Relating Polarimetric Radar Measurements to QLCS Cold Pool Properties and Damage Potential

Identifying the occurrence of and the underlying processes driving hazardous and violent weather conditions through use of observational tools remains, to an extent, challenging. Quasi-linear convective systems (QLCSs) are mesoscale convective storm systems that can form year round and often produce hail, severe winds, and possible tornadoes. A cold pool, or the negatively buoyant outflow air, is necessary for QCLS progression and plays a role in QLCS hazard production. However, probing the underlying cold pool processes occurring during storm evolution and attributing these processes to subsequent hazards are a challenge and typically are unachievable with the current operational observing infrastructure. Through the joint efforts of The Propagation and Evolution of Rotation in Linear Systems (PERiLS) project, sampling of the microphysical and thermodynamic properties of QLCSs is being achieved and providing insight to the dynamical processes taking place.

Here, we analyze the spatial and temporal variability of cold pool strength and assess if this information can be extrapolated from operational WSR-88D polarimetric radar data. We compare spatial patterns of polarimetric radar data from the COW and DOWs to direct Sticknet measurements of the spatiotemporal distribution of buoyancy gradients, and explore the relationship between the two. In particular, we make use of estimates of specific attenuation AH as a proxy for negative buoyancy production, given its closer association to hydrometeor mass compared to other dual-polarization products. We also apply the idea of a “separation vector” between regions of enhanced AH and enhanced ZDR as a proxy for hydrometeor size sorting, which has been shown to provide information on the storm’s tornadic potential. These separation vector calculations are conducted in a moving window along the QLCS, and compared to surface damage reports. The goal is to identify operationally observable proxies using the detailed, high-resolution PERiLS measurements. Ultimately, this can lead to a more robust indication of QLCS risk potential through use of the operational WSR-88D radar network.