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Heuristic clutter detection based on distribution properties (“histo cut”)

Heuristic clutter detection based on distribution properties (“histo cut”)#

Detects areas with anomalously low or high average reflectivity or precipitation. It is recommended to use long term average or sums (months to year).

[1]:

import wradlib as wrl
import numpy as np
import matplotlib.pyplot as plt
import warnings

warnings.filterwarnings("ignore")
try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    plt.ion()

/home/runner/micromamba/envs/wradlib-tests/lib/python3.11/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.3 when it was built against 1.14.2, this may cause problems
  _warn(("h5py is running against HDF5 {0} when it was built against {1}, "

Load annual rainfall acummulation example (from DWD radar Feldberg)#

[2]:

filename = wrl.util.get_wradlib_data_file("misc/annual_rainfall_fbg.gz")
yearsum = np.loadtxt(filename)

Downloading file 'misc/annual_rainfall_fbg.gz' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/misc/annual_rainfall_fbg.gz' to '/home/runner/work/wradlib/wradlib/wradlib-data'.

Apply histo-cut filter to retrieve boolean array that highlights clutter as well as beam blockage#

Depending on your data and climate you can parameterize the upper and lower frequency percentage with the kwargs upper_frequency/lower_frequency. For European ODIM_H5 data these values have been found to be in the order of 0.05 in EURADCLIM: The European climatological high-resolution gauge-adjusted radar precipitation dataset. The current default is 0.01 for both values.

[3]:

mask = wrl.classify.histo_cut(yearsum)

Plot results#

[4]:

fig = plt.figure(figsize=(14, 14))
ax = fig.add_subplot(221)
yearsum = wrl.georef.create_xarray_dataarray(yearsum).wrl.georef.georeference()
pm = np.log(yearsum).wrl.vis.plot(ax=ax)
plt.title("Logarithm of annual precipitation sum")
plt.colorbar(pm, shrink=0.75)
ax = fig.add_subplot(222)
mask = wrl.georef.create_xarray_dataarray(mask).wrl.georef.georeference()
pm = mask.wrl.vis.plot(ax=ax)
plt.title("Map of execptionally low and high values\n(clutter and beam blockage)")
plt.colorbar(pm, shrink=0.75)
ax = fig.add_subplot(223)
pm = mask.where(mask == 1).wrl.vis.plot(ax=ax)
plt.title("Map of execptionally high values\n(clutter)")
plt.colorbar(pm, shrink=0.75)
ax = fig.add_subplot(224)
pm = mask.where(mask == 2).wrl.vis.plot(ax=ax)
plt.title("Map of execptionally low values\n(beam blockage)")
plt.colorbar(pm, shrink=0.75)

[4]:

<matplotlib.colorbar.Colorbar at 0x7fc19430bf50>
../../_images/notebooks_classify_histo_cut_8_1.png
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