ENHANCING SPECIFIC DIFFERENTIAL PHASE ESTIMATION BY ISOLATING DIFFERENTIAL BACKSCATTERING PHASE TO IMPROVE SEVERE WEATHER DETECTION

Abstract

Polarimetric radar variables such as differential phase (ф_DP) and its range derivative, specific differential phase (KDP), are critical for improving quantitative precipitation estimation (QPE) and microphysics retrieval. However, conventional estimation methods for KDP are limited by measurement errors and artifacts arising from the differential backscattering phase (δ), particularly in regions of non-Rayleigh scattering. The presence of δ can introduce significant biases in ф_DP measurements, leading to errors in KDP estimates and misinterpretations of polarimetric signatures associated with hail and tornado debris. To address these issues, this study develops and applies a novel processing method that leverages classification techniques and Linear Programming (LP) to simultaneously estimate both KDP and δ, reducing error of bias and standard deviation in KDP estimates. The proposed method is implemented using Level II radar data from the WSR-88D network, specifically focusing on 0.5° plan position indicator (PPI) scans. Three cases are analyzed: a hailstorm with a well-defined positive δ signature and two tornado events exhibiting both positive and negative δ signatures, illustrating the variability introduced by resonance scattering effects. Comparisons between the classification-based LP estimator and the operational least-square-fit (LSF) method demonstrate the ability of the proposed approach to improve KDP estimation and reveal the diagnostic potential of δ for identifying hail and tornado debris. These findings support the use of δ as an additional observational parameter for polarimetric radar-based hazard detection.