Entry 7

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Authors

  • Scott M. Collis
  • Jonathan Helmus

Abstract

Removing Non-Meterological Returns from Arctic Radar Data.

The Atmospheric Radiation Measurement (ARM) Climate Research Facility operates a centimeter wavelength radar (3cm) at Barrow Alaska. This is used to document the large scale macrophysics (shapes and coverage), microphysics (ice, water, anisotropy, densities and sizes) and kinematics of clouds and precipitating particles. All this would be simple if clouds and precipitation were the only objects that interacted with the radar beam. Unfortunately this is not the case, especially in winter. The radar beam also interacts with power lines, insects and, in the case for this visualization example: Sea Ice. The notebook details techniques using radar texture to identify areas that are “significant returns” of meteorological origin and replaces areas deemed to be non-significant with a noise floor in order to give a background to grid down to. This is all done using the radar and grid common data models provided in the Python ARM Radar Toolkit [1] and takes advantage of geospatially aware plotting that uses Matplotlib Basemap. The final visualization that is being submitted for this competition uses slicing to show the impact on the three dimensional reflectivity data from the radar effectively removing the sea ice clutter return from the volumetric scene.

[1]https://github.com/ARM-DOE/pyart

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