Abstract
Cyclic mesocyclogenesis is the process by which a supercell produces multiple mesocyclones with similar life cycles. The frequency of cyclic mesocyclogenesis has been linked to tornado potential, with higher frequencies decreasing the potential for tornadogenesis. Thus, the ability to predict the presence and frequency of cycling in supercells may be beneficial to forecasters for assessing tornado potential. However, idealized simulations of cyclic mesocyclogenesis have found it to be highly sensitive to environmental and computational parameters. Thus, whether convective-allowing models can resolve and predict cycling has yet to be determined. This study tests the capability of a storm-scale, ensemble prediction system to resolve the cycling process and predict its frequency. Forecasts for three cyclic supercells occurring in May 2017 are generated by NSSL’s Warn-on-Forecast System (WoFS) using 3- and 1-km grid spacing. Rare cases of cyclic-like processes were identified at 3 km, but cycling occurred more frequently at 1 km. WoFS predicted variation in cycling frequencies for the storms that were similar to observed variations in frequency. Object-based identification of mesocyclones was used to extract environmental parameters from a storm-relative inflow sector from each mesocyclone. Lower magnitudes of 0–1-km storm-relative helicity and significant tornado parameter are present for the two more frequently cycling supercells, and higher values are present for the case with the fewest cycles. These results provide initial evidence that high-resolution ensemble forecasts can potentially provide useful guidance on the likelihood and cycling frequency of cyclic supercells.
Significance Statement
The rate at which supercell thunderstorms produce rotating updrafts, known as cycling, can provide information on tornado potential. This study’s purpose is to see if we can forecast the cycling rate of three supercells in an experimental model, called the Warn-on-Forecast System (WoFS). WoFS predicted higher cycling rates for supercells that cycled more frequently and lower cycling rates for supercells that cycled less frequently. These results provide a proof of concept that forecast models can potentially predict cycling frequency, which could be used to improve short-term forecasts of tornado likelihood. This is the first study examining the potential to predict cycling frequency in a forecast model; therefore, future work is needed to further analyze this potential using more case studies.
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