PNNL

Abstract

This study presents three separate processing methods to improve high-order moments estimated from 35-GHz (Kaband) vertically pointing radar Doppler velocity spectra. The first 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, Alaska. Multiple pathways through antenna side-lobes and reflections off a rotating scanning radar antenna located 2 m away from KAZR caused Doppler shifts in ground clutter returns from stationary targets 2.5 km away. After removing clutter in the recorded velocity spectra, the second processing method identifies multiple atmospheric peaks in the spectra and estimates high-order moments for each unique peak. Multiple peaks and high-order moments were estimated for both original 2-s and 15-s averaged spectra. The third processing method improves the spectrum breadth, skewness, and kurtosis estimates by removing 2-s velocity variability during 15-s averaging intervals. Assuming the cloud and precipitation microphysical properties do not change during the 15-s interval, shifting individual 2-s spectra to a common 15-s mean velocity before averaging removes 2-s temporal scale turbulent broadening. 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 peaks and then use multiple-peak moments for quantitative studies.