset'])

We can then use the ncss object to create a new query object, which facilitates asking for data from the server.



In [4]:

    
query = ncss.query()

We construct a query asking for data corresponding to latitude 40N and longitude 105W, for the current time. We also ask for NetCDF version 4 data, for the variables 'Temperature_isobaric' and 'Relative_humidity_isobaric'. This request will return all vertical levels for a single point and single time. Note the string representation of the query is a properly encoded query string.



In [5]:

    
from datetime import datetime
query.lonlat_point(-105, 40).time(datetime.utcnow())
query.accept('netcdf4')
query.variables('Temperature_isobaric', 'Relative_humidity_isobaric')









    Out[5]:





var=Temperature_isobaric&var=Relative_humidity_isobaric&time=2015-06-17T16%3A48%3A36.440540&longitude=-105&latitude=40&accept=netcdf4

We now request data from the server using this query. The NCSS class handles parsing this NetCDF data (using the netCDF4 module). If we print out the variable names, we see our requested variables, as well as a few others (more metadata information)



In [6]:

    
data = ncss.get_data(query)
list(data.variables.keys())









    Out[6]:





['isobaric3',
 'Temperature_isobaric',
 'Relative_humidity_isobaric',
 'station_name',
 'station_description',
 'latitude',
 'longitude',
 'time']

We'll pull out the variables we want to use, as well as the pressure values (from the isobaric3 variable).



In [7]:

    
temp = data.variables['Temperature_isobaric']
relh = data.variables['Relative_humidity_isobaric']
press = data.variables['isobaric3']
press_vals = press[:].squeeze()

Now we can plot these up using matplotlib.



In [8]:

    
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(9, 8))
ax.plot(temp[:].squeeze(), press_vals, 'r', linewidth=2)
ax.set_xlabel(temp.standard_name + ' (%s)' % temp.units)
ax.set_ylabel(press.standard_name + ' (%s)' % press.units)

# Create second plot with shared y-axis
ax2 = plt.twiny(ax)
ax2.plot(relh[:].squeeze(), press_vals, 'g', linewidth=2)
ax2.set_xlabel(relh.standard_name + ' (%s)' % relh.units)
ax.set_ylim(press_vals.max(), press_vals.min())
ax.grid(True)