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Record W4414651162 · doi:10.1175/jas-d-25-0033.1

What Forces the Rapid Vertical Acceleration and Vorticity Intensification near Ground in Tornadoes? Diagnostic Analysis Based on a Numerically Simulated Real Tornado

2025· article· en· W4414651162 on OpenAlexaff
Wei Huang, Ming Xue

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsBuoyancyVorticitySupercellTornadoVortexPerturbation (astronomy)Pressure-gradient forceMesocyclone

Abstract

fetched live from OpenAlex

Abstract A real-case supercell tornado simulation is analyzed to understand the rapid vertical acceleration of near-surface air parcels leading to intense vertical vorticity stretching and vortex intensification. The vertical acceleration is primarily due to effective buoyancy force and dynamic vertical perturbation pressure gradient force (VPPGF), and the latter is further decomposed into the splat and spin components by solving diagnostic pressure equations. Positive dynamic VPPGF is the dominant forcing responsible for near-ground vertical acceleration, while effective buoyancy is much smaller near ground. In the initial stage of tornado intensification, upward dynamic VPPGF is dominated by the spin term associated with the vorticity of the lowering tornado cyclone embedded within a mesocyclone because maximum vertical vorticity and associated perturbation pressure minimum are located off the ground. As the tornado further intensifies, the maximum vertical vorticity and corresponding perturbation pressure minimum shift to the ground level, and the spin-induced VPPGF becomes negative or downward. At this stage, the upward-splat-induced VPPGF is found to be responsible for promoting and supporting continued upward vertical acceleration and vorticity stretching near the ground. The splat component is largest near the ground and close to the corner region of the tornado because of the strong flow deformation there. Trajectory analyses of parcels entering the tornado further substantiate that the dominant term in the upward dynamic VPPGF transitions from the spin term before the maximum vertical vorticity lowers to the ground to the splat term after the lowering. As the air parcels rise, buoyancy becomes the primary force for continued updraft acceleration, aided by latent heating after reaching saturation. Significance Statement The important role of low-level intense vertical acceleration in tornadogenesis has been highlighted in recent studies because of the resulting near-ground vertical vorticity stretching. However, quantitative analyses on forces causing such vertical acceleration are generally lacking. The flow patterns responsible for the dynamic pressure gradient force (PGF) are in particular not well understood. This study finds that dynamic forcing is the primary driver of low-level vertical acceleration. In the early stage of tornado vortex intensification, the maximum vertical vorticity, being associated with a lowering mesocyclone/tornado cyclone, is located off the ground, and the upward dynamic PGF near the low-level tornado comes mainly from the spin term associated with mesocyclone/tornado cyclone rotation. As the tornado further intensifies, the maximum vertical vorticity shifts to the surface so that the associated spin term reverses sign, and the splat term associated with deformation flows becomes the dominant contributor to upward dynamic PGF. The important role of dynamic PGF associated with the splat term has not been explicitly recognized before in the tornado literature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.260
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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