TY - JOUR
T1 - What is the intrinsic predictability of tornadic supercell thunderstorms?
AU - MARKOWSKI, PAUL M.
N1 - Funding Information:
It is perhaps fitting to end this article with the following quote by Scorer (1978, p. 248): ‘‘This coming to terms with our limitations is actually much more satisfactory than it may seem because it amounts to making our objectives sensible. It is obvious that the deeper we probe into something the more complex it seems to be, and in the case of indescribably complicated motion, the only road to simplification is to decide that there shall be limits to the complexity we are prepared to study. Life is too short to spend it sorting out the infinite details of the flow on a single occasion, and anyone who did analyze them could have no assurance that the next occasion would be the same, nor that anyone else would be interested anyway.’’ Acknowledgments. This article is dedicated to the memory of Prof. Fuqing Zhang, with whom the author discussed the subject of tornado predictability over the years. The author thanks Steve Greybush, Zach Lebo, Dave Stensrud, and Yunji Zhang for their feedback on the direction of this research. Craig Schwartz and Ryan Sobash also are acknowledged for their assistance in interpreting smoothed tornado probabilities. Three anonynous reviewers helped improve the article, and for this the author is grateful. The work was partially supported by National Science Foundation Award AGS-1821885. The numerical simulations were performed on the Institute for Computational and Data Sciences Advanced CyberInfrastructure (ICDS-ACI) system at Penn State. A large fraction of the figures was created using the Grid Analysis and Display System (GrADS), developed by the Center for Ocean–Land– Atmosphere Studies. Figure 1 was created using GrADS software written by Bob Hart.
Publisher Copyright:
© 2020 American Meteorological Society.
PY - 2020/8
Y1 - 2020/8
N2 - A 25-member ensemble of relatively high-resolution (75-m horizontal grid spacing) numerical simulations of tornadic supercell storms is used to obtain insight on their intrinsic predictability. The storm environments contain large and directionally varying wind shear, particularly in the boundary layer, large convective available potential energy, and a low lifting condensation level. Thus, the environments are extremely favorable for tornadic supercells. Small random temperature perturbations present in the initial conditions trigger turbulence within the boundary layers. The turbulent boundary layers are given 12 h to evolve to a quasi-steady state before storms are initiated via the introduction of a warm bubble. The spatially averaged environments are identical within the ensemble; only the random number seed and/or warm bubble location is varied. All of the simulated storms are long-lived supercells with intense updrafts and strong mesocyclones extending to the lowest model level. Even the storms with the weakest near-surface rotation probably can be regarded as weakly tornadic. However, despite the statistically identical environments, there is considerable divergence in the finescale details of the simulated storms. The intensities of the tornado-like vortices that develop in the simulations range from EF0 to EF3, with large differences in formation time and duration also being exhibited. The simulation differences only can be explained by differences in how the initial warm bubbles and/or storms interact with turbulent boundary layer structures. The results suggest very limited intrinsic predictability with respect to predicting the formation time, duration, and intensity of tornadoes.
AB - A 25-member ensemble of relatively high-resolution (75-m horizontal grid spacing) numerical simulations of tornadic supercell storms is used to obtain insight on their intrinsic predictability. The storm environments contain large and directionally varying wind shear, particularly in the boundary layer, large convective available potential energy, and a low lifting condensation level. Thus, the environments are extremely favorable for tornadic supercells. Small random temperature perturbations present in the initial conditions trigger turbulence within the boundary layers. The turbulent boundary layers are given 12 h to evolve to a quasi-steady state before storms are initiated via the introduction of a warm bubble. The spatially averaged environments are identical within the ensemble; only the random number seed and/or warm bubble location is varied. All of the simulated storms are long-lived supercells with intense updrafts and strong mesocyclones extending to the lowest model level. Even the storms with the weakest near-surface rotation probably can be regarded as weakly tornadic. However, despite the statistically identical environments, there is considerable divergence in the finescale details of the simulated storms. The intensities of the tornado-like vortices that develop in the simulations range from EF0 to EF3, with large differences in formation time and duration also being exhibited. The simulation differences only can be explained by differences in how the initial warm bubbles and/or storms interact with turbulent boundary layer structures. The results suggest very limited intrinsic predictability with respect to predicting the formation time, duration, and intensity of tornadoes.
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U2 - 10.1175/MWR-D-20-0076.1
DO - 10.1175/MWR-D-20-0076.1
M3 - Article
AN - SCOPUS:85091175010
SN - 0027-0644
VL - 148
SP - 3157
EP - 3180
JO - Monthly Weather Review
JF - Monthly Weather Review
IS - 8
ER -