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Low-level updraft intensification in response to environmental wind profiles

Cite this dataset

Goldacker, Nicholas A.; Parker, Matthew D. (2021). Low-level updraft intensification in response to environmental wind profiles [Dataset]. Dryad.


Supercell storms can develop a “dynamical response” whereby upward accelerations in the lower troposphere amplify as a result of rotationally induced pressure falls aloft. These upward accelerations likely modulate a supercell’s ability to stretch near-surface vertical vorticity to achieve tornadogenesis. This study quantifies such a dynamical response as a function of environmental wind profiles commonly found near supercells. Self-organizing maps (SOMs) were used to identify recurring low-level wind profile patterns from 20,194 model-analyzed, near-supercell soundings. The SOM nodes with larger 0–500 m storm-relative helicity (SRH) and streamwise vorticity (ωs) corresponded to higher observed tornado probabilities. The distilled wind profiles from the SOMs were used to initialize idealized numerical simulations of updrafts. In environments with large 0–500 m SRH and large ωs, a rotationally induced pressure deficit, increased dynamic lifting, and a strengthened updraft resulted. The resulting upward-directed accelerations were an order of magnitude stronger than typical buoyant accelerations. At 500 m AGL, this dynamical response increased the vertical velocity by up to 25 m s–1, vertical vorticity by up to 0.2 s–1, and pressure deficit by up to 5 hPa. This response specifically augments the near-ground updraft (the midlevel updraft properties are almost identical across the simulations). However, dynamical responses only occurred in environments where 0–500 m SRH and ωs exceeded 110 m2 s–2 and 0.015 s–1, respectively. The presence vs. absence of this dynamical response may explain why environments with higher 0–500 m SRH and ωs correspond to greater tornado probabilities.


The soundings through April 2012 are derived from the Rapid Update Cycle model whereas later soundings are derived from the Rapid Refresh model. The base-state profiles were combined with the Storm Prediction Center’s mesoscale surface objective analysis (SFCOA), and the resulting profiles were interpolated to height above ground level from 0 km to 12 km in 50 m intervals. There were four variations of the interpolated wind profiles created: unaltered winds (ground-relative), winds rotated to align the 0 – 6 km vector wind difference with the abscissa (ground-relative rotated), winds converted to storm-relative (storm-relative), and winds converted to storm-relative and then rotated to align the 0 – 6 km vector wind difference with the abscissa (storm-relative rotated). These wind profile suites were then placed into self-organizing map algorithms (SOMs) trained on 0 – 500 m zonal and meridional wind profiles that returned four different lattices. The lattices consisted of the 16 most recurring 0 - 500 m wind profile patterns in the set of 20,194 profiles; the ground-relative and storm-relative rotated variations were used in Cloud Model 1 (CM1) simulations to analyze vertical velocity, relative vertical vorticity, and pressure perturbations within a simulated updraft evolving in each of these wind profiles. A total of 33 wind profiles (16 ground-relative, 16 storm-relative rotated, and one quiescent) were combined with a common dry adiabatic thermodynamic profile and then initialized and maintained by a constant, artificial vertical velocity tendency. The pressure perturbation fields were decomposed into buoyant, dynamic linear, and dynamic non-linear components, and associated vertical perturbation pressure gradient accelerations were analyzed to identify and quantify the supercell low-level dynamical response. Processing the dataset in this way allowed the authors to identify and quantify the supercell low-level dynamical response as a function of low-level (0 – 500 m) environmental wind profiles.

Usage notes

There is a general ReadMe file (README.txt) located within the included .tar files (data is hosted by Dryad and software is hosted by Zenodo). While there is a list of information about the 20,194 SPC soundings in the data .tar file (located in the “input_soundings_SOM” directory and named “SPC_Dataset_Information.txt”), the actual soundings themselves are not provided. Additionally, the Bunkers storm motion estimates, mixed-layer convective available potential energy (MLCAPE), mixed-layer convective inhibition (MLCIN), and mixed-layer lifted condensation level (MLLCL) for each of the 20,194 environments are not provided. Please contact the authors of the dataset entry and related journal article for more information.


National Science Foundation, Award: AGS-1748715