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    • An incomplete introduction to Python
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    • Data Input - Data Output
    • Attenuation correction
    • Beam Blockage Calculation using a DEM
    • Clutter and Echo Classification
      • Clutter detection using Gabellaapproach
      • Clutter detection by using space-born cloudimages
      • Heuristic clutterdetection
      • Fuzzy echoclassification
    • Georeferencing
    • Match spaceborn SR (GPM/TRMM) with ground radars GR
    • How to use wradlib’s ipol module for interpolation tasks?
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Clutter and Echo Classification¶

This section provides a collection of example code snippets to show clutter detection and correction as well as echo classification capabilities of \(\omega radlib\).

Examples List

  • Clutter detection using Gabellaapproach
    • Read the data
    • Apply filter
    • Plot results
  • Clutter detection by using space-born cloudimages
    • Read the radar data and count the number of tilts
    • Reconstruct the radar values
    • Construct the corresponding radar coordinates
    • Construct collocated satellite data
    • Estimate localisation errors
    • Identify clutter based on collocated cloudtype
    • Plot the results
  • Heuristic clutterdetection
    • Load annual rainfall acummulation example (from DWD radar Feldberg)
    • Apply histo-cut filter to retrieve boolean array that highlights clutter as well as beam blockage
    • Plot results
  • Fuzzy echoclassification
    • Setting the file paths
    • Read the data (radar moments and static clutter map)
    • Identify non-meteorological echoes using fuzzy echo classification
    • View classfication results
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