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Record W3022320462

Predictive Skill and Predictability of North Atlantic Tropical Cyclogenesis in Different Synoptic Flow Regimes

2017· article· en· W3022320462 on OpenAlexaff
Zhiyue Wang, W. Li, Melinda S. Peng, Xingwei Jiang, Ron McTaggart‐Cowan, Chris Davis

Bibliographic record

VenueAGUFM · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsPredictabilityExtratropical cycloneClimatologyCyclogenesisBaroclinityTropical cyclogenesisEnvironmental scienceForcing (mathematics)Trough (economics)Atmospheric sciencesGeologyCyclone (programming language)MathematicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

AbstractPractical predictability of tropical cyclogenesis over the North Atlantic is evaluated in different synoptic flow regimes using the NCEP Global Ensemble Forecast System (GEFS) reforecasts with forecast lead time up to two weeks. Synoptic flow regimes are represented by tropical cyclogenesis pathways defined in a previous study based on the low-level baroclinicity and upper-level forcing of the genesis environmental state, including nonbaroclinic, low-level baroclinic, trough-induced, weak tropical transition (TT), and strong TT pathways. It is found that the strong TT and weak TT pathways have lower predictability than the other pathways, linked to the lower predictability of vertical wind shear and midlevel humidity in the genesis vicinity of a developing TT storm. Further analysis suggests that stronger extratropical influences contribute to lower genesis predictability. It is also shown that the regional and seasonal variations of the genesis predictive skill in the GEFS can be largely explaine...

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.998

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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