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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 9
Atmos. Meas. Tech., 11, 4963-4980, 2018
https://doi.org/10.5194/amt-11-4963-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 11, 4963-4980, 2018
https://doi.org/10.5194/amt-11-4963-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 Sep 2018

Research article | 03 Sep 2018

Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra

Christopher R. Williams1, Maximilian Maahn2,3, Joseph C. Hardin4, and Gijs de Boer2,3 Christopher R. Williams et al.
  • 1Ann and H. J. Smead Aerospace Engineering Sciences Department, University of Colorado Boulder, Boulder, CO 80309, USA
  • 2Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado Boulder, Boulder, CO 80309, USA
  • 3NOAA Earth System Research Laboratory (ESRL), Physical Sciences Division, Boulder, CO 80305, USA
  • 4Pacific Northwest National Laboratory (PNNL), Richland, WA 99354, USA

Abstract. This study presents and applies three separate processing methods to improve high-order moments estimated from 35GHz (Ka band) vertically pointing radar Doppler velocity spectra. The first processing method removes Doppler-shifted ground clutter from spectra collected by a US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar (KAZR) deployed at Oliktok Point (OLI), Alaska. Ground clutter resulted from multiple pathways through antenna side lobes and reflections off a rotating scanning radar antenna located 2m away from KAZR, which caused Doppler shifts in ground clutter returns from stationary targets 2.5km away. After removing clutter in the recorded velocity spectra, the second processing method identifies multiple separate and sub-peaks in the spectra and estimates high-order moments for each peak. Multiple peaks and high-order moments were estimated for both original 2 and 15s averaged spectra. The third processing step improves the spectrum variance, skewness, and kurtosis estimates by removing velocity variability due to turbulent broadening during 15s averaging intervals.

Applying the multiple peak processing to Doppler velocity spectra during liquid-only clouds can identify cloud and drizzle particles and during mixed-phase clouds can identify liquid cloud and frozen hydrometeors. Consistent with previous studies, this work found that spectrum skewness assuming only a single spectral peak was a good indicator of two hydrometeor populations (for example, cloud and drizzle particles) being present in the radar pulse volume. Yet, after dividing the spectrum into multiple peaks, velocity spectrum skewness for individual peaks is near zero, indicating nearly symmetric peaks. This suggests that future studies should use velocity skewness of single-peak spectra as an indicator of possible multiple hydrometeor populations and then use multiple-peak moments for quantitative studies. Three future activities will continue this work. First, KAZR spectra from several ARM sites have been processed and are available in the ARM archive as a principal investigator (PI) product. ARM programmers are evaluating these processing methods as part of future multiple-peak products generated by ARM. Third, MATLAB code generating the Oliktok Point products has been uploaded as supplemental material for public dissemination.

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This study presents three signal-processing methods to improve estimates derived from a vertically pointing 35 GHz cloud radar deployed at Oliktok Point, Alaska. The first method removes ground clutter from the Doppler velocity spectra. The second method estimates multiple peaks and high-order moments from the improved spectra. The third method removes high-frequency variability in high-order moments by shifting original 2 s spectra to a common reference before averaging over a 15 s interval.
This study presents three signal-processing methods to improve estimates derived from a...
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