Hello everyone and thank you for reading my first piece of research review for the summer. Today, I will examine and review an article titled “Quantification of QLCS Tornado Genesis, Associated Characteristics, and Environments across a Large Sample” by James S. Goodnight, Devin A. Chehak, and Robert J. Trapp. QLCS (quasi-linear convective system) tornadoes have always interested me.
I find them fascinating because of how quickly they can form and the forecasting challenges they present to weather forecasters. Therefore, I was pleased to read this research and give a summary of what I found.
Introduction
The paper first started off by giving a general overview of QLCS tornadoes, such as QLCS tornadoes account for 20% of all tornadoes in the U.S. They have less lead time than traditional tornadoes. They are more common overnight and during the cool season. QLCS tornadoes are generally weaker than supercell tornadoes, but they can be just as deadly because of their overnight timing and faster spin-up time.
QLCS tornadoes start in two ways: pre-tornadic mesocyclone dominant (PMD) and shearing instability dominant (SID). The PDM process seems to be more prevalent in the warm season, and as well as providing some of the basis for the “three ingredient method” (3IM) proposed by Schaumann and Przybylinski in 2012 (James et al., 2022). The SID process seems to not work as well with the 3IM model and seems to be a factor in the poor lead time.
The primary focus of the study is to “generate robust information on such contributing processes for applications in forecast and warning operations” (James et al., 2016, 2017, and 2019 years were used, as these years represented different ranges of tornadic activity. Historic WSR-88 radar data were compared to local storm reports from the SPC. “The date, time, season, latitude, longitude, and EF rating of each confirmed and included event were recorded using the NOAA-compiled storm events database” (James et al., 2022). Radar reflectivity of >=40dbz must have existed for a tornado to be counted, and the general structure of the circulation must have fitted a certain criterion.
After this criterion was finished, each tornadic event was classified as PDM, SID, or other.
A PMD classification requires that there was a midlevel circulation before the tornado formed and must have a change of velocity of at least 10 m/s at a height of at least 2 km. The area of circulation must have also been observed 15 minutes before the NOAA storm report. To classify a SID event, “it must have formed within 25 km, and three-volume scans of the reported tornado, been associated with the parent QLCS and had a maximum ΔV of at least 10 m/s (James et al., 2022). Horizontal shearing instability (HSI) was also considered and “was estimated by calculating the distance between maximum inbound and outbound velocity peaks across the leading edge of the QLCS” (James et al., 2022).
Looking at the environment before the tornadoes formed, the paper used the 13 rapid refresh mesoscale model (RAP), which refreshes every hour. The latest model was used for each tornado i.e. if a tornado occurred at 15:23 UTC, the 15:00 UTC model was used. Two regions of the RAP were used, with one zone being slightly east of the other. “The slight eastward shift was used to reduce the inclusion of postfrontal model soundings” (James et al., 2022).
Results
There were 530 recorded QLCS tornadoes, with 190 (35.8%) SID, 319 (60.2%) PMD, and 21 (4%) others. About 75% majority of the cool season and 55% of the warm season QLCS tornadoes occurred in the Southeastern United States (<= 37 degrees north). It was found that 61% of SID tornadoes and 66% of PMD tornadoes occurred in the Southeastern United States. It was found that fewer SID tornadoes occurred in the cool season, but over half of the recorded SID tornadoes occurred in the warm season. 22% of PDM tornadoes occurred between 15:00 and 18:00 local time, compared to 5% of SID tornadoes.
This shows that solar loading is essential in PDM tornadoes. SID tornadoes had no peak time of development, but PDM tornadoes peaked at 22:00 and 10:00 local time. Of the 530 recorded tornadoes, only 39 caused EF2 damage, with 25 being PDM tornadoes. Looking at QLCS outbreaks, SID tornadoes were more likely to be associated with tornado outbreaks, while PMD tornadoes had a stronger circulation on radar.
Regarding warnings, “202 out of the 530 QLCS tornadoes in this dataset were warned by the NWS before tornado genesis, yielding a POD of 0.38” (James et al., 2022). The average warning time for the warned tornadoes was about 5 minutes. PDM tornadoes were warned more, with 43.6% of tornadoes warned, compared to 31.1% of SID tornadoes. PDM tornadoes seemed to be associated with higher CAPE values
Conclusions
This research review aimed to understand QLCS tornado formation, with the primary two ways of formation being shearing instability dominant (SID) and pre-tornadic mesocyclone dominant (PMD). To get this information, 530 QLCS tornadoes over three years were analyzed via Doppler radar, with 36% classified as SID, 60% as PDM, and 4% as other. One of this study’s key findings was that PDM tornadoes more commonly occur in the late afternoon.
More SID tornadoes happen in the warm season and there was little difference in the strength of the tornadoes. About 44% of PDM tornadoes were warned, compared to about 31% of SID tornadoes. Case studies in the future will look at how the three-ingredient method (3IM) would relate to the findings of this research.
I found this particular research article quite interesting. I never knew that there were different ways that a QUCS tornado forms. It was also quite interesting to learn about the locations and timing of these tornadoes and how different locations are more susceptible to different types of QLCS tornados. I highly recommend reading this paper if this topic interests you.
References
Goodnight, J. S., Chehak, D. A., & Trapp, R. J. (2022, November 18). Quantification of QLCS tornadogenesis, associated characteristics, and environments across a large sample. AMETSOC. https://journals.ametsoc.org/view/journals/wefo/37/11/WAF-D-22-0016.1.xml
