Identification of discrete updrafts within quasi-linear convective systems using gridded radar data and potential implications for tornadogenesis prediction

Quasi-linear convective system (QLCS) tornadoes are particularly difficult for forecasters to predict, resulting in short or even negative warning lead times. This is due in part to the relatively shallow nature of parent mesovortices and thus an increased reliance on low-level radar data, which are often absent in the U.S and worldwide. However, it is hypothesized that relatively deep, discrete updrafts may indicate portions of a linear system where tornadogenesis is most probable. Notably, these updrafts are easily identifiable in upper-tropospheric radar data, which are still available far from the nearest radar site, whereas low-level data can only be gathered relatively close to a radar. Using a dataset of QLCS tornadoes covering three years, it is shown that around 62% of tornadoes are preceded by discrete radar reflectivity cores several kilometers aloft, identified using gridded Multi-Radar Multi-Sensor (MRMS) data and indicating the presence of a deep updraft. Analysis of the near-storm environment at the time of tornadogenesis in each case reveals that 0-6 km and 3-6 km shear are especially good predictors of which tornadoes will be co-located with reflectivity cores, with greater shear resulting in more tornadoes having discrete updrafts in the mid-levels. We hypothesize that strong mid-level shear results in a negative pressure perturbation aloft that is supportive of deep QLCS updraft growth and that these updrafts indicate where low-level lifting is most capable of tilting and stretching vorticity to tornadic strength. The implications of null cases on the operational capability of reflectivity cores will also be discussed.