Yet, after dividing the spectrum into multiple peaks, velocity spectrum skewness for individual peaks is near zero, indicating nearly symmetric peaks. 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. 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. The third processing step improves the spectrum variance, skewness, and kurtosis estimates by removing velocity variability due to turbulent broadening during 15 s averaging intervals. Multiple peaks and high-order moments were estimated for both original 2 and 15 s averaged spectra. After removing clutter in the more » recorded velocity spectra, the second processing method identifies multiple separate and sub-peaks in the spectra and estimates high-order moments for each peak. Ground clutter resulted from multiple pathways through antenna side lobes and reflections off a rotating scanning radar antenna located 2 m away from KAZR, which caused Doppler shifts in ground clutter returns from stationary targets 2.5 km away. 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. This study presents and applies three separate processing methods to improve high-order moments estimated from 35 GHz (Ka band) vertically pointing radar Doppler velocity spectra. Contrary to expectations, the distance-weighting approach does not enhance the performance of LSPA, whether in terms of tracking accuracy or computation time. Since PDA constitutes a crucial building block of LSPA, we hypothesize that DWPDA, when integrated with LSPA, would perform better under the two performance metrics above. This distance-weighting approach, when combined with PDA, has been shown to enhance the tracking accuracy of PDA without significant change in the computation burden. Last, we consider an additional DA method that is a modification of PDA by incorporating a weighting scheme based on distances between position estimates and measurements. LSPA is known to be superior to PDA in terms of the former and to dominate JPDA in terms of the latter. We are interested in two performance metrics: tracking accuracy and computation time. We then apply these three DA methods for tracking multiple crossing targets in cluttered environments, e.g., radar detection with more » false alarms and missed detections. Second, we describe three methods of data association: probabilistic data association (PDA), joint probabilistic data association (JPDA), and LSPA. DA plays an important role when tracking multiple targets using measurements of uncertain origin. First, we discuss the problem of data association (DA), which is to infer the correspondence between targets and measurements. In this paper, we explore the performance of the distance-weighting probabilistic data association (DWPDA) approach in conjunction with the loopy sum-product algorithm (LSPA) for tracking multiple objects in clutter. Here, archiving of full Doppler spectra began in September 2003, and during the warmer months, a pronounced insect presence regularly introduces clutter into boundary layer returns. We focus on the MMCR operated by the Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) facility in Oklahoma. Our study looks beyond traditional radar moments to ask whether analysis of recorded Doppler spectra can serve as the basis more » for reliable, automatic insect clutter screening. Hence, screening of MMCR insect clutter has historically involved a laborious manual process of cross-referencing radar moments against measurements from other collocated instruments, such as lidar. When only radar Doppler moments are available, it is difficult to produce a reliable screening of insect clutter from cloud returns because their distributions overlap. The accurate detection and removal of insect clutter from millimeter wavelength cloud radar (MMCR) returns is of high importance to boundary layer cloud research (e.g., Geerts et al., 2005).
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