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Record W2951326012 · doi:10.1175/waf-d-18-0209.1

Investigating the Transition from Elevated Multicellular Convection to Surface-Based Supercells during the Tornado Outbreak of 24 August 2016 Using a WRF Model Simulation

2019· article· en· W2951326012 on OpenAlexaboutno aff
Kevin T. Gray, Jeffrey Frame

Bibliographic record

VenueWeather and Forecasting · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignUniversity of Oklahoma
KeywordsTornadoWeather Research and Forecasting ModelWind shearEyeConvectionGeologyStormAtmospheric sciencesPressure gradientMeteorologyMesoscale meteorologyMechanicsPerturbation (astronomy)ClimatologyPhysicsWind speed

Abstract

fetched live from OpenAlex

Abstract On 24 August 2016, a tornado outbreak impacted Indiana, Ohio, and Ontario with 26 confirmed tornadoes. Elevated multicellular convection developed into surface-based supercells that produced several tornadoes, particularly near a differential heating boundary. This convective mode transition is of particular interest owing to its relatively rare occurrence. A WRF Model simulation accurately captures the environment and storm evolution during this outbreak. Trajectory analyses indicate that the multicellular updrafts were initially elevated. Since nearly all of the vertical wind shear was confined to the lowest 1 km, significant rotation did not develop via tilting of horizontal vorticity until the storms began ingesting near-surface air. Near-surface vertical wind shear decreased outside of cloud cover owing to vertical mixing, while it was preserved under the anvil, allowing for large values of 0–1-km storm-relative helicity to persist north of a differential heating boundary. Analysis of the perturbation pressure field from the WRF Model output indicates that the development of relatively large nonlinear vertical perturbation pressure gradients coincided with when near-surface air began to enter the updrafts, resulting in upward accelerations in the lowest 2 km, below the level of maximum rotation. In strengthening updrafts, upward-directed buoyancy perturbation pressure accelerations may have offset the downward-directed nonlinear perturbation pressure accelerations above the level of maximum rotation, allowing the updrafts to intensify further.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

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

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 head0.049
GPT teacher head0.224
Teacher spread0.175 · 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 teacher head, 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

Citations10
Published2019
Admission routes1
Has abstractyes

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