Forecasting Left-Moving Supercells Using an Ensemble Data Assimilation System

As the dynamics behind splitting updrafts and storm motion has become better understood, differentiating between what encourages right- and left-moving supercells has become the subject of increasing study because of its implication for storm trajectory, strength and possible hazard type. Most research from storm-scale observations to numerical weather prediction in the United States has been focused on mesocyclones - right-moving supercells - as they typically lend themselves to all hazards possible and produce the vast majority of tornadoes. On the other hand, despite still being notorious for producing high-impact large hail and damaging winds along with their highly deviant motion, mesoanticyclones - left-moving supercells - have received much less attention. An assortment of cases were examined from the Great Plains spring 2016 severe weather season, with a particular focus on May 8th, that gave rise to varying favorabilities, lengths and intensities of anticyclonic supercells. Amidst NOAA Hazardous Weather Testbed operations, The NSSL Experimental Warn-on-Forecast System for ensembles (NEWSe) was run during these days. The goal of this study was first to determine how well the NEWSe model forecasted storm splits through qualitatively analyzing swaths of produced updraft helicity, then to interpret why the model gave the outcome it did by examining the environments it created through modeled soundings and visual analyses of dynamic parameters. While direct observations may be insufficient to help visualize the rapidly spatially- and temporally-changing conditions encouraging left-moving supercells, understanding how numerical analysis interprets these conditions will ensure a better understanding of mesoanticyclones themselves.