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Record W7029235775

Impact of numerical grid spacing and time step size on vortex Rossby waves in secondary eyewall formation in a simulation of hurricane Wilma (2005)

2012· dissertation· en· W7029235775 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2012
Typedissertation
Languageen
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsMcGill University
Fundersnot available
KeywordsEyeRossby waveVortexRossby radius of deformationComputer simulationWeather Research and Forecasting ModelUnstructured grid
DOInot available

Abstract

fetched live from OpenAlex

To understand the impact of numerical grid spacing and time step size on vortex Rossby waves in hurricanes going through an eyewall replacement cycle, multiple simulations with identical parameterization but with different numerics were carried out using the WRF model on hurricane Wilma (2005).The method of Empirical Normal Modes was then applied on the dataset in order to find radially outward propagating vortex Rossby waves (VRWs).It was found that for varying grid lengths, using high resolution can resolve these VRWs while a mixture of gravity and vortex Rossby waves was propagating from the eyewall using coarse resolution.An examination of the divergence of the Eliassen-Palm flux showed that high resolution is required to form a secondary eyewall.In terms of varying the time step size, differences were noted in the eyewall replacement cycles as well as the final shape of the eyewall, implying that non-converging numerical errors can impact strongly the vortex Rossby waves and therefore the dynamics of the hurricane.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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