Abstract
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 mid-levels, 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 co-polar correlation coefficient (ρhv) below the melting layer. This research expands on those studies using the Multi-Cell 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 mid-level 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, mid-level 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 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.
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