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).

import wradlib.clutter as clutter
from wradlib.vis import plot_ppi
import wradlib.util as util
import numpy as np
import matplotlib.pyplot as pl
import warnings

    get_ipython().magic("matplotlib inline")
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Load annual rainfall acummulation example (from DWD radar Feldberg)

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

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

mask = clutter.histo_cut(yearsum)

Plot results

fig = pl.figure(figsize=(14, 8))
ax = fig.add_subplot(121)
ax, pm = plot_ppi(np.log(yearsum), ax=ax)
pl.title("Logarithm of annual precipitation sum")
pl.colorbar(pm, shrink=0.75)
ax = fig.add_subplot(122)
ax, pm = plot_ppi(mask.astype(np.uint8), ax=ax)
pl.title("Map of execptionally low and high values\n(clutter and beam blockage)")
pl.colorbar(pm, shrink=0.75)
<matplotlib.colorbar.Colorbar at 0x7f158a6e8850>