Using wradlib

We want a customized package, but we do not want to reinvent the wheel! It’s quite useful to integrate other packages into the pylawr package. The most popular python package to process weather radar data is wradlib [Heistermann et al., 2013]. So why are we not using just wradlib? One aspect is that at the very beginning of the development of pylawr wradlib was not as well-developed as today, but the main aspect is we are facing other challenges due to our networked system architecture using X- and C-band weather radars. Additionally the low-cost single polarised X-band weather radars require more effort in preprocessing more background noise and clutter remains in the measurements compared to professional weather radar systems. All in all the pylawr package provides missing implementations, e.g. for online processing and flexible plotting routines. Nonetheless, we want to use the benefits from both packages. Wradlib provides suitable algorithms for clutter detection (see Using external filters) and attenuation correction (see Single radar attenuation correction), which we are using. Some wradlib application is shown below.

gabella = wradlib.clutter.filter_gabella(reflectivity.values[0], wsize=5,
                                         thrsnorain=0., tr1=6., n_p=8,
                                         tr2=1.3, rm_nans=False,
                                         radial=False,
                                         cartesian=False)[None, ...]
gabella_clt = ClutterMap('GabellaFilter', gabella.astype(int))
refl_filtered = gabella_clt.transform(reflectivity)