The Impact of Antenna Cross-Pol Contamination on Polarimetric Spectra From Ice Alignment Signatures

This study provides the first in-depth examination of the spectral polarimetric characteristics of the ice alignment signatures (IASs) in thunderstorms, emphasizing how aligned ice crystals affect spectral co-cross correlation coefficient (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s\rho _{xp}$ </tex-math></inline-formula>), linear depolarization ratio (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$sL_{\text{DR}}$ </tex-math></inline-formula>), and co-cross differential phase (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s\Phi _{xp}$ </tex-math></inline-formula>) using alternate transmission and simultaneous reception (ATSR) mode. IAS is associated with strong electric fields as a precursor to lightning activity and is often manifested by distinct cross-coupling streaks. However, radar observations of these features can be confounded by antenna cross-pol contamination (ACC), which mimics IAS patterns, particularly when strong precipitation cores are present in sidelobe directions. Using simulations that integrate radar antenna patterns, wave propagation, and the microphysics of the hydrometeors, we explore IAS and ACC under varying conditions and demonstrate that, despite some similarities, ACC and IAS exhibit distinguishable spectral and bulk polarimetric characteristics. This work enhances spectral radar analysis, providing robust criteria for differentiating IAS from ACC, and addresses cross-coupling effects by introducing tailored thresholds and phase characteristics. Our findings advance both theoretical radar spectral polarimetry and practical weather radar applications, offering a simulation framework to mitigate misinterpretations and improve severe weather detection.