Preprints
https://doi.org/10.5194/amt-2021-27
https://doi.org/10.5194/amt-2021-27

  09 Feb 2021

09 Feb 2021

Review status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Identifying Insects, Clouds, and Precipitation using Vertically Pointing Polarimetric Radar Doppler Velocity Spectra

Christopher R. Williams1, Karen L. Johnson2, Scott E. Giangrande2, Joseph C. Hardin3, Ruşen Öktem4,5, and David M. Romps4,5 Christopher R. Williams et al.
  • 1Ann and H.J. Smead Aerospace Engineering Sciences Department, University of Colorado Boulder, CO, 80309, United States
  • 2Brookhaven National Laboratory, Upton, NY, 11973, United States
  • 3Pacific Northwest National Laboratory, Richland, WA, 99354, United States
  • 4Department of Earth and Planetary Science, University of California, Berkeley, CA, 94720, United States
  • 5Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, CA, 94720, United States

Abstract. This study presents a method to identify and distinguish insects, clouds, and precipitation in 35 GHz (Ka-band) vertically pointing polarimetric radar Doppler velocity power spectra and then produce masks indicating the occurrence of hydrometeors (i.e., clouds or precipitation) and insects at each range gate. The polarimetric radar used in this study transmits a linear polarized wave and receives signals in collinear (CoPol) and cross-linear (XPol) polarized channels. The insect-hydrometeor discrimination method uses CoPol and XPol spectral information in two separate algorithms with their spectral results merged and then filtered into single value products at each range gate. The first algorithm discriminates between insects and clouds in the CoPol Doppler velocity power spectra based on the spectra texture, or spectra roughness, which varies due to the scattering characteristics of insects versus cloud particles. The second algorithm distinguishes insects from raindrops and ice particles by exploiting the larger Doppler velocity spectra linear depolarization ratio (LDR) produced by asymmetric insects. Since XPol power return is always less than CoPol power return for the same target (i.e., insect or hydrometeor), fewer insects and hydrometeors are detected in the LDR algorithm than the CoPol algorithm, which drives this need for a CoPol based algorithm. After performing both CoPol and LDR detection algorithms, regions of insect and hydrometeor scattering from both algorithms are combined in the Doppler velocity spectra domain and then filtered to produce a binary hydrometeor mask indicating the occurrence of cloud, raindrops, or ice particles at each range gate. Comparison with a collocated ceilometer indicates that hydrometeor mask column bottoms are within +/-100 meters of simultaneous ceilometer cloud base heights. Forty-seven (47) summer-time days were processed with the insect-hydrometeor discrimination method using U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar observations in northern Oklahoma (USA). All datasets and images are available on public repositories.

Christopher R. Williams et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-27', Anonymous Referee #1, 11 Mar 2021
    • AC1: 'Reply on RC1', Christopher Williams, 06 May 2021
    • AC3: 'Reply on RC1', Christopher Williams, 06 May 2021
  • RC2: 'Comment on amt-2021-27', Anonymous Referee #2, 19 Mar 2021
    • AC2: 'Reply on RC2', Christopher Williams, 06 May 2021
    • AC4: 'Reply on RC2', Christopher Williams, 06 May 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-27', Anonymous Referee #1, 11 Mar 2021
    • AC1: 'Reply on RC1', Christopher Williams, 06 May 2021
    • AC3: 'Reply on RC1', Christopher Williams, 06 May 2021
  • RC2: 'Comment on amt-2021-27', Anonymous Referee #2, 19 Mar 2021
    • AC2: 'Reply on RC2', Christopher Williams, 06 May 2021
    • AC4: 'Reply on RC2', Christopher Williams, 06 May 2021

Christopher R. Williams et al.

Christopher R. Williams et al.

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Short summary
In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.