How-to: Import radar data into Towerpy.#
Daniel Sanchez-Rivas1 and Miguel A. Rico-Ramirez1
1Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, United Kingdom
This notebook describes the process of importing radar data to towerpy.
Import relevant packages#
import towerpy as tp
import datetime as dt
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[1], line 1
----> 1 import towerpy as tp
2 import datetime as dt
ModuleNotFoundError: No module named 'towerpy'
Define working directory, file name and other parameters#
wdir = 'path/to/radarfile/'
fname = 'radarfile.dat'
rsite = 'myradarsite'
scan_time = dt.datetime(2018, 7, 14, 17, 25, 30)
Read radar data from other formats#
Here, the user can import the radar data using another library (such as wradlib). For instance, if the radar data is an HDF5 file (*.h5), wradlib provides all the tools to read the radar data from this type of file.
We will not import data but will create an object for the sake of clarity. This object refers to the one containing the radar data.
wrobj = []
Then, we will create a towerpy object:
rdata = tp.io.empty.Rad_scan(wdir+fname, f'{rsite}')
rdata.ppi_emptylike(nrays=360, ngates=1000, elev=0.6,
rad_vars='default', scandt=scan_time, tz='Europe/Berlin')
The ppi_emptylike function creates an empty_like object containing the fields that should be filled for the proper working of towerpy.
rdata.vars
{'ZH [dBZ]': nan,
'ZDR [dB]': nan,
'PhiDP [deg]': nan,
'rhoHV [-]': nan,
'V [m/s]': nan}
rdata.params
{'nvars': 5,
'ngates': 1000,
'nrays': 360,
'gateres [m]': nan,
'rpm': nan,
'prf [Hz]': nan,
'pulselength [ns]': nan,
'avsamples': nan,
'wavelength [cm]': nan,
'latitude [dd]': nan,
'longitude [dd]': nan,
'altitude [m]': 0,
'easting [km]': nan,
'northing [km]': nan,
'radar constant [dB]': 0,
'elev_ang [deg]': 0.6,
'beamwidth [deg]': 1.0,
'datetime': datetime.datetime(2018, 7, 14, 17, 25, 30, tzinfo=zoneinfo.ZoneInfo(key='Europe/Berlin')),
'datetimearray': [2018, 7, 14, 17, 25, 30]}
It is recommended to supply all the parameters within the rdata.params dictionary!
Then, we’ll populate the empty object (rdata) with the polarimetric variables.
rdata.vars['ZH [dBZ]'] = wrobj.ZH.values[0]
rdata.vars['ZDR [dB]'] = wrobj.ZDR.values[0]
rdata.vars['PhiDP [deg]'] = wrobj.PHIDP.values[0]
rdata.vars['rhoHV [-]'] = wrobj.RHOHV.values[0]
rdata.vars['KDP [deg/km]'] = wrobj.KDP.values[0]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[38], line 1
----> 1 rdata.vars['ZH [dBZ]'] = wrobj.ZH.values[0]
2 rdata.vars['ZDR [dB]'] = wrobj.ZDR.values[0]
3 rdata.vars['PhiDP [deg]'] = wrobj.PHIDP.values[0]
AttributeError: 'list' object has no attribute 'ZH'
Finally, we’ll add georeference information using the ppi_emptygeoref function:
rdata.ppi_emptygeoref(gate0=75, gateres=150.)
rdata.georef
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0.17453293, 0.19198622, 0.20943951, 0.2268928 , 0.2443461 ,
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0.34906585, 0.36651914, 0.38397244, 0.40142573, 0.41887902,
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6.19591884, 6.21337214, 6.23082543, 6.24827872, 6.26573201]),
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'range [m]': array([7.50000e+01, 2.25000e+02, 3.75000e+02, 5.25000e+02, 6.75000e+02,
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...,
[1.31230352e-04, 3.95675414e-04, 6.62766279e-04, ...,
1.57729908e+00, 1.58020086e+00, 1.58310529e+00],
[1.31230352e-04, 3.95675414e-04, 6.62766279e-04, ...,
1.57729908e+00, 1.58020086e+00, 1.58310529e+00],
[1.31230352e-04, 3.95675414e-04, 6.62766279e-04, ...,
1.57729908e+00, 1.58020086e+00, 1.58310529e+00]]),
'beamtop_height [km]': array([[1.44013879e-03, 4.32239998e-03, 7.20730601e-03, ...,
4.18768683e+00, 4.19320425e+00, 4.19872431e+00],
[1.44013879e-03, 4.32239998e-03, 7.20730601e-03, ...,
4.18768683e+00, 4.19320425e+00, 4.19872431e+00],
[1.44013879e-03, 4.32239998e-03, 7.20730601e-03, ...,
4.18768683e+00, 4.19320425e+00, 4.19872431e+00],
...,
[1.44013879e-03, 4.32239998e-03, 7.20730601e-03, ...,
4.18768683e+00, 4.19320425e+00, 4.19872431e+00],
[1.44013879e-03, 4.32239998e-03, 7.20730601e-03, ...,
4.18768683e+00, 4.19320425e+00, 4.19872431e+00],
[1.44013879e-03, 4.32239998e-03, 7.20730601e-03, ...,
4.18768683e+00, 4.19320425e+00, 4.19872431e+00]])}
Lets see how the data looks!
# tp.datavis.rad_display.plot_ppi(rdata.georef, rdata.params, rdata.vars)