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Record W4299410857 · doi:10.1002/essoar.10506709.2

Focusing and defocusing of tropical cyclone generated waves by ocean current refraction

2022· preprint· en· W4299410857 on OpenAlexaff
Rui Sun, Ana B. Villas Bôas, Aneesh C. Subramanian, Bruce D. Cornuelle, Matthew R. Mazloff, Arthur J. Miller, Sabique Langodan, Ibrahim Hoteit

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsKootenay Association for Science & Technology
FundersKing Abdullah University of Science and TechnologyNuclear Safety and Security CommissionNational Aeronautics and Space Administration
KeywordsTropical cycloneRefractionGeologyTropical waveClimatologyWavelengthCurrent (fluid)Cyclone (programming language)Wind waveAmplitudeRogue waveSatelliteSurface waveMeteorologyGeophysicsOceanographyPhysicsOptics

Abstract

fetched live from OpenAlex

Waves generated by tropical cyclones can have devastating effects on coastal regions. However, the role of ocean currents in modifying wave amplitudes, wavelengths, and directions is commonly overlooked in wave forecasts, despite the fact that these interactions can lead to extreme wave conditions. Here, we use satellite observations and wave modeling to quantify the effects of ocean currents on the surface waves generated during a tropical cyclone event in the Arabian Sea. As a case study, this paper documents beams of wave heights originating from the eyewall of a tropical cyclone caused by current-induced refraction. Alternating regions of high and low wave heights in the model simulations are consistent with observations and extend for thousands of kilometers all the way to 100 m isobath. Our results highlight the importance of accounting for wave refraction by currents in order to accurately predict the impact of tropical cyclone generated waves on coastal regions.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.784

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 integrity0.0000.001
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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 designOther design
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

Citations13
Published2022
Admission routes1
Has abstractyes

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