Anticipating QLCS Tornadogenesis for Decision Support: The Three-Ingredient Method During the 19-20 February 2017 South-Central Texas Tornadic QLCS Event

In the late evening hours of 19 February and into the early morning hours of 20 February 2017, a Quasi-Linear Convective System (QLCS) produced nine confirmed tornadoes over a three-hour period across South Central Texas. These nine tornadoes included three EF2s, two EF1s, and four EF0s, with the most impactful EF2 tornado tearing through a densely populated neighborhood of north-central San Antonio. This event was quite unique as the largest February tornado event on record for the Austin/San Antonio County Warning Area (CWA), as well as the second largest tornado event of any storm mode for the CWA in well over a decade.

Leading into the event, tornadoes were not well anticipated per model output of low 0-1 km and 0-3 km storm relative helicity (SRH) and a mostly outflow-dominated QLCS expected to occur. However, as the QLCS entered the San Antonio metro area, it rapidly intensified and began producing numerous mesovortices, and ultimately tornadoes. Research by Schaumann and Pryzbylinski (2012), as well as Stanford (2013) tested and developed the three-ingredient method for identifying favorable mesoscale environments for mesovortex generation, intensification, and tornadogenesis within QLCS structures. This presentation will review the three-ingredient method and apply it to the 19-20 February 2017 QLCS tornado event through radar and mesoscale analysis, demonstrating the three-ingredient method’s ability to provide better decision support services to core partners by highlighting the most favorable region for QLCS tornadogenesis in both the immediate term (0-60 minutes) as well as the forecast term (1-6 hours).