Analysis of Polarimetric Radar Downburst Precursors Using Automated Storm Identification and Tracking Weather and Forecasting

Downbursts pose a threat to life, property, and aviation, yet they remain challenging to predict. Prior studies have found radar-based downburst signatures such as divergent and convergent velocity signatures at the surface and midlevels, respectively; descending radar reflectivity (Z) cores (DRCs); present or descending specific differential phase (KDP) cores; and troughs of decreased differential reflectivity (ZDR) collocated with decreased copolar correlation coefficient (ρhv) below the melting layer. This research expands on those studies using the multicell identification and tracking (MCIT) algorithm to automate storm detection and analyze 53 downburst cases spanning most regions of the CONUS. Individual case analysis revealed that DRCs appeared in 83% of cases, descending KDP cores appeared in 85% of cases, and ZDR troughs and collocated ρhv drops appeared in 89% of cases. The magnitude of low-level divergence and midlevel convergence reached a threshold of 0.0025 s−1 in 68% and 83% of cases, respectively. Composite time series revealed that divergence displayed the most prominent signature near the surface; aloft, KDP at and 1 km below the freezing level, midlevel convergence, ZDR column area and volume, and VIL displayed the most prominent signatures. Differences were observed between geographic regions and thermodynamic environments, with lower velocity-related and higher KDP-related values most common in the eastern United States and environments with wind index (WINDEX) < 60; conversely, higher velocity-related and lower KDP-related values were most common in the western United States and environments with WINDEX > 60. These findings may help inform future polarimetric downburst detection and algorithm development efforts. Grant no. NA21OAR4320204