Identifying insects, clouds, and precipitation using vertically pointing polarimetric radar Doppler velocity spectra

Abstract

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 measured CoPol and XPol Doppler velocity spectra are used to calculate linear depolarization ratio (LDR) spectra. 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 vs. cloud particles. The second algorithm distinguishes insects from raindrops and ice particles by exploiting the larger Doppler velocity spectra 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 the 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. Forty-seven summertime days were processed with the insect–hydrometeor discrimination method using US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar observations in northern Oklahoma, USA. For these 47 d, over 70 % of the hydrometeor mask column bottoms were within ±100 m of simultaneous ceilometer cloud base heights. All datasets and images are available to the public on the DOE ARM repository.

Using cloud radar to investigate the effect of rainfall on migratory insect flight

“…The method outlined here relies only on the co‐polar Doppler velocity power spectrum, while the cross‐polar Doppler velocity power spectrum and LDR are not used. Since insects can fall below the detectability threshold in the cross‐polar velocity channel, and LDR are sometimes unavailable for non‐precipitating clouds (as discussed in Williams et al, 2021). As such, if an LDR threshold is required to be met for classifying pixels as insects, this will necessarily result in removing some of the insect signal in the co‐polar channel for range gates where is not reported.…”

“…We tested more complex algorithms that include velocity texture parameters and LDR (following Williams et al, 2021). The goal of the Williams et al (2021) algorithm is to classify each range gate as containing hydrometeors, insects or both; no attempt is made to separate overlapping insect and precipitation signals which is our goal here. When the data were also screened based on velocity texture parameter and LDR, the edges of regions of cloud and rainfall (in the spectral domain) were frequently misclassified as insects, as can also be seen in Fig.…”

“…As such, significant effort has been made towards identifying and removing insect returns from the cloud radar data (e.g. Luke et al, 2008; Williams et al, 2021). While insects are clutter for cloud studies, insect returns can also be used to explore insect behaviour under different atmospheric conditions (e.g.…”

Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system

“…But such approaches fall short when meteorological signals are mixed with clutter. Alternatively, cloud and/or precipitation signals can be discriminated from clutter properly if the clutter removal is made in the radar Doppler spectrum (Luke et al, 2008;Moisseev and Chandrasekar, 2009;Williams et al, 2018Williams et al, , 2021. For example, for stationary ground clutter signals characterized by the Doppler velocity of around 0 m s −1 , an interpolation method can be performed to remove the clutter after identifying the narrow spectral peaks (Williams et al, 2018).…”

“…For example, for stationary ground clutter signals characterized by the Doppler velocity of around 0 m s −1 , an interpolation method can be performed to remove the clutter after identifying the narrow spectral peaks (Williams et al, 2018). Williams et al (2021) have also used spectral linear depolarization ratio observations to identify asymmetric insect clutter. To the best of our knowledge, there is a lack of a non-polarimetric spectral approach to separate such nonstationary clutter signals.…”