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Record W4316466436 · doi:10.3390/rs15020486

Rapid Growth of Tropical Cyclone Outer Size over the Western North Pacific

2023· article· en· W4316466436 on OpenAlexaff
Yi Li, Youmin Tang, Shuai Wang, Xiaojing Li

Bibliographic record

VenueRemote Sensing · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsUniversity of Northern British Columbia
FundersHohai UniversityNational Natural Science Foundation of China
KeywordsTropical cyclonePercentileInflowClimatologyAtlantic hurricaneEnvironmental scienceOceanographyGeologyStatisticsMathematics

Abstract

fetched live from OpenAlex

The concept of rapid growth (RG) of tropical cyclones (TCs) in the north Atlantic basin was recently proposed. RG can represent a dangerous change in TC structure because it can rapidly ramp up the TC destructive potential. However, the nature of RG behaviour remains obscure over the western north Pacific (WNP), where nearly one third of global TCs occur. In this study, TC RG in the WNP is investigated using TC best-tracks and reanalysis of data. We first define TC RG in the WNP as an increase of at least 84 km in the radius of a gale-force wind within 24 h, corresponding to the 90th percentile of all over-water changes. Monte Carlo experiments demonstrate the robustness of the threshold. Similar to that occurring in the north Atlantic, RG in the WNP is associated with the highest level of destructive potential. In addition, RG over the WNP occurs closer to the coast than for TCs in the Atlantic and more RG events in the WNP are accompanied by rapid intensification, which may significantly increase their destructive potential in a worst case scenario. Composite analysis shows that certain dynamic processes, such as radial inflow, may play an important role in the occurrence of RG. This study suggests that, apart from rapid intensification, TC RG is another important factor to consider for TC-related risk assessment in the WNP.

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 score0.294
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.239
Teacher spread0.215 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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